U.S. Department of Transportation
Federal Highway Administration
1200 New Jersey Avenue, SE
Washington, DC 20590
202-366-4000
There is no definite answer. The decision must be made by each organization. Traffic patterns vary from site to site. In most urban cases, Monday traffic volumes are fairly similar to Tuesdays, Wednesday, and Thursdays. Fridays, however, tend to have lower morning volumes and slightly higher afternoon volumes than the other weekdays. In rural recreational areas, Mondays, like Fridays, can have substantially different volumes than the other weekdays. In other rural areas, Monday volumes tend to be similar to Tuesday through Thursday volumes. The procedures recommended in the TMG produce adequate estimates of AADT regardless of whether these days are included or excluded.
The answer to this question depends on the data that analysts will be factoring. If counts are routinely taken from noon-to-noon, then computation of the factors using noon-to-noon days is appropriate. If the days from short duration counts are always based on midnight start times (that is, the earliest hours of the data collection period are essentially discarded), then the days used in the factor computation should be based on calendar days. Analysis has shown that the use of either alternative has little impact on the AADT estimates
The best factoring results are obtained if the factors being applied are for the same year as the short duration counts being factored. That is, a short count taken in 2000 should be factored with continuous count data from 2000. This is done because significant events affecting the ratio of a short duration count to annual travel this year (e.g., a big snow storm) are accounted for in this year’s continuous count data. They were not present in last year’s continuous count data.
The drawback to using current year data for the factors is that computation must wait until the end of the year. States often wish to use AADT estimates from short counts taken during the current year before the end of the current year. One alternative is to create and use a temporary factor until the calendar year is complete. This factor is computed with the data from the previous 12 months. The temporary factor would be used until the final factors are computed. This final value would then be maintained as the annual estimate.
Another alternate is to use more than one year of data to compute seasonal factors. However, this technique does not account for annual conditions that affect traffic when it is applied to short duration counts that are from a different year.
Perhaps the simplest solution is to use the available AADT figure until a new one based on the current year factors is computed. Factors based on current year data are recommended.
There is no simple solution to this problem. These locations tend to have unique day-of-week patterns that reflect typical urban patterns on the weekdays, but rural patterns on weekends. Similarly, seasonal variation tends to be partway between the flat pattern found in most urban settings and the more varied peak patterns often found in rural areas. This occurs with commuter routes where the urban pattern extends outside the urban boundary. In most cases analyst judgment is the answer.
One alternative is to take longer short duration counts. A week-long count will provide the data needed to account for the day-of-week variation without factors. The factor application then only has to adjust for the seasonal component. Another solution may be to install a continuous counter for that route. Another may be to apply the appropriate factors outside the group boundaries as a special case
There is no single answer to this question. Statistics and the desire to have factors that yield annual AADT estimates with +10 percent accuracy with 95 percent confidence tend to require a factor group size of five to eight counters. A minimum of two counters is required to compute a standard deviation of the average factors that become the group factors. The standard deviation is used to estimate the reliability of the group factors. Recreational or special groups often have only a single continuous counter. Many States prefer to have additional counters to compensate for downtime and missing data problems.
Continuous counters track traffic at a point. Depending on the site, this can be expanded to a section or route. It is rarely practical to track area wide travel with continuous counters. Few agencies have the large number of continuous counters required to provide statistical reliability to the area wide travel estimates. In most cases, agencies use a limited number of continuous count locations to provide traffic trends at a limited number of sites. Individual road volumes are dramatically affected by local changes in land use and economic activity. The use of a small number of continuous count locations can result in highly biased VDT calculations. The FHWA uses the continuous count data reported monthly to the travel volume trends (TVT) system combined with the annual HPMS VDT estimates to track changes in monthly travel. A similar approach could be applied statewide for States with sufficient number of continuous counters.
A road segment for traffic counting is a section of road with homogeneous volume (i.e., the traffic volume does not change throughout the segment). Many State traffic programs divide their systems into traffic segments and physically count each segment to provide complete system coverage. Traffic volume is constantly changing and a perfect segment definition is not possible. For access-controlled systems, a definition between interchanges is the simplest. For non-controlled systems, the TMG recommends keeping a single segment until volume changes of plus or minus 10 percent are identified, at which point a new segment should be created.
There is no simple answer to this question. In most cases, when using portable vehicle classification equipment, FHWA’s 13 vehicle category classification system (Appendix C) have become the standard. However, it is certainly appropriate to further sub-divide these classifications to provide data on specific vehicles of interest. For example, Oregon DOT collects data on the use of triple trailer vehicles. This classification is a sub-set of class 13 (multi-trailer vehicles). Thus for their own purposes, these vehicles are a specific class of trucks. When Oregon reports data to the FHWA, the triple trailer class vehicles are simply combined with the other multi-trailer vehicles measured, and the total is reported as the volume in FHWA class 13. Permanent axle classifiers and WIM scales should also collect the FHWA 13 vehicle classes.
On many roadways, it is not possible to place axle sensors so that they accurately collect the 13 FHWA vehicle categories. However, it is possible to use two inductance loops or magnetic units to differentiate vehicles by total length. Four vehicle classes are recommended when collecting data in this fashion. These classes should reflect cars (and pick-up trucks), single-unit trucks, single-trailer combination trucks, and multi-trailer trucks. In some States, the multi-trailer truck category may be unnecessary, and these trucks can be incorporated into the combination category because there are few multi-trailer trucks. As the truck fleets and truck size and weight laws change, States not collecting data on these vehicles may have to revise their data collection process to collect the data. While use of the simplified vehicle classes does not meet the desired level of reporting for many purposes, collecting data in the simplified categories is far better than collecting no vehicle classification data at all, and it allows monitoring the presence of trucks in urban traffic
A reasonable answer to this question cannot be given without first understanding how the State proposes to factor short duration vehicle classification counts. If a traditional factoring approach is selected (i.e., something similar to the continuous count program operated by almost all States), then as many continuous classifiers as continuous counters should be operated. If the State chooses a classification count factoring approach that measures and applies road-specific factors, the number of counters required will increase significantly.
There are many factors to consider in the answer. As a rule of thumb, 25 to 30 percent of volume counts should be classified. In general, each State should undertake a vehicle classification count at least once every counting cycle that can be applied to each road segment under its control. This does not mean that each road segment should be counted. Instead, super segments consisting of combined roadway volume segments should be counted. Each super segment should be relatively homogeneous for truck traffic along its length. Each super segment should be counted at the same interval used by the State for collecting volume counts. Annual truck travel estimates can be derived from the counts. The annualized truck percentages can then be converted to estimates of truck travel for the entire super segment.
Traditional vehicle classifiers have difficulties operating accurately in urban areas because vehicle acceleration/deceleration makes speed and length calculations inaccurate, and because closely following vehicles result in misclassification of cars as trucks. On freeways, careful placement and calibration of either video or loop based classification equipment can produce accurate truck volume counts. To date, no inexpensive classifier is available that works accurately under stop-and-go arterial conditions.
For higher systems, permanent classifiers using loops or video may be the only alternative. On lower systems, there are locations where axle or magnetic (length) portable classifiers will work. In many cases, visual counts may be the last resort.
In simple terms, a traffic road segment is a section of roadway that has similar (or homogeneous) volume or classification characteristics. The difficulty comes from the fact that a homogeneous segment for traffic volume may not be a homogeneous segment for other purposes such as classification or pavement design purposes. For example, the road may change from asphalt concrete to Portland cement concrete even though the volumes being carried on that road do not change appreciably. When developing a count program for vehicle classification, it may be necessary to create classification roadway segments where truck volumes do not change significantly. A single classification count taken within a properly defined super segment provides the classification data for all segments within that super segment. The use of these super segments reduces the number of physical classification counts needed to provide adequate roadway coverage for truck volume information.
Analysis of available data indicated that no single set of vehicle lengths worked best for all States, as vehicle characteristics vary from State to State. As of November 2012, the vehicle length classification system that worked the best on combined data from all States is shown in Table I-1.
| Primary Description of Vehicles Included in the Class | Lower Length Bound > | Upper Length Bound < or = |
|---|---|---|
| Passenger vehicles (PV) | 0 m (0 ft) | 3.96 m (13 ft) |
| Single unit vehicles (SU) | 3.96 m (13 ft) | 10.67 m (35 ft) |
| Combination vehicles (CU) | 10.67 m (35 ft) | 18.59 m (61 ft) |
| Multi-trailer vehicles (MU) | 18.59 m (61 ft) | 36.58 m (120 ft) |
Source: Federal Highway Administration.
These criteria did an acceptable job of classifying vehicles into the four general categories. Considerable error was found in how well the length bins (and the corresponding classification results) performed when estimating aggregations of the FHWA 13 vehicle category classification system. A classifier can accurately measure vehicle length (for example as 34 feet for a given small vehicle combination), place that count in the correct length bin (in this example, the bin from 13 to 35 feet), but incorrectly classify that vehicle (in this case calling a small combination vehicle a single unit).
Table I-2 shows the errors associated with using vehicle lengths to estimate the four vehicle categories shown in Table I-1 when using the vehicle length boundaries shown in that table.
| Classification Based on Total Vehicle Length | |||||
|---|---|---|---|---|---|
| Classification Based on Configuration and Number of Axles |
PV | SU | CU | MU | |
| SU | 17.7% | 81.9% | 0.4% | 0% | |
| CU | 0% | 1.8% | 84.2% | 14.0% | |
| MU | 0% | 0.1% | 20.8% | 79.1% | |
Source: Federal Highway Administration.
Many States will be able to improve on these results by fine-tuning the length spacing boundaries to account for the characteristics of their trucking fleets. However, no amount of fine-tuning will lead to a perfect length classification system (where perfection is defined as the ability to use overall vehicle length to classify vehicles based on the number of units they include or the number of axles they use). This is because total vehicle length is not a consistent indicator of vehicle class as defined by these attributes. Consequently, highway agencies should be aware of the size and type of misclassification error that exists, and set their length boundaries to minimize error.
States should not abandon the axle based vehicle classification system. Data for all pavement designs are still based on FHWA’s 13 category vehicle classification system, which is an axle system.
WIM data is needed to address pavement design and other uses involving all types of pavement. Data collection mechanisms that provide quality data are needed under all conditions. Indeed, the dynamic forces that vehicles apply to the pavement may increase as the quality of the pavement decreases. Research and equipment activities under the auspices of the traffic monitoring program should continue under a variety of roadway conditions. However, under current equipment constraints, the collection of WIM data based on calibrated equipment and comparable to static weight data may only be possible on smooth and flat pavement. The TMG emphasizes the collection of quality WIM data at permanent installations in flat and smooth pavement to insure the quality and veracity of the resulting WIM data. The limited WIM data at these sites is then expanded based on specific road groups and detailed classification data to apply WIM estimates to the complete roadway system. Extended information on these issues is available from ASTM or the LTPP program
TVT reports vehicle miles traveled (VMT) on all U.S. public roads on a monthly basis. VMT change rates are also reported. The geography is done by individual State.
The TVT report is published within 60 days after the close of the given month. A copy of the report can be obtained at: https://www.fhwa.dot.gov/ohim/tvtw/tvtpage.cfm.
To subscribe the TVT, please visit the following website, select the eSubscribe button and then follow the instruction provided.
https://www.fhwa.dot.gov/policyinformation/travel_monitoring/tvt.cfm.
The TVT report is based on traffic data from the Highway Performance Monitoring System and on data submitted to the FHWA by State highway agencies throughout the entire U.S. The State highway agencies collect the data through continuous counters on public roadways.
Monthly continuous count data should be submitted to the FHWA within 20 days after the closing of the month. For example, the January 2013 data should be submitted to FHWA no later than February 20, 2013.
In the event that a State does not have a traffic counter on a given functional classification of roadway, the TVT procedure is to estimate the missing value(s) from other functional classes from the same State. If the State does not have any valid values, the average value from the surrounding States for the same roadway functional class is used. If no surrounding State data is available, National average(s) are used.
The Travel Monitoring Analysis System (TMAS) provides online data-submitting capabilities to State traffic offices to submit data to FHWA. Access to TMAS is obtained through the FHWA Division office in the individual State. This link provides contact information to all the Division offices:
https://www.fhwa.dot.gov/field.html#fieldsites.
A State should have 5 to 8 permanent sites for each grouping of functional classified roadway.
The Traffic Volume Trends (TVT) report is published monthly by the Federal Highway Administration (FHWA). The report estimates the vehicle miles traveled (VMT) by State and several functional classes of roads. The estimates are based on two sources of data:
The HPMS compiles data from the States annually concerning the condition and performance of all roads in the United States. HPMS includes the annual average daily traffic (AADT) by road segment. When these AADTs are multiplied by the length of each road segment and summed for all road segments and days of the year, they yield the annual VMT.
The States submit to FHWA traffic counts from their continuous counters each month. These continuous counters are permanent traffic counting devices such as inductive loops in the roadway. There are about 4,000 continuous counts that are reported to FHWA each month.
Continuous count data is submitted and processed using the Travel Monitoring Analysis System (TMAS). The FHWA runs quality control checks on all data received. Only data passing the checks are used for the TVT report.
Monthly average daily traffic (MADT) is computed from the continuous counts. Each MADT is compared with the MADT for the same month the previous year to yield a change rate. The change rates are averaged by functional class of road. If a State does not provide traffic data in time, their change rates are estimated from the surrounding States.
TVT estimates monthly VMT by combining the change rates for each month with the most recent annual VMT from HPMS. The TVT report is available to the public within 60 days after the close of the month. Data that covers a minimum of 30 States and 70% of the VMT is required for publication. The next month’s TVT report will include an update that covers more data.
The December TVT provides the first estimate of annual VMT for performance measures such as crash rates. When the annual HPMS data is available, they will supersede the total VMT from TVT.
The K-factor is the proportion of AADT occurring in the analysis hour. It is the ratio of analysis hour to annual average daily traffic. For example, the peak proportionality K-factor is the ration of peak hour to annual average daily traffic. By applying the peak hour proportionality K-factor to an AADT, design hour volume can be estimated. Other K-factors include the K30, which is the 30th highest peak hour divided by the annual average daily traffic (K100 is the 100th).
The most accurate way to do this is to have a continuous count station on every HPMS sample section. This is very unlikely because of the amount of money and staff needed to maintain and operate that type of system. However, every sample section should have K-factor data coded, and an estimate should be provided for sections without a direct measurement; zero coding is not an option for this data item.
States are using a variety of methods to develop K-factor estimates. In general, we encourage the States to use the same procedures for HPMS sample sections that are used to estimate K-factors for project level engineering and design decisions.
There are a number of estimating methods in use, including use of:
There are a number of estimating methods being used by States that are not common practice and that may require further investigation for applicability to other States. These include use of:
No. States should use K-factors from site-specific traffic monitoring data to the greatest extent possible. Any estimating procedures should make best use of the available information and sound traffic engineering judgment. In addition, they should be validated through the execution of a periodic test program that assesses the quality of the relationship between the estimate and factors computed from measured values. FHWA does not support the use of statewide values by functional class.
Every sample section should include associated truck percentage data; zero should only be associated with a site if there is no truck traffic on the section or if the percentage of trucks is less than one-half of one percent (result of rounding to nearest whole percent). Zero should not be associated with the site if the percent of trucks is unknown; an estimate of the value should be used instead. Associating truck percentage data with sites where only sections that have actual measured value results in too many sample sections with zero trucks; since the HPMS uses a single expansion factor for all variables, this practice distorts the information resulting from the expanded sample. In other words, having too many zero truck percentages will result in a lack of valuable information.
Where States are collecting data that results in rounded percentage of truck values of zero, a note in the submittal comment file is appropriate. This may be the case on high volume routes, especially in urban areas, where the volume of trucks may be significant but their percent of total AADT is insignificant.
The preferred way to eliminate this problem over time is to upgrade equipment used for short count purposes to counters that also provide vehicle classification information. Used where needed for short counts on HPMS sample sections, they will permit reporting measured values.
When it is necessary to use an estimate, the State should determine the best way to estimate percentages of trucks based on the information available. The most credible method is to assign known site-specific values to other samples that are located on the same route. Other methods include assigning known site-specific values to other samples that are located on similar facilities with similar traffic characteristics that are located in the same geographical area and are in the same volume group; or, assigning known site-specific values to other samples that are in the same functional class and are located in the same area type (rural, small urban, urbanized) with similar travel characteristics. Average statewide values calculated by functional class should not be used.
Supplemental methods and sources may be particularly useful in urban areas; some of these include turning movement studies, origin and destination studies, license plate surveys, design estimates and projections, and MPO/municipal data obtained for other purposes. Short-term visual observation of truck travel on a sample section can also be of help in developing an estimate. The HPMS analyst should enlist the assistance of the State traffic engineering or traffic operations unit in developing percentage of trucks estimates.
The percentage of average daily trucks should be reported as an annualized value. This is consistent with the new TMG that has as a goal of traffic monitoring programs the ability to adjust short-term truck data to represent truckAADT. Until States are able to estimate truckAADT values, percent truck data that best represents average conditions should be reported.
The percentage of peak trucks should be reported as the proportion of trucks in the traffic stream during the hour or period of peak total traffic flow on the sample section.
These questions do not have one specific answer and will require input from the States and FHWA for a general response to each question.
FHWA does not endorse one method over another. The States are encouraged to develop and test equipment and methods to meet the local conditions. Motorcycle detection and accuracy varies depending on the technology used and local environmental conditions existed. Some technologies are suited to better count motorcycles while classification can be much harder to perform. Tracking individual motorcycles may be possible while tracking grouped or multiple motorcycles closely grouped and spread in the lanes may be much harder to count and classify properly.
Given that equipment is different, there is no universal depth to be recommended. Installer should consult vendor specification and seek State DOTs for specific information.
Seasonal factors are used to convert average daily traffic (ADT) to annual average daily traffic (AADT). Axle adjustment is used to convert axle counts to volume. For capacity analysis, you may not have to apply any correction.
