Traffic Monitoring Guide

Chapter 3. METHODOLOGIES FOR TRAFFIC DATA COLLECTION AND PROCESSING

3.1 HOW TO DESIGN A CONTINUOUS COUNT TRAFFIC VOLUME DATA COLLECTION PROGRAM

3.1.1 Purpose

The purpose of a continuous traffic volume count program is to collect highly accurate continuous volume data to serve as the foundational temporal dataset for the entire traffic volume data collection program. This program forms the basis for the overall traffic monitoring program and in some States is referred to as the permanent count program. In the TMG, the program will be referred to as the continuous count station (CCS) program.

3.1.2 Continuous Count Station Program Objectives (Volume)

The objective of the continuous count traffic volume data collection program is to provide travel volume information, including the following traffic data: total volume, volume by direction, lane distribution factors and lane flow rates for lane closure policies, volume by lane by hour, day, month, or year as well as to develop time of day (TOD), day of week (DOW), month of year (MOY) and yearly (YTY) factors to expand short-term traffic volume counts to annual average daily traffic (AADT) volume statistics. This objective is the basis for establishing the number and location of CCSs operated by the State highway agency. Objectives of the continuous count program include the following:

Provide peak hour, 30th highest hour, and directional distribution data used by travel forecasters and roadway designers
Track traffic volume trends on specific roadway sections on the State highway system
Provide an anchor point for using ramp-balancing methods
Understand geographic differences in travel trends
Integrate with the HPMS traffic volume sample
Collect data on roadway sections where it is not possible or prohibitively expensive to collect data with portable counters
Provide travel statistics for determining historical and current facility usage by travelers, such as the AADT volumes, peak TOD traffic volumes, and traffic volume factors

3.1.3 Continuous Count Program Design Process (Volume)

Each agency develops its own balance between having larger numbers of CCSs (thus, increasing the accuracy and reliability of analyses that depend on data supplied by those counters) and reducing the expenditures required to operate and maintain those counters. The TMG recommendations provide sufficient flexibility for each agency to find an appropriate compromise among objectives.

The TMG recommends that the division responsible for traffic adjustment factor development operates at least the minimum number of continuous count locations needed to meet the accuracy and reliability requirement of the factoring program.

Additional travel and traffic volume data available through other count programs such as intelligent transportation systems (ITS), MPOs, cities, counties, and WIM programs (if separate), where the funding for the installation and operation of the counters comes from other sources, should be considered to supplement and expand the continuous count database. However, while the cost of equipment installation and operation of these supplemental continuous count programs is the responsibility of those other programs, the statewide traffic monitoring division should be responsible for ensuring its accuracy and making these data available to users. Determining how best to obtain, summarize, and report these data is an issue best addressed at the State level. Data management best practices can be learned from advanced travel monitoring programs. These examples are provided in Appendices E and J.

3.1.4 Establishing And Evaluating A Continuous Count Program (VOLUME)

Below is a list of steps that should be followed in establishing and evaluating a continuous count program for statewide traffic monitoring. These steps were designed for (1) developing a new program, and (2) checking and evaluating the existing program to ensure compatibility with the guidance. The results of these steps should be documented to allow for future benchmarking and improvement in the travel monitoring program.

The following steps are primarily focused on the traffic volume program component. Each step is described in detail in the sections following this list.

Continuous Volume Data Program Steps

Review Existing Continuous Count Program
1. Current Program
2. Traffic Patterns
3. Data Adjustment
4. Quality Control
5. Periodically Review
6. Summary Statistics
Develop Inventory of Available Continuous Count Locations and Equipment
1. Existing Data Sources for each data type
2. Other Sources
3. Data Use
Determine the Traffic Time Patterns to be Monitored
1. Time Patterns
2. Time of Day (TOD)
3. Day of Week (DOW)
4. Month of the Year (MOY)
5. Year to Year (YTY)
6. Factor Group Assignment
Establish Monthly Pattern Group
1. Traditional Approach
2. Cluster Analysis
3. Volume Factor Groups or other agency specific factor patterns
4. Recreation Factor Groups
Determine the Appropriate Number of Continuous Count Locations
Select Specific Count Locations (refer to Chapter 2 to assure that the selected technology or equipment would work in selected locations)
Compute Temporal and Axle Correction Factors

3.1.4.1 Step 1: Review the Existing Continuous Count Program

Current Program – The first step is to define, analyze, and document the current continuous count program. A clear understanding of the current program, its purpose, and customers will increase confidence in later decisions to modify the program. The review should explore the historical design, procedures, equipment, personnel, objectives, and uses of the information. This review should start with an inventory of the continuously operating traffic data collection equipment available (this would include features, limitations, age, and repair/failure rates). It should then progress to determining how the data are being used, who is using the data.
Traffic Patterns – Next, the available data should be reviewed to determine the different temporal traffic patterns that exist in the State and whether previous patterns have changed to establish whether the monitoring process should also change.
Data Adjustment – The next step is to review how the data are being adjusted for data discontinuities, and whether those data adjustment steps can be improved or made more efficient. Continuous traffic data are subject to discontinuities because of various reason such equipment malfunctions and sensor recording errors. The way a State identifies and handles errors or anomalies (e.g., due to weather, construction, special events) in the data stream is a key component of the program. Data adjustment should be made according to ASTM E27-59 Standard Practice for Highway Traffic Monitoring Truth-In-Data. Truth-in-data implies that agencies maintain a record of how data are collected and adjusted, and that each adjustment has undergone a strong statistically rigorous analysis. The emphasis is on documenting the process and implementing the documented process.
Quality Control – Of considerable interest in this review is how the quality of the data being collected and reported is maintained. Establishing the quality of the traffic data reported by the system and the outputs of the analysis process is a prerequisite for future improvements. Each State highway agency should have formal rules and documented procedures for these important quality control efforts. Data should not be discarded or replaced simply because they appear atypical. Instead, each State should establish systematic procedures that provide the checks and balances needed to identify invalid data, control how those invalid data are handled in the analysis process, and identify when those quality control steps have been performed.
Periodically Review – The State highway agency should periodically review whether the quality review and data summarization procedures are performed as intended or need to be revised. For States that currently do not have formal quality control procedures, Appendix C provides several examples of how States use data quality control procedures.
Summary Statistics – The last portion of the review process should entail the steps for creating summary statistics from the raw data collected by continuous counters. These procedures should be consistent from year to year, be replicable, and should accurately account for the limitations (such as gaps in data) that are often present in continuous count data.
Adjustment Factors – The review process should document how the continuous count program is being used to create and apply adjustment factors to short-term traffic counts to estimate AADT, as well as which highway locations require continuous counters because of the importance of tracking volume with a high degree of confidence at those locations. The collection of continuous data to determine AADT should only be necessary at a limited number of locations.

3.1.4.2 Step 2: Develop an Inventory of the Available and Needed Continuous Count Locations and Equipment

Existing Data Sources – The inventory of existing (and planned) continuous count sites ensures that the State's traffic monitoring effort obtains all the continuous count data that are available. The key to the inventory process is for the agency to identify not just the traditional continuous count sites but also other data collection devices that can supply continuous volume data. These secondary sites include, but are not limited to, the following:
- Continuous classification counters.
- Continuous weigh-in-motion sites.
- Travel and Traffic management systems sites such as ITS Ramp Metering
- Regulatory monitoring sites such as international border crossings and toll plazas.
- National Park Service counters.
- MPO, City, and Town count sites.
- Signalized intersections and ramp metering sites.
Posing more challenges are devices operated by other divisions within the State highway agency. Obtaining these data requires internal cooperation within the agency. The current emphasis on improved cost-efficiency in government means that in most States there is strong upper management support for full utilization of data resources, wherever they exist. The key to taking advantage of this support is to make the transfer of the data as automated as possible, so that little or no staff time need be expended outside of the continuous count data collection group to obtain the data.

The State highway agency should also look for data outside of its own agency (such examples are speed signs, toll facilities, ITS data sources, and others). While it may not be possible to obtain this data at the level provided by standard continuous count devices (i.e., hourly or other time increment such as 5, 10, or 15 minute, etc. or records by lane for all days of the year), it is often possible to obtain useful summary statistics such as average daily traffic (ADT) and weekly, monthly (seasonal) volume patterns from these locations. This summary data can be used to supplement the State's data at those locations and geographic areas. The accessibility of data from supplemental locations reduces the cost of collecting and increases access to useful data. Local data can also be provided to FHWA. To obtain this data, the State highway agency may have to acquire software that automatically collects and reports the data. The intent is to reduce the operating agency's staff time needed to collect and transmit the data. The easier this task is for the agency collecting the data, the more likely that the data can be obtained and integrated.
Data Use – Several State DOTs find a data business plan to be a useful tool for documenting the business needs for data and information. Data business plans help to document how data systems support current business operations, identify data gaps (i.e., where new data and information are needed to support current needs), and provide a structured plan for the development of enhanced data systems to meet future needs and include life cycle costs to make best use of limited resources.

3.1.4.3 Step 3: Determine the Traffic Patterns to Be Monitored

One of the most important tasks of the continuous count program is the monitoring of traffic volume trends and the tracking of temporal patterns around the State. Temporal patterns are used by the agencies to group continuous count sites together for development of traffic volume adjustment factors for factoring data from short-term count sites. Foremost among these trends is the monitoring of AADT at specific highway locations. The FHWA Traffic Monitoring Analysis System (TMAS) is a good source of data to evaluate traffic volume patterns over time, including the following patterns.

Time Patterns – Time of Day (TOD), Day of Week (DOW), Month of Year (MOY), and Year to Year (YTY) temporal and recurring event patterns are of great importance in the refinement of the continuous count program, since the effectiveness of the factoring process (and consequently the accuracy of most AADT counts) is a function of the temporal patterns observed around the State. Understanding what patterns exist, how those patterns are distributed, and how they can be cost-effectively monitored is a major component of the factor review process. Obtaining data from other sources (both volumes and speeds) and integrating the data with existing sources is beneficial for monitoring traffic and congestion patterns for factoring.
Time of Day Adjustments – Traffic volume variability by TOD is of great importance. TOD adjustment factors are needed to convert partial day counts to estimates of daily traffic, as well as for studies concerning roadway operations and to compute estimates of delay. FHWA research has shown that including partial day counts (for example more than 48 but less than 72) in the AADT process improves the AADT results.
Day of the Week distribution – Volume variation by DOW is related to site location (urban or rural) and the type of travel facility on which observations are made. Agencies would benefit from reviewing DOW traffic patterns to ensure homogenous traffic patterns exist for sites within the same factor group. For more information, see Appendix J that provides typical DOW variation in travel volumes.
Monthly Factors – The goal of this review is to assess the degree of monthly variation in traffic volume that exists in the State, as measured by the existing continuous count data, and to examine the validity of the existing factor grouping procedures that produces the monthly factors. The review consists of examining the monthly variation (attributed to month of the year or seasonality) in traffic volume at the existing continuous count locations, followed by a review of how roads are grouped into common patterns of variation. The goal of this review is to determine whether the State's procedures successfully group roads with similar monthly patterns, and whether individual road segments can be correctly assigned to those groups. The review of monthly patterns can be undertaken using one of several analytical tools:
1. Statistical cluster analysis (that can be performed using any one of several major statistical software packages such as SAS or SPSS)
2. Graphical and spatial examination (that uses GIS tools) of seasonal / monthly pattern data from individual sites.
States use different methods and analytical tools when grouping sites. Appendix J shows several different State practices and their current methods for grouping sites. Another method of reviewing MOY patterns is to visually review data from plotted graphs, making it a simple and easy method to follow. An example of MOY Pattern by the DOW graphs is provided in Figure 3-1. See Appendix J for graphing examples.

Figure 3-1. Example of Differences in Monthly Travel Patterns By Day of Week in Montana
The review process begins by computing the monthly average daily traffic (MADT) (see Section 3.1.4.7) and the monthly factors at each continuous count location. The monthly factors are then used as input to a computerized cluster analysis procedure. The patterns for individual sites can also be plotted electronically and printed so that patterns from different sites can be overlaid to visually test for similarities and/or differences. If the groups of roads reported by the cluster analysis are similar to the groups of roads already in use, or if the visual patterns of all continuous counts in each factor group are similar, then it can be concluded that the factor groups are reasonably homogeneous. Specifically, all the continuous count sites that make up each factor group have the same or reasonably similar MOY pattern.

The final factor groups are not necessary to be identical to the cluster analysis output for two reasons. For any given year, the cluster output is likely to be slightly different, as minor variations in travel, and traffic patterns are likely to be reflected in minor changes in the cluster analysis output.
Year to Year – Year-to-year comparisons of traffic volumes are also helpful to determine the traffic patterns to be monitored.
Factor Group Definition and Assignment – The remaining review step is to make sure that the groups are defined by an easily identifiable characteristic (e.g., site attributes) that allows assignment of each short-term count location to a factor group. The definition of each group must be specific and unambiguous so that analysts can correctly select the appropriate factor for every applicable roadway section. The groups should be defined is such a way that each roadway section can be uniquely assigned to a factor group.

For more details about temporal patterns, see details provided in Section 3.7.1.

Micromobility Traffic Pattern Identification

Like motorized traffic patterns and micromobility data traffic patterns are observed and identified. Traffic data provide patterns that indicate what type of facility usage is currently used such as a facility that has mostly commuters versus recreational traffic. For more information, see Appendix J that provides research findings, example traffic pattern identification graphs, and summarized analyses examples.

The TMG recommends that the division responsible for data collection help data users to track trends and prioritize investments in trail development, safety, and maintenance.

3.1.4.4 Step 4: Establish Temporal Pattern Groups

If the review of factor groups is not reasonably homogeneous, the definition of the groups is not clear, or new traffic patterns are emerging, it may be necessary to re-form the monthly factor groups.

The basic statistic used to create temporal traffic volume adjustment factor groups can be either the ratio of AADT to MADT, or the ratio of AADT to monthly average weekday traffic (MAWKDT). In many States, there are unique patterns related to rural roads, urban roads, and recreational areas. However, in some States, significant geographic differences in travel need to be accounted for in the seasonal factoring process. For example, rural roads in the northern half of the State may have different travel patterns than rural roads in the southern half of the State due to the differences in land use, climate, or major industries located in these regions. In addition, for some States clear patterns have failed to emerge.

The three prominent types of analysis to identify factor groups are described as follows. FHWA research "AADT from ADT Duration and Frequency" (FHWA 2014) has found the clustering method to be the most accurate method of the three approaches.

Traditional Approach – The more subjective traditional approach to grouping roads and identifying like patterns is based on a general knowledge of the travel patterns for a given road system combined with visual interpretation of the monthly pattern graphs. The advantage of the traditional approach is that it allows the creation of groups that are easier for agency staff to identify and explain to users. This happens because the grouping process starts by defining road groups that are expected to behave similarly based on some logic/rules defined by the analyst and utilizes known road descriptors. The hypothesis is then tested by examining the variation of the seasonal patterns that occur within these expected groups.

For example, the initial groups of roads that behave similarly could consist of roads of the same functional classification, or a combination of functional classifications. The groups should be further modified by the State highway agency to account for the specific travel characteristics within the State. Expected revisions include the creation of specific groups of roads that have travel patterns driven by large recreational activities, dominant business activities, or that exhibit strong regional differences.

Deciding on the appropriate number of factor groups should be based on the actual data analysis results; the analyst's knowledge of specific, relevant conditions; and the desired precision of the traffic estimates. As a general guideline, a minimum of five to six groups is usually needed. More groups may be appropriate if multiple recreational patterns need to be monitored or if significant regional differences exist.
Cluster Analysis – The cluster procedure is illustrated by an example in Appendix E where the monthly factors (ratio of AADT to MADT) at the continuous count stations are used as the basic input to the statistical procedure. An understanding of the computer software used for statistical clustering procedures is helpful but not required to interpret the software results. Two advantages of cluster analysis are that it allows for independent determination of similarity between groups, therefore making the groups less subject to bias, and identifies travel patterns that may not be intuitively obvious to the analyst. Accordingly, it helps agency staff investigate road groupings that might not otherwise be examined, which can lead to more efficient and accurate factor groups and provide new insights into the State's travel patterns.

The cluster analysis procedures have two weaknesses. One is the lack of uniformly agreed upon theoretical guidelines for establishing the optimal number of groups. While a mathematically calculated number of groups can be determined, the optimal number of groups required is hard to quantify and optimization criteria are not well defined. The second weakness in the cluster analysis approach is that the groups that are formed often cannot be adequately defined in terms of common road attributes/characteristics that are readily available to identify the road segments and short-term count locations that should be included in each of the resulting cluster groups.

Volume Factor Groups – Because of the importance and unique inter-regional nature of travel on the interstate system, States should consider maintaining separate volume factor groups for the interstate functional road categories. The interstate system will always be subject to higher data accuracy constraints because of its national emphasis and high usage levels. Most States maintain many continuous counts on the interstate system; therefore, separate interstate groups are easily created. An example of volume factor groups by aggregated interstate and non-interstate road categories is shown in Table 3-1. The TMG recommends these groups to be used as a minimum.

**Table 3-1. Minimum Recommended Volume Factor Groups**
Recommended Group	HPMS Functional Code
Interstate Rural	1
Other Rural	2, 3, 4, 5, 6, 7
Interstate Urban	1
Other Urban	2, 3, 4, 5, 6, 7
Recreational	Any

The first four groups are self-explanatory. The recreational group relies on subjective judgment and knowledge of the travel characteristics of the State. Usually, recreational patterns are identifiable from an examination of the continuous count data. The existence of a recreational pattern should be verified by knowledge of the specific locations and the presence of recreational travel generator(s). A roadway is likely a recreational road when the difference between the ratio of the highest hourly volume to AADT and the ratio of the thirtieth highest hourly volume to AADT is greater than one. No single method exists for determining recreational patterns. A typical commuter pattern roadway can operate as a recreational pattern on weekends or a weekday depending on events, etc. The best way to determine trip purpose is to conduct intercept surveys. The minimum group specification can be expanded as desired by each State to account for regional variation or other concerns. However, more groups result in the need for more continuous count stations, with a corresponding increase in program cost and complexity. Each State highway agency will have to examine the trade-offs carefully between the need for more factor groups and the cost of operating additional continuous count stations.

Special Considerations for Recreational Roads – Distinct recreational patterns cannot be defined based only on functional class or area boundaries. Recreational patterns are obvious for roads at some locations but nonexistent for other, almost adjacent, road locations. The boundaries of the recreational groups should be defined based on subjective knowledge. The existence of different patterns, such as for summer and winter, further complicates the recreational factor group. Therefore, the recommendation is to use a strategic approach to determine subjectively the routes or general areas where a given recreational pattern is clearly identifiable, establish a set of locations, and subjectively allocate factors to short-term counts based on the judgment and area knowledge of the analyst. The road segments where these recreational patterns have been assigned should be carefully documented so that these recreational factors can be accurately applied and periodically reviewed.

While this may appear to be a capitulation to ad hoc procedures, it is a realistic acknowledgement that statistical procedures are not directly applicable in all cases. However, recreational areas or patterns are usually confined to limited areas of the State and, in terms of total vehicle distance traveled (VDT), are small in most cases. The direct statistical approach will suffice for most cases.

The procedure for recreational areas is then to define the areas or routes based on available data (as shown by the analysis of continuous and control data) and knowledge of the highway systems to subjectively determine which short-term counts will be factored by which continuous count (recreational) location(s). The remaining short-term counts should be assigned based on the groups defined by the State.

FHWA pooled fund study document "Assessing AADT Accuracy Issues Related to Short-Term Count Durations" (Krile 2015b) provides the relative accuracy and precision associated with different durations of data collected (example 24 hours versus 48 hours of data). This document also evaluates the impact of day of the week and month of the year for AADT estimation where evaluation results are presented for different factoring methods and different factor grouping methods.

Table 3-2 shows the advantages and disadvantages of different methodologies for development of monthly factor groups.

**Table 3-2. Advantages and Disadvantages of Monthly Pattern Group Types**
Type	Advantages	Disadvantages
Traditional	Creation of groups is easier Application for factoring can be explained Easier to assign short-term count to a group	May not stand statistical scrutiny Relies on local knowledge and analyst judgment, may be biased
Cluster Analysis* (*FHWA Recommended Method)	Independent determination of similarity of groups without bias Traffic pattern can be found which may not be intuitively obvious Efficient and accurate factor groups	Lack of guidelines for establishing optimal number of groups Groups that are formed often cannot be adequately defined Difficult to assign short-term count to a group
Volume Factor Group	Consistent national framework for comparison among the States The precision of the seasonal factors can be calculated Easier to assign short-term count to a group	Functional or road classification may not be based on travel characteristics May not stand statistical scrutiny

3.1.4.5 Step 5: Determine Appropriate Number of Continuous Count Locations

Compute the Statistical Precision of the Factors

The precision of the temporal factors can be computed by calculating the mean, standard deviation, and coefficient of variation of each adjustment factor for all continuous count locations within a group.

In some cases, the mean, standard deviation, and coefficient of variation do not work for the typical factor group listing because the data do not fall within a normal statistical bell-shaped distribution and therefore other methods of computing the statistical precision need to be used to compute the factors.

The mean value for the group is the adjustment factor that should be applied to any short-term count taken on a road section in the group. The standard deviation and coefficient of variation of the factor describe its reliability. The coefficient of variation is a ratio of the standard deviation to the mean value expressed in a percentile form. The precision boundaries with respect to the error in the mean value of the estimate can be expressed in percentage terms using the coefficient of variation, where the error boundaries for 95 percent of all locations are roughly twice the coefficient of variation.

Coefficient of variation is equal to 100 times the standard deviation of the factor divided by the mean of the factor.

Where:

COV = coefficient of variation

s = standard deviation of the factor

X̄ = mean value of a factor

Typical monthly variation patterns for urban areas have a coefficient of variation under 10 percent, while those of rural areas range between 10 and 25 percent. Values higher than 25 percent are indicative of highly variable travel patterns, which reflect recreational patterns, but which may be due to reasons other than recreational travel.

Compute the Number of Sites

After establishing the appropriate temporal factoring groups and allocating the existing continuous count locations to those groups, the next step is to determine the total number of locations needed in each factor group to achieve the desired precision level for the composite group factors. Since the existing simple random sample selection, the standard equation for estimating the confidence intervals (i.e., the precision boundaries) can be used.

A statistical formula for estimating confidence intervals or precision boundaries.

The boundary is equal to the mean value plus or minus the Student's t-statistic (for a specific level of confidence and degrees of freedom) multiplied by the standard error (the standard deviation divided by the square root of the number of samples.

Where:

B = upper and lower boundaries of the confidence interval for a composite group factor

X̄ = mean value of a factor

t = value of Student's T distribution with 1 – aa/2 level of confidence and n-1 degrees of freedom

n =number of continuous count locations used to compute the monthly factor

α = alpha level for the selected level of confidence (for 95 percent confidence α =1-0.95)

s = standard deviation of the factor

This formula could be applied to compute factor precision for each month. The formula provides the results in the same units of measure as the factors used to compute mean and standard deviation values.

The results could be used to identify what short-term count months could potentially lead to the highest errors in AADT estimation and eliminate these months from the short-term count schedule. The precision estimation results could also be used to identify months where high variability within the group is observed and to change the grouping of the sites to reduce high variability and improve the precision for certain months.

The above formula can be used to estimate the sample size needed to achieve any desired precision intervals or confidence levels. Specifying the level of precision desired is a trade-off process. Very tight precision requires large sample sizes, which translate to expensive programs. Very loose precision reduces the usefulness of the data for decision-making purposes. Traditionally, the target precision for traffic factors has been plus or minus 10 percent of the mean value. A precision of 10 percent can be established with a high confidence level or a low confidence level. The higher the confidence level desired, the higher the sample size required. Furthermore, the precision requirement could be applied individually to each seasonal group or to an aggregate statewide estimate based on more complex, stratified random sampling procedures.

The recommended precision (i.e., reliability) levels for the adjustment factors are +/- 10 percent precision with 95 percent confidence for each monthly adjustment factor group, excluding recreational groups where no target precision requirement is specified (under +/-25% is desired).

For non-recreational roads with moderately stable seasonal traffic volume pattern, typically, the number of continuous count locations recommended is a minimum of 6 per factor group, although cases exist where more locations are needed if high traffic variability exists within the factor group.

The actual number of locations needed is a function of the variability of traffic patterns within that group and the precision desired; therefore, the required sample size may change from group to group.

Recreational factor groups usually are monitored with a smaller number of continuous counters, simply because recreational patterns tend to cover a small number of roads; it is not economically justifiable to maintain five to eight stations to track a small number of roads. The number of stations assigned to the recreational groups depends on the importance assigned by the planning agency to the monitoring of recreational travel, the importance of recreational travel in the State, and the different recreational patterns identified.

Select Number of Continuously Counting Sites for Pedestrian and Micromobility Monitoring Program

The micromobility monitoring program is modeled after the motorized traffic monitoring program.

The number of continuous counting sites an agency chooses to invest in is a factor of cost, geographic region, weather, traffic variability, and data sharing opportunities. In general, the number of continuous counting sites required to provide a statistically valid adjustment factors for micromobility monitoring program should follow the best practices and recommendations for the motorized program described in the previous section. It is further recommended that the micromobility program should have at least 5 total continuous counting sites per factor group.

According to the Transportation Research Record paper "Minimizing Annual Average Daily Nonmotorized Traffic Estimation Errors: How Many Counters Are Needed per Factor Group?" (NASEM 2019), four or more counters per factor group for bicycle and five or more for pedestrian traffic monitoring are recommended.

The number of sites is driven by: (1) expected standard deviation (or variability) within the group and (2) statistical sampling rules to achieve confidence in computed statistic. A decrease from 6 to 4 sites will result in 50% decrease in statistical measure of precision (or 50% increase in computed expected mean error range) (see values of t-statistic in Figure 3-2 in Section 3.2.6 for 4 and 6 sites).

There are significant differences between micromobility and motorized traffic monitoring. These differences include the number of monitoring sites, the amount of staff to manage the data, and how the data are managed (annual statistics creation and publication). Currently, most micromobility data collection programs have a much smaller number of monitoring locations than motorized vehicle count programs. Because of the smaller number of count stations and the relatively short length of micromobility traveler trips, the potential for determining overall system or network micromobility usage is limited. Without a formal micromobility data collection program, micromobility count locations are often chosen based on highest usage levels and/or a specific project or site need for information such as a strategic area of facility improvement. Establishing a formal micromobility data collection program includes developing standard methods for capturing data that contributes to the entire network or system. This includes following the motorized standard methodologies for selecting sites, purchasing equipment, and analyzing data. For example, one site selection criteria that is established for selecting monitoring sites is a "representative coverage sample" where micromobility sites are selected based on a need for representation of the entire network or system. Without a formal data collection program, limited data are collected with limited resources for specific data or project uses. These limited data collection activities and location samples represent a biased estimate of overall usage and trends for a city or State and are not recommended to be used for network or overall system usage conclusions.

3.1.4.6 Step 6: Select Specific Count Locations

Once the number of groups and the number of continuous count locations for each group have been established, the existing locations can be modified if revision is necessary. The first step is to examine how many continuous counters are located within each of the defined groups. This number is then compared to the number of locations necessary for that group to meet the required levels of factor precision (i.e., reliability). If the examination reveals a shortage of current continuous count locations, the agency should select new locations to place continuous counters within that defined group. Since the number of additional locations may be small, the recommendation is to select and include them as soon as possible. Additional issues that should be considered when selecting locations to expand the sample size are reviewed in the following paragraphs.

If a surplus of continuous counters within a group exists, then redundant locations may be eliminated. Also, some locations in a given factor group may fall out of the group (change in travel pattern or lack of complete data) for the given year, hence having extras in a group permits these types of small changes. However, it should be noted that additional locations increase the statistical reliability of the data and keeping them for that purpose is recommended. If the surplus is large and the reduction is desired, it should be planned in stages to ensure that the cuts do not affect reliability in unexpected ways. For example, if 12 locations are available and six are needed, then the reduction should be carried out by discontinuing locations annually over a period of 3 years. The sample size and precision analysis should be repeated each time prior to the sites being dropped from the group to ensure that the desired precision has been maintained without the data from the sites identified for deletion. Maintaining a few (two to three) additional surplus locations helps supplement the groups and compensate for equipment downtime or missing permanent site data.

Site and location reductions should be carefully considered, including the following:

Other uses of existing information or other reasons the sites are important – as mentioned previously, seasonality is not the only objective for use of continuous count data. Each State should ensure that these other criteria are met before discontinuation. Additional locations increase the reliability of the factored data.
Quality of the traffic data – continuous counter data are subject to many discontinuities due to downtime, which results in missing data, and to the issues of data adjustment and sensor issues.
Existing locations – available locations from traffic control or other programs may be candidates for upgrading to continuous status.
Location on or near HPMS sites – because of the direct linkage to the HPMS sample sections, these locations should be given priority.
Tie-in to the classification, speed, or weight programs – coordination with other programs is essential.
Distribution over geographical areas of the State.
Distribution by functional class system.
Random selection to reduce bias – new locations should be randomly selected.

Micromobility Program Site Distribution

It is critical to understand the travel behaviors and patterns of micromobility travelers to accurately determine where to count. Agencies that are new to micromobility traffic monitoring should follow the method of counting in locations that are most representative of a facility network such as locations that have high, medium, and low volumes and selecting sites that have geographical coverage of the entire network.

For motorized traffic, State DOTs have a short-term data program that provides traffic data for all Federal aid roads (spatial coverage) on their State highway system.

The number of short-term count locations for a micromobility program will depend on the available budget and the planned uses of the count data. For most regions getting started with counting micromobility travel, the short-term count program is best developed by working with other key stakeholders interested in collecting and using these data. By discussing needs and budgets, this group can identify and prioritize the special-needs short-term count locations that the available data collection budget can afford to collect. (These same discussions should also identify those key regional facilities that should be used for early deployment of permanent counters that will then be used to expand the short-term count data into estimates of annual and peak use.) The special needs counts will then provide the data needed to guide the development of a more statistically valid sample of short-term count locations.

The distribution of where to locate continuous counters should include a site selection methodology that is established to determine where an investment in continuous counting equipment is best utilized. Noteworthy practices for selecting sites have been documented and are provided in Appendix H for Nonmotorized Site Selection Methods for Continuous and Short-Term Volume Counting. Agencies should follow these guidelines when determining how many and where to install continuous counting sites.

As stated in these guidelines, it is recommended that agencies preform a short-term count for at least 2 weeks prior to installing continuous counting equipment to ensure travelers are present on the facility being considered for continuous counting instrumentation.

The reason for collecting a 2-week short-term count prior to installing continuous counting equipment is to ensure travelers are using the facility. It then provides a baseline dataset in which traffic patterns can be evaluated.

Evaluating traffic patterns can help to determine if a continuous counter is needed for representation of a traffic volume group such as high, medium, or low volume.

Once general monitoring locations have been identified, the most suitable counter positioning should be determined. It is critical to invest time in the site selection process, as determining where to count is the foundation of a statistically valid micromobility count program. The method for implementing site selection methods can be found in Appendix H – Nonmotorized Site Selection Methods for Continuous and Short-Term Volume Counting

The continuous count locations should provide geographical and volume density representative samples of high, low, and medium volume locations to achieve an overall statistically valid and un-biased estimate of facility usage in a city or statewide geographic region. This also applies to motorized traffic volume data programs.

Micromobility Screen Line (Mid-block) versus Intersection Counts

The two basic location types for nonmotorized traffic monitoring are:

Screen line counts that are taken at a mid-segment location along a micromobility facility (e.g., sidewalk, bike lane, cycle track, shared-use path).
Intersection crossing counts that are taken where a micromobility facility crosses another facility of interest.

Screen line (also known as mid-block) counts are typically used to identify general use trends along a facility and are analogous to most short-term motorized traffic counts. Although taken at a specific location, screen line counts are sometimes applied to the full segment length to calculate vehicle-miles of travel, pedestrian-miles of travel, and bicyclist-miles of travel.

Intersection crossing counts are typically used for safety and/or operational purposes and are most analogous to motorized intersection turning movement counts. Example applications include using intersection counts to determine exposure rates at high collision crossings, as well as to retime or reconfigure traffic signal phasing.

Intersection counts are typically more complicated than screen line counts and may require additional counters, primarily because multiple intersection approaches are being counted at once.

3.1.4.7 Step 7: Compute Temporal Factors

It is critically important to remember AADT statistics represent an estimate of volume. The AADT statistic represents a volume number for any "average" day of a calendar year and is not meant to be a precise number representing the exact number of vehicles on an exact day of the year. Rather, the AADT is an estimate that is significantly influenced by many factors such as (1) duration of count, (2) how well the selected factor represents the monthly and DOW patterns, and (3) traffic volume variability at the site, etc. Generally, for high-quality installation and optimal counting of selected sites, AADT is known to be accurate within count tolerances and bias for traffic volumes on any given day in the published AADT calendar year. AADTs based on short-term counts are calculated by applying adjustment factors to further advance the accuracy of the AADT statistic. FHWA provides a Reference Accuracy table as guidance on calculating AADTs (Table 3-3), which is based on the work performed in the "AADT from ADT Duration and Frequency" pooled fund study (FHWA 2014).

**Table 3-3. Reference Accuracy and Precision for AADT Estimates**
AADT Volume Range	Minimally 95% Probability, TCE Median Error (Bias) (%)	Minimally 95% Probability, 95% TCE Median Error Range (%)
500 - 4,999 (low)	+/- 2.0	+/- 34.0
5,000 – 54,999 (medium)	+/- 1.5	+/- 28.0
55,000+ (high)	+/- 2.5	+/- 28.0

*TCE – Traffic Count Estimate

Select computational approach and temporal dataset. Temporary factors can be developed in one of three ways:
- Computing using this year's factors.
- Computing a monthly rolling average (for example, the temporary July 2021 factor would be computed as the factor for the 12 consecutive months from August 2020 through July 2021).
- Applying last year's factors (if no other options are available).
The first of these approaches is best because it utilizes those values for factoring most closely related to the calendar year the count was taken. The second approach produces the timeliest adjustment factor but also requires the most labor-intensive data handling and processing effort (see Appendix D for an example of computing monthly rolling average). The last of these approaches is the easiest but also the least accurate, because the effects of this and last year's economic/environmental conditions are likely to be different.

The procedures for developing and using monthly factors to adjust short-term volume counts to produce AADT estimates are based on the structure of the traffic monitoring program. The method used should be detailed in the Traffic Monitoring System (TMS) documentation that States keep on record with the local FHWA Division office and update every 3 to 5 years.

MOY and DOW factors are most accurately developed and applied on a year-by-year basis. That is, a short-term count taken in 2019 should be adjusted with factors developed exclusively from continuous count data collected in 2019. This allows the adjustment process to account for economic and environmental conditions that occurred in the same year the short-term count was taken.

This recommendation creates challenges for the timing of factor computation and application. That is, if a short-term count is taken in the summer of this year, the true adjustment factor for this year cannot be computed until January of next year at the earliest, which may not be timely enough for many users. The recommendation is to compute temporary adjustment factors for estimating AADT before the end of the year, and then to revise that preliminary estimate once the year's true adjustment factors can be computed in January.
Identify statistical parameters necessary for factor computation. The monthly individual factors for each continuous count station are the ratio of the AADT to MADT. Alternatively, the State can combine the DOW adjustment and monthly adjustment into a single factor, for example the ratio of annual average daily traffic to monthly average weekday traffic (AADT / MAWKDT). This term, or a similar monthly or time of day adjustment, can be substituted directly for the ratio of AADT / MADT in the factor grouping and application process if desired.
Compute AADT, MADT, and MAWKDT for CCSs included in the factor group. For a counter site that operates 365 days per year without failure, the AADT can be computed by adding all the daily volumes and dividing by 365. Similarly, the MADT can be computed by adding the daily volumes during any given month and dividing by the number of days in the month.

Challenges with this approach are that few continuous count stations operate reliably during any given year. Most suffer at least small amounts of downtime because of power failures, communications failures, and other equipment or data handling problems. These missing hours or days of data can cause biases and other errors in the calculations, particularly when a moderate amount of data is lost in a block. As a result, a modified formula for computing these types of statistics that directly accounts for missing data has been adopted.

The AASHTO AADT formula was first developed over 30 years ago and computes an average daily traffic volume for each of seven DOW for each month and then computes an annual average value from those monthly averages, before finally computing a single annual average daily value (AASHTO Traffic Data Programs Guidance Book, 2009). This process effectively removes most biases that result from missing days of data, especially when those missing days are unequally distributed across months or days of the week.

The calculation of MADT using AASHTO method is similar to that of AADT. An average DOW traffic volume is first computed for a given month, and then all seven-day values are averaged to obtain MADT value. MAWKDT is similarly computed using average weekday (i.e., working day) values. However, each State can define the specific days present in the MAWKDT calculation. For example, some States do not count Fridays for routine short-term traffic counts and therefore choose not to include Fridays in the computation of MAWKDT.

Since 2016, FHWA has recommended the use of this new AADT procedure.

FHWA recommended procedures for AADT and MADT computation are documented in Assessing Accuracy Issues with Current Known Methods in AADT Estimation from Continuous Traffic Monitoring Data (Krile 2015a). The FHWA method produces statistically significant better results and improved AADT estimates. The new FHWA procedure has several improvements over the above AASHTO procedure, including that it specifically incorporates all valid temporal data records (i.e., volume records for time periods of less than one full day, such as hourly, 5-minute or any time increment count records). This procedure corrects mathematical flaws that exist in the AASHTO AADT computation that slightly undervalue both weekday volumes over the course of a year and the traffic occurring in months of the year with 30 or 31 days. Finally, this method allows for datasets like ITS data that may be valid data but contains missing time increments to be utilized to obtain valid MADT and AADT values from such sources as signalized intersection and ITS archives.

The FHWA procedure first computes monthly average daily traffic, while retaining all valid temporal traffic records (even valid partial day counts) and accounting for the actual number of each day of the week in that month for that year. (That is, in some years, January has five Mondays, and in other years it has four Mondays. This approach specifically takes the number of Mondays into account when computing MADT.) The procedure then averages the 12 monthly values, while accounting for the number of days in each month. The recommended procedure is expressed mathematically below:

$A-A-D-T equals the following fraction: the numerator is the sum from m equals 1 to 12 of d sub m, multiplied by M-A-D-T sub H-P comma m; the denominator is the sum from m equals 1 to 12 of d sub m.$

Where:

AADT = annual average daily traffic $MADT sub m equals the following fraction: the numerator is the sum from j equals 1 to 7 of w sub jm, multiplied by the sum from h equals 1 to 24 of the following quantity: 1 divided by n sub hjm, multiplied by the sum from i equals 1 to n sub hjm of Volume sub ihjm; the denominator is the sum from j equals 1 to 7 of w sub jm.$

MADT_HPm = monthly average daily traffic for month m

VOL_ihjm = total traffic volume for ith occurrence of the hth hour of day within jth day of week during the mth month

i= occurrence of a particular hour of day within a particular day of the week in a particular month (i=1,…n_jmh) for which traffic volume is available

h= hour of the day (h=1,2,…24) – or other temporal interval

j= day of the week (j=1,2,…7)

m= month (m=1,…12)

n_hjm= the number of times the hth hour of day within the jth day of week during the mth month has available traffic volume (n_hjm ranges from 1 to 5 depending on hour of day, day of week, month, and data availability)

w_jm= the weighting for the number of times the jth day of week occurs during the mth month (either 4 of 5); the sum of the weights in the denominator is the number of calendar days in the month (i.e., 28, 29, 30, or 31)

d_m= the weighting for the number of days (i.e., 28, 29, 30, or 31) for the mth month in the particular year
Compute monthly factors for CCSs included in the factor group. Monthly factors for each CCS are computed by the ratio of AADT to MADT or AADT to MAWKDT. These computations are done for each of the 12 calendar months.
Compute monthly factors for the factor group. Group monthly factors are computed as the average of the factors for all continuous count sites within the group. These computations are done separately for each of the 12 calendar months. Both the individual continuous count and the group factors should be made available to users in tabular and computer-accessible form.

3.2 HOW TO DESIGN A VEHICLE CLASSIFICATION COUNT (AXLE AND LENGTH) DATA PROGRAM

3.2.1 Program Design Process Overview

This section discusses the process for establishing a continuous vehicle classification count program and presents two alternative methods for the development of factor groups for classification.

The continuous vehicle classification data collection program is related to, but can be distinct from, the traditional continuous traffic volume count program.

In addition, factoring of vehicle classification counts (i.e., motorcycle and heavy vehicle volume counts) should be performed independently from the process used to compute AADT from short-term volume counts.

Highway agencies should collect classification data (which also supply total volume information) in place of simple volume counts whenever possible.

The general steps required to develop a statewide vehicle classification count program are similar to the volume count program steps presented in Section 3.1. The general continuous count program design guidelines outlined in Section 3.1 also apply to vehicle classification continuous count station program. In addition to these guidelines, the vehicle classification count program design also includes traffic volume patterns by vehicle class review process focused on (1) identifying unique traffic patterns for each major class of vehicle in the State, (2) evaluating if the previously identified patterns have changed, and (3) determining whether the monitoring process should be adjusted. Distributions of vehicle volume by vehicle class should be analyzed and distribution patterns identified for different road types.

The specific guidance for executing vehicle classification count program design steps is provided in the ensuing sections.

Duration of Short Counts

Vehicle classification counts should be a minimum of 48 hours of continuous data. Vehicle counts longer than 48 hours are useful, particularly when those counts extend over the weekend, since they provide more comprehensive volume by vehicle class information by DOW. However, in some locations it is difficult to keep portable axle sensors in place for periods that significantly exceed 48 hours.

Longer duration counts from 72 hours to 7 days are encouraged and recommended whenever possible. One advantage to a 7-day duration of a short-term count is no DOW factor needs to be applied to annualize the short-term count to an AADT count program.

Other count durations can produce reasonable results in some cases but are not recommended for general use. Equipment that can collect data in time increment (such as 15-minute, hourly, etc.) traffic bins should be used for the general program. In urban areas or for special studies, the use of shorter intervals, such as 15 minutes, may be appropriate. The use of 48-hour periods is recommended because:

The accuracy of the annual load estimates of 48-hour counts is better than that of 24-hour counts.
Significant improvement in quality control capabilities become possible with the comparison of one day's hourly traffic counts against the second day's counts.
Longevity of the sensors/road tube and equipment has improved to provide longer-duration counts.

According to the HPMS Field Manual (FHWA 2016), "For HPMS reporting, States are permitted to perform counting using durations shorter than 48 hours for roadway functional classes arterial and interstate. For functional classes of collector and local roadways, if a State has a monitoring duration that is less than 48 hours, they must be able to demonstrate no loss in data quality based on documented statistical analysis provided to FHWA's Office of Highway Policy Information via FHWA's Division Office located in their respective States.

Also, for 48-hour counts (two full 24-hour days) are required for all HPMS full extent and sample data including those off the State highway system except where otherwise noted. Where axle correction factors are needed to adjust raw counts, they should be derived from facility-specific vehicle classification or weigh-in-motion (WIM) data obtained on the same route or on a similar route with similar traffic in the same area. Factors that purport to account for suspected machine error in high traffic volume situations shall not be applied to traffic counts used for HPMS purposes, including volume group assignment. In high volume situations and on controlled access facilities, it may be more appropriate to use continuous or short-term ramp counts in conjunction with strategic mainline monitoring than to use short-term counts on all mainline locations (see "ramp balancing" for details)."

3.2.2 Step 1: Review the Existing Continuous Vehicle Classification Count Program

Reviewing existing vehicle classification count programs should determine which highway locations require continuous vehicle classification equipment to effectively capture the travel patterns of all vehicle classes with a high degree of confidence. The review process should also document whether and how the continuous vehicle classification (CVC) program is being used to create and apply adjustment factors to short-term vehicle classification traffic counts to estimate annual average volumes by type of vehicle.

Four primary reasons for installing and operating permanent vehicle classifiers for continuous traffic monitoring purposes include the ability to:

Provide a highly accurate measure of traffic volume by vehicle class at a limited number of specific sites around the State.
Track the changes in those volumes over time with a high degree of accuracy.
Determine the temporal patterns (TOD, DOW, MOY) of different vehicle types on different roadways across the State; (if per vehicle data is stored, CVCs can provide axle correction factor data) (see Section 3.7.1 for more detail).
Create adjustment factors and factor groups that allow application of the factors for converting short-term classification counts into annual average estimates of vehicle volume by vehicle type.

3.2.3 Step 2: Develop an Inventory of Available Vehicle Classification Count Locations and Equipment

Highway agencies should check the accuracy of their vehicle classification data collection and take appropriate actions to assure that their vendor-specific classification algorithms correctly classify all vehicle types on their roadways (within 10% of the counting tolerances, bias, and error by class).

The inventory process should document whether and how the continuous vehicle classification program is being used to create and apply adjustment factors to short-term vehicle classification traffic counts to estimate annual average volumes by type of vehicle. The inventory review process should also determine which highway locations require continuous vehicle classification equipment to capture the travel patterns effectively of all vehicle classes with a high degree of confidence. Another item to check: are the CVC classification methods the same as the ones implemented in short-term counting equipment?

3.2.4 Step 3: Determine the Traffic Patterns to Be Monitored

If sufficient data are available, they should be evaluated to determine what unique traffic volume patterns exist for each different class of vehicles. For example, motorcycles have different DOW and monthly travel patterns than single-unit trucks. The development of factor groups and factor procedures for different classes of vehicles should be undertaken.

At a minimum, States should investigate whether they need different factor groups and processes for six aggregate classes of vehicles: motorcycles (MC), passenger cars (PV), light-duty trucks (LT), buses (BS), single-unit trucks (SU), and multi-unit combination trucks (CU)

In some cases, two or more of the above classes of vehicles may be included in one set of factors when these vehicles can be shown to have similar travel patterns.

The review of temporal patterns can be undertaken using one of several analytical methods described in Section 3.2.5.

The intent of the temporal pattern review is to assess the degree of temporal variation that exists in the State as measured by the existing vehicle classification data program and to examine the validity of the existing factor grouping procedures that produce the appropriate temporal factors. If sufficient data are available, they should be evaluated to determine what unique travel and traffic patterns exist for each of the different classes of vehicles.

The review consists of examining the monthly, DOW, and TOD variation in vehicle traffic volume for each class of vehicles (at a minimum for MC, PV, BS, LT, SU, and CU) at the existing vehicle classification locations, followed by a review of how roads are grouped based on common patterns of DOW and monthly variation.

3.2.5 Step 4: Establish Monthly Pattern Groups

3.2.5.1 Understand Differences in Travel Patterns for Different Vehicle Classes

Previous studies have shown that the 6 vehicle types listed above (MC, PV, BS, LT, SU and CU) are likely to have unique TOD, DOW, and monthly (seasonal) patterns (and the corresponding adjustment factors).

Continuously operating classification counters are needed to monitor these travel patterns so that these patterns can be detected and accounted for in the monthly adjustment factors and to support engineering and planning analyses. For example, if the large increases in weekend motorcycle travel are not accounted for, short-term classification counts will significantly underestimate the number of miles traveled annually on motorcycles, thus biasing national and State safety analyses. This effect is even more important due to most short-term class counts taking place on a Monday thru Thursday days of the week and most motorcycle travel takes place on a Friday thru Sunday days of the week.

3.2.5.2 Vehicle Classes Used for Factoring

Regardless of the approach taken for the computation and application of truck volume by class seasonal adjustment factors, it is recommended that adjustment factors be computed for six generalized HPMS vehicle classes (see VM-1 and HPMS Summary types listed in Table 3-4).

Table 3-4 maps the six-vehicle class groupings used in one of the HPMS data sets to the FHWA 13-vehicle category classes:

**Table 3-4. HPMS Vehicle Class Groups/FHWA Vehicle Classes**
HPMS Summary Table Vehicle Class Group*	FHWA 13-vehicle Category Classification Number
Group 1: Motorcycles (MC)	1
Group 2: Passenger Vehicles equal to or under 121" (PV)	2
Group 3: Light trucks over 121" (LT)	3
Group 4: Buses (BS)	4
Group 5: Single-unit vehicles (SU)	5,6,7
Group 6: Combination-unit vehicles (CU)	8,9,10,11,12,13

* These groupings are used to report travel activity by vehicle type in the Vehicle Summaries dataset for HPMS.

Highway agencies may adjust these categories to reflect their vehicle fleets and travel patterns best, as well as the capabilities of the classification equipment in their programs. (Note that where data show similar patterns, the passenger car and light truck categories can be combined into one set of factor groups.)

Several reasons support these recommendations. There is known to be more variability in the data for lower-volume roadways. With low volumes, even small changes result in high-percentage changes that make the computed factors highly unstable and unreliable. Even on moderately busy roads, many of FHWA's 13-vehicle classes (illustrated in Appendix A) will have mathematically unstable patterns simply because their volumes are low.

A second reason is that computing factors for the individual 13-vehicle classes may introduce too much complexity. There is no gain in separately annualizing extremely variable and rare vehicle classification categories.

The HPMS reports data for six aggregated vehicle classes. This aggregated vehicle classification reporting eliminates the issue with underrepresented and unstable low volume vehicle classes. Therefore, the HPMS 6 vehicle classification groups are recommended for factor development, unless some of the vehicle types included in the HPMS aggregated classes have large volumes and different travel patterns than other vehicles included in that aggregated vehicle class.

Micromobility Classes for Factoring

Classifying micromobility travelers is a relatively new and emerging process, as monitoring and detection equipment continues to be improved and adapted to capturing total volumes of these travelers. Two clearly defined classes are micromobility device users. Future classifications of micromobility devices are likely to include bicycle, pedestrian, hoverboards, e-scooters, and other electronically powered vehicles. See Chapter 2 for more details on equipment and Chapter 6 for more information on future third-party data sources that might provide micromobility use datasets.

3.2.5.3 Select Factoring Methodology

The following two recommended factoring procedures both have advantages and disadvantages. Both are complementary and can be combined as appropriate. The first procedure involves the use of roadway-specific factors. The second is an extension of the traditional traffic volume factoring process involving the creation of groups and the development of average adjustment factors for each of the groups. There are multiple approaches that can be used for road grouping that are discussed in more details later in this section. States are encouraged to use the presented alternative factor procedures or develop other alternatives that effectively remove temporal bias.

Either applying factors to a road or assigning road segments into groups involves making decisions to resolve issues. A factor process may result in one set of factors for cars, another set of factors for trucks, and the combination of both to arrive at a total volume. A factor process may also require more than one set of factors for trucks where different truck types are factored separately. Some roads could conceivably fit in one factor group for cars, a second factor group for single-unit trucks, and a third factor group for combination trucks. Resolutions should be made by each State between the need for accuracy and reductions in complexity in the approach to remove temporal bias.

Two basic elements to the factor development process are the computation of the factors to apply to the short-term counts and the development of a process that assigns these factors to specific counts taken on specific roadways. The roadway-specific and the traditional procedures approach these two aspects of the factoring process differently. The result is two different mechanisms for creating and applying factors, each with its own strengths and weaknesses.

Alternative #1: Roadway-Specific Factor Approach

One option is the process implemented by the Virginia Department of Transportation (VDOT). VDOT operates continuous counters on all major roads, and the counters are used to develop road-specific factors. A short-term classification count taken on a specific road is adjusted using factors taken from the nearest continuous classification counter on that road. A factor computed for a specific road is not applicable to any other road.

As a result, a continuous classification counter should be placed on every road for which an adjustment factor is needed. This requires many continuous vehicle classification counters and substantial resources. However, it ensures that a road can be directly identified with an appropriate factor and provide considerable insight into the movement of freight and goods within the State. The rule for assigning factors to short-term counts is simple and objective.

Identifying a specific road with a specific factor removes a major source of error in the computation of annual traffic volumes by removing the spatial error associated with applying an adjustment factor. Further, it produces factors that are applicable to all trucks using that road. The fact that different truck classes (single-unit versus combination trucks) exhibit different travel patterns is irrelevant, since all patterns are computed for that road. Having road-specific continuous classification counters also greatly reduces the number of short-duration counts that are needed, since the continuous counters provide classification data for road sections near the count locations. The quality of data from continuous classification counters is superior to that of short counts.

Finally, this approach has the advantage of simplifying the calculation of adjustment factors, the application of those factors, and the maintenance of the program. For example, there is no need to develop groups, and the application is performed one road at a time. Problems with continuous counters only apply to the affected roads and prioritization of counter problem correction can be based on road priority.

The most important disadvantage with this approach is cost. It is expensive to install, operate, and maintain large numbers of continuous traffic counters. The larger the system to be covered, the larger the cost. However, this approach may apply effectively to the interstate, where sufficient continuous counters may be available. It can also be applied to roads where current CVC counters are installed.

A second disadvantage is that many roads are quite long and the travel characteristics of any given type of vehicle traffic over their length can change significantly. An adjustment factor taken on a road segment may not be applicable to another segment a few (two to three) miles down the road if a significant vehicle generation activity takes place along that stretch of roadway. When these factors are applied to count locations that are further away from the continuous counter, the potential for error increases and the precision of the estimate diminishes. Traffic patterns change because of economic activity, traffic generators, or road junctions. Caution is recommended when significant traffic generators in the intervening space between the count and the continuous counter exist. Not only does this further increase the number of continuous counters required, but it also creates difficulty in selecting between the two continuous classification counters when a short-term count falls in between.

The high cost may be mitigated by using road-specific factors for the most important truck roads and the traditional factor groups for routes without continuous classification counters. When continuous counters fail, traditional factoring techniques can then be used to provide adjustment factors on those roads. This combination of the traditional and roadway-specific factors may be an effective compromise between these two techniques.

Alternative #2: The Traditional Factor Approach

The traditional factor process involves categorizing roads that have similar traffic patterns for all six HPMS vehicle classes. A sample of data collection sites is then selected from within each group of roads, and factors are computed for each of the CVC data collection sites within a group and then averaged for the group. A definition is provided for each group to describe characteristics that explain the observed pattern, which is used to allow the objective assignment of short counts to the groups. For example, a group might be defined as all roads in counties that experience heavy beach traffic, as these roads have unique seasonal and DOW recreational traffic. Similarly, for truck factors a logical grouping might be all roads serving heavy north/south or east/west through trucking movements, versus those roads that serve primarily local delivery movements.

The truck travel patterns appear to be governed by the amount of long-distance through-truck traffic versus the amount of locally oriented truck traffic, the existence of large truck traffic generators along a road (e.g., agricultural or major industrial activity), and the presence or absence of large populations that require the delivery of freight and goods. Understanding how these and other factors affect truck traffic is the first step toward developing truck volume factor groups. Developing this understanding requires analysis of the existing continuous vehicle classification data already being collected by the State within the context of the commodity movements and economic activity happening in the State. The steps required to gain this understanding are described below.

Several methods can be used to determine whether various sites belong together. A statistically rigorous approach to testing the precision of the selected groups requires the use of analytical statistical tools, an examination of all the truck classes used, and the comparison of statistical reliability for the different types of statistics produced, with the users' need for those reliability statistics. This is a complex and difficult analysis. The analysis can be simplified by concentrating on the most important vehicle classes and statistics produced. However, even with these simplifications, trade-offs are necessary. No designated group will be optimal for all purposes or apply perfectly to all sites. For example, in one group of roads, the single tractor-trailer volumes on roads within each group may have similar travel characteristics, but the single-unit truck volume patterns are quite different from each other.

At some point, the analyst will need to determine the proper balance between the precision of the group factors developed for these two classes of trucks, or they will have to accept different factor groups for different vehicle classes. Each road may end up in multiple factor groups depending on what vehicle classification volume is being factored. Use of multiple groups may result in a more accurate factor process but will result in a more complicated and confusing procedure.

The trade-offs between alternative factor groups can only be compared by understanding the value of the precision of each statistic to the data user. In most cases, this is simply a function of determining the relative importance of different statistics. For example, if 95 percent of all trucks are single tractor-trailer trucks, then having road groups that accurately describe tractor-trailer vehicle patterns is more important than having road groups that accurately describe single-unit truck patterns. Similarly, if single-unit trucks carry the predominant amount of freight (this occurs in mineral extraction areas), then the emphasis should be on forming road groups that accurately measure single-unit truck volume patterns. If a road group has a significant presence of motorcycles, buses, and/or recreational vehicles (typically recreational roads), the road grouping process should focus on temporal similarities in travel for these vehicle types.

3.2.5.4 Create Initial Factor Groups (Traditional Approach)

The creation and application of adjustment factor groups (TOD, DOW, and MOY) by class of vehicle is a topic that is still new. Some State DOTs have yet to develop these factoring procedures, and considerable research still needs to be accomplished. Several methods can be used to create factor groups using the traditional approach, including knowledge-based grouping, statistical clustering, area of influence, and a hybrid approach that combines methods.

Knowledge-Based Grouping

States should use the available classification data and knowledge to begin the development of traffic patterns. Traffic patterns are governed by a combination of local service activity, local economic activity, local freight movements, and through-truck movements. Extensive passenger vehicle and truck through-traffic movements are likely to result in higher night passenger vehicles and truck travel and higher weekend passenger vehicles and truck travel. Through-traffic can flatten the monthly fluctuations present on some roads and create monthly patterns on other roads not associated with the economic activity occurring in the land abutting that roadway section.

Similarly, a road primarily serving local freight movements will be highly affected by the timing of those local freight movements. For example, if the factory located along a given road (not subject to significant amounts of through-traffic) does not operate at night, there may be little freight movement on that road at night.

Functional road classification can be used to a limited extent to help differentiate between roads with heavy through-traffic and those with only local traffic. Interstates and principal arterials tend to have higher through-truck traffic volumes than lower functional classes. However, there are urban interstates and principal arterial highways with little or no through-truck traffic, just as some roads with lower functional classifications can carry considerable through-truck volumes. Therefore, functional classification of a road by itself is an insufficient identifier of truck usage patterns. To identify road-usage characteristics, additional information should be obtained from either truck volume data collection efforts or the knowledge of staff familiar with the trucking usage of specific roads or the transportation planning and land development offices. The truck volume data patterns, especially TOD patterns from short-term counts and DOW and monthly patterns from continuous classifiers, identify travel patterns for different types of vehicles. These patterns should then be discussed with staff working on freight planning activities to understand and help identify trucking patterns in ways that allow both grouping of continuous counters and assignment of short-term count location to those groups.

Among the types of patterns that can be identified through this combination of data and communication with staff are various local, regional, and through-travel patterns. For example, local truck traffic can be generated by a single facility such as a factory, or by wider activity such as agriculture or commercial and industrial centers. These point or area truck-trip generators create specific seasonal and DOW patterns, much as recreational activity creates specific passenger car patterns. Truck trips produced by these generators can show large monthly changes (such as from agricultural areas) or constant (such as flow patterns produced by many types of major industrial plants). Where these trips predominate on a road, truck travel patterns tend to match the activity of the geographic point or area that produces those trips. In addition, changes in the output of these facilities can have dramatic changes in the level of trucking activity. For example, a labor problem at a West coast container port may produce dramatic shifts in container truck traffic to other ports. This results in significant changes in truck traffic on major routes serving those ports. Expansion or contraction of factory production at a major automobile plant in the Midwest can cause similar dramatic changes on roads that serve those facilities.

An understanding of the commodity flow within the State is important for road grouping process. Specific commodities tend to be carried by specific types of heavy vehicles (trucks). Understanding the types of heavy vehicles typically used to carry specific commodities is critical to understanding the trucking patterns on a road and how those patterns are likely to change (e.g., coal trucks in Kentucky and Pennsylvania) within the State.

Geographic stratification and functional classification can be used to create truck factor groups that capture the temporal patterns and are reasonably easy to apply. An initial set of factor groups might look something like that shown in Table 3-5. However, the two keys to the creation of groups are that (1) the data should show that traffic patterns within grouped sites are in fact similar, and (2) those groups should be designed in such a manner that short-term counts can be easily and accurately assigned to the correct factor groups. Therefore, as groups are formed, specific roads may need to move from one group to another to ensure that both constraints remain true.

Definitions like those above group roads with as homogenous truck travel patterns as possible and provide easy identification of the groups for application purposes. They present a starting point to begin the identification process necessary to form adequate groups.

**Table 3-5. Example Classification Factor Groups**
Rural	Urban
Interstate and arterial major through-truck routes	Interstate and arterial major truck routes
Other roads (e.g., regional agricultural roads) with little through traffic	Interstate and other freeways serving primarily local truck traffic
Other unrestricted truck routes	Other unrestricted truck routes
Other rural roads (e.g., mining areas, agricultural, weather, mountain/coastal, geographic area)	Other roads (non-truck routes)
Special cases (e.g., roads primarily used for recreational travel, roads serving ports, etc.)

Grouping Using Cluster Analysis

Performing a statistical cluster analysis using truck volumes by vehicle class (as illustrated in Section 3.1.4 for total volume) will help to identify the natural patterns of variation and to place the continuous counters in variation groups. This will help in identifying which groups may be appropriate and in determining how many groups are needed.

Grouping Using Hybrid Approach

One of strengths of the cluster analysis is that it identifies groups only by variation. The weakness is that it does not describe the characteristics of the group that allow application of the resulting factors to other short counts. The example definition in Table 3-5 does exactly the opposite. It clearly establishes group characteristics but cannot indicate whether the temporal variation is worth creating separate groups or not. As is the case for AADT group procedures, a hybrid approach that combines the statistical methods and knowledge-based assignments provides a good way to establish the appropriate groups.

3.2.6 Step 5. Develop Monthly Factors

All roads within the defined factor group should have similar types of vehicle volume by class patterns. To verify this condition, the continuous counter data available within the group should be used to compute the temporal adjustment factors of interest (TOD, DOW, MOY, or combined) for each of the vehicle types desired, and then compute the mean and standard deviation for the group as a whole. Visual assessment of the TOD, DOW, and MOY factor plots can also help to determine whether the travel patterns at the continuous sites are reasonably similar.

The assumptions and computation of the adjustment factors by vehicle class is similar to AADT factors. An example of a combined monthly and weekday factor computation for a vehicle classification site is shown below.

Adjustment Factor sub c comma June equals A-A-D-T-T sub c divided by M-A-W-K-D-T-T sub c comma June.

Where:

Adjustment Factor_{C, June} = a multiplicative factor for a specific vehicle type C used to convert a 24-hour count taken on any weekday in June to an estimate of annual average daily traffic for a vehicle type C

AADTT C = annual average daily (truck) traffic volume for a specific vehicle type C

MAWKDTT _{C, June} = monthly average weekday (truck) traffic volume for the month of June for a specific vehicle type C

This formulation assumes a multiplicative application of the computed adjustment factor. AADTT_c is equal to the average 24-hour count for vehicle class c times the adjustment factor. Many States use the inverse of the above formula and apply the resulting factor by dividing the average 24-hour volume obtained from their short-term count by the adjustment factor. The example in Table 3-6 demonstrates factors computed for a single month and shows how these monthly adjustment factors differ by vehicle class.

**Table 3-6. Example of Monthly Factors by Vehicle Class at a Single Site**
Measurement	Motorcycles	Car and Light Trucks	Buses	Single-Unit Trucks	Combination Trucks	Total Volume
MADTc	35	4,874	52	227	1,639	6,826
AADTc	33	5,499	57	288	1,653	7,530
Monthly Factor (AADTc/MADTc)	0.95	1.13	1.10	1.27	1.01	1.10

Computing the mean (or average) for the monthly (e.g., June) factor using data from all sites within the factor group yields the group factor for application to all short-term counts (e.g., weekdays in June) taken on road segments within the group. The standard deviation of the factors within the group describes the variability of the group factor. The variability can be used to compute the precision of the group factor and to estimate the number of continuous classification counter locations needed.

The variability of each statistic computed for the factor group will have a different level of precision. For example, the June factor will have different precision than the July factor. The precision will also vary for each of the vehicle types analyzed.

3.2.6.1 Test the Quality of the Selected Groups

The information on estimated precision of group factors must be reviewed to determine whether the roads grouped together have similar individual vehicle travel patterns.

The trade-offs between alternative factor groups should be evaluated considering the value of the precision of each statistic to the data user. For example, if 95 percent of all trucks are single tractor-trailer trucks, then having road groups that accurately describe tractor-trailer vehicle patterns is more important than having road groups that accurately describe single-unit truck patterns. Similarly, if single-unit trucks carry the predominant amount of freight (this occurs in mineral extraction areas), then the emphasis should be on forming road groups that accurately measure single-unit truck volume patterns.

The quality of a given factor group can be examined in two ways. The first is to examine graphically the traffic patterns present at each site in the group. The second method is to compute the mean and standard deviation for various factors that the factor group is designed to provide. If these factors have small amounts of deviation (for example, the 95% confidence interval is within +/- 10% of the mean value of each monthly factor), the roads can be considered to have similar characteristics. If the standard deviations are large, the road groupings may need to be revised.

There can be cases where the factors will not improve the annual volume estimates, particularly in high vehicle volume variability situations. An alternative is to take multiple site-specific classification counts at different times during the year to measure monthly changes and develop annual estimate. This can be an effective way to estimate annual traffic volume for individual vehicle classes more accurately for high profile projects, if an agency can afford this additional data collection effort. This alternative can also be used to test the accuracy of the annual estimates derived from the group factors.

3.2.6.2 Determine the Precision of the Factors

Use this formula to compute the precision of the adjustment factor for a selected month for a group of n sites for each vehicle class:

$D equals plus or minus t sub alpha divided by two comma n minus one, multiplied by the following fraction: s divided by the square root of n.$

Where:

D = precision of the group factor (95% confidence interval)

α = level of significance, the probability of rejecting the Null Hypothesis when it is true, for the selected level of confidence (for 95 percent confidence α = 1 - 0.95 = 0.05)

s = standard deviation of the group factors for the selected month

n = number of sites in factor group

The ratio standard error equation is called standard error

Note that the precision of the group is affected by the standard deviation (as a measure of homogeneity or diversity) of group factors and by the sample size. Increasing the number of continuous counter locations within a group will also improve the precision of the group factor for groups made of small number of sites. However, for fairly homogeneous groups increasing the number of continuous classification counter locations beyond 6 only marginally improves the precision of the group factor application at specific roadway sections, as demonstrated in Figure 3-2.

Figure 3-2. Relationship between Statistic and Number of Sites

3.2.6.3 Refine the Factor Groups

If the factor groups selected have reasonably homogenous travel patterns (i.e., the variability of the factors is low), then the groups can be used for factor development and application. If the factors for the group are too variable, then the groups may need to be modified. These modifications can include the creation of new groups (by removing the roads represented by some continuous classification counters from one group and placing them in a new group), and the realignment of counters within existing groups (by shifting some classification counters and the roads they represent from one existing factor group to another). This process continues until a judgment is made that the groups are adequate.

Be aware, as noted earlier, that if precise adjustment factors are desired, it is possible that the factor process will require different factor groups for each vehicle class. That is, traffic patterns for combination trucks may be significantly different (and affected by different factors) than the traffic patterns found for smaller, short-haul trucks. These patterns may in turn be sufficiently different from passenger vehicle patterns that three different factor groupings may need to be developed. In such a case, passenger car volumes may need to be adjusted using the State's existing factor process since total volume tends to be determined by passenger car volumes in most locations, while single unit trucks are factored with data obtained from different groups of counters. Combination trucks are factored with counts obtained from those same counters but aggregated in a different fashion. Then the three independent volume estimates will need to be added to produce the total AADT estimate.

3.2.7 Step 6 Determine the Appropriate Number of Continuous Vehicle Classification Locations

Once groups have been established and the variability of the group factors computed, it is possible to determine the number of count locations needed to create and apply factors for a selected level of precision. Note that because each statistic (i.e., factors computed for different months) computed for a group has a different level of variability, each statistic computed will have a different level of precision.

The first step in determining the number of sites per group is to determine which statistics will guide the decision. In general, the key statistics are those that define the objective of the formation of groups, that is typically, the correction for temporal bias in truck volumes. The combined DOW and monthly factor, computed for the truck-trailer combination vehicles during the months when short-term counts are taken, may well be the most appropriate statistic to guide the group size for the interstate/arterial groups. For other groups, the single-unit trucks may be more appropriate.

If counts are routinely taken over a nine-month period, the one month with the most variable monthly adjustment factor (among those nine months) should be used to determine the variability of the adjustment factors and should thus be used to determine the total sample size desired. In that way, factors computed for any other month have higher precision.

For most factor groups, at least six continuous counters should be included within each factor group.

If it is assumed that some counters will fail each year because of equipment, communications, or other problems, a margin of safety may be achieved by adding additional counters.

3.2.7.1 Collect Additional Data and Refine the Established Process

States are encouraged to convert as many of their continuous counters to classification as possible and to analyze the available data to understand individual vehicle travel patterns and variation. A substantial continuous vehicle classification program allows States to refine the classification count factoring process as needed. The addition of new continuous count locations allows the comparison of newly measured truck travel patterns with previously known patterns. This is true even for the road-specific factoring procedure since traffic patterns along a road can change dramatically from one section to another. One way of adding new count locations is to move counter locations when equipment or sensors fail and need replacement at an existing continuous site.

If a new data collection site fits well within the expected group pattern, that site can be incorporated into the factor group. However, if a new site shows a truck travel pattern that does not fit within the expected group pattern, a reassessment of the truck volume factoring procedures may be appropriate. Modifications include moving specific roads or road sections from one factor group to another, creating new factor groups, and even revising the entire classification factoring process.

The factoring process should be reviewed periodically to ensure that it is performing as intended. For the first few years after initial development or until the process has matured, these evaluations should be conducted every year. After that, the classification process should be reviewed every 3 years (or the same review cycle used for the AADT group factor process).

3.2.8 Step 7 Compute Final Group Temporal Adjustment Factors

Current practice applies temporal adjustments to the total volume and then estimates volumes for vehicle types using the observed classification proportions. This approach works if the seasonal traffic volume patterns of individual vehicle types are the same as the total volume profile. Otherwise, traffic volume for some vehicle types will be under-estimated or over-estimated.

A more accurate and appropriate approach is to apply monthly adjustment factors individually to six aggregated FHWA vehicle classes (MC, PV, LT, BU, SU, CU). The following example shows application of adjustment factors to short-term motorcycle count sample. All other vehicle classes will follow the same procedure. For more information on computing temporal adjustment factors, see Section 3.1.4.

Example of Applying Adjustment Factors to Motorcycles

The DOW traffic pattern for motorcycles differs from that of other vehicle types, so short-term counts for motorcycles should be factored separately. The TMG allows flexibility in the creation of DOW factors. It suggests that factors may be computed on an individual basis (seven daily factors) or as combined weekday and weekend factors. The definition of "weekday" and "weekend" is a function of traffic patterns. In urban areas, Fridays are more similar to weekdays than weekends. In some rural areas, they are closer to weekends. It is also permissible to treat weekdays as Monday – Thursday; treat weekends as Saturday and Sunday and treat Fridays as a third factor adjustment group.

In practice, few short-term counts are taken on weekends, unless the State performs seven-day short-term counts, so the only data available for weekends are from continuous traffic counters and classifiers. This is a problem for correctly estimating motorcycle VDT, as motorcycles may have significant weekend travel on routes or areas that are not near a continuous classifier, therefore underestimating annual motorcycle VMT, which is an important statistic for evaluating the safety of motorcycle travel. The solution is to: 1) install additional continuous vehicle classifiers; 2) make sure that at least some of the available permanent classifiers are placed on roads that are used for recreational motorcycle travel; or 3) take classification counts that include some weekdays and extend over weekends where recreational motorcycle travel is expected to occur in order to account for differences in DOW motorcycle travel on those roads.

The following example shows how to correctly estimate the motorcycle AADT (AADMCT). First, take the data from a continuous automatic vehicle classifier and determine the monthly average daily motorcycle traffic.

Next, determine the factor group that the short-term count location belongs to and extract the monthly factors computed for motorcycles. These factors are computed as the ratios of the monthly average daily motorcycle traffic to the AADMCT for each month, see Table 3-7.

**Table 3-7. Motorcycle Traffic Volume Estimation**
Month	MADT	Monthly Factor
January	47,376	1.05
February	45,285	1.10
March	50,574	0.99
April	51,040	0.98
May	51,662	0.97
June	52,320	0.95
July	51,320	0.97
August	52,416	0.95
September	50,824	0.98
October	51,564	0.97
November	49,188	1.02
December	45,806	1.09
AADMCT	49,948	1.00

Next, use continuous count data to calculate the average daily traffic by vehicle type for each day of the week for the year. For motorcycles, the computed parameter will be ADMCT by DOW. Then compute DOW motorcycle correction factors (MCF) as the ratio of the AADMCT and the DOW ADMCT. Table 3-8 shows an example of the ADMCT by day of week.

**Table 3-8. ADMCT by Day of Week**
Dav	ADMCT b DOW	Resulting DOW MCF
Monday	396	1.26
Tuesday	403	1.24
Wednesday	405	1.23
Thursday	428	1.1
Friday	655	0.76
Saturday	725	0.69
Sunday	483	1.03
ADMCT	499

Compute the MCF for each DOW:

Monday MCF	= ADMCT/Monday AADMCT = 499/396 = 1.26
Tuesday MCF	= ADMCT/Tuesday AADMCT = 499/403 = 1.24
Wednesday MCF	= ADMCT/Wednesday AADMCT = 499/405 = 1.23
Thursday MCF	= ADMCT/Thursday AADMCT = 499/428 = 1.17
Friday MCF	= ADMCT/Friday AADMCT = 499/655 = 0.76
Saturday MCF	= ADMCT/Saturday AADMCT = 499/725 = 0.69
Sunday MCF	= ADMCT/Sunday AADMCT = 499/483 = 1.03

Therefore, a short-term class count is first factored for seasonality and then for the day of week.

For the motorcycle example, a short-term vehicle classification count was taken on the same route as the continuous site analyzed above, about 10 miles to the south. Vehicle counts by classification were taken on two weekdays in August, with Table 3-9 showing the results for motorcycles.

**Table 3-9. ADT Calculation Example**
Date	ADT	AADT
Aug. 14 (Tues)	518	50,761
Aug. 15 (Wed)	494	51,231
Average	506	50,996

Since we are using separate DOW factors, we will do the adjustments for each DOW first and then average the adjusted daily values. The two counts are adjusted using both the seasonal (monthly) factor for August,

which is 0.95, and the appropriate DOW factors (1.24 and 1.23 respectively).

518 x 0.95 x 1.24 = 610

494 x 0.95 x 1.23 = 577

These two ADT_m estimates are then averaged to provide the estimate of AADT_m.

(610 + 577) / 2 = 594

Because of the special DOW motorcycle factors, weekday motorcycle counts are increased to more accurately estimate the annual average daily motorcycle travel. This considers the likelihood of higher weekend motorcycle travel. The other vehicle classes would need to be adjusted for the day of week, too, so that the total volume is correct.

Example of Applying Adjustment Factors to Other Vehicle Classes

The same process should be performed with each of the vehicle classes. At the end of the process, the total of the different vehicle classes should then be compared against the AADT computed for the volume only factor and the various volumes adjusted proportionately to account for any differences in those two AADT estimates. (The AADT computed from volume only will be the more accurate estimate of total volume and should serve as the control total.)

A simplified example is shown in Table 3-10. (Note that this table shows the different day of week and monthly adjustments for each class. This example illustrates the need for adjusting vehicle classification volumes if applicable. Although this example uses a daily total volume count, additional time increments can be used for adjusting vehicle classification volumes such as hour of the day or 15-minute data.

FHWA recommends using all hours if data is collected beyond 24 or 48 hours. FHWA also recommends collecting and storing data in individual vehicle record (IVR) format so all vehicles are captured when collecting and reporting data.

If necessary, deleting the first and last hour is done because the time it takes to set the counter up might not represent the entire hour (or 15-minute time increments) and per-vehicle records need to be represented according the defined time increment.

**Table 3-10. ADT Vehicle Type Computation Example**
Date	MC Volume	PV Volume	LT Volume	Bus Volume	SU Volume	CU Volume	Total Volume
Aug. 14 (Tues)	518	30,705	11,215	58	4,103	4,162	50,761
Aug. 15 (Wed)	494	31,689	11,834	48	3,697	3,469	51,231
Tuesday Factor	1.24	1.02	1.02	1.06	0.88	0.8
Wednesday Factor	1.23	1.00	1.00	1.03	0.89	0.79
August Factor by Class	0.95	0.97	0.97	0.81	0.84	0.91
AADT Based on Tuesday	610	30,380	11,096	50	3033	3030	48,199
AADT Based on Wednesday	518	30,705	11,215	58	4,103	4,162	50,761
Average	494	31,689	11,834	48	3,697	3,469	51,231

ADT computed from total volume = (50,761 + 51,231) x 0.95 x 0.98 DOW factor) = 47,477

Difference of average computed from total volume minus average computed by class specific factors and then summed = -668

Date	MC Volume	PV Volume	LT Volume	Bus Volume	SU Volume	CU Volume	Total Volume
Fraction of Traffic	0.012	0.635	0.234	0.001	0.060	0.058
Proportional Adjustment (Fraction of Vehicles x Error)	-8	-424	-157	-1	-40	-38
Final AADT by Class (Volume + Proportional Adjustment)	585	30,135	11,131	44	2,858	2,724	47,477

3.3 HOW TO DESIGN A CONTINUOUS VEHICLE WEIGHT DATA MONITORING PROGRAM (WEIGHT)

3.3.1 Statewide WIM Program

Truck and axle weight data are used as a primary input to a number of a State highway agency's most significant tasks. For example, traffic loading is a primary factor in determining the depth of pavement sections. It is used as a primary determinant in the selection of pavement maintenance and rehabilitation treatments. The total tonnage moved on roads is used to estimate the value of freight traveling on the roadway system and is a major input into calculations for determining the costs of congestion and benefits to be gained from new construction and operating strategies. Vehicle classification and weight information are also key components in studies that determine the relative cost responsibility of different road users. The number, weight, and configuration of trucks are also major factors in bridge design and the analysis of expected remaining bridge life.

The Steps required to develop a statewide WIM program are generally similar to volume and class count program steps but also have some differences due to the extent of the program, the intended data uses, and WIM equipment cost and capabilities. The steps to create and maintain the weight portion of the continuous traffic monitoring data program include:

STEP 1 – Review Existing Weight Data Collection Program

STEP 2 – Develop an Inventory of Existing WIM Sites and Assess WIM Site Locations STEP 3 – Determine the Roadway Groups to Be Monitored by WIM

STEP 4 – Establish Load Factor Groups

STEP 5 – Determine Number of Weight Data Collection Locations STEP 6 – Select New Sites to Meet WIM Program Needs

STEP 7 – Integrate the WIM Sites with the Remaining Count Program

3.3.2 Step 1. Review the Existing Weight Data Collection Program

Of all the traffic monitoring activities, WIM requires the most sophisticated data collection sensors, the most controlled operating environment (strong, smooth, level pavement, even traffic flow at constant highway speed), and the costliest equipment set up and calibration. It is important to consider these complex requirements during weight data program review.

An excellent introduction to WIM is provided in the WIM Pocket Guide (FHWA 2018). The Guide consists of three main text documents, four instructional video supplements, and six appendices showcasing noteworthy practices. The video supplements and appendices are only available on the FHWA website:
https://www.fhwa.dot.gov/policyinformation/knowledgecenter

3.3.2.1 Heavy Vehicle Weight User Needs

In addition to reviewing the physical requirements for WIM systems, the needs of the end users (customer of the WIM data) should be considered.

Heavy vehicle weight data are used for a wide variety of tasks. In the TMG, heavy vehicle refers to buses and heavy trucks, not light trucks such as pick-ups. These tasks include, but are not limited to, the following:

Pavement design
Pavement maintenance
Bridge design
Geometric design
Environmental analysis, such as air quality and noise
Pavement and bridge loading restrictions
Development and application of equitable tax structures
Determination of the need for and success of weight law enforcement actions
Determination of the need for geometric improvements related to vehicle size, weight, and speed
Determination of the economic value of freight being moved on roadways
Determination of the need for and effect of appropriate safety improvements
Noise abatement
Highway cost allocation studies
Axle Correction Factors

3.3.2.2 Truck Weight Data Summaries

State highway agencies summarize and report truck weight data in many ways. Three types of summaries are commonly used including:

Gross vehicle weight (GVW) (usually by vehicle class), including average GVW and frequency distribution by weight bins.
Axle load spectra, which are axle load frequency distributions by load bins. The AASHTOWare Pavement ME Design (PMED) software that follows AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) Manual of Practice (MOP) requires the normalized (i.e., percentile) axle load spectra by axle group (singles, tandems, tridems, and quads) and vehicle class representing a typical day of each calendar month, other applications also require information about axle load spectra for pentads and other axle groupings.
Equivalent single axle load (ESAL) values, which are used as key inputs to the traditional AASHTO 93 pavement design procedures. ESAL value of 1 corresponds to 18,000 lb single axle load. In the ESAL method, each axle load is assigned a load equivalency factor (LEF) representing an equivalent number of 18,000 lb loads with respect to pavement damaging potential. The LEFs are multipliers that are applied to the reported axle loads to convert them to ESAL values. LEFs are dependent on axle group, pavement type, and pavement serviceability index. LEF factors are also called ESAL per-axle factors. The cumulative ESAL value estimated for the entire pavement service life is used as input for traditional AASHTO 93 pavement design. ESAL per truck values are used to characterize and compare the pavement damaging potential of different vehicles.

Each of these summary statistics can be developed for a specific vehicle class, specific site, a group of sites, geographic region, or an entire State or, depending on the needs of the analysis and the data collection and reporting procedure. In addition to the summary loading statistics, FHWA requires submission of axle and truck loading data in the IVR format.

The role of the traffic-monitoring program is to provide the user with the data summaries needed. The summaries can be required for any one of several levels of summarization. For example, it may be appropriate to maintain axle-loading distributions for each of the FHWA heavy vehicle classes (classes four through thirteen, see Figure 3-5) so that these statistics are available when needed for pavement design—such as with the AASHTO MEPDG MOP. It is recommended that axle loading (all vehicle classes or classes 4 to 13) be stored in the IVR format, since it offers the most detail for later reporting. Even if a more aggregated classification system is used for most analyses by an agency, the more detailed data collected by WIM systems should be retained for later use, as these raw per vehicle data are the only source of other key statistics—such as the headway between trucks, or studies looking at changes in the truck characteristics like average tandem axle spacing—which are used as engineering design assumptions. For AASHTO MEPDG MOP design of jointed plain concrete pavements, axle-to-axle spacing of 12 to 15 feet is an important input parameter for predicting mid-slab cracking.

The truck weight summary statistics can be computed with FHWA's TMAS software, with software supplied by the WIM system vendor, or with software developed specifically for use by the State highway agency as part of its traffic database.

Axle Load Spectra

The basis for all truck loading estimates is the axle load distribution table, also called an axle load spectrum (or the plural form called spectra). An axle load spectrum is computed using IVR data collected by WIM systems for individual vehicle passes over the WIM sensors. It describes the distribution of axle weights for each axle group (single, tandem, tridem, quad, and penta+) and for each class

of vehicles. Load spectra are frequently normalized (i.e., expressed as percentile distribution) so that the

table shows the fraction of axles within specific load ranges for a given class of vehicles. Table 3-11 shows an example of normalized (i.e., expressed as a percentile frequency distribution) load spectra for single and tandem axles for class 9 trucks obtained from one of Florida DOT WIM sites.

Understanding and accounting for monthly variations in vehicle weights is becoming increasingly important for both economic analyses and pavement design procedures. The AASHTO MEPDG MOP pavement design procedures require input of a normalized axle loading distribution representing a typical day of each calendar month. Therefore, the traffic data collection process should be able to detect and report differences in loads by month (because the number of trucks or the weights of individual trucks vary) during the year. Appendix F contains AASHTOWare Pavement ME Design (PMED) software requirements for axle loading distribution factor reporting.

**Table 3-11. Example of a Normalized Load Spectrum for Vehicle Class 9 Single and Tandem Axles from FDOT**
Steering Axle Distribution		Tandem Axle Distribution
Load Bin, lb	%	Load Bin, lb	%
0 to 999	0.00	0 to 1999	0.00
1000 to 1999	0.06	2000 to 3999	0.00
2000 to 2999	0.06	4000 to 5999	0.13
3000 to 3999	0.19	6000 to 7999	0.26
4000 to 4999	0.45	8000 to 9999	10.27
5000 to 5999	1.85	10000 to 11999	17.49
6000 to 6999	6.96	12000 to 13999	10.27
7000 to 7999	24.25	14000 to 15999	7.47
8000 to 8999	36.50	16000 to 17999	6.00
9000 to 9999	23.10	18000 to 19999	6.00
10000 to 10999	5.36	20000 to 21999	5.68
11000 to 11999	0.89	22000 to 23999	5.23
12000 to 12999	0.26	24000 to 25999	3.25
13000 to 13999	0.06	26000 to 27999	3.57
14000 to 14999	0.00	28000 to 29999	4.53
15000 to 15999	0.00	30000 to 31999	8.36
16000 to 16999	0.00	32000 to 33999	7.28
17000 to 17999	0.00	34000 to 35999	2.43
18000 to 18999	0.00	36000 to 37999	1.40
19000 to 19999	0.00	38000 to 39999	0.26
20000 to 20999	0.00	40000 to 41999	0.13
21000 or more	0.00	42000 or more	0.00

Traffic Loading Estimates Derived from Axle Load Spectra

Once developed, axle load spectra are often converted into other statistics, such as ESAL, GVW, or total traffic loading statistics. Thus, agencies should have means to develop the normalized axle loading distributions using their WIM data.

Axle load spectra and the resulting ESAL and GVW statistics can be derived directly only from WIM sites or static weight scales. Because WIM equipment is expensive to install and maintain, WIM data are available at only a few locations in a State. Thus, at most road sites, truck weight data items cannot be measured directly. Instead, the traffic loading estimates are obtained by combining a representative, normalized axle load spectra collected elsewhere in the State and a site-specific count of volume by vehicle classification.

Multiplying the site-specific truck volume statistics (AADTT or MADT by vehicle class) by the normalized axle load spectra and by the number of axles per truck yields site-specific estimate of the annual or monthly average daily traffic loading by vehicle class and axle group for that site. Summing the products across axle groups provides an estimate of the traffic load by vehicle class. Further summation across all vehicle classes provides an estimate of the average daily total traffic load.

That is, the site-specific classification count is used to determine how many trucks of a particular type travel on the road. The WIM data determine how many axles of each type are present for each class of trucks and the weight of each of those axles. For example, if a road section carries 100 Class 9 trucks in a day, it likely experiences approximately 100 single axles and 200 sets of tandem axles. (Directions for developing and applying representative axle load spectra for load factor group are given later in this chapter.)

Alternatively, if the average GVW for each vehicle class is known or computed based on WIM data, multiplying the number of trucks within a given class by the average GVW for vehicles of that class yields the total number of pounds or total traffic load applied by that class on that roadway. Adding these values across all vehicle classes yields the total load carried by that road.

ESAL Computation

The axle load distribution by axle load range can be converted into an ESAL. To make this conversion, an ESAL per axle factor, also called AASHTO axle load equivalency factor (LEF), is assigned to each axle load measured at the WIM location. ESAL per axle factors can be obtained from the 1993 edition of the AASHTO Guide for Design of Pavement Structures Appendix F Tables D.1 to D.18 Axle Load Equivalency factors. These values vary by pavement type, slab thickness (for rigid pavements), or pavement structural number (for flexible pavements), and pavement terminal serviceability index.

LEF values depend on axle load magnitude, axle group, pavement type, pavement structure (structural number for flexible and slab depth for rigid), and pavement terminal serviceability index. The LEF value times the number of axles within that axle load range yields the number of ESALs for that axle group and load range. Summing these values across all load ranges, all axle groups, and all vehicle classes yields the total number of ESALs applied to the roadway. The number of axles by load range is computed from IVRs collected by WIM equipment.

3.3.3 Step 2. Inventory Existing WIM Sites and Assess WIM Site Locations

The State should conduct a detailed inventory of its WIM assets and assess its existing WIM data collection sites against WIM program goals and objectives. An existing WIM site may require relocation or pavement remediation because of failure of the pavement surrounding the WIM sensors or failure of the WIM equipment. To make this determination, the need for that WIM site should be evaluated. Sites that are still needed should be reinstalled and/or pavement remediated. If that site is no longer needed or if other higher priority locations exist, the WIM equipment should be moved to another site. Based on users' needs, the additional sites may become necessary.

The State should assess if WIM sensor locations are conducive to accurate measurement of the vertical forces applied by vehicle wheels to sensors in the roadway while the truck travels over the sensors. These forces are used for estimating axle and truck weights as if the truck was stationary. The task is complicated by a variety of factors, including the following:

Each sensor registers the vertical force of each axle for only a brief time.
The weight estimated by the WIM system based on that measurement varies because while the vehicle is in motion, the truck and its components may experience up and down motion, generating dynamic forces. If the truck mass is moving upward when an axle crosses the WIM sensor, the weight applied by that axle is lower than the static value. If the truck mass is landing, the weight applied is greater than the static value. (In addition, truck components such as shock absorbers or the type of load being carried are also in motion, affecting the axle weight at any given instant in time.) The WIM systems are designed to account for this variation but can only account for modest vertical truck movements. Removal or the reduction of vehicle dynamics is an important part of a successful WIM site.
Some sensors (strip or line sensors) register only a portion of the tire weight at any given time. Because the sensor is smaller than the footprint of the tire, the pavement surrounding the sensor physically supports some portion of the axle weight throughout the axle weight measurement.
Sensors should be capable of weighing more than one axle in quick succession. That is, the sensor should be able to recover quickly enough so that one axle weight does not affect the measurement of the following axle. At a minimum each WIM lane should measure both the left and right axle weight at least once.
Roadway geometries such as grade, slope, and horizontal and vertical curves can cause shifts in vehicle weight from one axle to another or from one side to another, which would not be present if the truck was at rest on a flat scale platform.
Vehicle acceleration or braking, torque from the drive axles, wind (head or cross), the style and condition of vehicle's suspension system, and a variety of other factors can also cause shifts of weight from left to right and one axle to another.
To provide the ability for accurate weight data collection, all WIM sites should have sensors installed flush with the road (i.e., permanently installed sensors).

Strip or line WIM sensors register only a portion of the tire weight at any given time. Because the sensor is more narrow than the footprint of the tire, the pavement surrounding the sensor physically supports some portion of the axle weight throughout the axle weight measurement.

The effects of many of these factors can be minimized through careful WIM site selection and design of the WIM site. The WIM Pocket Guide (FHWA, 2018) provides detailed information about the desired WIM site features, the recommended WIM sensor configurations, strengths, and weaknesses of different WIM sensor technologies.

The site should be selected and designed to reduce the dynamic motion of passing vehicles. However, achieving these design controls requires restrictions on site selection, which means that WIM systems cannot be placed as easily or as universally as other traffic monitoring equipment. They should not be placed in rough pavement or pavement that is in poor condition. To help States identify those locations where pavement conditions are conducive to the placement and operation of WIM equipment, the FHWA-LTPP program has developed a software module called the Optimal WIM Locator (OWL) that is part of the Profile Viewing and Analysis (ProVAL) software system. The OWL software uses pavement profile information to identify optimal WIM sensor locations. Both ProVAL and the OWL module are free. Information on both ProVAL and the OWL module can be obtained through the LTPP Customer Support Service Center.

WIM sensors work most accurately when they are placed flush with the roadway. Sensors that sit on top of the roadway cause two problems with WIM system accuracy: 1) They induce additional short-wave-length dynamic motion in the vehicle axles; and 2) They can cause the sensor to measure the force of tire deformation (which includes a horizontal component not related to the weight of the axle) in addition to the axle weight. This means that permanent installation of the sensors and/or frames that hold the sensors is normally better for consistent, accurate weighing results. The use of permanent WIM sensors installed flush with the road surface is recommended as a means of improving the quality of the data.

3.3.4 Step 3. Determine the Roadway Groups to Be Monitored

The primary objective of the weight data collection program is to obtain a reliable measure of the axle weights and axle-to-axle spacings and GVW per vehicle for different heavy vehicle classes (primarily for FHWA classes 4 to 13).

The data collection plan for truck weight accounts for the following:

The needs of State and Federal agencies for truck weight data.
The capabilities and limitations of WIM equipment.
The resource constraints found at many State highway agencies.
The variability of truck weight data.

The weight data collection program is based on collecting accurate axle weights for at least all heavy trucks that can be applied with confidence and statistical precision to all roads in a State.

A single statewide average statistic such as GVW or ESAL per truck is not applicable to all parts of the State or all road types. Trucking characteristics can vary significantly by type of road or by geographic area within a State. Therefore, it is important to collect data and maintain summary statistics for different regions or roads in the State. For example, the truck traffic in urban areas often has different truck weight characteristics than those in rural areas. Roads that serve major agricultural regions often have different loading characteristics than roads that serve resource extraction industries. Roads that serve major industrial areas within an urban area tend to carry much heavier trucks than roads that serve general urban and suburban areas. Roads that serve major through-truck movements often experience different truck weights than roads that serve primarily local truck traffic.

An effective truck weight program should identify these differences and include a data reporting mechanism to provide users with data summaries that correctly describe specific truck loading characteristics.

The procedure is to group the State's roads into load factor categories, so that each of those groups experiences similar per-truck loads, at least for the heavy vehicle classes (including busses and trucks) that are dominant on the roadway. All roads within the same load factor group should have freight traffic with similar characteristics and are subject to similar axle weight and GVW limits, including the monthly variations of these limits.

For example, roads that experience trucks carrying predominantly heavy natural resources should be grouped separately from roads carrying only light, urban delivery loads.

The weight data collection program is analogous to both the CCS and CVC programs for collecting temporal pattern information for volume and vehicle classification data. For the WIM sites that provide data for pavement designs based on AASHTO MEPDG MOP, a minimum of 12 calendar months of WIM data (January to December) are needed to develop monthly axle load distribution inputs for pavement design.

Within each load factor groups, the State should operate several WIM sites (see ensuing sections how to determine the number of sites based on weight data variability and the desired precision of the estimate) with permanently installed in-road sensors. These sites should be used to identify monthly weight patterns, as well as per-truck loads that apply to all roads in the group. Where possible (given budget and staffing limitations), WIM sites within each load factor group should be monitoring truck and axle weights continuously to provide more reliable measures of seasonal changes in traffic loading. The number of continuous sites that a State should operate is primarily a function of:

Each State's ability to supply the resources needed to monitor the sites to ensure the provision of accurate data throughout the year.
User needs and requirements for weight data, including roads and/or bridges that are high priority for WIM data users or WIM program sponsors.
Monthly truck loading patterns observed for each load factor group (lesser number of continuous sites if monthly variation in per-truck loads is insignificant or very stable and well understood and accounted for in sampling plan).

However, if the State has limited data on monthly weight patterns or if prior data collection has shown the pattern to be inconsistent, then a larger number of continuous counters may be needed. Performing additional vehicle weighing, both by operating continuous WIM sensors and by collecting data at more than the minimum number of sensor sites, will allow a State to determine whether the initial groups selected carry similar truck traffic. Where new data collection shows that monitored roads do not carry traffic with loading characteristics similar to those of other roads in the group, the State should either create new road groups (and collect more truck weight information) or revise the existing road groups to create more homogeneous groups. To determine the number of sites per group, see Step 5.

WIM sites with permanently installed WIM sensors used for seasonal data sampling, in lieu of continuous monitoring, are being phased out by State agencies due to low cost effectiveness, higher qualified personnel requirements, and availability of reliable remote data downloading options. However, if seasonal WIM data sampling is used, the seasonal monitoring sites should have permanently installed and regularly calibrated WIM sensors (annually or seasonally, in case of temperature-dependent sensors like piezo-polymer) to assure WIM data accuracy.

3.3.5 Step 4. Establish Load Factor Groups

Figure 3-3 illustrates the reason why roads should be stratified into load factor groups. It shows distributions of tandem axle weights for Class 9 trucks at different sites. Each of these distributions exhibits a different set of loading conditions, ranging from very heavily loaded to lightly loaded that would result in different pavement design outcomes.

The key to the design of the truck weight data collection effort is for the highway agency to be able to successfully recognize these differences in loading patterns, and to collect sufficient data to be able to estimate the loads that are occurring under these different conditions.

Source: FHWA Report FHWA-HRT-13-090.

Figure 3-3. Tandem Axle Load Distributions with Different Loading Conditions

One important consideration when creating load factor groups is whether different road groups will be created for each class of heavy vehicles or whether each road segment is assigned to only one group that is primarily formed based on the similarities in truck weight of the most common or voluminous heavy vehicles. The most common approach historically has been to assign each roadway to one truck weight road group. However, the FHWA LTPP program has developed MEPDG axle loading defaults by grouping WIM sites independently for each class of vehicles (MEPDG Traffic Loading Defaults Derived from Traffic Pooled Fund Study FHWA 2016). As a result, the LTPP created a catalog of axle load spectra for each of FHWA vehicle classes 4 through 13 representing commonly observed, as well as special (such as very heavy or very light) loading conditions and labeled these spectra as light, moderate, heavy, or very heavy loading conditions. The grouping was performed entirely mathematically using a clustering technique. The final groups were subjected to engineering review to confirm whether similarities and differences in the identified loading patterns make sense from a pavement engineering point of view.

Regardless of whether a single road segment is assigned to one or more groups, two key aspects of group formation are:

The truck loading patterns (or typical per truck loads) at sites within each group should be similar.
It should be relatively easy to assign each road accurately and consistently in the State to a load factor group so that the group values can be applied as needed.

Finally, it is important to note that for roads with separated right-of-way for different directions of travel, the two different directions of travel can be placed in different groups (such as roads serving primarily resource extraction and agricultural to/from sites). For example, the loaded direction might be assigned to a group with a heavy loading pattern, while the other direction of travel (the side carrying primarily empty trucks) might be assigned to a light group. Where the two directions share a single pavement design, the entire road should be assigned to the heavier group for pavement design purposes.

Selecting Approach to Form Load Factor Groups

As with the factor grouping processes described earlier for both vehicle classification and total volume, the basis for the load factor group formation process can be either intuitive knowledge-based or mathematical, or a combination of these two approaches.

The intuitive approach is where descriptive information is used along with professional knowledge to create groups of roads that should have similar truck loading patterns due to the nature of the truck loads they carry. This approach is the easiest to apply but often produces groups that have higher variability within the group.

The mathematical approaches (most commonly based on cluster analysis) generally create more homogeneous groups but tend to result in groups that are harder to define using the available data or attributes, making assignment of roadway sections to groups more difficult. As a result, combination approaches are often used that start with basic intuitive groups (e.g., geographic stratifications or descriptive road classifications such as urban/rural or interstate/non-interstate) and then apply cluster analysis to test the initial assumptions or to determine more uniform sub-groups within the basic geographic/roadway classifications.

Intuitive or Local Traffic Knowledge-Based Grouping

With this approach, the initial load factor groups should be based on a combination of known geographic, industrial, agricultural, and commercial truck loading patterns, combined with knowledge of the truck routes and legal weight limits that occur on specific roads. These initial concepts should then be tested by examining the actual truck weight data collected at WIM sites operated by the State to determine if roads that are expected to have similar loading patterns indeed have similar patterns. The intent is to identify those roads where large numbers of trucks are heavily loaded versus those routes where large numbers of trucks are not carrying heavy weights.

The resulting road groups should be easily identified by users of truck weight data within the State. They should provide a logical means for discriminating between roads that are likely to have very high load factors (such as ESAL/truck) and roads that have lower load factors (i.e., between roads where most trucks are fully loaded and roads where a large percentage of trucks are either partially loaded or empty). Unless a road is primarily serving a resource extraction or agricultural facility, it is unlikely that the road (in the direction used by the empty trucks to go to the facility) will have the majority of trucks primarily empty.

In addition, States should incorporate knowledge about specific types of very heavy vehicles into their weight grouping process so that roads that have significant volume of those very heavy trucks (at least 10 percent or more of all trucks with weight exceeding Federal legal limit) are grouped together, and roads that are not likely to carry those trucks are treated separately. For example, roads leading to and from major port facilities might be treated separately from other roads in that same geographic area, simply because of the high load factor that is common to roads leading to/from most port facilities. Other examples of roads that are likely to have high percentages of heavy trucks are the roads with a high percentage of trucks involved in the following: agricultural goods movement, natural resource extraction, construction, heavy industries (e.g., automotive) and debris/garbage removal.

For a State, it is recommended to start with the initial intuitive groups based on a more simplistic approach. For example, insight into geographic differences in truck travel, combined with the knowledge of road use by heavy commercial trucks (i.e., the percentages of through-trucks and local delivery trucks) can be used to define roads where loading patterns are dominated by local industry or long-haul truck traffic.

Other professional knowledge-based criteria that can be used to create truck load groups include:

The presence of agricultural products that create specific loading patterns and are carried in specific types of trucks. For example, wheat-growing areas might need to be grouped separately from those that grow cherries because these two products have different densities, different weights on a truck, and because their harvest and hauling seasons are different.
The types of industrial areas, such as resource extraction operations that ship large amounts of material by truck. For example, roads serving coal truck movements may be grouped separately from roads that experience few coal trucks.
The distance over which the trucks are likely to travel. (e.g., roads where trucks deliver cargo over long distances across multiple States, or roads with truck travel between cities within a region where drivers can make a round trip in one day, or roads with truck travel within a general urbanized area where drivers make multiple trips in a day). Trucks traveling longer distances are more likely to be full, and thus heavier, than trucks operating within half a day of their base, which are likely to be full leaving their depot but are often empty when returning.
Urban or rural roads, because urban areas often have considerably higher numbers of partially loaded trucks and trucks that travel empty after unloading at urban destinations. Note that some roads functionally classified as rural that are located between two large cities (within 200 miles of each other) may experience urban rather than rural trucking patterns because trucks routinely make day-trips between those cities, traveling full in one direction and possibly empty in the other.

This approach could then be improved (as needed) over time as more weight data are collected and analyses are carried out. Information can be extracted from existing truck weight databases to determine logical and statistical differences that can be used in the formation of load factor groups. As an example of a load factor group, Washington State developed five basic truck-loading patterns to determine total freight tonnage carried by all State highways. These five groups were defined as:

Group A – Serves major statewide and interstate truck travel. These routes are the major regional haul facilities.
Group B – Serves primarily intercity freight movements, with minor amounts of regional hauling. These routes also serve as produce transfer routes, serving rail and barge loading facilities.
Group C – Serves farm to market routes and regional commerce.
Group D – Serves suburban industrial activity.
Group E – Serves primarily local goods movement and specialized products.

A starting point for developing load factor groups is shown in Table 3-12. The example begins with the groups identified in the vehicle classification section. The truck loading groups defined should be coordinated with the vehicle classification groups identified earlier. Differences in the two sets of groups are likely since the groups are defined to meet different purposes (seasonal differences in truck volume and loading variation). However, they both reflect truck travel characteristics that are directly related. A similar group definition will greatly simplify the understanding and applicability of the patterns. The groups may need further redefinition over time as information is gained.

**Table 3-12. Example Truck Loading Groups**
Rural	Urban
Interstate and arterial major through-truck routes with high percentage of heavy freight trucks	Interstate and arterial major truck routes with high percentage of heavy freight trucks
Other roads (e.g., regional agricultural with little through trucks)	Interstate and other freeways serving primarily local truck traffic
Other unrestricted truck routes	Other unrestricted truck routes

These are examples. Each State highway agency should select the appropriate number and definition of truck groups based on its economic and trucking characteristics and the need for heavy vehicle travel patterns in their State.

Grouping Based on Cluster Analysis

MEPDG Traffic Loading Defaults Derived from Traffic Pooled Fund Study (FHWA 2016) contains detailed instructions on how to use a cluster analysis to identify and group similar axle load spectra. This section summarizes and generalizes this approach.

The generic cluster process for forming load factor groups consists of the following steps:

Develop a normalized annual axle load spectrum for each vehicle class and each WIM site.
Compute a single statistic for each load spectrum that represents the nature of that spectrum. (For example, if the primary reason the group is being created is for pavement design, a statistic should focus on importance of heavy axle loads. A general ESAL is this type of statistic. A general ESAL is computed by keeping pavement inputs constant in the load equivalency factors LEF or ESAL per axle formula to allow comparison of traffic loading between different roads. If the main use of the load factor group is for estimating total tonnage on State routes, then mean GVW per truck class may be used as the best single statistic).
If the load spectra for all heavy vehicle classes at a WIM site are to be assigned to only one truck weight road group, determine how loading patterns for each class of vehicle will be weighed by importance. (This step is not necessary if groups will be formed for each type of axle for each class of vehicle.)
Perform a cluster analysis using traffic loading summary statistic of choice, stopping when clusters reach the point where the difference between clusters becomes large enough that the use of different clusters causes statistically and practically different outcomes when used in planned analyses.

Step 3 is the most challenging task in this process because different classes of trucks are likely to have different loading patterns at any given site. That is, some classes of trucks will be heavier at one site (Site A) than at other sites (e.g., Sites B through G), while a different set of vehicle classes will be lighter at Site A than at the remaining sites. For example, grouping may be heavily weighted towards similarities in class 9 axle load spectra, if class 9 carry large percentage of heavy loads for the load factor group.

In Step 4, the traffic loading summary statistic (ESAL/truck, average GVW, or load spectra) that represents the vehicle class loading conditions being used to group sites is entered into a statistical clustering program. The output of that process can then be tested to determine the reliability of the groups created.

Hybrid Approach Combining the Intuitive and Clustering Approaches

This approach combines features of the Intuitive and Clustering Approaches. First, professional judgment is used to initially segregate roads into specific categories or groups. For example, based on data from classification and WIM sites, the State may know that specific roads carry large volumes of very heavy Class 7 and Class 10 trucks due to the nature of industry served by those roads (e.g., coal or other heavy natural resources). These roads may be segregated from roads that carry more diverse heavy vehicle traffic prior to running cluster analyses. These roads may be used as one group, or a cluster analysis may be performed using only data from WIM sites on these special roads—using Class 7 and Class 10 loading conditions as the key cluster variable. A separate cluster analysis may then be applied—using Class 9 loading conditions as the cluster variable—for all other roads in the State.

This hybrid approach to truck weight road group creation is intended to improve the group creation process by allowing application of professional knowledge in limited ways, while preserving the statistical integrity of the group creation as much as possible with the clustering approach whenever current knowledge does not provide clear definition of truck load groups.

Testing the Quality of Selected Load Factor Groups

The initial formation of load factor groups should be reviewed to determine whether the road segments grouped together have similar truck weight characteristics. More likely it will not be possible to form homogenous groups for different truck classes, and trade-offs will have to be made. Examining available data from the existing WIM sites is the first step. For example, at Site A, the Class 9 truck weight pattern may be dominated by urban delivery trucking patterns where Class 9 trucks are equally split between loaded, unloaded, and partially loaded conditions. At the same site, Class 7 and Class 10 vehicles may all be carrying very heavy loads. At Site B, the majority of Class 9 trucks are fully loaded, while Class 7 and Class 10 are also carrying heavy loads. At Site C, the original Class 9 urban pattern is present, but the Class 7 and 10 vehicles are much lighter than elsewhere.

If all vehicle classes have equal importance, a statistically based cluster analysis might group all three of these test sites or it might separate all three sites, depending on the criteria set when applying the clustering approach.

However, not all trucks are equally important. Some truck classes are heavier than others and thus, may be more important to the users (pavement and bridge engineers). Class 5 is considered a truck but is generally so light that it creates little pavement damage, while Classes 7 and 10 can be extremely heavy. While trucks in Classes 7 and 10 tend to be very heavy, in most States and on most roads, these classes are a very small percentage of the traffic stream and contribute a relatively modest amount of total pavement damage. On a few roads, these trucks are very prevalent and drive the pavement design equation. In most cases, however, Class 9 vehicles tend to produce the majority of pavement loading. These trucks tend to be less damaging per vehicle than Classes 7, 10, and 13, but they tend to constitute a very large percentage of truck volumes, thus contributing the most to the cumulative heavy loads (heavy loads are those that exceed 3/4 of the legal load limit). Therefore, understanding of loading patterns for Class 9 trucks is very important for weight data users involved in pavement design.

Determination of the relative importance of different truck classes and selection of truck loading grouping statistics is very important. When deciding how to balance the importance of different truck classes to the grouping process, a combination of how heavy each class is and how frequently they are observed are important considerations. The type of vehicle considered the most important should be given priority (for example, vehicle that contributes 40% or more of the total cumulative traffic load) in the load factor grouping process. This can be computed by multiplying the volume of that class of trucks times their average weight. This simplifies the grouping process, although it downplays the importance of lower volume truck classes in that process.

Determining the Precision of Estimates from Load Factor Groups

An estimate of the precision of the mean of a variable for any load factor group can be computed using the standard deviation of the mean estimate (such as mean single axle load, mean tandem axle load for loaded trucks, mean gross vehicle weight, or mean generic ESAL) and the number of sites in the load factor group. An example of this computation is shown in Table 3-13. In the example, assume that a State has determined that all rural interstate roads have similar truck weight characteristics based on seven WIM sites. Statistics from those WIM sites are shown in Table 3-13. Based on these data, it can be assumed that all rural interstate roads in the group have a mean gross vehicle weight of 54,000 lb for Class 9 trucks and 1.63 ESALs per Class 9 truck (the general ESAL computations assumes SN = 3, pt = 2.5).

**Table 3-13. Example of Statistic Computation for Load Factor Group Precision Estimates**
Site	Mean Class 9 GVW	Mean Class 9 ESAL
1	50,000 lb	1.64
2	57,000 lb	1.72
3	64,000 lb	1.84
4	46,000 lb	1.45
5	45,000 lb	1.34
6	55,000 lb	1.65
7	62,000 lb	1.78
Group Mean, m	54,000 lb	1.63
Group Standard Deviation, s	7,500 lb	0.18
Coefficient of Variation,	0.14	0.11
Standard Errors of Mean,	2,800 lb	0.07
Estimated Precision of the Mean with 95 Percent Confidence, D	+/- 6,900 lb	+/- 0.17

The precision of the group mean can be estimated for the selected confidence level as approximately plus or minus t_{a/2, n-1} times the standard error of the mean (which is the standard deviation of the sample s divided by the square root of the number of sites n):

$M-M equals plus or minus t-t sub alpha-alpha divided by two comma n-n minus one, multiplied by the following fraction: s-s divided by the square root of n-n.$

Where

D = the desired precision of the estimate, expressed as a fraction

s = the standard deviation for the group, computed using the selected traffic loading statistic

ta/2, n-1 is a critical value for t-interval that can be found using the Student's t distribution tables for the selected level of confidence (For example, α/2 = (1-0.95)/2 = 0.025 for 95 percent confidence level) and appropriate degrees of freedom (i.e., one less than the number of samples, which for seven WIM sites is roughly 2.45.)

n = the number of sites in the group

In the above example, note that the coefficient of variation (computed as standard deviation divided by the mean) for the two statistics (GVW/vehicle and ESAL/vehicle) are different, even though both variables come from the same set of vehicle weights. This is because the ESAL formula applies different ESAL per axle factors to loads of different magnitudes. Therefore, the grouping results are more homogeneous with respect to heavy loads than to average GVW.

The level of precision will be different for each vehicle class due to variability in mean weights and ESALs per truck observed between different sites in the load factor group for each vehicle class. For example, the precision of the mean GVW value for Class 9 trucks will be different from that value for Class 11 trucks.

3.3.6 Step 5. Determine the Appropriate Number of Weight Data Collection Locations

The precision formula can be used to determine how many WIM sites should be included within each load factor group. Each State highway agency should determine what traffic loading summary statistic it wants to use (such as ESAL per truck or GVW), select how precisely it wishes to estimate that statistic, and compute the number of WIM locations needed to obtain the desired degree of confidence.

This step involves several decisions:

The first decision is to determine whether the goal of the load factor groups is to report the mean statistics within each group with a given level of precision (e.g., the mean ESAL/Class 9 truck for rural interstates is 1.56 + 0.15 with 95 percent confidence) or the default values or load factors that will be applied to other sites in the group to estimate loading. This decision primarily affects the grouping process.
- – If the intention is to develop precise mean values for the group, the key tends to be the number of WIM sites included in each group. (The precision is low for groups with too few sites based on the statistical formula used to estimate the precision.)
- – If the intention is to develop good default values or load factors, the key to the grouping process is to have more and very homogenous groups (groups in which truck weights are very similar for all sites within the group, making standard deviations very small).

States that emphasize predicting mean values for groups will have fewer groups but larger numbers of data collection sites within each group, whereas States that emphasize site-specific estimates will have more load factor groups but fewer sites within each group.

The second decision that affects the grouping process is the selection of the statistic to be the basis for the precision estimates. Use of multiple statistical parameters (such as load spectra) for grouping is possible but requires more complex multivariate clustering techniques and informed decisions about what weights (by importance) to assign to different statistical parameters. Because the precision of each statistic will vary, a State should select a single statistic that is most meaningful for the analyses that use the truck and axle weight data. Normally, this means selecting a specific vehicle class and a specific weight variable. The recommended statistics for use in selecting sample sizes are either the mean generic ESAL per Class 9 truck or the mean GVW for Class 9 trucks. Class 9 trucks are recommended because they are the most common throughout the country, and they tend to carry a high percentage of the loadings on most major roads. The GVW statistic is easily understood by technical and non-technical people and could be converted to measures of commodity flow. However, the average GVW statistic hides some attributes about vehicle loading, such as proportion of heavy tandem axles, typical weight and percentage of loaded truck or percent of overloaded axles. At the other extreme, use of a full axle load spectrum (such as single and/or tandem axle load spectra) for road grouping puts equal weights by importance to all loads in the distribution, resulting in clusters that may not be as meaningful for the end user (e.g., pavement designer) who may be primarily interested in similarities in heavy loads and not interested in similarities in distribution of light loads. The ESAL per truck statistic puts high importance on heavy loads that are important for pavement designers and thus, provides a better parameter for grouping of roads based on similarity in heavy loads. The general ESAL per truck statistic is a good parameter for road clustering, if pavement design is the primary purpose for creating road groups.
The third decision is how precise to estimate the target statistic. Precision levels are normally stated in terms of percentage of error within a given level of confidence (e.g., the GVW/vehicle estimate is within plus or minus 15 percent of the mean value with 95 percent confidence). Decreasing the size of the acceptable error or requiring higher levels of confidence both increase the number of WIM sites required. Conversely, accepting lower levels of precision and/or confidence allows fewer WIM sites per load factor group and lower data collection costs. However, the low precision or higher error must be acceptable to the weight data users.For example, pavement users require high precision of load factors associated with the heavy loads (80 percent or more of the federal legal load limit) but not with the light loads (Traffic Loading Defaults Derived from Traffic Pooled Fund Study, FHWA 2016). For users of ESAL-based pavement design approach, changes in average ESAL per truck of 25 percent or less are not significant from the design perspective, assuming typical highway truck mix scenarios (not movers of super heavy loads).

The number of WIM sites within a group is estimated as:

$n equals the following fraction: the numerator is the quantity t sub alpha divided by two, squared, multiplied by the quantity s squared; the denominator is D squared.$

Where:

n = the number of samples taken (in this case, the number of WIM sites in the group)

t = the Student's t distribution for the selected level of confidence and appropriate degrees of freedom (one less than the number of samples, n)

α = level of significance, the probability of rejecting the Null Hypothesis when in it is true, for the selected level of confidence for 95 percent confidence α = 1 - 0.95 = 0.05)

s = the standard deviation for the group, using the selected traffic loading statistic

D = the desired precision of the estimate, expressed as a fraction

The parameters are computed from available truck weight data. D is selected as part of the previously described decision process (see above). The number of sites, n, can be computed after selecting the value for alpha (α) and looking up the appropriate term for t_α/2 with n-1 degrees of freedom. Similarly, if n is given, it is possible to solve directly for the value of t_α/2 and therefore (α). The example given below illustrates the basic process of comparing sample size with the precision levels each sample size achieves.

Table 3-14 shows the same truck weight statistics used in Table 3-13, except two additional weight sites have been added. These two sites experience heavier vehicle and consequently have increased the mean values and the standard deviations for GVW/vehicle and ESAL/vehicle for the group. These new WIM sites did not improve group homogeneity.

Increasing the number of WIM stations included in the sample to 15 sites (and assuming that those new WIM sites would not increase the standard deviation of the sample) would improve the confidence in the mean value of the GVW/vehicle estimate for the load factor group to 59,000 lb +/- 6,400 lb with 95 percent confidence.

**Table 3-14. Statistics Used for Sample Size Computation**
Site	Mean Class 9 GVW	Mean Class 9 ESAL
1	50,000 lb	1.64
2	57,000 lb	1.72
3	64,000 lb	1.84
4	46,000 lb	1.45
5	45,000 lb	1.34
6	55,000 lb	1.65
7	62,000 lb	1.78
8	77,000 lb	2.01
9	75,000 lb	1.95
Group Mean	59,000 lb	1.71
Group Standard Deviation	11,600 lb	0.22
Coefficient of Variation	0.197	0.13
Standard Error of Mean	3,900 lbs	0.07
Estimate of Precision for the Mean with 95 Percent Confidence*	+/- 8,900 lb	+/-0.17

* for a critical value for t-interval equal to 2.306 for eight degrees of freedom (9 sites minus 1)

Changing the number of sites included in a load factor group has four effects:

It changes the computed mean value for the group.
It changes the computed sample standard deviation for the group. New sites may increase or decrease this value.
It changes the denominator used to compute the standard error, which is the statistic used to determine how well the mean value computed from that group of roads estimates the mean value for the population being sampled. Additional sites would increase this value.
It changes the value of t_a/2 used to compute the size of the confidence interval applied to estimates produced for that group. Additional sites would decrease this value.

In general, the more sites (with similar values of the selected summary loading statistic) included in a group, the better the estimates produced by that group. However, those improvements could be lost due to an increased standard deviation, if the new sites would have the mean values of the selected summary loading statistic (GVW and/or ESAL/truck values) that are different from the group mean values, as was demonstrated by the example in Table 3-14.

The benefit of adding sites is significant for small groups but decreases as the number of sites within a group increases. The effect of using the Student's t distribution to compute the precision (recall formula $M-M equals plus or minus t-t sub alpha-alpha divided by two comma n-n minus one, multiplied by the following fraction: s-s divided by the square root of n-n.$ ) means that a significant decrease in the value of t can be obtained by simply adding locations up to a sample size of six, as demonstrated for 95 percent confidence interval (α/2=0.025) and the number of sites n. For example, a sample size of six sites has a 20 percent smaller confidence interval at the 95 percent level of confidence than a sample size of five sites, all other things being equal. A sample size of six sites has a 250 percent smaller confidence interval at the 95 percent level of confidence than a sample size of 3 sites, all other things being equal. Beyond six sites, the benefits gained by adding sites begin to decrease quickly. More than six sites in a group may be appropriate, particularly if the State is unsure of its truck weight patterns or high variability in mean values of a selected weight summary statistic are observed.

Based on this analysis, six WIM sites per load factor group are recommended. The exception to the six-site rule is for truck weight road groups that contain very few roads. These will tend to be specialty roads (e.g., roads leading into and out of quarries) that have unusual loading conditions but that are not applicable to many other roads in the State. If improvements in precision are needed beyond what affordable increases in sample size will achieve, the primary option is to change the make-up of the load factor groups, i.e., create new subsets of roads that will serve as the load factor groups. If this change produces a significant decrease in the standard deviation that offsets the increase in t_α/2 caused by the lower sample size, then the State will benefit from an improvement in the precision of its weight estimates along with a smaller data collection sample size.

Selecting the acceptable level of error or a precision of load factor group is an iterative process. First, the desired target precision is selected. Next, the variability of data in the load factor group is examined. This examination may result in the need to collect more data or to adjust the assignment of roads within load factor groups. If the State cannot meet the initially selected precision levels (either because it cannot create sufficiently homogenous groups or because it cannot collect data at enough sites), the desired precision levels have to be relaxed to reflect the quality of the estimates that can be obtained and the users of the data should be informed about the reduced precision.

3.3.7 Step 6. Select New Sites to Meet WIM Program Needs

The selection of new WIM sites should be based on the needs of the WIM data collection program and the site characteristics of the roadway sections that meet those needs. The WIM site locations should be selected based on specific users' needs and program requirements from a list of candidate sites that meet all site requirements. The needs of the data collection program include, but are not limited to, the following:

The need to obtain more vehicle weight data on roads within a given load factor group.
The need to collect data in geographic regions that are poorly represented in the existing WIM data collection effort.
The need to collect data on specific facilities of high importance (e.g., interstate highways or other national highway system routes).
The need to collect data for specific research projects or other special needs of the State.
The need to collect weight information on specific commodity movements of importance to the State.

States should place WIM equipment only in pavements that allow for accurate vehicle weighing. See Chapter 2 for description of physical site characteristics of the roadway sections for installation of WIM systems and the recommended configurations for WIM sensor arrays. Additional information about physical site characteristics could be found in the FHWA WIM Pocket Guide.

WIM Equipment Selection

The WIM Pocket Guide (FHWA, 2018) contains an in-depth discussion about WIM equipment selection. The following are the most critical factors that should be considered when making WIM equipment selection:

User needs for data accuracy and consistency and length of data collection
WIM equipment and sensor reliability and longevity
Type of WIM equipment already installed and agency's prior experience with WIM equipment
Type of array installed
Ease of equipment installation, operation, maintenance, and calibration
Pavement requirements for different WIM sensors
Number of WIM Lanes

The following issues should be considered when selecting the number of lanes of WIM to install:

Available funding
Cost of installation
Program objectives to be met
Design of current WIM installations in the State
Trade-offs between obtaining more complete coverage at each site versus less coverage at each site but getting more sites covered

Significant differences in loads by direction of travel may occur. The collection of WIM data in at least one lane in each direction of travel at each site allows a clear assessment of directional differences in weights and loadings. WIM differences by travel lane are difficult to generalize, although the outside lanes (referred as "truck lanes" by pavement engineers) typically carry heavier vehicles. For multi-lane facilities, covering two lanes in each direction provides the most cost-effective alternative.

A WIM site covering all lanes and direction of travel provides the most complete data collection coverage. If some lanes are not monitored by WIM sensors, each WIM site should have, at a minimum, a portable classification count by direction and travel lane to measure truck travel in the lanes not being monitored by WIM system. Continuous classification in those lanes is preferable.

3.3.8 Step 7. Integrate the WIM Sites with the Remaining Count Program

In addition to weight, WIM systems also provide counts of vehicle volume by classification, speed, and total volume. Consequently, WIM sites could also provide volume and vehicle classification count data and take the place of volume and classification counts required to meet the needs described in Sections 3.1 and 3.2. However, agencies should note that physical characteristics of many road sections prevent the collection of accurate weight data, and additional resources are needed to maintain and calibrate WIM equipment and process WIM data.

Sites selected for WIM data collection should be located within HPMS volume sample sections, if possible. If two alternative sites exist to meet a specific need and one is already an HPMS sample site, it should be given priority over the alternative (all other factors being equal). If neither site falls on an HPMS sample section, the selected WIM site should become an HPMS sample section the next time the HPMS sample is revised. The HPMS volume and classification data should be collected at the same time as the WIM data, using the same equipment where practical. This reduces the staffing and resources needed to collect these HPMS data and directly ties the different data items.

3.3.8.1 Total Size of the Weight Data Collection Program

The size of the weight data collection program should be a function of the variability of the truck weights, accuracy, and precision desired to monitor and report on those weights. WIM program size and coverage should address specific needs of weight data users.

A small State may start with two basic load factor groups of roads: interstate and non-interstate groups, or one group roads with majority of through-way trucks and another group of roads with majority of local delivery trucks. To improve statistical precision, a minimum of 6 WIM sites per load group is recommended, leading to a minimum of 12 weighing locations in a State to support two load factor groups. If more than two loading patterns, meaningful to data users, are identified within a State but the budget can support only a limited number of WIM sites, it is recommended to have fewer sites per group but more groups covering different loading patterns then to have fewer groups with larger number of sites. The number of locations could be further reduced if the State works with surrounding States to collect joint vehicle weight data representing the same load factor group. If the variability in load factors computed for individual sites within a group is too high and leads to significantly different outcomes for the data user (for example, pavement thickness design varies by over 0.5 inches), it is recommended to create an additional load factor group (MEPDG Traffic Loading Defaults Derived from Traffic Pooled Fund Study, FHWA 2016).

A larger State with highly diverse trucking characteristics might have as many as 10 or 15 distinct load factor groups of roads, and accordingly 60 to 90 WIM sites, with a corresponding increase in the number of continuously operating WIM locations. Most States will be between the two extremes presented, and the number of weighing locations should fall somewhere between 12 and 90 locations.

3.4 HOW TO DESIGN A SHORT-TERM DATA COLLECTION PROGRAM

Short-term traffic volume counts are traditionally the primary focus of most statewide traffic monitoring efforts. They provide the majority of the geographic (spatial) diversity needed to provide traffic volume information on the State roadway system. Short-term traffic data collection typically includes any of the following: total traffic volume, volume by vehicle classification and micromobility counts.

The recommended short-term volume-counting program is divided into coverage count and special needs count primary subsets. The coverage count subset covers the roadway system on a periodic basis to meet both point-specific and area needs, including the HPMS reporting requirements. The special needs subset comprises additional counts necessary to meet the needs of other users. This second category of counts can be further subdivided into counts taken to meet State-specific statistical monitoring goals, to provide increased geographic coverage of the roadway system, and to meet the needs of specific project or data collection efforts.

Short-term counts ensure that adequate geographic coverage exists for all roads under the jurisdiction of the highway authority. The coverage counts ensure that at least some data exist for all roads maintained by the agency. How much data to be collected when providing adequate geographic coverage is a function of each agency's policy perspective. Significant utility can be gained from having at least hourly volume estimates by lane at coverage counts, since those data can be used to obtain a much more accurate understanding of travel and traffic volume peaks during the day.

3.4.1 Steps to Develop Short-Term Count Data Collection Program

The following steps should be used to develop a short-term count data collection program. These same steps are applicable to the development of a short-term classification count program:

Divide the road system into homogeneous traffic volume segments; determine the count locations needed to cover the system over a maximum cycle of six years.
Determine the count locations required to meet the HPMS and other data needs by reviewing HPMS manuals.
Determine the count locations and data collection needs of specific projects that will require data in the next year or two. This entails working with the offices that will request these data to determine their data needs. This coordination should occur on a continuous basis to ensure counts for special projects are collected when needed.
Overlay the counts on maps of the highway system including the location of functioning continuous counters.
Determine how counts can be combined to make best use of available counting resources.
Schedule the counts to use the available data collection crews and equipment efficiently.

These steps are intended to reduce count duplication and increase the efficiency of the data collection staff.

3.4.2 Duration of Counts

Longer duration short-term counts produce modest but statistically significant improvements in accuracy of AADT estimates. Details about accuracy gains can be found in Assessing AADT Accuracy Issues Related to Short-Term Count Durations (Krile 2015b). For example, if a 48-hour count serves as the basis for the AADT estimate—as opposed to a 24-hour count—there is around a 5 percent increase in the probability that an AADT estimate is within +/- 10 percent of actual AADT. In general, short-term counts on higher-volume roads can be more accurately converted into AADT estimates from shorter duration counts than those from lower-volume roads. Thus, most improvement in accuracy is obtained when counts are conducted for longer periods on lower-volume roads.

Micromobility Duration of Counts

The use of automatic counter equipment can substantially extend the duration of short-term counts. If automatic counters are used, then the minimum suggested duration is 7 days (such that all weekday and weekend days are represented). The TMG recommends micromobility short-term data collection programs collect hourly (or more granular, 15-minute, etc.) traffic volumes that include a minimum of seven consecutive days. The Transportation Research Record publication "Minimizing Annual Average Daily micromobility Traffic Estimation Errors: How Many Counters are Needed per Factor Group?" (Nordback 2019) states that results from using continuous count data from 102 sites across six cities, findings confirm that mean absolute percent error (MAPE) in estimated AADT is minimized when at least seven-day short-term counts are collected.

Depending on several other factors for Micromobility (e.g., day-to-day count variability, the total number of short-term monitoring sites, and the number of automatic counters), the preferred duration of automatic counts is 14 days at each location.

Having 14 days of hourly count data allows duplicate DOW of data to be collected and provides assurance that for at least one of each DOW is available, in case of weather, equipment failure, or other field-related issues cause one day or one hour to be eliminated due to erroneous data.

Manual counts can be used for validation of the automated equipment and should be used to verify the quality of the data collected from automated equipment. It is recognized that agencies might not have the available resources to purchase automated equipment and may need to partner with other agencies or obtain grant funding for automated portable counters. Partnering with agency traffic operations may allow automated camera technologies to be used, but processing of the video data is labor intensive and may be limited to the collection of 12-hour counts.

If micromobility traffic levels have been counted several times showing results that are high and consistent from day to day, then future counts at the location may be conducted for shorter periods and/or fewer days. However, a longer-duration count period (14 days) will be needed to determine how variable the micromobility traffic is for both TOD and DOW.

3.4.3 Spacing and Geographical Coverage Methods for Grouping Count Locations (for Factoring)

The spacing between short-term counts in a roadway is subject to agency discretion. The method for section length determination should be detailed in the State TMS plan. The primary objective is to count enough locations on a roadway so that the traffic volume estimates available for a given highway segment accurately portray the traffic volume on that segment. Generally, roadway segments are treated as homogenous traffic sections, meaning traffic volumes are the same for the entire segment. For a limited access highway, this is true between interchanges. However, it is also true for all practical engineering purposes for a rural road where access and egress along, for example, a 10-mile segment is limited to a few driveways and low volume, local access roads. Highway agencies are encouraged to examine existing traffic volume information to determine how best to segment their roadway systems to optimize the number and spacing of short-term counts. A rule of thumb that has been used in the past to define these traffic count segments is that traffic volume in each roadway segment be within 10 percent. An alternative approach would be to define limits using a graduated AADT volume scale such as the one shown in Table 3-15.

**Table 3-15. Estimating Spacing of Short-Duration Counts**
Beginning Segment AADT	Adjoining Segment AADT Within
100,000 or more	+/- 10%
50,000 – 99,999	+/- 20%
10,000 – 49,999	+/- 30%
5,000 – 9,999	+/- 40%
1,000 – 4,999	+/- 50%
500 – 999	+/- 60%
250 – 499	+/- 80%
Less than 250	+/- 100% or 250 vehicles (whichever is greater)

Breaking the system into large segments reduces the number of counts needed but also the reliability of the resulting traffic estimates for any given section of that large roadway segment. Use of small segments increases the reliability of a specific count but also the number of traffic counts needed.

The character of the road systems and the volumes carried has a major impact in the definition of segments. For roads where access is controlled (such as the interstate system), a simple definition of segments between interchanges is appropriate. For lower systems, clear traffic volume breaks are not always apparent and other rules of thumb (such as major intersections) should be applied. Rural and urban characteristics also require different handling. For the lowest volume roads, the 10-percent rule of thumb may be too narrow, and a wider definition should be sought. Careful definition of roadway segments can significantly reduce the number of counts needed to cover all highways within an agency's jurisdiction, while still providing the accurate volume data required for planning and engineering purposes.

3.4.4 Count Frequency

Once roadway segments are finalized, FHWA recommends as a rule that each roadway segment be counted at least once every 6 years. This ensures that reasonable traffic volume data are available for State needs, and that all roadway segments are correctly classified within the proper HPMS volume groups when State highway agencies compute statewide VDT as part of their required Federal reporting. HPMS further requires counting every 3 years for higher functional class roadways.

Not all count locations should be counted on a 6-year basis. Some count locations should be counted more often. According to the HPMS Manual (FHWA 2016), count locations should be counted on a minimum 3-year count cycle and the State's traffic monitoring program shall cover all NHS and Principal Arterial System (PAS) roadway sections (i.e., Interstates, Other Freeways and Expressways, and Other Principal Arterials) on a 3-year cycle or better; at least one-third of these roadway sections should be counted each year. The remaining two-thirds counts must be estimated based on a documented process in accordance with the TMG and the Field Manual (FHWA 2016). The State shall cover all roads on these systems, not just State-owned roads, so data provided by MPOs, cities, or counties should be included in the count cycle.

A minimum of one-third of all NHS and Principal Arterial System (PAS) roadway sections (i.e., Interstates, Other Freeways and Expressways, and Other Principal Arterials) shall be counted each year; all other monitoring should be on a minimum 6-year cycle. The roadway sections to be counted should be randomly selected from each sample stratum (volume group), with minor adjustments as necessary for strata with numbers of sections not divisible by three or having less than three samples. A single count may be used for several sections between adjacent interchanges on controlled-access facilities.

In general, roadway sections that experience high rates of growth require more frequent data collection than those that do not experience growth. Therefore, roads near growing urban centers and expanding recreational sites should be counted more frequently than roads in areas where activity levels have hardly changed for many years. Counting roads more frequently in volatile areas also allows the highway agency to respond with confidence to questions from the public about road use (a common concern in high-growth areas) and ensures that up-to-date by-lane and directional statistics are available for the roadway design, maintenance, and repair work that is common in high-growth areas.

The short-term count data collection program itself can be structured in many ways. One simplistic approach is to randomly separate all the roadway segments into unique sets and count one of these sets each year. However, this approach does not always lend itself to efficient use of data collection staff and equipment. Grouping counts geographically leads to more efficient data collection activity, but results in the need to account for the geographic bias in the data collected when computing annual average traffic statistics or looking at trends in travel and traffic growth around the State.

In addition, most highway agencies collect data at some sites on a cycle shorter than 6 years. For example, more frequent counts (3-year cycle) are required on HPMS sections, and most States count higher system roads more frequently as well. Still, considerable flexibility is allowed in the structure of each agency's short-term count program. According to the HPMS manual, the Count Cycles and Coverage section states the following:

A State should have minimum count cycles and coverage as follows:

Minimum 3-year count cycle – The State's traffic monitoring program shall cover all NHS and Principal Arterial System (PAS) roadway sections (i.e., Interstates, Other Freeways and Expressways, and Other Principal Arterials) on a 3-year cycle or better; at least one-third of these roadway sections should be counted each year. The remaining two-thirds counts must be estimated based on a documented process in accordance with the TMG and the Field Manual. The State shall cover all roads on these systems, not just State-owned roads, so data provided by MPOs, cities, or counties should be included in the count cycle.
Minimum 6-year count cycle – The State shall also have a traffic count program on a 6-year cycle or better for all non-NHS lower functional system roadway sections (i.e., minor arterials, major collectors, and urban minor collectors). Traffic data for ramps, as defined in Chapter 4, are also to be collected on a 6-year cycle or better.

3.4.5 Special Needs Counts

The HPMS standard sample meets the need for computation of a statistically reliable measure of statewide travel. The data collected also cover many highway agency's needs. However, there remain traffic data needs that cannot be met by the short-term count program. This is where an effective short-term program supplemented by special counts can substantially fill the gap.

Non-HPMS data needs vary dramatically from State to State and from agency to agency. Non-HPMS data needs vary dramatically from State to State and agency to agency based on factors such as individual agency and State responsibilities and legal environments.

A consequence of this variety of traffic data needs is that no single traffic monitoring program design fits all cases. Therefore, the philosophy of the special needs element is to provide highway agencies wide flexibility to design this portion of their monitoring program in accordance with their own self-defined needs and priorities. The guidance in this report is intended to provide highway agencies with a framework within which they can ensure that they collect the data they need.

The special needs portion of a data collection program can be divided into two basic portions:

Statistical samples for developing system-wide summary measures,
Point-specific estimates intended to meet project requirements and other studies defined by the highway agency.

All Special Events and Data Need Counts

Scheduled events typically call for the need to collect both continuous and short-term counts. Other events or special needs (such as project-specific counts) also warrant further traffic volume data collection.

Steps to collect special count data include:

Request information from the special need requestor (location, purpose, duration, etc.).
Schedule the collection of date(s). This schedule typically includes the event dates, and may include the day before the event, and day after the event.
Deploy the counters to collect the special count data (typically a mobile/portable counter that can be used in multiple count locations).
Download the data collected.
Analyze, report, and share the data using tabular and/or graphical representation.

The graphical example of collecting micromobility special needs program counts in a graphical representation is provided in the following example from the Pedestrian Traffic and Safety on Four Anishinaabe Reservations Case Study (Lindsey 2020), conducted by the Minnesota DOT. This data collection activity demonstrates the benefit of using an already-existing micromobility counter to collect special event data. The special event data shows an increase in bicycles and pedestrian volumes in 2020, as compared to historical averages. In this study, special need counts were collected using video detection to count bicyclists and pedestrians. Figure 3-4 shows the special 2020 traffic volume event total volume data by month of the year.

Figure 3-4. Weekly Bicycle and Pedestrian Traffic

Data were requested to obtain information for the purpose of understanding how parks and trails shape the world and conversely how the world shapes parks and trails. Without Minnesota DOT's and Minnesota DNRs permanent micromobility volume counters, there would be no baseline to understand and compare traffic volumes during events such as the 2020 special traffic volume event. Ultimately, data are collected to help make better and more-informed decisions. In the early days of the 2020 special traffic volume event, Minnesota DOT and Minnesota DNR simply wanted to understand, and share, how Minnesota's trails were being impacted. There were many questions: How much of the increase in trail use is attributable to the special traffic volume event? Should trail use be encouraged or cautioned? Are busy trails still safe to use? What time of day will traffic be highest? Although Minnesota DOT and Minnesota DNR could not give definitive answers to all those questions, they wanted to interpret and share the available data to people understand what was happening and make better-informed decisions.

Data collected in Minnesota during the special traffic data event provided useful information, so the Governor was able to visualize the effectiveness of any executive orders that were given to see if people were staying home and not traveling.

The traffic data program in Minnesota provided the funding and installed most of the continuous bicycle and pedestrian counters while working with the Office of Transit and Active Transportation on establishing a counting program like the motorized program. Since the Office of Transit and Active Transportation was seeing increases in the traffic volume, Minnesota leadership and data users were interested in accessing refreshed data daily that provided better information on changes throughout the State. Therefore, Minnesota DOT decided to implement a GIS dashboard showing the daily changes in volume and class.

Minnesota DOT reports that evidence is essential and simply providing counts, estimates of interactions and images from the counting technology, provided a basis for action. Information about the volumes of pedestrian crossings provided an additional rationale for Tribal transportation managers and county and State engineers to review proposed projects and identify opportunities for incremental improvements that could be incorporated into projects already planned and funded such as the trail along Mission Road to TH 210. Proposed countermeasures varied by intersection and included vegetation removal and line-of-sight improvements, new lighting, crosswalk improvements, rectangular rapid flashing beacons with advanced warning signs, ADA-compliant ramps, pedestrian education programs, realignment of intersections, and, at one intersection, a pedestrian hybrid beacon.

3.4.6 Statistical Samples in the Special Needs Program

Statistical samples such as the HPMS are the most efficient way to estimate population means and totals. Most statistical samples involve the collection of data at randomly selected locations to compute unbiased estimates of population means and totals. Random sampling is a very efficient mechanism for computing these totals.

The HPMS Field Manual (FHWA 2016) and Sampling Techniques (Cochran 1977) provides descriptions of how the HPMS samples are developed and implemented. These documents are useful in helping design a sampling program to meet objective needs. The keys to successfully designing a statistical sampling plan are defining the objectives, understanding the variability of the data being sampled, having a clear understanding of what statistics should be computed, and establishing the needed accuracy and precision of the estimates. Any statistical samples developed should make use of the available data from the short-term element to minimize the duplication of effort, as much as possible. One possible use of statistical samples is to estimate VMT for the local functional systems, where extensive mileage makes the collection of traffic data very costly.

3.4.7 Point-Specific Estimates in the Special Needs Program

Unfortunately, the random selection of count locations required by most statistical samples is an inefficient mechanism for meeting many site-specific traffic data needs. For example, HPMS guidance indicates that areas of the State selected for counting in a program year should be selected on a random basis. It further notes

that highways with high variability should be counted more often than those with low variability, and highways with high traffic volume should be counted more extensively than those with low volume (HPMS Field Manual page 5-4). However, this quasi-random selection of count locations may not satisfy many site-specific traffic data needs necessary for pavement and bridge design calculations, for example. While statistical techniques are adequate for statewide VMT estimations, these averages or totals are not viable substitutes for actual counts taken at a specified location and time frame.

However, if pavement needs to be designed for that section of roadway, a statewide average or total is not a substitute for one or many traffic counts specific to that road section.

Consequently, data needs require agencies to collect data at locations that are not part of the short-term program. However, by maximizing the use of available data, it is possible to keep the number of these special counts to a minimum and to save resources for other data collection and analysis tasks. No additional data should be collected if existing data meet the desired need.

Special counts are generally required for specific project needs. Project counts are undertaken to meet the needs of a given study (for example, a pavement/corridor study, rehabilitation design, or a specific research project).

These cover a range of data collection subjects and are usually paid for by project funds. Project counts are traditionally taken on relatively short notice, and they often collect data at a greater level of detail than for the short-term or the HPMS parts of the program. Often, the need is not realized until after a project has been selected for construction, and insufficient time exists by that date to schedule the project counts within the regular counting program. However, where it is possible to include project counts within the regular count program's schedule, significant improvements in staff utilization and decreases in overall costs can be achieved.

Many different types of counts can fall within the special need's element. Counts are taken by many public and private organizations for many purposes including intersection studies, signal warrants, turning movements, safety analysis, and environmental studies. As much as possible, these activities should be coordinated within the program umbrella.

In general, roadway sections that experience high rates of growth and recreational areas require more frequent counting than those that do not experience growth. Counting roads frequently in volatile areas allows the highway agency to respond with confidence to questions from the public about road use (a common concern in high-growth areas), while also ensuring that up-to-date statistics are available for the roadway design, maintenance, and repair work that is common in high-growth areas. Many agencies prefer the use of several counts a year to understand the traffic variability inherent in high growth better. Likewise, recreational roads usually experience major traffic peaking at specific times necessitating frequent data collection times.

High-growth areas (if not necessarily roads with high volume growth) can usually be selected based on knowledge of the highway system and available information on the construction of new travel generators, highway construction projects, requirements for highway maintenance, applications for building permits, and changes in population. Recreational areas are also well known to experienced transportation professionals.

3.4.8 Coordinating the Short-term and Special Needs Counts

Cost efficiency in the traffic-monitoring program is best achieved by carefully coordinating the different aspects within the program, which includes both the continuous and short-term counts. It also includes the short-term, HPMS, and special needs counts.

In theory, the highway agency would start each year with a clear understanding of all the counts that need to be performed. The list could then be examined to determine whether one count could be used for more than one purpose. For example, a classification count at one interstate milepost might easily provide the data required for both that count and a volume count required at the next milepost, since no major interchanges exist between those mileposts. By careful analysis of traffic count segments, location, and data requirements, it is often possible to significantly reduce the total number of counts required to meet user needs.

The next step is to compare the reduced list of count locations with locations covered by continuous counters (volume, classification, weight, and ITS). Continuous counter locations can be removed from this list, and the remaining sites are the locations that require short-term counts. These locations should then be scheduled to make best use of available staffing and resources.

To make this scenario work, it is necessary to understand where data should be collected and the kinds of data that need to be collected. This can be difficult to do because some requirements, such as those for project counts, are not identified until after the count schedule has been developed. Many project count locations and project count needs can be anticipated by examining the highway agency's priority project list and from knowledge of previous requests for data. Project lists detail and prioritize road projects that need to be funded soon, normally including road sections with poor pavement that require repair or rehabilitation, locations with high accident rates, sections that experience heavy congestion, and roadways with other significant deficiencies. While priority lists are rarely equivalent to the final project selection list, high-priority projects are commonly selected, analyzed, and otherwise examined. Making sure that up-to-date, accurate traffic data are available for the analyses helps make the traffic database useful and relevant to the data users and increases the support for maintenance and improvements to that database and entire traffic counting programs.

3.4.9 Adjusting Short-term Volume Counts to AADT

Short-term volume counts usually require several adjustments to convert a daily traffic volume raw count into an estimate of AADT. The specific set of adjustments needed is a function of the equipment used to collect the count and the duration of the count itself. Almost all short-term counts require adjustments to reduce the effects of temporal bias, if those short-term counts will be used to estimate AADT. In general, a 48-hour axle count is converted to AADT with the following formula:

A-A-D-T sub h-i equals Volume sub h-i, multiplied by M sub h, multiplied by D sub h, multiplied by T sub h, multiplied by A sub j, multiplied by G sub h.

Where:

AADT_hi = the annual average daily travel at location i of factor group h

VOL_hi = the 48-hour axle volume at location i of factor group h

M_h = the applicable monthly (seasonal) factor for factor group h

D_h = the applicable DOW factor for factor group h (if needed)

T_h = the applicable TOD factor for factor group h (if needed for any partial day counts)

A_i = the applicable axle-correction factor for location i (if not a traffic volume or class count, i.e., for counts collected using a single pneumatic road tube)

G_h = the applicable yearly change (i.e., growth or decline) rate factor for factor group h (if needed)

This formula should be modified, as necessary, to account for the traffic count's specific characteristics. For example, if the short-term count is taken with an inductive loop detector instead of a conventional pneumatic axle sensor, the axle correction factor (A_h) is removed from the formula. Similarly, if the count is taken for seven consecutive days, the seven daily volumes can be averaged, substituted for the term VOL_hi, and the DOW factor (D_h) removed from the equation. Lastly, growth factors are only needed if the count was taken in a year other than the year for which AADT is being estimated. For the TOD factor, this may be by hour or other time increment and is meant to adjust for any partial day counts taken. This factor can be a weighted value by hours in the day or other method. It is best to include any valid partial time increments for any portable counts including extra hours in the count duration. Including 49 hours of a 48-hour count improves the result of an annualized short-term count.

3.5 HOW TO COLLECT SPEED DATA USING PORTABLE COUNTS

Traffic monitoring devices used for vehicle classification or WIM can also provide vehicle speed data for use in speed and other safety studies. For example, dual sensor-based event recorders that record the passage of individual vehicles and/or their axles collect vehicle speed data because of the time stamps associated with each passing axle/vehicle and the recorded distance between axle sensors. This same information is collected by portable vehicle classifiers, which use vehicle speed measurements in the calculations of axle spacing and overall vehicle length. Many portable, non-intrusive detector systems can also be used to collect vehicle speeds at locations where data collection crews cannot safely place portable axle detectors.

The key to successful portable classification and speed data collection efforts is to ensure that the data collection equipment is carefully calibrated after it has been placed (the measurement of the distance between

portable sensors is important) and that the data collection electronics connected to those sensors have been set to collect the desired speed bins. Crews should perform an on-site calibration process each time they place equipment on the roadway by using a laser speed-monitoring device to compare equipment output with the speed data being collected or using a vehicle of known axle-distance to calibrate the axle spacing reported by the portable counter.

To ensure that short-term speed data collection is cost effective, it is important that the traffic data collection office reach out to the safety management office within the agency before developing the annual traffic data collection plan. This allows early identification of locations for which speed data are needed, thus ensuring the inclusion of those data locations within the routine short-term count data collection program.

3.6 HOW TO DESIGN A SHORT-TERM CLASSIFICATION DATA COLLECTION PROGRAM (AXLE AND LENGTH)

Short-term vehicle classification counts serve as the primary mechanism for collecting information on heavy vehicle volumes. They provide the geographic distribution necessary to meet the general agency needs and the needs of its customers, as well as the site-specific knowledge needed for the more detailed technical analyses of users.

Large numbers of transportation analyses are starting to require more and better vehicle class data volume information. Vehicle volume information has become particularly important for pavement design, freight mobility, planning, safety, and project programming decisions.

The TMG recommends that State highway agencies aim to collect at least 25 to 30 percent of their entire short-term count program with vehicle classification counting equipment.

FHWA's 13-Vehicle Category Classification

Figure 3-5 provides FHWA's 13-vehicle category classification recommended for short-term vehicle classification data collection. The table provides class naming (class 1, class 2, etc.), class types (motorcycle, passenger car, four tire single unit vehicles, etc.), and illustrative images of vehicles that represents each class.

Certain truck configurations utilize axles that can be lifted when the vehicle is empty or lightly loaded. The position of these axles—sometimes called lift axles, drop axles, or tag axles—affects the classification category into which the vehicle falls. To maintain consistency between visual and axle-based counts, the TMG recommends that only axles that are in the dropped position be considered when classifying the vehicle. While this promotes consistency, it may induce difficulty when interpreting summary classification statistics at certain locations. For example, a site that has quarry traffic may exhibit directional differences in vehicle classification even though the same trucks may be traveling one direction loaded (with axles down – class 7) and the other direction empty (with axles lifted – class 6).

Most vehicles can be easily classified into this system by a human observer. In addition, some States permit specific vehicle types that are not legal in other States. (For example, some western States allow tractors to pull three trailers, while most States do not allow more than two trailers.) States often wish to track these unusual vehicle types, and therefore add additional vehicle categories to FHWA's 13 categories that meet their specific traffic monitoring needs. When these States purchase vehicle classification counters, they require that the vendors install their State-specific classification methods in the data collection electronics or post-processing software.

However, these modified FHWA classification systems are not the only classification systems of interest. Many engineering and planning analyses do not require data in the detailed FHWA 13 categories but do require information on truck volumes versus car volumes. Thus, many engineering and planning analyses use either a simple car/truck split or they use a very simplified truck classification system; commonly a 3- or 4-bin classification system based on vehicle length, number of axles, or other vehicle attributes.

The most common length classification systems essentially consist of four generalized length bins that approximate the following four categories of vehicles: cars, small trucks, large trucks, and multi-trailer trucks. States that use only three truck classes will combine the large truck and multi-trailer truck classes. (These States tend to be States where multi-trailer trucks are rare.) Unfortunately, unlike the FHWA 13-vehicle category classification, there is no common definition across the States that indicates the vehicle length at which a car becomes a truck. The States, therefore, set their own length definitions for these classification systems.

Besides its simplicity, one advantage of the length classification systems is that vehicle length can be easily calculated by a number of sensor technologies that do not require axle sensors. Thus, many of the sensor technologies that can collect volumes by vehicle length can be placed above or beside the roadway, which will limit or eliminate the need for staff to place sensors in the lane of travel.

Class 1 Motorcycles			Class 7 Four or more axle, single unit
Class 2 Passenger Cars

			Class 8 Four or less axle, single trailer

Class 3 Four tire, single unit
			Class 9 5-Axle tractor semitrailer
			Class 9 5-Axle tractor semitrailer
Class 4 Busses			Class 10 Six or more axle, single trailer
			Class 10 Six or more axle, single trailer
			Class 11 Five or less axle, multi-trailer
Class 5 Two axle, six tire, single unit			Class 12 Six axle, multi-trailer
			Class 12 Six axle, multi-trailer
			Class 13 Seven or more axle, multi-trailer
Class 6 Three axle, single unit

Source: Federal Highway Administration.

Figure 3-5. FHWA'S 13-Vehicle Category Classification

The primary disadvantage of the length-based classification is that it does not correlate as well as the FHWA 13-vehicle category classification system to several of the key vehicle attributes used in specific types of analyses. For example, a major input to pavement design is traffic load, and that in turn is driven by the number and weight of axle loads being applied. The FHWA 13-vehicle category classification system directly accounts for the number of axles within the classification system. The FHWA classification system also does a good job of identifying specific vehicle types (e.g., classes 7 and 10) that are often particularly heavy. This results in better traffic load estimation and thus better pavement analysis.

Jurisdictions should adopt classification systems that are compatible with the 13-vehicle category classification system. Systems with fewer categories should be combinations of the FHWA classes, and systems with more categories should be subdivisions of the FHWA classes.

Length-based classification systems do not account for specific axle configurations, and thus the connection between the number and weight of axles within the different length classifications is far more nebulous. Similarly, the FHWA axle-based system does a good job of differentiating the number of multi-unit vehicles on the roadway, while the length-based systems are most often not able to track the number of vehicles pulling one or more other units. The number of units in each vehicle is a key variable being tracked for safety purposes; thus the length classification systems are much less useful for the kinds of safety analyses that are interested in the exposure rates associated with multi-unit vehicles.

Some State agencies use some combination of both FHWA's 13-vehicle category classification system and a simpler length-based system. The length-based system is used in those physical road segments where it is not possible to place axle sensors. Length-based is also used when the advantages of simplicity outweigh the loss of detail and precision that comes from using the more sophisticated axle-based classification system. Approval is required by a State's FHWA Division office for use of length class in any data submitted to FHWA.

Short-term Vehicle Classification Counts

Short-term classification counts are vitally important to the computation and submittal of the HPMS full extent traffic data and vehicle summary table. The vehicle summary table requires VMT by six vehicle types (motorcycles, passenger cars, light trucks, buses, single-unit trucks, and combination trucks) by six classes of roads (interstate, other arterials, and other roads for both urban and rural roads). Consequently, when collecting classification data, States should look to count at least these six vehicle classes whenever possible.

Given the growing need for data on truck volumes, a more comprehensive approach is required to provide classification data than what the TMG has historically recommended. The current recommendation is based on the following objectives:

Increasing the accuracy and availability of truck volume data.
Improving the truck volume data for national studies.
Improving the truck volume data used for site-specific studies.
Decreasing the per-count cost of collecting classification data by having more classification counts in the traffic data collection program.

Short-Term Classification Count Program Design Considerations

The classification short-term count program should be designed to operate like a traditional volume coverage program to provide a minimum level of travel by vehicle type data on all system roads. The basic short-term program would be supplemented by special counts as needed to meet site-specific data needs. At a minimum, the TMG recommends that State highway agencies collect 25 to 30 percent of their short-term counts with classification counting equipment. Agencies that can exceed this figure are encouraged to do so. The ability to meet or exceed this goal depends on agency perspective and is a function of the equipment available and the nature of the road system. Classification data are difficult to collect in many urban settings because of safety or equipment limitations.

To develop a classification coverage program, the highway system should be divided into vehicle classification segments similar to what is currently performed for volume and described in Section 3.1. Vehicle classification segments should carry a homogeneous volume of each class of vehicle. In practice, development of these section definitions is a judgment call since the definition is usually based on the available classification data combined with specific knowledge of the system. The more classification data and the better knowledge of trucks available, the easier and better the definition will be. The availability of truck or commercial vehicle flow maps during the road segmentation process is very useful. Most vehicle classification segments are expected to span several traffic volume segments because truck traffic can remain constant despite changes in total traffic volume (that is, changes in car volumes do not necessarily result in changes in truck volume). With time, as more data and information become available, the definition of segments will improve. As with traffic volume, the classification segments will change over time as roadway and traffic characteristics change and as more classification data help to better define the segments. Periodic reassessments will be necessary to maintain the classification segment inventory and keep it current.

Many caveats apply to the development of the classification short-term count program. Each agency will have to develop a classification inventory system to cover the roads that meet its needs.

Table 3-16 illustrates some of the considerations used in developing traffic segments and classification coverage programs based on the functional classification (and use) of roadways.

**Table 3-16. Traffic Segments and Classification Short-Term Program Based on Functional Classification and Truck Activity**
Functional Classification	Truck Traffic Activity	Classification Segment Lengths	Number of Classification Segments	Traffic Volume Segments
Higher FC roads, i.e., Interstates, Principal Arterials – Other Freeways and Expressways, etc.	High	Long	Few, but this number should be about equal to 25-30% of volume >counts	Few – encompasses classification segments
Lower FC roads, i.e., Minor Arterial, Major and Minor Collector, Local	Combination of Low, Medium, High	Combination of long and short segments where traffic generators are found	Depends upon number of defined traffic segments; 25-30% of volume counts	Depends upon length (extent) of road and level of truck activity; higher the fluctuation in truck activity, the more counts should be taken at locations with changes in traffic volumes; traffic volume segments should encompass the classification segments

In some cases, the individual class of traffic may not change over large expanses of road, and a small number of classification segments will cover the road. In the Interstate system, for example, classification segments may extend over several interchanges and be very long. The character of the highway and the traffic it carries will play a major role in the definition of these segments and in the number of classification counts needed. Roads that serve significant individual traffic-generating activities (e.g., ports, quarries) will necessitate more classification segments, more classification counts, and more frequent revision than roads through regions that experience little unique by class travel activity.

The structure of the road system is superimposed by a system of traffic volume segments that allow the traffic-counting program to cover it. Likewise, the traffic volume segments will be covered with a smaller subset of vehicle classification segments that allow the establishment of a vehicle classification program that covers the system and provides comprehensive truck data.

The vehicle classification segment inventory will allow a determination of how much classification counting is needed and how many of the volume counts should be classification counts. A general rule of thumb is that 25 to 30 percent of the coverage volume counts should be classification. This depends on the actual volume coverage program in operation, the character of the road system covered, and many other considerations. The general rule of thumb applies to the traffic volume program recommendation using a coverage program over a 3-year cycle.

Traffic engineering judgments are greatly needed to determine how to integrate classification and volume counting. Different agencies will make specific decisions depending on many considerations. In some cases, the availability of low-cost classification equipment can almost justify the conversion of most counting to classification. The gain in information on different classes of vehicles, combined with the elimination of the error introduced by axle correction, will likely justify the extra cost. Many of the newer counters perform classification, and many agencies that have acquired the new equipment thar classifies rather than only collecting volume data. The trend is to go toward collecting and storing all vehicle types in a per-vehicle format. However, changes in program direction, the acquisition of new equipment, and the implementation of program changes do not occur overnight.

Some lower-volume roads do not have the volume of different classification of vehicles (mainly recording of trucks) to justify the full conversion of volume counting to classification. These are the roads where the installation of classifiers based on road tubes is easier and where equipment limitations are not a problem. However, once a classification count is taken, additional repetitive counts may not improve the individual classification volume estimates. In these cases, a decision to save a little time, effort, and funding could be appropriate.

On higher-volume roads, repetitive classification may greatly enhance the understanding of individual classification travel variability and result in better classification estimates. However, on these roads the collection of classification data is often more difficult due to multiple lanes and counting for longer periods in all lanes. In the higher-volume systems, portable equipment installation may not be safe or effective, and the installation of more expensive equipment is the only solution.

Such constraints may dictate a slower conversion from the current data collection program to the recommended program that emphasizes classification counting. Still, all highway agencies need to understand the use of their roadways by different vehicle types (motorcycles, buses, and trucks), and consequently counting of the six vehicle types is an important task. To help achieve that objective, another useful rule of thumb is that a minimum of one vehicle classification count should be taken on each road each year to ensure a minimum of data available annually to represent each road. Where practical, these counts should be taken at existing HPMS volume sample sections to ensure the quality of classification data reported to the HPMS.

Many caveats apply to this rule of thumb as well. For long roads (such as roads that extend across an entire State), far more than one count should be taken; for roads that change character (e.g., a route may be primarily a farm-to-market road in one place but become a major freight hauling road in another), several classification counts would be appropriate.

Roads that experience significant changes in truck traffic due to changes in industrial activity and/or junctions that lead to truck generators may need classification counts on either side of the junctions where truck activity levels change. For minor routes, a single classification count may be all that is needed. Finally, some agencies may decide to take additional vehicle classification counts whenever resources permit simply because of a specific vehicle type plays a major role in defining coverage program segments and to ensure quality data are available to meet traffic data user needs.

The implementation of a comprehensive classification coverage program requires direct integration into the standard volume counting program activities. The manner of scheduling, equipment, staff, and resources should be adequately considered.

It may not be necessary to perform vehicle classification counts at the same location every year. Any placement within the defined segment should provide adequate representation and any additional counts taken help to verify the annual estimate provided. Likewise, it would be best to collect classification counts randomly (by location and time of year). In fact, counts taken at different times of the year provide independent estimates that will help to verify and/or improve the segment estimate. Careful scheduling of the data collection effort may also be necessary to measure important, seasonal truck movements such as those due to harvesting or other highly seasonal events.

The recommended cycle of monitoring for the classification program is 3 years. The schedule of counts should be developed to ensure that coverage of each classification segment occurs at least once within a 6-year cycle.

Whenever possible, vehicle classification counts should be taken within the HPMS volume sample sections. This results in direct estimates for each sample section, thereby allowing the expansion of the truck percent variables in the HPMS to valid system estimates of the six types of vehicular travel.

Short-Term Vehicle Classification Count Duration

The recommended minimum length of monitoring for vehicle classification data is 48 consecutive hours.

Other count durations can produce reasonable results in some cases but are not recommended for general use. Equipment that can collect data in hourly (or other time increments such as 5, 10, or 15 minutes, etc.) traffic bins should be used for the general program. In urban areas or for special studies, the use of shorter intervals, such as 15 minutes, may be appropriate. The use of 48-hour periods is recommended because:

The accuracy of the volume by class estimates of 48-hour counts increase for low-volume classes.
Significant improvement in quality control capabilities become possible with the comparison of one day's hourly traffic counts against the second day's counts.
Longevity of the sensors/road tube and equipment has improved to provide longer duration counts.

Counting throughout the day for an entire 24-hour period is important to determine accurate daily volumes, particularly on roads that carry substantial numbers of trucks. Counts that are less than 24 hours are not recommended but if collected, will need to be adjusted to daily totals using a daily adjustment factor to convert the shorter period to a 24-hour estimate that is then used to calculate an AADT. This adjustment factor should be obtained from more extensive classification counts on similar roads because the time-of-day distribution of truck volume is not the same as that for total volume. The daily volume will need to be converted to an annual estimate by using the appropriate temporal factors. Any deviation to not collecting 48 hours of class and/or 24/48 hour for volume should be detailed in the State TMS plan and approved by the FHWA Division Office.

Vehicle classification counts of longer than 48 hours are useful, and a minimum of 48 hours is recommended particularly when those counts extend over the weekend, since they provide better DOW volume information. Whether a highway agency can conduct longer counts is a function of short-term data collect program size, staff utilization, and other factors. Longer duration counts from 72 hours to 7 days are encouraged.

Other Special Needs Counts

As with traditional volume counting, the vehicle classification count program requires special counts in addition to those collected for coverage to meet needs that the short-term program does not cover. Traditionally, these counts have been primarily project related.

Project Counts

In some States, a significant number of classification counts are project related. Most commonly, these counts are taken to determine the truck traffic on a road segment that requires a traffic load estimate as an input for a pavement rehabilitation design. Collection of the data specifically for the road segment being rehabilitated ensures that the count data reflect current conditions and that the data used in the geometric and structural design procedures are accurate enough to ensure adequate performance of the new pavement over the design life of the project. Common reasons for project counts include pavement design, operational design (e.g., signal timing or testing the need for truck climbing and/or passing lanes), geometric design, and corridor studies. Each project count can have different requirements for duration, spatial frequency, and types of summary measures that must be produced.

The establishment of a classification short-term program will allow a more complete understanding of specific types of traffic on the highway systems and optimistically limit the need for additional counting to only special cases.

Urban Classification Count Programs

The need for classification data in urban areas is pressing. Unfortunately, these are some of the most difficult places for current data collection equipment to operate. Existing counter technologies have significant difficulty classifying vehicles in conditions where vehicles do not operate at constant speed, where vehicles follow very closely, or where stop-and-go traffic occurs. This is particularly true for equipment that relies on inductive loops and axle detectors.

However, this does not mean that vehicle classification counts cannot be taken in urban areas. Agencies must simply take special care in selecting the technologies they use, the sensor and array that works best, and the locations where they place the equipment to ensure that the data collected are valid. Research efforts to investigate new technologies should continue. Several new technologies (ITS and segmented sensors), video imaging and various laser-based technologies, can classify accurately in urban conditions when they are correctly placed and calibrated. Traffic monitoring sites that combine multiple technologies help to overcome challenges with monitoring traffic in urban areas.

Studies can be undertaken to identify the classification segments where classification data needs exist. Examples of this include transit studies and motorcycle traveling roadway studies. The first step is to identify current installations where classification data may already be collected by ITS installations, State continuous counters, tolls, "your speed is" signs, bridges, traffic signals, etc. Retrieving those data reduces the need for the use of portable data collection equipment at as many sites. Secondly, identify the remaining locations where the

portable data collection program can collect data using current technology. Subtracting these sites from the set of all needed locations should result in a set of locations where data cannot be collected using current means. The use of manual counts or visual counts is often a last resort in cases where data cannot be collected by other means. Finally, a determination can be made of the counting/classification program needed to provide system short-term and meet special count needs. Also, State DOTs can obtain counts from data partner agencies such as cities, counties, metropolitan planning organizations, and others to supplement their existing traffic data repositories.

Classification data also offer the additional advantage of providing speed data that are often used in air quality analysis and other urban studies. Likewise, speed studies provide classification data, thereby offering an opportunity for coordination and reduced data collection.

Integration of the Short-term Count Program with Other Programs

At first glance, the short-term program recommended for classification counts can seem large. It is true that the recommended program is an expansion over previous recommendations. The expansion is due to the maturation of vehicle classification technology and an expansion in the need for truck data. Many States already actively collect substantial amounts of classification data (25-30 percent of all CCS sites are classification stations) to meet their own data needs.

The first level of integration is that classification counts should replace traditional volume counts on road sections where classification counts are taken. Therefore, for every classification count taken, one less volume count is needed. (In most cases, this still requires an increase in data collection resources because it takes more staff time as well as more physical data collection equipment to set classification counters than it does to set traditional volume counters for the same number of lanes of data collection.) Use of classification counters to provide total daily volume estimates also has the advantage of providing direct measurement of daily volume since there is no need for an axle correction factor.

The short-term count program should also be integrated as much as possible with the project count program. Existing project counting activities can eliminate the need for short-term counts. Similarly, existing short-term counts can often supply project information, if the existing short-term count meets the informational needs of the project. Metadata to be included with the short-term count is very important.

Finally, the classification count program should be integrated with other travel and traffic surveillance systems, particularly those involving regulation of the trucking industry (such as mainline sorting scale operations upstream of weight enforcement stations), as well as surveillance systems installed as part of travel and traffic management, safety, and traveler information systems.

3.7 HOW TO ACCOUNT FOR TRAFFIC VARIABILITY

This section discusses the concepts of different types of variability found in traffic patterns and describes how this variability affects the design of a strong traffic monitoring program. Traffic volumes typically vary over time and space. That is, traffic volumes are different at 8 a.m. than they are at 8 p.m. Similarly, traffic patterns are different on urban freeways and on rural farm-to-market roads. A good traffic monitoring program collects data to meet many needs; therefore, a roadway agency should design data collection efforts that provide the roadway agency with an accurate understanding of exactly what these patterns are and how they are changing over time.

3.7.1 Traffic Variability

Technology allows agencies to collect enough data to accurately describe how travel and traffic varies over time and space. Travel and traffic varies over a number of different temporal measures including:

Time of day (time/hour of day, event-driven).
Day of week.
Month of the Year (season of the year).
Year to year.

Travel and traffic varies from place to place and can also vary directionally too. Not only do roads carry different volumes of traffic, but also the characteristics of the vehicles using those roads change from facility to facility. One road with 5,000 vehicles per day may have very little bus traffic, while another road with the same volume of vehicles may have 1,000 trucks per day mixed in with 4,000 cars. Similarly, one road section may be traversed by 100 motorcycles per day while a nearby road is used by 1,000 partially loaded trucks. Directional variations also exist.

Time-Of-Day Variation

The rate of road usage typically changes during the day. In most locations, traffic volumes increase during the day and decrease at night, as can be seen in Figure 3-6. FHWA studies have determined that most truck travel falls into one of two basic time-of-day patterns: one pattern is centered on travel during the business day (see Figure 3-6), and the other pattern shows almost constant travel throughout the twenty-four-hour day (on predominantly throughway routes or near facilities with deliveries or dispatch 24 hours a day).

Figure 3-6. Example of Differences in Time-of-Day Traffic Volume by HPMS Vehicle Class

Passenger cars tend to follow either the traditional two-mode urban commute pattern or the single-mode pattern commonly seen in rural areas, where traffic volumes continue to grow throughout the day until they begin to taper off in the evening, as can be seen in Figure 3-6. Trucks serving local deliveries also exhibit a single mode that typically peaks in the early morning (many trucks make deliveries early in the morning to help prepare businesses for the coming workday). The other truck pattern (travel constantly occurring throughout the day) is common with long-haul trucking movements.

The traffic at any given site comprises some combination of these types of movements. In addition, at any specific location, time-of-day patterns may differ significantly because of local trip generation patterns that differ from the norm.

Because the volumes of the six different vehicle types are very different from one site to another, the effect of these different time-of-day patterns on summary statistics such as percent motorcycles, percent buses, percent trucks, percent bicycles, percent pedestrians, and total volume can be unexpected. Often, in daylight hours, urban car volumes are so high in comparison to truck volumes that the car travel pattern dominates, and the percentage of trucks is extremely low. However, at night on that same roadway, car volumes may decrease significantly while through-truck movements continue so that the truck percentage increases considerably, and total volume declines less than the car pattern would predict. Figure 3-6 shows that during nighttime the TOD percentages of combination unit trucks drop less abruptly than for the other vehicle classes.

Because these changes can be significant, it is important to account for them in the design and execution of the traffic monitoring program, as well as in the computation and reporting of summary statistics.

Every portable class count should be, at a minimum, annualized into the six vehicle types utilized in the HPMS vehicle summary table regardless of the data collection duration (48/72 hours…etc.)

Day-Of-Week Variation

Day of the week patterns provide another way of viewing traffic volume data. Examples of DOW variation can be found in Figure 3-7.

Source: Federal Highway Administration.

Figure 3-7. Example of Differences in Traffic Volume by Day of Week for Florida Site

Month of Year (Seasonal) Variation

Both car and truck traffic change over the course of the year. Monthly changes in volume of traffic have been tracked for many years with permanent traffic counters. Climate, proximity to major metropolitan areas or a major business venue, and primary vehicle use have an effect on seasonal travel pattern. Figure 3-8 shows an example of a typical MOY patterns by HPMS vehicle type for a rural road in Wyoming. Figure 3-8 shows that a higher percentage of annual travel happens during summer months, especially for motorcycles. Combination unit trucks show the least seasonality among the HPMS vehicle classes shown in Figure 3-8.

Source: Federal Highway Administration.

Figure 3-8. Example of Monthly Volume Pattern for a Rural Road in Wyoming

On a monthly basis, most States track travel and traffic patterns, and they base the patterns being followed on some combination of functional classification of roadway and geographic location. Geography and functional classification are used as readily available surrogate measures that describe roads that follow that basic pattern. Geographic stratification is particularly important when different parts of a State experience very different travel behavior. For example, travel in areas that experience heavy recreational movements follow different travel patterns than those in areas without such movements. Even in urban areas where travel is more constant year-round, cities with heavy recreational activity have different patterns than cities in the same State without heavy recreational movements.

Not surprisingly, truck traffic has monthly patterns that are different from automobile patterns. Some truck movements are stable throughout the year. These movements are often identified with specific types of trucks operating in specific corridors or regions. Other truck movements have high monthly variability, for example, in agricultural areas. It has even been shown that the weights carried by some trucks vary by season. This is particularly true in States where monthly load restrictions are placed on roads and where weight limits are increased during some winter months. Where this happens, States should track monthly changes for the development of adjustment factors.

As with DOW patterns, tracking of monthly changes in volumes is useful to calculate adjustments needed for various analyses. If annual statistics are needed for an analysis, it is necessary to adjust a short-term traffic volume count taken in mid-August to account for the fact that August traffic differs from the annual average condition.

Truck volume patterns can vary considerably from car volume patterns. Roads that carry significant volumes of through-trucks tend to have very different monthly patterns than roads that carry predominately local freight traffic. Roads that carry large volumes of recreational travelers often do not experience similarly large increases in truck traffic but do often experience major increases in the number of recreational vehicles, which share many characteristics with trucks but have significant differences in weights.

Thus, it is highly recommended that States monitor and account for monthly variation in truck traffic directly, and that these procedures be independent of the procedures used to account for variations in the six vehicle types.

Directional Variation

Most two-way roads exhibit differences in flow by direction by time of day. The traditional urban commute involves a heavy inbound movement in the morning and an outbound movement in the afternoon. On many suburban roads, this directional behavior has disappeared, replaced by heavy peak movements in both directions during both peak periods.

In areas with high recreational traffic flows, directional movements change the DOW traffic patterns as much as the TOD patterns. Travelers often arrive in the area starting late Thursday night and depart on Sunday.

Truck volumes and characteristics can also change by direction. One example of directional differences in trucks is the movement of loaded trucks in one direction along a road, with a return movement of empty trucks. This is often the situation in regions where mineral resources are extracted. Volumes by vehicle classification can also change from one direction to another, for example when loaded logging trucks (classified as 5-axle tractor semi-trailers) move in one direction, and unloaded logging trucks (which carry the trailer dollies on the tractor and are classified as 3-axle single units) move in the other. Another example of directional changes in volumes by vehicle classification are roads leading to/from quarries or landfills. On these roads, single-body trucks with 4+ axles (dump trucks) equipped with liftable axles are reported as Class 7 (when trucks travel loaded and liftable axles are in the lowered position) in one direction and as Class 6 in the opposite direction (when trucks travel empty and liftable axles are in the elevated position).

Tracking these directional movements as part of the statewide monitoring program is important for not only planning, design, and operation of existing roadways, but as an important supplement to the knowledge base needed to estimate the impacts that new development will generate in previously undeveloped rural lands.

Geographic Variation

The last type of variation discussed is spatial variation. That is, how volumes change from one roadway to another, or from one location on a road to another location on that same road. This type of differentiation is taken for granted for traffic volumes. Some roads simply carry more vehicles than others do. This concept is readily expanded to encompass the notion discussed above, that many of the basic traffic volume patterns are geographically affected (e.g., California ski areas have different travel patterns than California beach highways).

It is important to extend these concepts even further to recognize that truck travel also varies from route to route and region to region. It is just as important to realize that differences in truck travel can occur irrespective of differences in automobile traffic.

One important area of interest in traffic monitoring is the creation of truck flow maps and/or tonnage maps. These maps (analogous to traffic flow maps) show where truck and freight movements are heaviest. This is important for the following:

Prioritizing maintenance and roadway improvement funding.
Instituting geometric and pavement design and maintenance guidelines that account for expected truck traffic.
Studying the effects of regulatory changes in freight and goods movements (such as the abandonment of existing freight rail lines).

When travel and traffic patterns for all six vehicle types are plotted on traffic flow maps, they often reveal that vehicle traffic routes exist irrespective of the total traffic volume and/or the functional classification of the roads involved. Vehicles use specific routes because those roads lead from the vehicle's origin to their destination.

Because truck flows (both truck volumes and weights) play such an important (and growing) role in highway engineering functions, it is vital that States collect truck volume data that describe the geographic changes that exist. Which roads carry large freight movements? Which roads carry large truck volumes, even if those volumes are a small percentage of total traffic volume? Which roads restrict or carry light volumes of freight?

3.7.2 Programs and Technology Designs that Account for Traffic Variability

The variability described in this section should be measured and accounted for in a micromobility traffic data collection and reporting program. The data collection program should also identify changes in these traffic patterns as they occur over time. To meet these needs in a cost-effective manner, statewide traffic monitoring programs generally include the following:

Short-term data collection efforts.
Permanent, continuously operating, data collection sites.

The short-term counts provide the geographic coverage to understand traffic characteristics on individual roads, streets, shared-use paths, and pedestrian facilities, as well as on specific segments of those facilities. They provide site-specific data on the TOD variation and can provide data on DOW variation in nonmotorized travel, but they are mostly intended to provide current general traffic volume information throughout the larger monitored network. However, short-term counts cannot be directly used to provide many of the required data items desired by users. Statistics such as annual average traffic cannot be accurately measured during a short-term count. Instead, data collected during short-term counts are factored (adjusted) to create these annual average estimates.

To develop those factors, an agency should have an adequate number of permanently operating traffic monitoring sites. Permanent data collection sites provide temporal data on monthly (seasonal) and DOW trends. Continuous count summaries also provide very precise measurements of changes in travel volumes and characteristics at a limited number of locations.

Importantly, while the basic traffic variables required for short- and permanent-duration counts is the same (i.e., volume, volume by class, speed, weights), these two types of data collection efforts place different demands on data collection technology, and thus the equipment well suited for short-term counts is not always well suited for permanent counting and vice-versa. The implications of these different types of data collection durations on equipment selection are discussed in the following sections.

3.7.3 Micromobility Permanent Data Program

Comprehensive information on micromobility travel and traffic variability applies all the same concepts as in the motorized traffic monitoring program if enough data are collected throughout the network. Although comprehensive information on micromobility traffic variability is limited because very few public agencies have collected and analyzed continuous micromobility traffic data to date, it is expected that future analyses will contribute a much better understanding to the traffic monitoring guidance contained here. For this section, a single continuous monitoring location (e.g., Cherry Creek Trail, Denver, Colorado) is used to illustrate variability at that site. This single example may not be indicative of micromobility travel and traffic variability at other U.S. locations, especially those with a mild climate year-round.

There are multiple goals for understanding the TOD, DOW, and monthly variations in micromobility travel. One important goal is to estimate annual average daily use of micromobility facilities from short-term counts. This important statistic, referred to as AADT when applied to motorized vehicle traffic, is the most common reporting and comparison measure of facility use.

A modest number of permanent, continuously operating data collection sites.
A large number of short-term data collection efforts.

However, short-term counts cannot be directly used to provide many of the required data items desired by users. Statistics such as annual average traffic cannot be accurately measured during a short-term count. Instead, data collected during short-term counts are factored (adjusted) to create these annual average estimates.

The development of those factors requires the operation of at least a modest number of permanently operating traffic monitoring sites. Permanent data collection sites provide data on seasonal and DOW trends. Continuous count summaries also provide very precise measurements of changes in travel volumes and characteristics at a limited number of locations.

The process for collecting continuous micromobility traffic data should follow the same steps outlined in the volume traffic program design, as follows:

Review the existing continuous count program.
Develop an inventory of available continuous count locations and equipment.
Determine the traffic patterns to be monitored.
Establish pattern/factor groups.
Determine the appropriate number of continuous monitoring locations.
Select specific count locations.
Compute monthly, DOW, and hour-of-day (if applicable) factors to use in annualizing short-term counts.

The following sections provide additional detail for implementing these steps.

Pedestrians, bicyclists, scooters, and other micro-powered travelers are grouped together as micromobility traffic. A need exists to provide additional information for separate monitoring of pedestrian, bicycle, scooter, and other micro-powered traffic.

Like motorized traffic monitoring, there are many continuous volume count program design processes that help to define micromobility noteworthy practices for program development. These steps, while similar to motorized, have been documented below for details specific to micromobility travel.

Steps 1 and 2: Review the Existing Continuous Count Program; Develop an Inventory of Available Continuous Count Locations and Equipment

The first two steps are to inventory, review, and assess what your agency currently has (regarding permanent monitoring locations and equipment). This may be a short exercise for some agencies, as permanent continuous counts are much less common than short-term pedestrian and bicyclist counts.

However, these first two steps should not be bypassed simply if an agency does not have permanent count locations. Because nonmotorized traffic levels are typically higher on lower-volume and lower functional class roads/streets as well as shared-use paths and pedestrian facilities, city and county agencies and MPOs have often been more active than State DOTs in monitoring micromobility traffic.

Therefore, if a State DOT traffic data collection program will monitor micromobility traffic, it should coordinate with local and regional agencies as it inventories and reviews existing continuous counts. Additionally, it should inquire with departments other than the transportation or public works department. The following agencies or entities may have installed permanent pedestrian and bicyclist counters:

City or county parks and recreation department (e.g., on shared-use paths).
National or State parks (e.g., on internal or connector paths).
Public health departments (e.g., monitoring physical activity).
Retail or business associations (e.g., on pedestrian malls or plazas).
Pedestrian and/or bicyclist advocacy groups.

The process outlined for motorized traffic volume is equally applicable for micromobility traffic.

Traffic Patterns

If existing continuous count data are available, they should be analyzed to determine typical traffic patterns and profiles:

How do counts vary throughout the day?
How do counts vary by day of the week?
How do counts vary by month or season?
How do counts vary for inclement weather and other special events?
How does traffic vary by street functional class and the presence of bike or pedestrian facilities?
How do traffic patterns and profiles compare at different locations in areas with different land use and demographic characteristics?

Data Processing

In reviewing the current program and existing micromobility data, one should also understand the basics of how data are processed by the field equipment and loaded into the final repository, whether that be a stand-alone spreadsheet, a mode-specific database, or a traffic monitoring data warehouse. The following elements should be considered:

What formats (e.g., data structure, time intervals, metadata) are available and/or being reported from the field equipment?
What quality assurance and quality control processes are applied to the field data?
Are suspect or erroneous data flagged and/or removed?
What summarization or adjustment procedures are applied to the field data?
How does the current process/system address missing data (e.g., due to equipment hardware, software, or communications errors)?
Are estimated or imputed values flagged or documented with metadata?
Are the nonmotorized data stored/integrated with motorized data? Alternatively, is there an entirely separate process?
Are data summarization processes automated to the fullest extent possible? At what points are manual review and/or intervention required?

Subjective data manipulation or editing should be avoided. Instead, appropriate business rules and documented and approved procedures should be used in combination with supporting metadata to address missing or invalid data.

Summary Statistics

The final step in reviewing the existing program is to consider summary statistics, both those that are currently computed as well as those that may be needed. Permanent count locations should be providing count data

24 hours per day, 365 days per year; however, this continuous data stream is often summarized into a few basic summary statistics, like AADT. Because of the greater monthly variability of micromobility traffic, other summary statistics may be more relevant:

Monthly (seasonal) average daily traffic (seasonal average daily traffic [SADT] is a traffic statistic used by the National Park Service in addition to AADT. SADT computation includes those months that contain at least 80 percent of the annual traffic).
Average daily traffic by month and day of week.
Peak hour volumes for peak seasons (i.e., different user types in summer and winter for shared-use paths).

The review of existing and needed summary statistics should be based on those users and uses that have been identified earlier in this process. In this way, one can ensure that the variety of users has the required information to make decisions.

Step 3: Determine the Traffic Patterns to Be Monitored

After reviewing the existing micromobility program (both what is being done and what is needed), Step 3 is to determine those traffic patterns that are to be monitored. Part of this determination will depend upon the functional road classes and bicyclist and pedestrian facilities of interest. For example, do State DOTs want to collect micromobility count data on local streets, shared-use paths, and pedestrian facilities that are considered off-system (i.e., not included on the State highway system)?

Once the micromobility network to be monitored has been defined, an agency should determine the most likely types of traffic patterns that are expected on this network. In most cases, the nonmotorized network will include facilities that have a mix of commute, recreational, and utilitarian trips. Depending upon the relative proportions of these different trip types, distinct traffic patterns will emerge. These patterns should be used in the Step 4 to establish seasonal pattern groups.

The most common way to determine typical traffic pattern groups is through the visual analysis and charting of existing data. Continuous count data are preferred for this step, but short-term counts (multiple full days, but not 2-hour counts on a single day) may also be used with caution.

Step 4: Establish Monthly Pattern Groups

In the previous step (Step 3), existing nonmotorized data were used to determine the traffic patterns that are to be monitored. In Step 4, this information is used to establish unique traffic pattern groups that will be used as the foundation for the monitoring program.

In some cases, nonmotorized count data may not be available in Step 3 to determine the most likely traffic pattern groups. In these cases, previous analyses of nonmotorized data from previous studies or of similar locations should be used as a starting point. Once more nonmotorized data are gathered in your region, these traffic pattern groups can be refined based on your local data.

Previous (but limited) research indicates that nonmotorized traffic patterns can be classified into one of these three categories (each with their own unique TOD and DOW patterns):

Commuter and work/school-based trips – typically have the highest peaks in the morning and evening.
Recreation/utilitarian – may peak only once daily or be evenly distributed throughout the day.
Mixed trip purposes (both commuter and recreation/utilitarian) – has varying levels of these two different trips purposes or may include other miscellaneous trip purposes.

For more detailed visualization examples, see Appendix J.

Overall climate conditions will strongly influence seasonal patterns. Day-to-day weather conditions will also influence specific daily or weekly patterns but should not have a seasonal impact.

Facility type and adjacent land use are important variables; however, these will influence the mix of trip purpose, which is likely the strongest predictor of TOD and DOW traffic patterns.

Step 5: Determine Appropriate Number of Continuous Monitoring Locations

If equipment budgets are not constrained, then a rule of thumb is that about three to five continuous count locations should be installed for each distinct factor group (based on trip purpose and seasonality). The number of permanent count locations can be refined and/or increased as more data are collected on nonmotorized traffic.

Step 6: Select Specific Count Locations

Once the number of locations within factor groups has been established, the next step is to identify specific monitoring locations. Several considerations should be addressed in this step.

Differentiating pedestrian and Micromobility device traffic — Will pedestrian and bicyclist traffic be separately monitored at each permanent count location? In the case of shared-use paths, pedestrians and bicyclists will be traveling in the same space, and specialized equipment should be used to differentiate these different user types. In other situations, it may be preferable to monitor bicyclists separately from pedestrians. Exclusive bicycle lanes or separated bicycle paths can be instrumented with inductance loops (permanent) or pneumatic tubes (short-term) that will not count larger/heavier motorized vehicles. Pedestrian malls, sidewalks, or walkways can be instrumented with a single-purpose infrared counter if bicyclists are not typically present.

Selecting representative permanent count locations — Although it may be tempting to select the most heavily used locations for permanent monitoring, one should focus primarily on selecting those locations that are most representative of prevailing nonmotorized traffic patterns (while still having moderate nonmotorized traffic levels). In some cases, permanent count locations may be installed at low-use locations if higher use is expected after pedestrian or bicycle facility construction. The primary purpose of these continuous monitoring locations is to factor/annualize the other short-term counts. Continuous counts at a high-pedestrian or high-bicyclist location may look impressive but may not yield accurate results when factoring short-term counts.

Selecting optimal installation locations — Once a general site location is identified, the optimal installation location should be chosen for the specific monitoring technology and equipment. In most cases, the optimal location is:

On straight, level sections of road or trail, not on curves or on or near a steep grade.
On smooth pavement or other compacted surface.
Where the traveled way is clearly delineated, and deviation is not common.
For infrared sensors, not near water or in direct sunlight.
For infrared sensors, not directly facing the roadway unless a vertical barrier exists.
For inductance loop detectors, not near high-power utility lines that could disrupt or distort the detection capability.

Step 7: Compute Adjustment Factors

The computation of adjustment factors should follow a similar process as motorized traffic volumes outlined in Section 3.2.5. These adjustment factors will be calculated for each unique nonmotorized traffic factor group as determined in Step 4.

In practice, very few agencies have applied monthly or DOW adjustment factors to short-term nonmotorized counts. The current prevailing practice is to collect short-term counts during those dates and times that are believed to be average, thereby reducing the perceived need for adjustment. However, this practice should evolve to a more traditional traffic monitoring approach as more permanent nonmotorized count locations are installed.

3.8 TRAFFIC DATA PROCESSING

3.8.1 Calculations and Computations for End-of-Year Processing

This section presents basic statistics or estimates derived from the vehicle classification program. Statistics discussed include:

MADT – Monthly Average Daily Traffic
AADT – Annual Average Daily Traffic
AADTT – Annual Average Daily Truck Traffic (used for pavement design and management purposes)
AVDT – Annual Vehicle Distance Traveled
DVDT – Daily Vehicle Distance Traveled

3.8.2 Methods for AADT Calculations for CCS

There are three basic procedures for calculating AADT. The first two have been used historically, while the third is being currently recommended by FHWA to produce a more statistically reliable outcome.

A simple average of all days.
An average of averages (the American Association of State Highway Transportation Officials (AASHTO) method) (See Appendix L).
The new FHWA AADT method with any time increment (Krile 2015a) – this method is recommended over the other two methods.

In the first of these techniques, AADT is computed as the simple average of all 365 days in a given year (unless a leap year). When days of data are missing, the denominator is simply reduced by the number of missing days.

The advantage to this approach is that it is simple and easy to program. The disadvantage is that missing data can cause biases (and thus inaccuracy) in the AADT value produced. Blocks of missing days of data (for example, data from June 15 to July 15) can bias the annual values by removing data that has specific characteristics. On a heavy summer recreational route, missing data from June 15 through July 15 would likely result in an underestimation of the true AADT for that road.

When the simple average is used to compute average monthly traffic, the missing data often bias the results (this bias depends on what days are missing and how much of the data is missing) when an unequal number of weekday or weekend days are removed from the dataset. Because continuous count stations may have equipment down time during a year thus missing one or more days of data, AASHTO adopted a different approach for calculating AADT. Details of AASHTO method for AADT computation are found in Appendix L.

There are two limitations with the traditional AASHTO method. One limitation is that the AASHTO AADT equation uses only complete days of data. This means that the loss of one or more hours of data due to errors in the data collection process results in the loss of a full day of data from the AADT computation, reducing the number of useful time increments and providing a reduced accuracy of the resulting AADT estimate. The second limitation is that the averaging process used in the AASHTO method produces a small bias in the resulting AADT estimate.

There are two biases affecting the AASTHO method, the first is the under valuing of months with fewer than 31 days, and the second is the day-of-week bias caused by the number of days of week present in each month, which varies from month to month and year to year.

As a result, FHWA is recommending the use of the following AADT formulation for computing AADT. This computation is performed in two steps. The first step computes monthly average daily traffic from the available hourly (or other temporal period) count records. The formula will work equally well with any temporal interval data, such as the 5-minute or 1-minute data frequently recorded by ITS-based traffic management systems. The second step then computes AADT from the twelve available monthly values. These two mathematical steps are as follows:

$MADT sub m equals the following fraction: the numerator is the sum from j equals 1 to 7 of w sub jm, multiplied by the sum from h equals 1 to 24 of the following quantity: 1 divided by n sub hjm, multiplied by the sum from i equals 1 to n sub hjm of Volume sub ihjm; the denominator is the sum from j equals 1 to 7 of w sub jm.$

and

$A-A-D-T equals the following fraction: the numerator is the sum from m equals 1 to 12 of d sub m, multiplied by M-A-D-T sub H-P comma m; the denominator is the sum from m equals 1 to 12 of d sub m.$

Where:

AADT = annual average daily traffic

MADT_m = monthly average daily traffic for month m

VOL_ihjm = total traffic volume for i^th occurrence of the h^th hour of day within j^th day of week during the m^th month

i = occurrence of a particular hour of day within a particular day of the week in a particular month (i=1,…n_hjm) for which traffic volume is available

h = hour of the day (h=1,2,…24) – or other temporal interval

j = day of the week (j=1,2,…7)

m = month (m=1,…12)

nhjm = the number of times the h^th hour of day within the j^th day of week during the m^th month has available traffic volume (n_hjm ranges from 1 to 5 depending on hour of day, day of week, month, and data availability)

w_jm = the weighting for the number of times the j^th day of week occurs during the m^th month (either 4 of 5); the sum of the weights in the denominator is the number of calendar days in the month (i.e., 28, 29, 30, or 31)

d_m = the weighting for the number of days (i.e., 28, 29, 30, or 31) for the m^th month in the particular year

**Table 3-17. Example Abbreviated Dataset (Selected Hours) Showing Partial-day Counts**
Day	DOW	HR 0	HR 1	HR 2	HR 3	HR 4	HR 5	HR 6	HR 7	HR 8	HR 9
1	Tue	78	35	37	70	180	512	599	620	624	641
2	Wed	56	38	33	71	165	428	562	582	658	664
3	Thu	50	39	30	63	174	483	596	629	644	596
4	Fri	22	24	15	56	147	415	431	537	666	624
5	Sat	136	78	47	63	98	208	303	451	641	770
6	Sun	89	48	34	25	29	34	81	168	226	307
7	Mon	65	39	25	56	188	495	615	614	622	642
8	Tue	56	32	26	70	167	535	584	M**	M**	595
9	Wed	16	12	15	46	142	419	444	404	392	334
10	Thu	57	31	22	61	165	485	593	624	644	586
11	Fri	16	21	16	60	150	398	422	383	374	391
12	Sat	118	67	56	68	107	201	301	446	611	669
13	Sun	92	39	DST*	30	18	41	81	118	195	243
14	Mon	42	26	16	20	17	65	158	195	218	216
15	Tue	39	37	30	65	159	514	597	608	629	660
16	Wed*	76	31	36	61	182	496	NA	NA	NA	NA

*For more information on this method, please refer to TRB Paper Number: 16-2477 (2016).

**Maintenance

3.8.3 Procedure for AADT Calculation Based on CCS Data Using the FHWA Formula

AADT calculations are demonstrated in the steps below using the new FHWA AADT methodology.

Step 1 — determine the time increment of the data (hourly, 10 minutes, 5 minutes or 1 minute…)

The new FHWA AADT formula will work with data in any time interval. All time intervals need to be the same length.

Apply steps 2 to 6 to each month of data.

Step 2 — sum volumes for all time increments by each day of week

Sum volumes for each time increment for 12:05 am to 12:09:59, and so on for each time increment for each DOW.

For 5-minute increment of data, sum the values for Sunday at 12:00:00 am to 12:04:59 am for each time this data is available for a given month (it could be 1 or up to 5 values that are summed for a given month).

**Table 3-18. Example of Hourly Totals for Time-of-Day and Day-of-Week**
May 2019 Time	1^st Sunday Volume	2^nd Sunday Volume	3^rd Sunday Volume	4^th Sunday Volume	5^th Sunday Volume	Total
12:00:00 AM	18	23	21	17	NA	79
12:05:00 AM	15	20	18	16	NA	69
12:10:00 AM	10	21	16	15	NA	62
12:15:00 AM	13	18	16	14	NA	61
12:20:00 AM	16	19	18	NA	NA	53
12:25:00 AM	12	17	15	NA	NA	44
12:30:00 AM	12	16	14	NA	NA	42
12:35:00 AM	10	14	12	12	NA	48
12:40:00 AM	8	15	12	10	NA	45
12:45:00 AM	7	12	10	10	NA	39
12:50:00 AM	8	10	9	8	NA	35
12:55:00 AM	5	8	7	7	NA	27
1:00:00 AM	7	8	9	6	NA	30
1:05:00 AM	6	7	9	7	NA	29

Step 3 — determine the average volume for each time increment for every DOW (Average DOW Increment Volume)

**Table 3-19. Example of Day-of-Week Averages**
May 2019 Time	1st Sunday Volume	2nd Sunday Volume	3rd Sunday Volume	4th Sunday Volume	5th Sunday Volume	Total	Average DOW Increment
12:00:00 AM	18	23	21	17	NA	79	20
12:05:00 AM	15	20	18	16	NA	69	17
12:10:00 AM	10	21	16	15	NA	62	15
12:15:00 AM	13	18	16	14	NA	61	15
12:20:00 AM	16	19	18	NA	NA	53	18
12:25:00 AM	12	17	15	NA	NA	44	15
12:30:00 AM	12	16	14	NA	NA	42	14
12:35:00 AM	10	14	12	12	NA	48	12
12:40:00 AM	8	15	12	10	NA	45	11
12:45:00 AM	7	12	10	10	NA	39	10
12:50:00 AM	8	10	9	8	NA	35	9
12:55:00 AM	5	8	7	7	NA	27	7
1:00:00 AM	7	8	9	6	NA	30	8
1:05:00 AM	6	7	9	7	NA	29	7

Step 4 — sum all average time increment volumes for each DOW

Step 5 — correctly weight each DOW volumes for how many DOWs are present for the given month/year

For example, when one month has 5 Tuesdays vs. 4 Tuesdays, each DOW volume is weighted by the actual number of individual days that specific day is present in the given month for the given year.

Step 6 — average the DOW volume values across all DOWs to obtain a Monthly Average Daily Traffic (MADT)

Step 7 — correctly weight for the number of days in each of the 12 MADTs (i.e., use the number of days in a month as weighting factors in computing weighted average)

Step 8 — average the DOW values together to obtain an AADT for the year

The limitation of this method is that it requires at least one time increment for each day of week in each month of the year. The disadvantage of the method is that each MADT is not as consistent from year to year as the AASHTO method. The advantages of using this method are that it more accurately provides AADT estimates when data are missing and provides better estimates than the other two AADT methods, it allows for partial day data to be utilized, it removes the known bias in the AASHTO method, it provides for any time increment of data for AADT estimates (e.g., 1 min, 5 min, 15 minute, hourly), and it allows for ITS and other non-traditional sources of data for seamless AADT estimates provided every time increment is present for each day of the week for a given month.

Micromobility AADT Calculations

Calculating AADTs for bicycle, pedestrian, and other micromobility modes is still evolving and how to report these AADTs is also non-uniform across traffic monitoring programs. However, several methods for calculating micromobility AADTs are being researched and studied.

Rounding AADTs

The rounding of AADTs is acceptable for HPMS purposes when following the scheme recommended by the AASHTO Guide (AASHTO 2009). The TMG does not recommend this unless it is common practice for the State to round all traffic data in its traffic monitoring database and is applied to all traffic data consistently. This applies to the reporting of volume and vehicle classification data.

Rounding should be performed after all adjustments to the raw count data have been made and should NOT be performed when calculating percent single-unit and combination trucks. Low-volume counts (e.g., 0.2%) should not be rounded to report zero as a volume or as a percent since this will not accurately represent the presence of the minimal volumes and will also show no change in trends. A zero should only be reported when the actual count is zero.

The following guidance should be followed regarding rounding of AADTs:

When sufficient data are available to develop a reasonable AADT estimation from permanent continuous counters for the current year, rounding is not recommended.

3.8.4 Computation of AVDT and DVDT

The annual vehicle distance traveled (AVDT) is computed by multiplying the daily vehicle distance traveled (DVDT) by the number of days in the year. The HPMS software calculates the DVDT, and FHWA also computes the AVDT for each segment.

The DVDT is calculated by multiplying the section AADT by the section length to compute section-specific DVDT. (A roadway section or subsection is a State-owned or off-system roadway identified by an eight-digit code. Each roadway section is defined by a beginning and ending milepost in the Roadway Characteristics Inventory (RCI).

Aggregate DVDT estimates at any stratification level (volume group, functional class, area type, statewide, or other combinations of these) can be derived by summing the DVDT of the appropriate strata. For example, to obtain estimates of rural interstate DVDT, sum the DVDT estimates for each volume group strata within the rural interstate functional system group.

The HPMS standard sample sizes are defined in terms of AADT within strata (described in the HPMS Field Manual (FHWA 2016)). To estimate the precision of DVDT estimates, a complex procedure is needed to account for the variation in AADT and for the variation in section length. The equation to estimate the sampling variability of aggregate DVDT estimates is given in Sampling Techniques (Cochran 1977).

Estimates of DVDT or AVDT for specific HPMS vehicle classes also can be derived by multiplying DVDT strata figures by the appropriate percentages derived from the vehicle classification counts and aggregating to the strata totals as done for volume.

An estimate of the standard error of a stratum DVDT estimate is given by the following equation:

$S sub h equals the square root of the following: the fraction N sub h times the quantity N sub h minus n sub h, all divided by n sub h times the quantity n sub h minus 1, multiplied by the following quantity in brackets: the sum of D sub hi squared, plus the quantity sum of D sub hi divided by the sum of L sub hi, squared, multiplied by the sum of L sub hi squared, minus 2 times the quantity sum of D sub hi divided by the sum of L sub hi, multiplied by the sum of D sub hi times L sub hi.$

Where:

S_h = standard error of DVDT estimate in stratum h

N_h = number of full extent sections in stratum h

n_h = number of sample sections in stratum h

D_hi = DVDT of section i in stratum h

L_hi = length of section i in stratum h.

Example:

If:

N_h = 10, n_h = 5, D_hi = 5.00 miles, L_hi = 1.00 mile, then S_h = ± 5%

This equation, and a complete discussion of ratio-estimation procedures, is presented in Sampling Techniques (Cochran 1977). The estimates produced by this process are conservative since the errors introduced by using factors to develop AADT estimates have been ignored. The assumption is that these errors are normally distributed and therefore will cancel out when aggregated. The equation shows that estimates of the standard error of aggregate VDT for HPMS strata are derived by summing the squared standard errors of the appropriate strata and taking the square root of the total. Coefficients of variation and confidence intervals can be derived by standard statistical procedures.

The example above provides a rule of thumb and the expected precision of statewide DVDT estimates (excluding local functional class). Because of this assumption, precision estimates are conservative. Computation of annual DVDT estimates with the complete HPMS standard sample by using the AADT from each HPMS standard sample would be expected to approximate the stated precision. It is important to note that precision and accuracy are different concepts. For example, traffic volumes can vary from year to year but still yield accurate (i.e., unbiased) volume estimates.

3.8.5 Estimating Annual Average Daily Traffic Volumes from Short-Term Counts of More Than 24-Hours

When the data are collected cover 48 or more hours, the data should be summarized to represent a single daily count. This can be accomplished in two ways, depending on how the factoring process is performed:

If individual DOW factors are used (e.g., a different factor for Tuesdays than for Wednesdays), then each 24-hour count can be converted into an estimate of annual average daily traffic, and the different daily values averaged into a single estimate of AADT.
If a general DOW adjustment (e.g., a single weekday to average DOW adjustment), the individual time increment volumes can be averaged. (Only data for complete hours should be used. Partial hours should be discarded.) These averages are then totaled to produce a single daily volume, which can then be adjusted for seasonality and day of week.

Using extra hours of more than 48 but under 72 is recommended by FHWA. However, it is not recommended to truncate a 50-hour count to just 48 to fit the 2-day count number of hours needed. Utilizing the extra hours collected improve the resultant ADT to AADT values. Use of the new FHWA method of calculating AADTs provides less biased and more accurate results than other methods, such as the AASHTO method. There is a published informational guide from this research that describes four preparation steps for safety data integration and an eight-step process for developing a random stratified sampling scheme and AADT estimates for non-Federal aid-system (NFAS) roads (see Collection and Estimation of AADT on Lower-Volume Roads (Tsapakis 2016)).

Short-term volume counts require several adjustments to convert a daily traffic volume raw count into an estimate of AADT. See Section 3.4.9 for computational details.

3.8.6 Estimating Annual Average Daily Truck Traffic Volumes from Short-Term Counts

Computation of AADTT (by vehicle class) from a short-term count requires the application of one or more factors that account for differences in TOD, DOW, and monthly (seasonal) travel by vehicle type traffic patterns. These adjustments are the same as those applied to traditional volume counts, except that they should be applied by individual vehicle classification when working with classification count data.

3.9 FACTORS

There are two categories of factors—one is the annualization of traffic data and the other is for traffic engineering analyses. A factor is a number that represents a ratio of one number to another number. K, D, T, and peak hour factor are factors best computed from data collected at continuous count stations and are used in engineering analyses. The FHWA Traffic Data Computation Method Pocket Guide (FHWA 2018) is an excellent source of information about computing different traffic parameters, including factors.

The temporal factors are time-of-day (TOD), day-of-week (DOW), month-of-year (MOY) and year-to-year (YTY) factors. These factors are computed from continuous count station data for use in adjusting short-term count data to estimates of AADT.

3.9.1 Temporal Factors

Time-of-Day Factoring

TOD factoring is an FHWA-recommended methodology that should be implemented in traffic data processing software. TOD factors are used to estimate daily volume from less-than-24-hour samples. Currently, several DOTs use a traffic data processing software platform that does not support or provide the features to support TOD factoring.

Day-of-Week Factoring

DOW factors are used to correct for bias according to the day of the week. For example, Maine uses a DOW factor that averages each hour of the day for Monday, beginning at midnight and ending on Friday at noon. This factoring process is possible in the state of Maine because 98% of the short-term counts are collected during the same timeframe in which the DOW factor is calculated.

Month-of-Year Factoring

The monthly factor is used to correct for MOY bias in short-term counts. Directions on how to create and apply monthly factors are provided in the general discussion of factoring in Section 3.2.5 Step 4. Those procedures are recommended for the HPMS reporting, discussed further in Chapter 5. States may choose to select alternative monthly adjustment procedures if they have performed the analytical work necessary to document the applicability of their chosen procedure.

Year-to-Year Factoring

States use a YTY factor to account for increases and decreases in traffic volume from year to year. For example, in Washington State, the same factor groups as used for DOW and MOY factoring are used for YTY factoring. Not having distinct growth factor groups can produce sub-optimal results (particularly for the more rural groups and when the groups are not partially defined by geographic area) but no special work is needed to maintain them

(a benefit given how much effort is already put into maintaining and refining factor groups).

Another example of YTY factoring is from North Carolina where the State DOT has implemented a county-level factoring process. This is an effective factoring method in North Carolina because the variation of traffic volume from county to county is high, and some rural counties are experiencing a decline in population where other urban areas are gaining population. States should consider developing different YTY factors by vehicle type.

Example of various factors can be found in Appendix J.

3.9.2 Axle Correction Factoring

The traffic data collection that is dependent on pneumatic tubes that count axles rather than vehicles requires adjustments by applying an axle correction factor to represent vehicles. Equipment that detects vehicles directly (such as inductive loops or vehicle classification counters) does not require axle adjustment. Axle correction factors are developed from vehicle classification or WIM counts reported in IVR formats. The Glossary provides definition of axle correction factors.

In general, the higher the percentage of multi-axle vehicles on a road, the more error will be introduced into the data by not using axle correction factors.

Axle correction factors can be applied at either the individual point or the system level; specifically, from either specific vehicle classification counts at specific locations/roadways or from a combination of vehicle classification counts averaged together to represent an entire system of roads or factor grouping. This is an example of creating axle factor groups. Another example is described below.

Because truck percentages (and consequently axle correction factors) change dramatically from road to road, even within functional classes and HPMS strata,

The TMG recommends that axle correction factors be developed for specific roads from vehicle classification and WIM counts taken on that road whenever possible.

Where possible, the axle correction factor applied to an axle count should come from a classification count performed nearby, on that same road, and from a vehicle classification count that was taken during the same approximate period as the volume count. Figure 3-9 provides an example on how Axle Correction Factors (ACFs) can vary by DOW and MOY.

Source: Federal Highway Administration.

Figure 3-9. Example of Differences in ACF by DOW and MOY in Montana

For roads where these adjustment factors are not available, a system-wide factor is recommended. The system-wide factor should be computed by averaging the ACFs computed in the vehicle classification count sample within a functional classification of roads. However, other methods can also be used. Where State highway agencies have developed a by-vehicle-type route classification system, this classification system may be substituted for the functional class strata.

Computation of Axle Correction Factors

The TMG recommends that axle correction factors be developed using individual vehicle records formatted data from continuous counting axle class sites or WIM sites.

Similar to applying factors by each vehicle class, the axle correction factoring methods apply factors by using the minimum six HPMS vehicle classes.

Emphasis on the collection of classification data should minimize the need for ACFs. Whenever possible, ACFs needed to convert axle counts to vehicles should be developed from vehicle classification counts taken on the specific road. In addition, the classification count should be taken from the same general vicinity and on the same day of week (a weekday classification count is usually sufficient for a weekday volume count) as the axle count it will be used to adjust. Where a classification count has not been taken on the road in question, an average ACF can be estimated from the WIM and continuous classification sites that have per vehicle data. These values should be made publicly available for anyone collecting data from a single road tube count in the state.

Axle correction factors are required for all single road tube counts.

Example of ACF Computation

Table 3-20 provides an example of ACF computation. In the table, vehicle volume is computed by dividing the total number of axles counted by the average number per vehicle. Appropriate numbers should be computed at each site. States have different axle class systems. Some States have automated software to create these factors by axle factor groups; not all States are the same; and not all States group axle factors the same way.

**Table 3-20. Example Calculation of the ACF**
Road ID	Daily Vehicle Volume Count (A)	Daily Number of Axle Count (B)	Average Number of Axles Per Vehicle (K)=B/A
R200B	54,267	135,124	2.5
R120A	1,968	4,546	2.3
R280K	240,656	579,019	2.4

Example of Applying ACF

Table 3-21 illustrates the process of applying ACFs to convert axle counts to traffic volume estimates. The table provides a conservative estimate of the number of axles per vehicle for the FHWA 13-vehicle category classes. In the table, vehicle volume is computed by dividing the total number of axles counted by the average number of axles per vehicle. Thus, an axle count of 4,465 axles would be equal to a vehicle volume of 1,795

(4,465 / 2.49 = 1,795).

Multiplicative axle correction factors can be derived as the inverse of the average number of axles per vehicle. In the above example, the factor would be 0.40 (the inverse of 2.49). The number of vehicles (1,795) would then be estimated by multiplying the number of axles (4,465) times the factor (0.40).

**Table 3-21. Number of Axles Per Vehicle**
FHWA Vehicle Class	Daily Vehicle Volume	Total Number of Axles	Average Number of Axles Per Vehicle
1	100	200.0	2.0
2	1,400	3,080	2.2
3	45	103.5	2.3
4	15	31.5	2.1
5*	20	40.0	2.0
6	40	120.0	3.0
7	5	21.0	4.2
8	15	58	3.9
9	120	600	5.0
10	5	32	6.4
11	15	73.5	4.9
12	5	30	6.0
13	10	75	7.5
Total Volume	1,795
Total Number of Axles		4,465
Average Number of Axles Per Vehicle			2.49

*Class 5 – vehicles with trailers should not be put in class 5 (i.e., class 5 cannot have a trailer). For example, an RV with a boat/etc. should not be classified as 5.

3.9.3 Factoring Short Term Counts

Short-term volume counts require several adjustments to convert a daily traffic volume or vehicle classification raw count into an estimate of AADT.

A factoring process is necessary to adjust short-term counts to best represent an annualized estimate. A similar process should be used to annualize motorized and micromobility traffic counts.

Depending on the count duration, type of automated equipment used, and presence of inclement weather, there may be up to five factors that could be applied:

Time-of-day: If less than a full day of data is collected or partial days are present in the total multi-day count, this factor adjusts a sub-daily count to a total daily count.
Day of week: If data are collected on a single weekday or weekend day, this factor adjusts a single daily count to an average daily weekday count, weekend count, or DOW count.
Month/season-of-year: If less than a full year of data is collected, this factor adjusts an average daily count to an annual average daily count.
Occlusion: If certain types of automatic counter equipment are used, this factor adjusts for occlusion that occurs when pedestrian or cyclists passing the detection zone at the same time (i.e., side-by-side or passing from different directions). This factor is applied to correct for known under-biased count results from the occlusion of some of the count results. Documenting the process utilized for occlusion factoring should be included in the TMS.
Weather: If short-duration counts are collected during periods of inclement weather, this factor adjusts an inclement weather count to an average, typical count. Documenting the process utilized for weather factoring should be included in the TMS.

The last two factors typically apply for micromobility counts only. The micromobility data submittal formats in Chapter 4 provide the capability to report these five types of adjustment factors in five separate factor groups.

Adjustment factors should be developed for distinct factor groups, which are groups of continuous counters that have similar traffic patterns. The continuous counters in the factor groups provide year-round traffic counts and permit the short-term counts to be annualized in a way that minimizes error.

Many State DOTs have data warehouse tools that already perform the factoring process for motorized traffic counts. See Section 3.2.8 for a computational example showing application of factors for motorized traffic counts. Many of these tools and factoring processes could be used for micromobility traffic factoring, given some adaptation. Since State DOTs already have motorized traffic monitoring programs in place, many micromobility programs can take advantage of making investments in existing software and trained traffic data analysts.

3.9.3.1 Micromobility Example: Factoring Short-Term Counts

The following simplified example illustrates the process of calculating an estimate of annual average traffic based on a short-term count pedestrian and bicyclist count. The example is for mixed-mode, micromobility traffic (i.e., bicyclists and pedestrians combined) along the Midtown Greenway, a shared-use path in Minneapolis, Minnesota. An active infrared counter was used at the permanent monitoring location along the Greenway, near an intersection with Hennepin Avenue (Lindsey 2012).

For this example, the Minneapolis Department of Public Works installed a temporary infrared sensor to count traffic for 48 hours on a Friday and Saturday in February 2012 on a different shared-use path where no monitoring previously had occurred (Monitoring Site A). The 24-hour mixed-mode traffic count for Friday was 175 and the

24-hour count for Saturday was 250. What is a reasonable estimate of annual traffic (AADT) at Site A? DOW and monthly ratios or adjustment factors from the Midtown Greenway-Hennepin Avenue location can be used to obtain this estimate.

Table 3-22 presents the following actual mixed-mode traffic count statistics for 2011 at the Hennepin Avenue monitoring location along the Midtown Greenway:

Annual average daily traffic (AADT).
Monthly average daily traffic (MADT).
Ratio of mean day of week traffic to MADT for each month.
Ratio of MADT to AADT for each month.

The steps in using these factors to obtain estimates of annual traffic and AADT for Site A are:

Use the 2011 Friday and Saturday mean daily traffic ratios for February to calculate an average adjustment factor for the February 2012 48-hour monitoring period.

Estimate the MADT for February 2012.
Use the MADT/AADT ratio from February 2011 to estimate the 2012 AADT and 2012 annual traffic.
Use the 2011 Friday and Saturday mean daily traffic rations for February to calculate an average adjustment factor for the February 2012 48-hour monitoring period. From Table 3-22, the average Friday traffic in February 2011 was 1.04 times February average daily traffic, and the average Saturday traffic was 1.27 times February average daily traffic. Therefore, for the Friday-Saturday monitoring period, the average daily traffic was 1.16 times the February average daily traffic.
Using this ratio, the 2012 February average daily traffic can be calculated from the 2012 48-hour traffic count:
2012 February average daily traffic = ((175 + 250)/2) / 1.16 = 183
From Table 3-22, the February MADT/AADT ratio is 0.18 (i.e., February average daily traffic is 18% of annual average daily traffic). This factor then is used to calculate AADT and annual traffic for 2012 for Site A:
Site A AADT = (183 / 0.18) = 1,023

Site A cumulative annual traffic = 1,023 x 365 = 373,422

This example could easily be extended for counts of different duration (e.g., daily, or weekly or peak hour). To extrapolate 2-hour, peak hour counts, hourly adjustment factors from the continuous monitoring sites would be needed. While the general process would be the same, extrapolation from peak hour counts would introduce additional uncertainty into the estimates of AADT and annual traffic.

**Table 3-22. 2011 Mixed-Mode Traffic Count Statistics for Midtown Greenway Near Hennepin Avenue, Minneapolis, Minnesota**
Measurement	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sept	Oct	Nov	Dec
Annual Average Daily Traffic	1,975	1,975	1,975	1,975	1,975	1,975	1,975	1,975	1,975	1,975	1,975	1,975
Monthly Average Daily Traffic (MADT)	239	354	586	1,807	2,753	3,699	4,099	3,896	2,805	1,960	886	495
Ratio of MADT to AADT	0.12	0.18	0.30	0.92	1.39	1.87	2.08	1.97	1.42	0.99	0.45	0.25
Ratio of Sunday ADT to MADT	0.89	1.33	0.89	1.55	0.88	1.29	1.18	1.34	1.06	1.20	0.75	1.11
Ratio of Monday ADT to MADT	1.01	0.66	1.10	1.10	0.98	0.95	0.98	0.87	1.22	0.96	1.00	1.08
Ratio of Tuesday ADT to MADT	1.10	0.74	0.91	0.96	1.27	0.89	0.91	0.74	0.86	1.03	1.01	1.07
Ratio of Wednesday ADT to MADT	1.15	0.96	0.93	0.76	1.11	0.96	0.94	1.07	0.99	0.87	1.03	0.97
Ratio of Thursday ADT to MADT	1.06	1.00	1.03	0.88	0.93	0.96	0.90	1.03	0.85	0.87	0.97	0.92
Ratio of Friday ADT to MADT	0.97	1.04	0.84	0.78	0.79	0.96	0.95	0.88	0.87	0.82	1.31	0.91
Ratio of Saturday ADT to MADT	0.88	1.27	1.34	1.03	1.02	1.02	1.09	1.15	1.23	1.16	0.91	0.98

Source: Greg Lindsey, University of Minnesota.

Other resources providing information on micromobility data formatting can be found in the Guidebook on Pedestrian and Bicycle Volume Data Collection (NASEM 2014).

3.9.3.2 Micromobility Weather Factor

Weather can be a significant factor in the level and variability of micromobility traffic and should be considered when developing a short-term micromobility program. Monthly (seasonal) weather patterns (such as cold winters or hot/humid summers) are expected by micromobility travelers and will result in relatively consistent patterns from year to year. However, heavy precipitation or unexpectedly hot or cold weather may introduce abnormal variations on a given time of day or day of year. These variations can both generate unusually high levels of activity (e.g., a very nice day) or depress otherwise expected levels of activity (due to very bad weather).

If automatic counter equipment is used for short-term counts in typical weather, then the minimum suggested duration is 7 days (such that all weekday and weekend days are represented). This duration provides an average of 5 weekdays and 2 weekend days. However, if atypical heavy precipitation or inclement weather occurs during this entire 7-day period, agencies should consider extending the duration to 7 days.

When heavy precipitation or inclement weather occurs with manual observers, the counts should be extended over multiple days at the same time. Local judgment should be used to determine whether to include inclement-weather days into a multi-day average.

Because of inclement weather's influence on micromobility traffic, weather conditions should be recorded in a nonmotorized traffic monitoring program. The micromobility data submittal format in Chapter 4 recommends three weather-related attributes:

Precipitation (yes/no): Did measurable precipitation fall at some time during data collection?
High temperature: Approximate high temperature for either the day (if a day or longer count) or the duration of the count (if the count is less than a day in duration).
Low temperature: Approximate low temperature for either the day (if a day or longer count) or the duration of the count (if the count is less than a day in duration).

Historical weather data can be obtained from several different sources and does not necessarily have to be collected at the exact count location.

Months/Seasons of Year for Data Collection

The specific months/seasons of the year for short-term counts should be chosen to represent average or typical use levels, which can be readily determined from permanent continuous counters (thereby underscoring the importance of these automatic continuous counters). In most climates in the U.S., the spring and fall months are considered the most representative of annual average nonmotorized traffic levels.

Short-term counts may be collected during other months/seasons of the year that are not considered average or typical; however, a factoring process will be necessary to adjust these counts to best represent an annualized estimate of micromobility traffic.

3.9.4 Considerations for Collecting Motorcycle Data and Correction Factoring

Motorcycles are the most dangerous motor vehicles for both operators and passengers of any age. Moreover, data from the NHTSA's Fatality Analysis Reporting System (FARS) indicate disturbing trends in motorcycle safety:

Trends accompanying the rising motorcyclist death toll include a dramatic increase in motorcycle ownership, particularly by riders over 40 years old, along with changes in other factors such as motorcycle size; and
The rate of increase in fatalities has outpaced the rate of increase in motorcycle registrations.
To assess motorcycle safety, it is necessary to know the number of crashes as well as the corresponding exposure to determine a fatality rate. One of the key exposures is the motorcycle miles traveled (MC data are the denominator for exposure and crash rates):
Motorcycle exposure data are used to inform national decisions and establish motorcycle-related policies and safety countermeasure programs.
Motorcycle exposure data are an important part of current safety performance measures, which measure the number of motorcycle fatalities per vehicle registrations and per million miles traveled.
Motorcycle travel data, especially by roadway functional system, helps the DOT to better understand the distribution of travel and devise effective design and operational measures for both reliable and safe travel of motorists. Motorcycle travel data are a critical element for developing effective safety countermeasures.

A successful example of one State's ability to detect motorcycles using inductive loops and piezo sensors comes from the State of Virginia. The Virginia DOT (VDOT) worked with their vendor to develop a four-channel loop board that meets the required performance standard and a piezo card that provides improved detection of motorcycle axles by analyzing complex waveforms and rejecting energy from adjacent lanes. VDOT attributes their ability to detect motorcycles to their installation standards for loops and piezos. Loops are installed with four turns of wire and no splices, using wire that meets International Municipal Signal Association (IMSA) Specification 51-7; and they are now installing two piezos stacked in a single saw cut. Virginia and Wisconsin DOTs also use length-based classification devices of about seven feet to distinguish motorcycles from other compact vehicles. To reduce the potential of undercounting Class 1 vehicles using the combination of loops and piezos, VDOT takes an additional step of using six bins instead of a single bin for all vehicle counts that cannot be classified (Schinkel 2008). One of these bins, Bin 21, is used for vehicles whose length is less than 7 feet, but for which fewer than two axles are detected. On two-lane, two-way roads, this condition usually indicates that the piezo at a classification site has begun to fail and did not detect one or both axles of a Class 1 vehicle. Accordingly, on these roads, Bin 21 vehicles can be assigned to Vehicle Class 1.

Traffic Data Collection and Motorcycles

A State DOT should be able to provide users with an estimate of the amount of traffic by vehicle class by road segment—including motorcycle travel. Motorcycle volume and percentage estimates should be available for the date when data were collected and as annual average estimates corrected for yearly, monthly, and DOW variation.

Complicating the process for annualizing motorcycle counts is that travel patterns for motorcycles are usually different from those for cars, buses, or trucks. Motorcycle volume patterns are primarily recreational patterns, although commuter travel may be significant in some cases. Consequently, motorcycle travel is frequently heavily dependent on the DOW (higher on weekends), season (higher in summer), and special events (e.g., rallies).>Recreational motorcycle travel may also concentrate on specific roads more than car or truck travel typically does. (That is, some specific roads are commonly used by large groups of motorcycles for "group rides"—therefore creating very large increases in motorcycle VMT on a relatively modest series of roads and days of the year.)

The TMG recommends that a vehicle classification-counting program include both extensive, geographically distributed, short-term counts and a smaller set of continuous counters. This same guidance works for effectively collecting motorcycle travel, but accurately estimating motorcycles does require some refinement of the traditional count program to account for motorcycle patterns, simply because many of the traditional data collection plans are structured specifically around understanding the movements of cars and trucks.

The first change required to the traditional traffic monitoring program is for States to develop a process for converting short-term counts to estimates of AADT that specifically factor short-term motorcycle counts (as well as specifically factor the other vehicle classes) based on the travel patterns observed for each of those classes. Without motorcycle-specific adjustments, short-term classification counts yield biased annual estimates of motorcycle travel.

As noted in the HPMS Field Manual (FHWA 2016), although motorcycles are a small percent of travel, they have significant safety issues that require attention for estimating their travel exposure. Continuous counters provide an understanding of how typical motorcycle travel varies by day of the week and month of the year. To provide motorcycle specific adjustment factors, States should account for motorcycle travel patterns when selecting locations for permanent vehicle classification counters.

To capture motorcycle movements and more effectively estimate annual motorcycle VMT, some short-term counts should be conducted in places and times where motorcyclists are known to travel such as weekend rallies, runs, and shows and on other roads used for recreational motorcycle travel. Some of these data should be collected using thoughtfully sited, permanent count sites placed on recreational routes with motorcycle travel. This data collection effort yields basic motorcycle traffic statistics including geographic variability and the TOD distribution.

A sufficient number of locations should be monitored to meet HPMS requirements. Motorcycle travel is reported under the HPMS summary travel as a proportion of total travel by roadway functional class. The State should have motorcycle and other vehicle class travel data for all the roadway functional classes. If the stations are sufficiently distributed according to road type and by traffic volume, a simple average of the observed proportions from all stations can be reported on the summary travel table (see HPMS Field Manual, FHWA 2016).

Traffic data collection, including motorcycle data collection, is eligible for Federal funding under a wide range of Federal-aid highway programs.

Considerable improvement in the accuracy of motorcycle counts area has been made in the past few years. Montana utilizes bi-wheel path counting for proper motorcycle counting, which is especially effective during motorcycle rallies where motorcycles may be doubled-up into one lane. Many State DOTs use a wider 6' by 8' loop in the lane to provide a large lane coverage that prevents motorcycles from not being detected by loops. This wider lane width of 8' requires that each bi-lane array be staggered so that interference or "cross talk" between loops does not occur. Multiple technologies can be used successfully for this activity, although each technology has its own strengths and limitations.

Axle, visual, and presence sensors can all be used successfully for collecting motorcycle volumes as part of vehicle classification counts, although each provides a different mechanism for classifying vehicles. Within each of these three broad categories are an array of sensors with different capabilities, levels of accuracy, performance capabilities within different operating environments, and output characteristics. Each type of sensor works well under some conditions and poorly in others. For example:

Motorcycles in parallel or staggered formation may confuse detectors unless wheel path counting sensor set-up or segmented sensor technology is used.
Adjusting detector sensitivity for trucks may lead to reduced detection of motorcycles.
Some combination trucks may be misclassified as a single-unit truck followed by a motorcycle (the rear tandem axle) when the loop incorrectly detunes in the middle of the vehicle.

Full-lane width sensors should be installed for the most accurate classification detection of vehicles, unless by wheel path counting is being done.

Using more sophisticated axle sensors and data collection electronics that can monitor left and right wheel paths independently can improve the ability of axle sensor-based classification equipment to count all motorcycles in a group, while also correctly counting and classifying cars and trucks. For axle sensor arrays that are placed in a staggered formation, a motorcycle will usually hit one sensor but not both; the system will likely record this as a vehicle with missing axle detection and classify it as a passenger car by default—unless the data collection electronics are specifically designed to look for motorcycles. For this reason, FHWA recommends full-lane width axle sensors (road tube, tape switches, or piezo sensors) rather than half-lane width sensors.

Side-looking radar provides length-based classification and detects motorcycles. Inductive loops can work well if properly installed and maintained, but they too can have problems with motorcycles traveling in groups, especially when riding in slightly staggered side-by-side configurations in individual lanes. Conventional loops can also be hard to tune to capture motorcycles while at the same time not having that same sensitivity setting, resulting in over counting of cars and trucks. Some studies have shown that accuracy of counting in all classes can be improved by using inductive loop signature technology and calibrating sites to be accurate.

Accuracy improvements have also been shown to occur when using 6' by 8' foot loop layouts instead of the conventional 6' by 6' configuration and when installing piezos, the full lane width should be used. Full lane width for road tube sets for class counts are also recommended.

Quadrupole loops also known as figure-8 style loop detectors have enhanced sensitivity for detecting motorcycles, bicycles, and smaller cars. The Montana DOT provides an example of loop lane configuration placement installation diagrams illustrating a best practice, see Appendix I.

Sensors that cover a small area such as magnetometers have problems detecting motorcycles or groups of motorcycles.

All vehicle classifiers should be annually calibrated and regularly tested, and it is a good idea to involve motorcycles traveling in groups as part of those tests to ensure that motorcycles are properly counted. It is also advisable to use a test standard such as ASTM E2532-06, Standard Test Methods for Evaluating Performance of Highway Traffic Monitoring Devices.

If length-based classification is used, it should accommodate motorcycle identification as one of the groups. The FHWA Pooled Fund Program Report TPF-5[192], Loop and Length Based Vehicle Classification (Minge 2012) prepared for Minnesota DOT provides more information.

Axle sensors, loops, and road tubes that detect the presence of vehicles should be placed—and loop sensitivities set—in the travel way of motorcycles to assure their detection. Sensors that detect vehicles over the width of a lane are preferable to those that are partial lane.

All vehicle classes are important; no vehicle class should be shortchanged. It is the responsibility of each agency to make the best decision as to the types of automatic vehicle classifiers to purchase, install, calibrate, and maintain so that their classification (both axle and/or length) data accurately represent travel and traffic conditions.

3.10 QUALITY ASSURANCE – STARTS WITH THE DATA QUALITY ACT REQUIREMENTS

Data quality assurance processes are a critical component of any well-designed traffic monitoring program. The TMG recommends that each agency improve the quality of reported traffic data by establishing quality assurance processes for traffic data collection and processing. Each highway agency should have formal, documented rules and procedures for their quality control efforts.

A comprehensive and quality documented process will also assist in a smooth succession when there is turnover in staff.

FHWA recommends traffic monitoring programs consider the following seven data quality principles:

Data quality is more than correcting data.
Data assessment identifies process improvements.
Quality control process checks for valid data, not necessarily accurate data.
Data need to be useful, not just accurate.
Quality problems are not (necessarily) caused by people.
Inserted non-quality data does not improve quality.
Recounts and poor decisions are more costly than ensuring initial data quality.

Appendix C provides a compendium of data quality control criteria for State highway agency implementation.

Return to top

Page last modified on May 18, 2026