The primary purpose of the Highway Performance Monitoring System (HPMS) is to serve data and information needs to reflect the condition and operating characteristics of the nation's highways. HPMS data support the analyses needed for the biennial condition and performance reports to Congress. One of the required data elements for the HPMS program is vehicle-miles traveled (VMT). VMT is derived from average annual daily traffic (AADT), so an accurate measure of AADT is essential. Traffic data collected on the highest volume routes have the most significant impact since these data represent a large share of total statewide and national travel. These routes are also often the most difficult locations to monitor. State and public agencies use various strategies to develop effective counting programs at these locations.
The objective of this project is to investigate and document information that can be shared with states on various procedures being used to estimate and report traffic data on high-volume routes. This study focuses on the accurate collection of traffic data on high-volume routes, as well as the processes that accompany the collection of these data. The study develops best practices and guidelines for improving the quality of AADT estimates on these high-volume routes.
The information for developing this report was gathered through review of published literature and telephone interviews with representatives of state Departments of Transportation (DOTs). Representatives of the top 13 states with the highest mileage of highways with high traffic-volumes were interviewed. The states are: California, Texas, Florida, Georgia, Illinois, Massachusetts, Maryland, Michigan, Ohio, New York, New Jersey, Virginia, and Washington.
The following are summaries of the major findings from the interviews and literature review.
Each state uses data collection equipment by different manufacturers. States are comfortable with the performance of current equipment.
Road tube is the primary equipment for short-term counts and inductive loops for permanent counts.
Equipment problems such as failures, damage, or loss of communication, are common to all states interviewed regardless of the type of equipment.
Non-intrusive equipment are not widely used for data collection. DOTs however recognize the advantages of these devices.
The states interviewed employ the following approaches for data quality control and assurance:
Data processing rules and checklists.
Staff training and use of guidelines.
Stringent adherence to calibration and set-up routines.
Proven algorithms for classifiers.
Tight control on vendors' compliance with guidelines.
Proven software and data processing methods.
The major issues and challenges facing state DOTs and other agencies are:
Safety of the traffic data collection crew is the primary concern in collecting data on high-volume routes.
Collecting traffic data in stop-and-go traffic conditions is a challenge.
Traffic congestion precludes reliable classification counts.
Equipment failures, communication problems, and inability to secure road tubes are common problems.
Construction and incidents also impact traffic data collection activities.
Institutional issues, including funding constraints and lack of interagency cooperation, were noted to impact traffic data collection activities.
Data processing and quality control and assurance are challenges especially for high traffic-volume routes.
Based on the findings from the interviews and literature review, the best or common practices were identified to address the issues and challenges. Table ES-1 summarizes the practices adopted by states to overcome or mitigate the issue and challenges.For each category, the best or common practices are described and illustrated with examples from the states. The examples are intended to illustrate the successes of the various approaches in addressing the issues, and also to serve as resources to states seeking guidance. Additional sources of information relevant to the practices are also identified in the report. Further detailed resource information is provided on the accompanying CD to supplement information presented in this report.
|Category||Practice Areas||Issues Addressed|
|A1. Training and Guidelines||
Data Collection Equipment
|B1. Equipment Selection, Calibration and Maintenance||
|B2. Use of Non-Intrusive Equipment||
C1. Use of Safety Strategies
C2. Ramp Balancing
C3. Innovative contractual Practices
C4. Use of ITS Data
Data Processing and Quality Control
|D1. Data Processing and Quality Control Procedures||
|D2. Adjustment Factors and Growth Factors||
For traffic data gathering and processing, each state DOT follows a set of procedures, chooses, and uses equipment that best meets their specific needs. The guidelines are intended as a guide or reference source based on states' experiences and lessons learned to help states seeking direction or guidance on addressing common or specific issues relating to traffic data collection and processing for high-volume routes.
The following steps are considered useful for traffic monitoring on high-volume routes.
Define high-traffic volume — It is important to define a high-traffic volume route in terms of traffic volume. It is recommended that 50,000 AADT be used as the threshold.
Identify high-volume locations — The next step is to identify routes carrying high traffic volume. This is important in selecting and planning installation of data collection equipment.
Select data collection strategies — Several strategies for collecting traffic data on high-volume routes have been identified. These strategies are being practiced in some states and are designed to address the issues and challenges associated data collection on high-volume routes. These include:
The following are recommended elements in data processing and quality assurance of AADT data. These are intended to guide states in validating and evaluating the quality of data from different sources and for different applications. Methods of calculating adjustment and growth factors are also included.
Data validation — Data processing to verify validity and completeness is carried out using either in-house software packages or legacy mainframe programs.
Adjustment factors and growth factors – Adjustment factors based on TMG recommendations are needed to convert short-term volume counts to AADTs. Several approaches have been identified to guide the states in selecting those that best meet their needs.
Assessment of data quality — The recently developed framework1 for assessing traffic data quality is recommended for use in assessing the quality of data from different sources and for different applications. The framework presents a comprehensive methodology for evaluating the quality of traffic data using a set of quality measures.
ITS data offer a valuable source of traffic data especially to the HPMS program. Some state DOTs rely on ITS-generated data to report AADT for HPMS for parts of their program, other states have concerns about the quality and reliability of such data. Potential approaches to encourage the use of ITS data for traffic monitoring applications include:
Resource sharing — Merge ITS field infrastructure (like inductive loops and sensors) with traditional traffic counting devices to allow the use of the traffic counters/ classifiers alongside ITS devices. It is recommended that a program be developed that combines ITS and traditional traffic monitoring.
Compatible equipment — Investigate the use of compatible equipment or sensor-sharing arrangements where the signals from in-road sensors are split into two devices. The intent would be to use ITS sensors for traditional data gathering without impacting ITS operations.
Strategic locations — The need to select strategic locations for ITS sensors is critical to traffic monitoring. Identifying and locating ITS sensors strategically would also allow the sensors to be maintained jointly by the traffic monitoring group and ITS groups.
Supplemental data source — Increase use of data from ITS data archives could supplement HPMS and traffic monitoring programs States are encouraged to develop ITS data archives based on experiences in other states.
Selection of data collection equipment is determined by individual state experiences, needs, and conditions. The following are expected to guide the selection of equipment and technologies.
Advances in detection technology — Improvements in loop installations and vehicle counters have reduced greatly the problems with inductive loops. Advanced vehicle counters with loop signatures-based detection and classification promise to build upon the improvements.
Equipment calibration and testing — Accuracy testing of equipment is often done at the time of procurement rather than during regular operations. In order to test equipment installed in the field for accuracy, it is necessary to develop quick and easy methods for field personnel, including such methods as visual displays on counters or manual counts prior to setting up short-term counts.
Equipment maintenance — The use of maintenance contracts for rapid restoration of Automatic Traffic Recorders (ATRs) is a strategy being used or considered by some states interviewed. Tasks for such contracts include performing regular maintenance of equipment, on-call duties, and installation of new sites.
Non-intrusive equipment — Out of 13 states interviewed, 10 indicated they either use or are testing non-intrusive detection equipment. States need to develop specifications or criteria that non-intrusive detectors must satisfy to help in their selection. These specifications or criteria should include installation and calibration guidelines, functionality requirements (e.g., volume accuracy, classification accuracy), testing procedures, and equipment specifications, including power supply issues, weather-related issues.
Testing and evaluation results — Sharing information about the capabilities or experiences with new and improved technologies and vendors is considered important to state DOTs. A clearinghouse of vehicle-detector information would be useful to state DOTs in comparing and selecting detection equipment. The Vehicle Detection Clearinghouse (VDC), a multi-state, managed by the Southwest Technology Development Institute (SWTDI) at New Mexico State University (NMSU) (www.nmsu.edu/~traffic) is a valuable resource for information on technology, evaluation, testing results, and level of use by states.
The practices and guidelines presented in this report are intended as a reference for states to improve the quality of traffic data collection and processing on high-volume routes especially. The guidelines are not intended as uniform standards that all states must follow, and they are not intended to replace existing successful practices. The following are general conclusions from this examination of current data collection and processing practices.
A high-volume route is usually not defined solely in terms of traffic volume, but rather in terms of the difficulty in installing data-collecting equipment. It is recommended that a threshold of 50,000 AADT be used in defining high-volume routes.
Safety to data collection crew was identified as a major deterrent to data concern on high-volume routes. As such, many states view training and guidelines on safety are crucial to improving data collection on high-volume routes. States have adopted several practices to improve data collection, processing, and reporting for high traffic-volume routes. These practices are designed to address the challenges and issues associated with high-volume routes.
Equipment problems are common to all states interviewed, regardless of the type of equipment. Non-intrusive equipment is increasingly being used or considered for data collection by several states. States need to develop specification and criteria to guide the selection and testing of such equipment.
The potential for using ITS data for HPMS has been recognized by many states. Increasingly, stated are tending to ITS-generated data for HPMS reporting. Several states like Florida, Ohio, Michigan, and Illinois have successfully used ITS data for HPMS reporting. Other states are experimenting with using ITS data sources for HPMS reporting.
Descriptions of intrusive and non-intrusive data collection equipment are provided to identify the limitations, advantages, and evaluation results and provide a guide to technology selection.
1Traffic Data Quality Measurement, Battelle for FHWA, Office of Highway Policy Information, 2004
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