U.S. Department of Transportation
Federal Highway Administration
1200 New Jersey Avenue, SE
Washington, DC 20590
202-366-4000
Traffic monitoring is performed to collect data that characterize the use and performance of a roadway, trail, or path traveled by motorized vehicles, micro powered devices such as electrical bicycles, and nonmotorized devices such as bicycles and pedestrians. This chapter addresses the high-level design of a traffic monitoring program and provides guidance on how data planning is used to support the program design and development at State highway agencies and Metropolitan Planning Organizations (MPOs). It explains the importance of having such a program from three perspectives: statewide, regional/sub-area, and roadway facility/corridor-specific traffic monitoring.
This chapter is organized into the following four sections:
1.2 Traffic Monitoring Program Purpose (WHY)
1.3 Traffic Data Types Introduction (WHAT)
1.4 Traffic Data Program Users (WHO)
1.5 Traffic Data Program Design Consideration (HOW)
The TMG has existed since before the passage of Intermodal Surface Transportation Efficiency Act (ISTEA).
It has provided guidance to traffic monitoring programs across the Nation. The primary audience for this guide is State highway agencies, MPOs, and other professionals who are interested in traffic monitoring activities, equipment, and data. The TMG is updated on a routine basis to reflect legislation and regulation changes, technology development, and new program and initiative needs.
Traffic data monitoring programs can include both motorized and nonmotorized. Agencies may have two separate data collection programs, such as one for motorized and one for micromobility (that encompasses pedestrian traffic) travel monitoring. Differences between these programs are described below. Both programs are typically organized by continuous and short-term counting programs. Within the monitoring programs, there are two primary data collection sub-programs that include a volume and classification data collection program. In addition, motorized traffic monitoring programs also include a weight data collection program.
Motorized and Micromobility Program Differentiators
One of the key differences in the state of practice between motorized and micromobility traffic monitoring is the scale of data collection. Most micromobility data collection programs have a much smaller number of monitoring locations, and these limited location samples may not accurately represent the entire geographic area of interest. In many cases, the micromobility monitoring locations have been chosen based on highest usage levels or strategic areas of facility improvement. Given limited data collection resources and specific data uses, these site selection criteria may be appropriate. However, one should recognize that these limited location samples might represent a biased estimate of overall usage and trends for a city or State. More research is needed to identify statistically representative site selection criteria.
A second key difference is that micromobility traffic will typically have higher use on lower functional class roads and streets as well as shared-use paths and pedestrian facilities, simply because of the more pleasant environment of lower speeds and volumes of motorized traffic. In addition, pedestrian and non-motorized vehicles are not permitted to use/operate on limited access facilities such as interstate highways. Conversely, motorized traffic monitoring focuses on higher functional class roads that provide the quickest and most direct route for motorized traffic.
A third key difference is that technologies for counting micromobility traffic for pedestrians, hover boards, e-bikes, bicyclists, etc. are not always scheduled to collect data for the same period such as 24 hours, 48 hours, or 2 weeks of consecutively collected (15 minute or hourly) data.
Finally, a fourth key difference is that technologies for counting micromobility traffic are still evolving, and error rates associated with different technologies are not well known. All methods for counting both motorized and micromobility traffic have error rates and provide estimates that only approximate actual use; however, the error rates for technologies used to count motorized traffic generally are better understood, as are the procedures for managing or reducing these errors.
The Intermodal Surface Transportation Efficiency Act (ISTEA) put forth the need to establish a systematic statewide Traffic Monitoring System (TMS) and directed FHWA to promulgate regulations for State highway agencies to establish a functional TMS where traffic data items such as volume, classification, and weight data can be collected, processed, and reported.
Subsequently, both the Transportation Equity Act for the 21st Century (TEA-21) and the Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA-LU) contained Federal funding for the State TMS. When SAFETEA-LU expired in July 2012, there was a substantial number of traffic monitoring stations established by State highway agencies, which is evidenced by both the quality and quantity of data submitted to the FHWA through both the Highway Performance Monitoring System (HPMS) and the Traffic Monitoring Analysis System (TMAS).
When Moving Ahead for Progress in the 21st Century Act (MAP-21) replaced SAFETEA-LU in 2012, the Federal requirement that each State should have an operational traffic monitoring system to be in place was deemed met.
Since that time, the mission of TMS has expanded under the authority of both MAP-21 and the Fixing America's Surface Transportation (FAST) Act to include for performance management, safety, and many other Federal-aid highway programs as detailed in the Federal statutes listed below.
23 U.S.C. 134: Metropolitan Planning Organizations (MPOs), in cooperation with states and transit operators, develop long-range, performance-driven transportation plans and Transportation Improvement Programs (TIPs).
23 U.S.C. 135: Statewide Transportation Improvement Program (STIP) To support performance-based planning and the development of long-range statewide transportation plans, States must collect and analyze data to establish performance targets and track progress toward national goals such as safety, infrastructure condition, and system reliability.
23 U.S.C. 150: National Goals and Performance Management Measures (prescribes the legal requirements of systematic traffic monitoring data)
23 U.S.C. 503: Research and Technology Development and Deployment (mandates the FHWA to conduct the biennial Condition & Performance (C&P) Report to Congress and the need for State-supplied traffic data).
23 CFR 420.105(b): State DOTs are required to provide comprehensive transportation data to the FHWA to facilitate legislative reporting, system performance evaluation, federal funding analysis, and the accurate calculation of state funding apportionments.
23 CFR part 450: Planning Assistance and Standards (specifies the legal requirement to rely on data obtained from the traffic monitoring system)
23 CFR part 490: National Performance Management Measures (prescribes more specific legal requirements of systematic traffic monitoring data)
40 CFR part 93, subpart A Transportation Conformity Requirements (defines traffic data dimensions) 40 CFR part 93, subpart A General Conformity Requirements (defines traffic data dimensions)
The chapters and appendices in the TMG include the following:
Chapter 1 – Traffic Monitoring Program Introduction: Federal Authority, Data Users and Data Sources, Business Planning and Design – This chapter provides a TMG introduction with an overview of the guide's organization and an overview of traffic monitoring program, including: traffic monitoring program purpose, traffic data customers and common traffic data requirements, and an introduction of essential elements of traffic monitoring program.
Chapter 2 – Traffic Data Collection Technology and Equipment - This chapter provides an overview of different vehicle detection technologies to collect motorized and micromobility traffic data. It also contains practical guidance on selecting traffic monitoring location, sensor layout, and equipment installation and calibration.
Chapter 3 – Methodologies for Traffic Data Collection and Processing - This chapter describes methodologies for collecting traffic data from moving vehicles and pedestrians, including guidance for continuous and short-term data collection plans. It provides guidance on how to collect traffic data to support various FHWA, State, and local-level programs. This chapter addresses limitations related to traffic monitoring program design, duration of data collection, and natural traffic variations and their potential effect on computed traffic data statistics. This chapter also contains methodology and practical examples of parameter computations based on collected traffic data.
This comprehensive chapter also provides guidance on the following:
This chapter provides guidance and examples on coordinating activities for transportation management and operations functions within State DOTs, including the following:
Chapter 4 – Traffic Monitoring Data Formats - This chapter describes data and file formats for traffic data collection to satisfy FHWA traffic data reporting requirements. This chapter also defines the data record formats and data submittal frequency for reporting volume, speed, vehicle classification, and weight data for motorized vehicles and also describes the data formats for reporting Micromobility data. It describes Individual Vehicle Record (IVR) formats, as an alternative method for submitting traffic data to FHWA. The traffic data formats described in this chapter are an addition to the traffic data submitted annually to FHWA as part of the HPMS submittal.
Chapter 5 – This chapter describes HPMS requirements for traffic data reporting. The traffic data items reported in HPMS are identified, and the use of these data is also explained in this chapter. This chapter also provides detailed information about the FHWA TMAS software submittal system, including a discussion about why TMAS is important and how data partners (State DOTs, MPOs, Counties, Cities, etc.) provide data to meet the Federal data needs. Traffic data represent a significant portion (25%) of the HPMS data reported to FHWA annually. Many of these datasets are provided from a state's traffic monitoring program.
Chapter 6 – This chapter provides an overview of innovative ways of collecting traffic data using emerging new technologies from connected and autonomous vehicles and from the Internet-of-Things (IoT) environment, including discussion about acquiring third-party traffic data.
There are a few differences between the 2016 TMG and the 2022 TMG. First, information has been combined and condensed to reduce the total number of chapters from 7 to 6. Second, the 2022 TMG now provides the new term, micromobility. The term micromobility is an all-inclusive updated and modern definition of a nonmotorized program and incorporates pedestrians, human-powered bicycles scooters, hoverboards, e-bikes, and other micro-powered traffic. Chapter 3 provides a separate subsection dedicated to the micromobility program methods for traffic data collection and processing. Third, pipe-delimited file formats are now an acceptable way to submit data (see Chapter 4). Additionally, a new informational Chapter 6 has been added providing information on how to acquire and work with third-party traffic and traffic monitoring data, and data analysis.
Traffic data provide the foundation for transportation decision making including both program and project development. For example, Congress relies on historical, current, and projected traffic data when developing various surface transportation statutes, regulations, and funding authorizations. Other Federal programs established by surface transportation legislation such as the Highway Safety Improvement Program, the National Highway Performance Program, the National Highway Freight Program, the Congestion Mitigation and Air Quality Program, and others rely on traffic data for their implementation. Each of these programs demand accountability and transparency and traffic data that meets or exceeds known quality thresholds and fulfills such demands.
On statewide and regional scales where State highway agencies and MPOs are primarily responsible, key traffic data elements such as volume, class, and weight data provide critical pieces of needed information. The ability to understand the future needs of the transportation system is quantified with traffic data during the planning process and is critical to developing transportation planning products such as the Transportation Improvement Program (TIP) and Statewide Transportation Improvement Program (STIP).
Transportation air quality conformity determination relies on quality traffic data, as do other environmental impact evaluations such as highway noise analysis.
For capacity improvement projects, traffic data offer the ultimate justification for a project's scope and design concept. Traffic data also offer information and data for safety projects. Traffic data enable safety trend analysis and the development of specific mitigation strategies and are used in post-crash analyses.
The importance of traffic data to road and bridge design, construction, operations, and maintenance processes are obvious, as a significant percentage of all public roadways are under repair, maintenance, and upgraded at any given time throughout the year. Traffic data help make all work zones and maintenance activities less impactful and safer for the traveling public. And, traffic data enable reliable decisions in granting access points and median openings to both businesses and citizens, ensuring an efficient highway system.
Traffic data are among the most critical data and information pieces in the transportation decision-making process. The traffic monitoring program is to ensure that the collection and processing of needed traffic data is efficient, effective, and consistent.
There are several data program types that can be organized into sub-programs within a traffic monitoring program. These include volume, classification, weight, travel time (speed), and origin-destination data programs.
The different traffic monitoring data types are designed to meet specific program needs. While volume data provide an aggregate sense of demand, vehicle classification data offer insight on how different vehicle types contribute to the overall demand as well as to congestion, safety, air quality, and so on. Weight data provide necessary information for pavement and bridge design and maintenance decisions and are also used in various safety and security programs. Travel time and travel speed data offer information for travel reliability, enabling better trip planning and performance management. Origin-Destination data provide needed information on demand analysis and corridor usage, through traffic and cost allocation analysis.
There are two basic programs to obtain volume, class, weight, and speed data: a continuous count data program and short-term data program.
The following sections contain more detailed descriptions of each traffic data type and traffic data collection subprograms.
Volume data refers to the total number of vehicles or micromobility passing through a point on a roadway, bikeway, or walkway in a predefined time interval.
All highway agencies should have access to data collected from continuous counters. Agencies should work with each other to ensure that enough data are collected and shared to allow calculation of accurate adjustment factors needed to convert short-term traffic counts into estimates of annual average daily traffic (AADT). Chapter 3 provides guidance on how to structure continuous count programs, how to determine the appropriate number of counters for adjustment factor development, and how to apply those factors.
The continuous counts allow an agency to understand temporal (time-of-day (TOD), day of week (DOW), month-of-year (MOY) and multi-year – year-to-year (YtY)) changes in traffic volume, speed, class, and weight. This analysis provides the mechanism needed to convert short-term counts into accurate annualized estimates. Adjustments to short-term count data are also required to remove the temporal bias for an annualized purpose such as AADT, Single Unit Truck (SU) AADT, and Combination Unit (CU) Truck AADT.
Speed data represent a vehicle traversing through a segment of roadway. When the length of the roadway segment is sufficiently short, the speed is also called a spot speed. Regardless of the segment length, speed data are assumed to be "space mean speed," meaning speed is equal to segment length divided by the time a vehicle takes to traverse through the segment.
Vehicle classification data refers to the counts of vehicles by specific vehicle type. The standard vehicle classification categories are the 13-vehicle types adopted by the FHWA and are used for geometric design, pavement and bridge design, safety analysis, performance measurement, and environmental impact analysis and other programs. Vehicle classification data have the same unit as volume data (i.e., the number of vehicles passing through a point or short roadway segment in a predefined time period). Some States also collect length-based classification data. Chapter 3 provides details on the 13-vehicle types.
Weight data refers to vehicle wheel path (if collected by wheel path), axle and gross vehicle weight. The collection of vehicle weight data is typically only applicable to trucks and not the light-duty passenger vehicles (classes 1-3). The rationale for this is that both the axle weight and gross vehicle weight of regular passenger vehicles have virtually no design impact on a roadway's pavement or the loading of a bridge. For the purposes of traffic monitoring, weight data are typically collected by the Weigh-In-Motion (WIM) systems installed in a roadway.
Travel time characterizes the time it takes for a vehicle to travel from point A to point B along a roadway. While stationary sensors, such as those used in both the continuous count program and the short-term count program, can measure travel time, their applicability is limited due to coverage limits. Current probe-based devices located inside moving vehicles enable travel time collection for many highway segments in the system without the need to install any specialized hardware by a highway agency.
Vehicle Occupancy refers to the number of persons inside a vehicle. Traditionally, vehicle occupancy data are collected through roadside observations, personal or household surveys. While these traditional methods work well, they are costly. More recently, roadside cameras are being used to collect such information. Also, advanced statistical methods have proven that vehicle occupancy data can be derived from crash records supplemented by other data sources.
Lane occupancy can also be used as a direct measure of congestion. Many urban freeway and arterial performance monitoring programs use lane occupancy measurements to describe the onset and duration of congested conditions. For example, the Washington State Department of Transportation uses lane occupancy values above 35 percent to indicate the formation of stop-and-go congestion.
Inductive loop detectors and other devices that mimic loop output, such as video image-based counters, can produce lane occupancy statistics that describe the percentage of time that a vehicle occupies the detection zone. This value can be converted into a reasonable measure of lane occupancy.
Roadway Occupancy is well defined in the FHWA Traffic Data Pocket Guide (FWHA 2018) under the traffic data computation method section.
Headway is the time between the front of one vehicle and the front of the next vehicle, and gap is the distance between the back bumper of one vehicle and the start of the next vehicle. Traffic monitoring devices that time stamp the passage of either individual vehicles or the axles of individual vehicles can be used to report the headway between vehicles and/or the time gap between vehicles. These statistics are useful for several specific operational analyses but are not routinely reported as an output of most traffic monitoring programs.
Consequently, most headway and gap information is collected and reported as part of special studies.
Origin and destination (OD) data refers to location data related to where a vehicle enters a road and where it exits a road. For example, a truck entered Interstate 95 at Exit # 23 and got off the interstate at Exit # 127. Exit #23 and Exit #127 are the origin and destinations. Historically, OD data are most likely obtained from surveys. Probe-based technologies have made OD data collection easier and less labor intensive. Also, new research associated with existing loop detector technology led to inductive loop signature capability for vehicle re-identification and can make OD data collection a regular component of traffic monitoring.
State highway agencies are requested to submit motorized traffic monitoring program data, including volume, speed, class, and weight data from their continuous counting stations monthly to the FHWA TMAS [OMB CONTROL NUMBER: 2125-0587]. In addition, traffic volumes for all roadways are required to be submitted annually to the FHWA through the HPMS reporting process [OMB CONTROL NUMBER: 2125-0028].
HPMS reporting requires State highway agencies to provide traffic and travel data elements by individual predefined HPMS segments once a year. The following elements are a sample of traffic and travel data and derived statistical parameters included in the HPMS submission:
State agency traffic data programs are required to submit traffic data monthly. To facilitate this requirement, FHWA provides access to the TMAS. This software supports traffic data uploads to the FHWA National Traffic Database. For a complete list of all TMAS quality control methods, contact FHWA. The system also provides data download, quality control/quality checking, and reporting features. TMAS continues to evolve and now contains micromobility traffic monitoring data.
Refer to Chapter 5 for a more detailed discussion of TMAS.
The benefits that accrue from having comprehensive and high-quality traffic data should be balanced against the costs necessary to implement and sustain an effective and efficient traffic monitoring program.
Therefore, in planning and designing a traffic and travel monitoring program, it is critical to consider your customer user needs and the benefits that result from the timely delivery of quality traffic data for decision support.
Users of traffic data, including micromobility data, generally fall into the following categories:
Traffic data usage continues to expand and grow in terms of the types and numbers of users. Table 1-1 illustrates the range of uses for the most common types of traffic and travel data, including traffic counts, vehicle classification, vehicle weights, and vehicle speed.
Highway Activity |
Traffic Volume |
Vehicle Classification |
Vehicle Weight |
Vehicle Speed |
|---|---|---|---|---|
Design |
Highway geometry |
Pavement design, bridge design |
Pavement design, bridge design, and monitoring |
Highway geometry |
Engineering Economics |
Benefit of highway improvements |
Cost of vehicle operation |
Benefit of truck climbing lane |
Costs associated with congestion |
Finance |
Estimates of highway revenue and toll revenue |
Highway cost allocation |
Highway cost allocation |
User travel time costs |
Legislation |
Selection of highway routes |
Speed limits and oversize vehicle policy |
Changing weight limits on highways |
Speed limits |
Maintenance |
Selecting the timing of maintenance by lane volume for lane closure policies Prioritizing activities Determine bridge responsibility (State vs. local) Determine highway striping responsibility (State vs. local) |
Selection of maintenance activities |
Pavement management, bridge management |
Work zone safety measures |
Operations |
Signal timing, by lane volumes Traveler information emergency evaluation |
Development of control strategies and speed by class Freight |
Weight enforcement activities Freight |
Setting speed limits and speed by class Traveler information |
Planning |
Location and design of highway systems Assignment/change of Federal Functional Classification Prioritizing projects |
Forecasts of travel by vehicle type |
Truck lanes Truck ramps Freight |
Congestion measurement systems |
Environmental Analysis |
Air quality analysis, noise impact analysis |
Forecasts of emissions by type of vehicle |
Emissions by type of vehicle |
Project-level analyses |
Safety |
Design of traffic control systems and accident rates |
Safety conflicts due to vehicle mix and accident rates |
Weight limits and regulations |
Design of safety systems |
Statistics |
Annual Average daily traffic Vehicle Miles Traveled |
Travel by vehicle type |
Average weight by vehicle class |
85th percentile |
Private Sector |
Location of service areas Development planning Business loans |
Marketing keyed to particular vehicle types |
Trends in freight movement Truck lanes |
Accessibility to service areas |
Administration , Other |
Performance measurement, resource allocation, emergency operations, asset management |
Lane use Tax administration |
Enforcement |
N/A |
Statewide traffic monitoring programs are designed to collect four types of data: volume, speed, classification, and weight. The information obtained from statewide traffic monitoring programs is also the primary information resource for almost all general queries about road use in the State. These data provide a critical framework for effective decision making.
Many users, both inside and outside of State highway agencies, periodically need basic traffic statistics, and those statistics should be readily available and comparable throughout the State and between States. Requests for statewide data can range from how vehicle miles of travel are changing to computing carbon emissions to whether specific roads carry enough volume to warrant new construction activity. A comprehensive statewide counting program allows an agency to confidently and effectively answer a wide range of key policy and business questions. The ability to describe how much traffic is using a road reflects positively on the agency's ability to effectively perform its responsibilities and manage its budget. Highway agencies that cannot provide direct, timely, and accurate answers to these basic queries risk losing their credibility and subsequently the support of decision makers and the taxpaying public.
While several traffic volume statistics are used in traffic analyses, primary interest for the design of statewide traffic monitoring programs are AADT and average daily vehicle distance traveled (DVDT). Because DVDT is computed by multiplying the roadway segment AADT by the length of that segment, the primary goal of most traffic monitoring programs is to develop accurate AADT estimates, which can then be expanded to estimates of travel.
A traffic monitoring program can be implemented at regional or sub-area levels to address additional information needs that are not met as part of a general statewide program. Traffic monitoring at this level is generally more detailed than the statewide program and may include roads that are not part of the statewide program.
Regional or sub-area monitoring plans are generally designed to answer specific questions of regional importance such as traffic movements that cross jurisdictional borders (e.g., they may provide data that are used to allocate State resources between jurisdictions within the region). Regional traffic monitoring data such as congestion and travel time reliability are used to answer key scoping questions for upcoming regional projects.
Similarly, detailed regional traffic counts can be vital to maintaining or improving the economic vitality of communities that depend on recreational movements. Data collection in geographic areas with recreational traffic movements may use different methods or times that would otherwise be collected for general State traffic monitoring purposes.
Facility-specific monitoring plans are the most detailed level of traffic monitoring programs. Roadway facility-level monitoring provides data needed at the project level. A minimum of four data items are typically produced as part of these monitoring efforts: AADT, K-Factor, D-Factor, and truck percentages. However, monitoring efforts may also collect data items that are needed for specific project purposes such as vehicle speed distributions, turning movements, lane distribution factors and vehicle weight by class or by axle.
Facility-specific traffic monitoring programs are designed to provide the site-specific traffic statistics needed for roadway project development and planning studies. Special counts are added to the overall traffic count database throughout the calendar year to capture special traffic data as needed. They are also used to collect the detailed data needed to design, implement, and refine traffic operations plans (e.g., traffic signal timing or event planning). Well-designed facility monitoring plans are fundamental to the effective management and operation of heavily used roadways.
The TMG is designed to provide guidance to States, MPOs, and local agencies in establishing and maintaining a traffic monitoring program. Other sources of information include the AASHTO Guidelines for Traffic Programs (AASHTO 2009), the WIM Pocket Guide (FHWA 2018), ASTM and both the FHWA Traffic Detector Handbook (FHWA 2006) and HPMS Field Manual (FHWA 2016). HPMS Field manual is incorporated by reference into 23 CFR part 490 by 23 CFR 490.111(b)(1).
The following sections describe the purpose and framework for establishing a traffic monitoring program. These programs are designed to collect traffic data within a defined geographic area for a State, region, or at the roadway-specific level. The types of data collected primarily consist of volume, speed, classification, and weight data for motor vehicles. Many traffic programs are adding micromobility counting to their existing motorized count programs.
Figure 1-1 shows the recommended framework for a traffic monitoring program that consists of two basic components:
CONTINUOUS COUNT PROGRAM – A continuous count program refers to the processes and procedures used to collect traffic data from permanently installed counters operating on a continuous basis throughout the entire 24 hours in a day, year-round. Data from the continuous program provide temporal information such as patterns of time of day, day of week, week of month, and month of year. A continuous count program helps to understand temporal time-of-day (TOD), day-of-week (DOW), and month-of-year (MOY) changes in traffic volume.
SHORT-TERM COUNT PROGRAM – The short-term count program refers to the processes and procedures to collect traffic data from installed sensors for a continuous period of less than one-year. The duration of the short-term counts can be as short as 24 hours or, more preferably, a week or longer period. The short-term count program is also referred to as the "coverage count program or portable counts program."
Continuous count stations (CCSs) form the basis for the overall traffic monitoring program. A continuous count is a volume count derived from permanently installed counters for a period of 24 hours each day over 365 days (except for leap year) for the data-reporting year. In some States, this is referred to as the permanent count program. In the TMG, this program is referred to as the continuous count program.
Continuous count programs have many objectives that can vary from State to State. For example, continuous count station data can be used to
The number and location of the counters, type of equipment used, array, sensor technology, and the analysis procedures used to manipulate data supplied by these counters are determined by these objectives. As a result, it is of the utmost importance to establish, refine, and document the objectives of the program. Only by thoroughly defining the objectives, and designing the program to meet those objectives, will it be possible to develop an effective and cost-efficient program.
The continuous vehicle classification (CVC) data collection program is related to, but distinct from and often is a subset to, the traditional Continuous Count Program.
FHWA supports highway agencies programs that are designed to collect classification data (which also supply total volume information) in place of simple volume counts whenever possible.
Traditional Continuous Count Programs require motorcycle factoring, transit vehicles, and other types of traffic volume data. Class factoring traffic data programs should include a minimum of the 6 vehicle types in the HPMS Vehicle Summary table.
Transportation agencies perform short-term counts for a variety of purposes including meeting Federal reporting needs such as HPMS, supplying information for individual projects (pavement design, planning studies, etc.), and providing broad knowledge of roadway use. Portable short-term counts also ensure geographic diversity and coverage. When properly designed and executed, the short-term counting program allows network-wide traffic data coverage at a low cost. The short-term counting program is most efficient if these various data collection efforts are coordinated so that one count program meets multiple needs. Examples of coordination include: sharing counting schedules with city/county/MPO staff; putting technology solutions in place that include access to software that encourages the integration/dissemination/conversion of schedules/data collected from city/county/MPO and State agencies; providing city/county/MPO and public customers axle correction factors (ACF) and other factors; and establishing a data governance committee that crosses agency jurisdictions including national, State, county, city, and MPO boundaries.
Short-term count stations collect data using portable traffic counting equipment and sensors. Short-term data collected by such stations should be adjusted to represent an AADT) number. For a detailed definition of short-term counting station or related terms, please see the glossary of terms. These short-term volume stations often require ACF and temporal factors to properly annualize them.
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. These traffic collection locations do not need ACF.
The two types of short-term counts include coverage counts and special counts. The coverage count program subset covers the roadway system on a periodic basis to meet both point-specific and area needs, including the HPMS reporting requirements.
The TMG recommends that the short-count data collection consist of a periodic comprehensive coverage program over the entire system on a maximum 6-year cycle. The coverage plan includes counting the HPMS sample and full-extent sections on a shorter (maximum) 3-year cycle to meet the national HPMS requirement.
The coverage program is supplemented with a special needs program subset where additional counts are performed, as needed, to meet other more specific data needs. The special needs program represents many different operations and may include the following:
One of the key differences between micromobility and motorized traffic monitoring is the scale of data collection. Most micromobility traffic monitoring programs have a much smaller number of monitoring locations, and these limited location samples may not accurately represent the entire geographic area of interest. In many cases, the micromobility monitoring locations have been chosen based on highest usage levels or strategic areas of facility improvement. Given limited data collection resources and specific data uses, these site selection criteria may be appropriate. However, one should recognize that these limited location samples might represent a biased estimate of overall usage and trends for a city, county, or State. Additional research is needed to identify statistically representative site selection criteria.
A second key difference is that micromobility traffic will typically have higher use on lower functional class roads and streets as well as shared-use paths and pedestrian facilities, simply because of the more pleasant environment of lower speeds and volumes of motorized traffic. Conversely, motorized traffic monitoring focuses on higher functional class roads that provide the quickest and most direct route for motorized traffic.
A third key difference in current practice is a tendency to use very short-term counts (i.e., as short as 2 hours) for micromobility traffic monitoring, primarily because of the perceived difficulty of automatically counting pedestrians and bicyclists (as well as the desire to collect age and bicycle helmet use data).
Although this practice is not prohibited by the TMG, data users should recognize that these very short-term counts can introduce significant overall error when micromobility traffic use is low and inherently variable.
If short-term micromobility counts are needed, then it is essential that longer counts be taken to establish hourly patterns and a statistical basis for extrapolation of these counts. This issue is addressed in more detail in Chapter 3.
Finally, a fourth key difference is that the monitoring technologies for counting micromobility still are evolving, and error rates associated with different technologies are becoming available. All methods for counting both motorized and micromobility traffic have error rates and provide estimates that only approximate actual use; however, the error rates for technologies used to count motorized traffic generally are better understood, as are the procedures for managing or reducing these errors.
National Cooperative Highway Research Project (NCHRP) Report 797: Guidebook on Pedestrian and Bicycle Volume Data Collection (NASEM 2014) provides additional information.
The majority of micromobility locations will be monitored using short-term counts and special needs counts although in micromobility programs, the distinction between short-term counts and special needs counts may not be as clearly defined in some programs. Short-term counts are often performed on specific facilities based on certain needs for that facility (e.g., before-after use). One limitation of this data lies in the uncertainty over whether the data collected at that a specific facility are representative of other similar facilities and can therefore be expanded to a sub-area or regional estimates of overall micromobility travel.
DOT traffic monitoring programs should be comprehensively evaluated every 5 years, at a minimum, to ensure compliance with users' needs and Federal regulations, account for changes in road usage, and address equipment or personnel needs.
This comprehensive evaluation should include auditing all aspects of the program including equipment inventories, site selection procedures, data collection practices, validation, quality control, analyzing data, staffing levels, data dissemination practices, and data user base review and documentation updates. The state DOT should provide its FHWA Division Office with the results from the comprehensive evaluation. A comprehensive travel monitoring program evaluation should provide an agency with a strategic business plan that documents program strengths and deficiencies with targeted recommendations for minimizing deficiencies and leveraging data program assets for a broad range of agency needs. A comprehensive program evaluation is recommended every 5 years because travel monitoring equipment and technology, as well as Federal regulations requiring travel monitoring data, can change over time, ultimately requiring travel monitoring program changes.
Conducting a thorough program evaluation identifies potential savings of time, resources, and budget and recommends business practice improvements that eliminate unnecessary or inefficient monitoring processes or data management practices. Examples to consider include (but are not limited to) the following:
Many agencies already rely on obtaining advice from partner Federal, State, and local agencies related to budgeting, monitoring equipment, resource allocations, etc. When conducting a comprehensive program evaluation, a similar industry practice (i.e., peer benchmarking) is advised.
Managing a travel monitoring program requires many different skills including budgeting, resource allocations, statistical analyses, and quality evaluation of the travel monitoring program's data. The remainder of this section describes the steps for conducting the program evaluation.
The program evaluation review should include the following elements.
This business process review should be updated every 5 years to ensure the optimum use of resources with respect to stakeholder needs. States have embarked on business process reviews to document their processes, identify gaps, and improve their traffic and travel monitoring programs.
Data business planning is an important component of any State DOT traffic and travel monitoring program because it ensures that customer needs are met and the most efficient methods are deployed. It also provides accountability, transparency, and other strategic management benefits such as documenting what duties each data program staff perform. Several States have well-documented programs and can be used as references (see Appendices C and D).
Source: Federal Highway Administration.
Figure 1-1. Steps for Establishing a Data Program
Coordinating count programs and sharing data are beneficial to all public agencies. Sharing data collection resources often reduces costs of purchasing and installing equipment. Access to additional counts provides another opportunity for data usage in quality assurance activities that save money, increase quality, and make reporting of data easier, allowing all data to be integrated into one platform. For example, data may exist in different formats or be collected with different standards (e.g., 24-hour versus 48-hour counts). With carefully planned and implemented management strategies in place (such as creating a data governance committee, implementing QA/QC data procedures, and having a scalable enterprise-wide data warehousing solution), an agency cannot only overcome data sharing challenges, but benefit significantly through data sharing noteworthy practices.
Agencies should work together to reduce duplication in the number and location of permanent, continuous data collection devices. Agencies should share the data they collect (e.g., a State DOT could use monthly and DOW information collected at permanent sites operated by a county or city as part of developing adjustment factors for a specific urban area). A single count location can supply information for many purposes (e.g., permanent, continuous weight-in-motion sites supply weights, spacings, classification, speed, and volume data). Opportunities to share data exist not only among agencies but also within agencies. Ensuring that planning, operations, maintenance, and construction groups share the data they collect can substantially increase the availability of traffic and travel monitoring data and benefits derived, while reducing the overall cost of data collection.
Key sources for urban traffic data are the traffic surveillance systems used for traffic management and control. The ITS program offers highway agencies the ability to receive and utilize continuous traffic monitoring data at high-volume locations. Access to these data requires proactive efforts by the agencies involved (both the producer of the data and the user of the data), as archiving and analysis of surveillance data are traditionally less important to the operations groups that build, operate, and maintain these ITS systems. Without proactive efforts by the respective agencies, benefits of ITS data can be lost because operations groups spend their limited resources on
operational improvements rather than on the archiving and analysis software needed to maintain useful traffic and travel statistics. When configured properly, the traffic monitoring assets can also supplement ITS assets and provide critical information for operations.
Another critical factor of a well-designed traffic monitoring program is thorough and complete documentation. States should maintain adequate documentation to support the decisions made and to allow future reexamination of those decisions as experience is gained in such areas as the factoring process. Developing program documentation is recommended for traffic data programs. For example, HPMS annual report process documentation is required. HPMS annual reporting also requires reporting of metadata pertaining particularly to traffic data (see 23 CFR 420.105(b)).
Documentation is recommended for any processes or methods used in data collection and analysis that may affect the outcome of the traffic data reported.
Metadata should include documentation describing specific data items and datasets. For example, traffic metadata should describe whether AADT values have been seasonally adjusted, whether AADT values are drawn directly (raw unadjusted data) from vehicle count data, or whether AADT is adjusted by annual growth/change.
Data program documentation should be a shared responsibility between the Traffic Monitoring Staff and IT divisions and the data-collecting business units within the organization. These units should bear the responsibility to document the business needs and benefits of the traffic programs so that executives internally, as well as external entities such as legislatures, are aware of the importance of continued funding and resource allocation to support these critical programs. Establishing structured documentation procedures includes having well-defined change tracking mechanisms to ensure that the prioritization of requested system changes is in accordance with the primary goals and objectives of the agency.
All types of documentation that are used to support traffic monitoring programs become a significant part of the repository of information about the program. This important information is useful at the national level for modeling travel trends, conducting highway safety and weight studies, as well as supporting State needs and uses for the data.
