U.S. Department of Transportation
Federal Highway Administration
1200 New Jersey Avenue, SE
Washington, DC 20590
202-366-4000
Prepared for
Office of Highway Policy Information
Federal Highway Administration
U.S. Department of Transportation
Washington, D.C.
This report is a work prepared for the United States Government by Battelle. In no event shall either the United States Government or Battelle have any responsibility or liability for any consequences of any use, misuse, inability to use, or reliance on the information contained herein, nor does either warrant or otherwise represent in any way the accuracy, adequacy, efficacy, or applicability of the contents hereof. The United States Government does not endorse products or manufacturers. Trade or manufacturers' names appear herein only because they are considered essential to the purpose of the document.
The authors gratefully acknowledge the support and guidance of Mr. Fred Orloski of the Office of Highway Policy Information, Federal Highway Administration throughout this project. The authors would also like to thank Mr. Harshad Desai and Mr. Jeff Patten of the Office of Highway Policy Information , Federal Highway Administration for their review comments on the draft report.
The authors also acknowledge the participation and assistance of the following representatives of the state Departments of Transportation for participating and providing valuable inputs during the interviews.
| Joe Avis | California Department of Transportation (Caltrans) |
| Brian Domsic | California Department of Transportation (Caltrans) |
| Harshad Desai** | Florida Department of Transportation |
| Nabeel Akhtar | Florida Department of Transportation (FDOT) |
| David Adams | Georgia Department of Transportation (GDOT) |
| Rob Robinson | Illinois Department of Transportation (IDOT) |
| Mike Baxter | Maryland State Highway Administration (MDSHA) |
| Philip Hughes | Massachusetts Highway Department (MHD) |
| Mike Walimaki | Michigan Department of Transportation (MDOT) |
| Louis Whiteley | New Jersey DOT (NJDOT) |
| Todd Westhuis | New York State Department of Transportation (NYSDOT) |
| David Gardner | Ohio Department of Transportation (ODOT) |
| Tony Manch | Ohio Department of Transportation (ODOT) |
| Jim Neidigh | Texas Department of Transportation (TxDOT) |
| Tom Schinkel | Virginia Department of Transportation (VDOT) |
| John Rosen | Washington DOT (WsDOT) |
| ** Now with FHWA | |
The project team members are:
Dr. Edward Fekpe, Principal Investigator, (Battelle)
Mr. Deepak Gopalakrishna (Battelle)
Dr. Dan Middleton (Texas Transportation Institute)
Mr. Shawn Turner (Texas Transportation Institute)
Guidelines for Data Collection for High-Volume Routes
2.0 Traffic Monitoring on High-volume Roads
3.0 Best or Most Common Practices used by States. 12
4.0 Traffic Data Collection Equipment for High-Volume Locations
5.0 Guidelines for Data Collection for High-Volume Routes
Websites of State DOT Traffic Monitoring Groups
List of Tables
Table ES-1: Best or Most Common Practices used by States
Table 2.1: Miles of High-volume HPMS Segments in States
Table 3.1: Best or Most Common Practices used by States
List of Figures
Figure 3.1: Independent Array Installation of Road-tubes (Virginia DOT)
Figure 3.2: Washington DOT Zones for Data Collection
Figure 3.3: California Ramp Balancing Guidelines
Figure 3.4: California's Checklist for Editing Traffic Counts
Figure 3.5: Virginia's Quality Flags and Error Messages from the Information System
| ADT | Average Daily Traffic |
| AADT | Average Annual Daily Traffic |
| AASHTO | American Association of State Highway and Transportation Officials |
| ARTIMIS | Advanced Regional Traffic Interactive Management and Information System |
| ATR | Automatic Traffic Recorder |
| AVC | Automatic Vehicle Classifier |
| CATS | Chicago Area Transportation Study |
| Caltrans | California Department of Transportation |
| DIA | Detector Isolation Assembly |
| DOT | Department of Transportation |
| DTS | Digital Traffic Systems |
| EIS | Electronic Integrated Systems |
| FDOT | Florida Department of Transportation |
| FHWA | Federal Highway Administration |
| GDOT | Georgia Department of Transportation |
| HPMS | Highway Performance Monitoring System |
| IDOT | Indiana Department of Transportation |
| ILDOT | Illinois Department of Transportation |
| ITS | Intelligent Transportation Systems |
| MDOT | Michigan Department of Transportation |
| MITS | Michigan Intelligent Transportation Systems |
| MPO | Metropolitan Planning Organization |
| NJDOT | New Jersey Department of Transportation |
| NYSDOT | New York State Department of Transportation |
| ODOT | Ohio Department of Transportation |
| ORADS | Off Road Axle Detection Sensors |
| PeMS | Performance Measurement System |
| PennDOT | Pennsylvania Department of Transportation |
| QC/QA | Quality Control/Quality Assurance |
| RTMS | Remote Traffic Microwave Sensor |
| RWIS | Roadway Weather Information System |
| SHA | State Highway Administration |
| TEA-21 | Transportation Equity Act for the 21st Century |
| TxDOT | Texas Department of Transportation |
| TMCs | Traffic Management Centers |
| TMG | Traffic Monitoring Guide |
| TTI | Texas Transportation Institute |
| VDOT | Virginia Department of Transportation |
| VID | Video Image Detection |
| VMI | Vehicle Magnetic Imaging |
| VMT | Vehicle Miles Traveled |
| WAN | Wide Area Network |
| WIM | Weigh-in-Motion |
| WSDOT | Washington State DOT |
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.
Data Collection and Processing Approaches
Data Collection Equipment
Quality Assurance and Control
The states interviewed employ the following approaches for data quality control and assurance:
Issues and Challenges
The major issues and challenges facing state DOTs and other agencies are:
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.
Table ES-1: Best or Most Common Practices used by States
| Category | Practice Areas | Issues Addressed |
|---|---|---|
A. General |
A1. Training and Guidelines |
|
B. Data Collection Equipment |
B1. Equipment Selection, Calibration and Maintenance |
|
B2. Use of Non-Intrusive Equipment |
|
|
C. Data Collection |
|
|
C2. Ramp Balancing |
|
|
C3. Innovative contractual Practices |
|
|
C4. Use of ITS Data |
|
|
D. Data Processing and Quality Control |
|
|
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.
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.
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:
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.
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.
The Federal Highway Administration (FHWA) is responsible for assuring that adequate highway transportation information is available to support its own functions and those of the Administration and Congress. The primary purpose of the Highway Performance Monitoring System (HPMS) is to serve these data and information needs to reflect the condition and operating characteristics of the nation's highways. The HPMS program is a cooperative effort involving state highway agencies, local governments, and metropolitan planning organizations (MPOs) working in partnership to collect, assemble, and report the needed data and information. FHWA maintains data submittal software and analytical models and techniques that can utilize the HPMS data to conduct the necessary planning and programming.
The data needed by the FHWA include highway length, lane-miles, and travel data to support the apportionment of Federal-aid highway funds under the Transportation Equity Act for the 21st Century (TEA-21). HPMS data also support the analyses needed for the biennial condition and performance reports to Congress and are the source for much information used in a variety of publications and media.
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. To report VMT for the HPMS, a jurisdiction must be able to count and classify vehicles accurately, use the count data to estimate AADT, and it must have a reasonably accurate total of its centerline-miles of highways.
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.
There are several possible sources of traffic data for high-volume routes that are not being fully utilized. Data collected by other agencies for other purposes, although supported by FHWA programs, are not always used for a variety of reasons, including accuracy, reliability, reference to HPMS section locations, and data management. However, states are using successful procedures that are not widely shared or even shared internally with appropriate state HPMS and traffic monitoring staffs. As a result, the best methods available to estimate AADTs and alternatives for improving data quality for HPMS are not being fully utilized.
The primary 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 information will help improve HPMS traffic monitoring programs in urban areas. 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 will yield a report of best practices and guidelines for improving the quality of AADT estimates on these high-volume routes.
The remainder of the report is organized as follows:
To maintain a manageable document size, additional documentation about practices are also provided on an accompanying CD. These include detailed documentation on traffic monitoring guidelines, contractor specifications, data quality guidelines, equipment evaluations, and performance specifications.
A user guide to the CD is provided as an appendix to the document. Sections with references to documents on the CD include hyperlinks in the main text of the report to the corresponding documents.
This chapter presents the research approach used in this study and highlights the main findings.
The information for developing this report was gathered through review of published literature and telephone interviews with representatives of state Departments of Transportation (DOTs). This section summarizes the findings from the literature review and interviews.
Documents, conference proceedings and articles published in recent years dealing with HPMS, traffic data collection procedures and traffic monitoring equipment systems were reviewed. A complete listing of references is provided at the end of this document. The literature review focused on AADT monitoring on high-volume roads. Several states with significant mileage of roadways with high AADT volumes were identified. The review did not identify any state practices that are specific to high traffic-volume locations. State DOTs use a variety of programs directed at improving their traffic monitoring programs especially in urban locations, ranging from the use of Intelligent Transportation System (ITS) sensors to better training of agency personnel to collect data on urban/multi-lane facilities. State DOTs are also investigating new technology and equipment for use in urban areas. The most common equipment used by states are inductive loop and piezoelectric sensors for permanent counts and pneumatic tubes for short-term counts. Non-intrusive devices are not commonly used due to concerns with vehicle classification.
The Urban Transportation Monitor[2] conducted a recent survey of traffic engineers in the U.S. and Canada to obtain information about traffic counting issues. The survey was sent out to 700 transportation professionals at public agencies via email. The following are some of the relevant findings based on responses received from 124 cities (i.e., 18 percent response rate):
The equipment mostly used for traffic data collection at permanent count stations is inductive loops while pneumatic road-tubes are mostly used for short term counts. Factors dictating the selection of permanent count locations include (high) traffic volumes and functional highway classification. Both permanent and short term count stations are used primarily for traffic volume data collection. Speed and classification data are secondary. The survey also revealed that consultants are extensively used in traffic data collection.
The respondents also listed some desirable improvements with counter equipment to include ability to import count data into software applications such as MS Excel, Access; increased durability, reliability, and accuracy.
The survey noted that data quality control (QC) was primarily done by the agency staff. Majority of the QC software used are provided by equipment manufacturers. Few agencies use in-house or third party software for quality control. About 36 percent respondents did not use any QC software. Several areas of improvements in the processing of traffic data were identified e.g.:
The survey results show that the present average error level reported by the respondents is closer to 5 percent. Most of the respondents (74 percent) indicated that their counts were accurate to about 95 percent (or 5 percent inaccuracies). In fact, 96 percent of the respondents indicated that the error in counts is less than 10 percent.
With regards to data sharing, the survey indicated that 79 percent of the responding agencies do not have any inter-local agreements that coordinate traffic collection activities. It was observed that the lack of coordination among agencies can lead to duplication of effort and an inability to share resources toward making traffic counting in a metropolitan area more efficient.
The main source of information for developing the best or common practices is interviews with traffic monitoring program managers and personnel from selected state DOTs. To determine the states to contact for information, those with the highest mileage of highways with high traffic-volumes were identified using HPMS 2001 data and National Highway Planning Network (NHPN) databases. Typically, high-volume routes have volumes in excess of 50,000 AADT. However, given that the definition of high traffic volume varies from agency to agency and from state to state, three threshold values were used: 50,000, 75,000 and 100,000 AADT (Table 2.1). The top 13 states with high traffic volumes were selected: California, Texas, Florida, Georgia, Illinois, Massachusetts, Maryland, Michigan, Ohio, New York, New Jersey, Virginia, and Washington.
An interview guide was developed to facilitate the data collection process. Prior to the actual interviews, the guide was distributed to the state representatives. The interview guide was structured to capture information on various aspects relating to
(i) Traffic data collection approaches to high-volume routes
(ii) Data processing methods and practices
(iii) Data quality assurance practices and
(iv) Equipment for traffic data collection.
Table 2.1: Miles of High-volume HPMS Segments in States
| State Name | AADT > 50,000 | State Name | AADT > 75,000 | State Name | AADT > 100,000 |
|---|---|---|---|---|---|
California |
2647 |
California |
1850 |
California |
1470 |
Texas |
1429 |
Texas |
867 |
Texas |
633 |
Florida |
1122 |
New York |
476 |
New York |
286 |
New York |
798 |
Florida |
434 |
New Jersey |
276 |
New Jersey |
740 |
New Jersey |
403 |
Illinois |
244 |
Michigan |
679 |
Ohio |
363 |
Georgia |
240 |
Ohio |
663 |
Virginia |
317 |
Florida |
239 |
Georgia |
569 |
Michigan |
308 |
Ohio |
188 |
Maryland |
542 |
Massachusetts |
303 |
Maryland |
182 |
Virginia |
528 |
Georgia |
300 |
Michigan |
170 |
Massachusetts |
504 |
Illinois |
291 |
Washington |
156 |
Illinois |
423 |
Maryland |
276 |
Virginia |
156 |
Pennsylvania |
420 |
Washington |
217 |
Massachusetts |
142 |
Telephone interviews were conducted with 12 state representatives. An on-site in person interview was conducted with Ohio DOT (ODOT) representatives. A summary of the interview responses was sent to the respondents to confirm the accuracy and completeness of the information provided during the interviews.
Information from the literature review and interviews were analyzed to identify best or most common practices used by state DOTs as well as the equipment used. Each of these practices is described in detail, including use, technologies, and points of contact. Finally, based on the practices and a review of equipment, some basic guidelines were developed to aid state DOTs in improving their HPMS programs.
This section presents highlights of the current state of the practice with respect to traffic data monitoring. These findings are derived primarily from the interviews with state representatives.
Traffic data collection for HPMS reporting is managed primarily by the state DOTs and their district/zonal offices in all the interviewed states. Ohio, Florida, Michigan, Massachusetts, Washington, and California collect all the counts on the state highway system using state DOT staff through the district offices. Georgia, Maryland, New Jersey, New York, Virginia, Texas, and Illinois contract out their traffic data collection activities either fully or partially to private agencies. In all states, city, MPOs and local agencies are involved in data collection for minor roads to varying extents.
Continuous counts are used by state DOTs for HPMS reporting where possible. Automatic Traffic Recorders (ATRs) are used for continuous counts that are 24-hour counts for every day of the year. ATRs are permanently installed on or near the roadway. Continuous counts provide volume and classification data as well as data needed to calculate daily, monthly, and seasonal variations in traffic to develop adjustment factors to apply to short-term counts. Continuous counts are carried out by State DOT personnel in all states except Virginia, where contractors are responsible for the equipment and data collection.
Short counts comprise the bulk of the data collection program for HPMS. Short-count durations range from 24 hours, 48 hours (recommended by the Traffic Monitoring Guide [TMG]) to a full week (California). HPMS counting cycles range, depending on functional class, from annually (e.g., Texas) to once every three years. Short-counts are often a mixture of volume only, and volume and classification counts. Each state has its own methods of calculating adjustment factors with the data from ATRs and classification stations based on TMG guidelines for converting short-term volumes into AADTs. California, Florida, and Washington have detailed documentation on the calculation of adjustment factors. Most of the states interviewed use contractors to some extent to collect short-count data.
Data collected from continuous and short-term counts are processed in central offices of most state DOTs, although in some states, the district offices also do some preliminary data quality checks. Typically, state DOTs download and review daily volume counts (ADTs) for accuracy, completeness and validity. Review of traffic counts is often automated using either in-house or off-the-shelf software packages applying various traffic editing rules and traffic checks.
The primary objective of this project is to identify the best or common practices used by state DOTs and other agencies for collecting, processing and reporting traffic data on routes carrying high volumes. The definition of high-volume traffic routes varies from agency to agency. In fact, there is little evidence in the literature to indicate that state DOTs identify the segments for special emphasis for AADT monitoring based only on traffic volumes. An arbitrary definition of 20,000 AADT was used by FHWA in the Highway Information Quarterly Newsletter (www.fhwa.dot.gov/ohim/hiqsep01.htm). Other definitions include those used by the New York State DOT (NYSDOT) Pavements Group (High-Volume > 80,000 AADT).
Interviews with state DOTs did not provide specific definitions for high-volume routes. Several factors influence state DOT concerns with traffic-volume monitoring in urban areas not only the volume of traffic on the roadway. 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.
In general, roadway geometry, safety of data collection personnel, congestion, and multilane facilities were identified as factors used in identifying locations where data collection, especially short-term counts, is a problem. These locations invariably carry high traffic volumes.
This section summarizes the major findings relating to the state-of-the-practice in traffic monitoring reported by state DOTs.
Data Collection and Processing Approaches
Data Collection Equipment
The use of non-intrusive equipment was primarily for volume data. These devices are not widely used for data collection due to lack of knowledge on the capabilities and limitations. High-cost was also identified as a deterrent. DOTs however recognize the advantages of these devices. Some states have either tested or use limited non-intrusive technology.
Quality Assurance and Control
The states interviewed employ the following approaches for data quality control and assurance:
Issues and Challenges
The findings were analyzed to identify major issues facing state DOTs and other agencies in collecting data on high-volume routes. The major issues and challenges are listed below and discussed in detail in the following sections:
Safety of the traffic data collection crew was identified as the primary concern in installing equipment on high-volume routes. This applies to all types of data collection equipment.
Collecting traffic data in stop-and-go traffic conditions was identified as a major challenge. This includes technological limitations of sensors under those traffic conditions.
Equipment failures (e.g., sensor), communication problems, and inability to secure road tubes throughout the duration of the counts was also identified as an issue associated with collecting traffic data on high-volume routes.
This section discusses the issues and challenges associated with data collection on high-volume routes in detail. In order to improve the quality of data for high-volume routes, these issues need to be addressed.
Safety of the traffic data collection crew was indicated by all the states interviewed as the primary concern in conducting short-term counts. Ohio, Massachusetts, Washington, Texas, Illinois, and New Jersey mentioned safety as the primary concern in collecting data on high-volume routes. Massachusetts indicated that the major distinction between regular routes and high-volume routes relates to the safety procedures that need to be employed to protect staff and the traveling public.
Traffic data collection in stop-and-go traffic conditions was identified as a major challenge. Stop-and-go traffic often results in volume and classification errors due to equipment limitations. Detectors that work on vehicle presence detection fail under these situations, resulting in erroneous data.
Similar to stop-and-go traffic, heavy congestion or high-volume traffic precludes reliable classification. For example, in congested traffic, the class tables provided by the vendors frequently fail to determine whether four counted axles represent two cars or one truck. It is also difficult and unsafe to install and remove data collection equipment under such traffic conditions.
Equipment failures (e.g., sensors), communication problems, and inability to secure road-tubes properly throughout the duration of counts are factors that affect the quality of data collected on high-volume routes. Some equipment failures are caused by external factors such as vandalism, utility operations, pavement repair and maintenance, pavement surface striping, and pavement deterioration.
Construction was identified as an impediment in data collection, but most states interviewed consider anticipated construction activities when planning their counting programs. However, the effect of construction on alternative routes is a concern, as it can result in abnormal data during a particular year on a given route. For example, construction on a major highway might result in increased traffic on nearby or alternate county and local roads. Unless clearly specified, the final user of the data has no way of knowing the underlying reasons for abnormality in the data.
Incidents are often more troublesome from a traffic data collection standpoint for the obvious reason that they are unforeseen. An incident on a section with ATRs can result in significant data losses.
Data quality and assurance were identified as important issues. The ability to process and assess the quality of data from different data collection equipment efficiently was noted as a challenge especially for high-volume routes. While states do not have a separate process for high-volume routes, they expect their processes to be robust enough to verify the validity of data for such traffic conditions.
The institutional issues were based on information from the literature review. The Volpe National Transportation Systems Center (VNTSC) conducted a survey of traffic monitoring in urban areas for FHWA (Volpe, 1997). The study noted that funding and staffing cutbacks have hurt data collection efforts in the recent past, and continue to pose a threat in the future. It also concluded that successful coordinated data collection programs were based on a spirit of cooperation and professionalism among all involved parties within a region. While current programs generally provide the data that is needed, data quality and accessibility are major concerns.
The best or common practices were identified to address these issues and challenges based on the findings, issues, and challenges described above. The next chapter presents detailed descriptions of the practices with examples.
The purpose of this chapter is to describe the various practices that address the issues and challenges associated with data collection, processing, and reporting for high traffic-volume routes. Table 3.1 aligns the issues to the practices adopted by states to overcome or mitigate them. The practices are grouped into four major categories: (A) general (the issues apply to all categories), (B) data collection equipment, (C) data collection, and (D) data processing, quality control, and quality assurance.
The descriptions are based on the information gathered through the interviews of sample states and supplemented by information from the published literature. The practice areas are illustrated with examples of use by states. Additional sources of information relevant to the practices are also identified. Furthermore, additional documentation for each practice area is included on an accompanying CD. Where possible, hyperlinks to these documents are provided. The documents on the CD include traffic monitoring guidelines, HPMS field guides, contractor specifications, training materials, equipment evaluations and specifications, and data quality assessments.
Table 3.1: Best or Most Common Practices used by States
| Category | Practice | Issues Addressed | Examples |
|---|---|---|---|
A. General |
A1. Training and Guidelines |
|
|
B. Data Collection Equipment |
B1. Equipment Selection, Calibration and Maintenance |
|
|
B2. Use of Non-Intrusive Equipment |
|
|
|
C. Data Collection |
C1. |
|
|
C2. Ramp Balancing |
|
|
|
C3. Use of Innovative contractual Practices |
|
|
|
C4. Use of ITS Data |
|
|
|
D. Data Processing and Quality Control |
D1. |
|
|
D2. Adjustment Factors and Growth Factors |
|
|
Description
Improving HPMS data collection on high-volume roads is often pursued by training and providing guidelines to personnel and agencies, since high-volume routes have special requirements with regards to placement of equipment and data quality verification. Several agencies provide focused training to the staff involved in data collection and processing.
Examples of Use by States
Additional Information on CD
B1. Equipment Selection, Calibration, and Maintenance
Issues Addressed
Description
Agencies are trying to maximize performance of existing technologies such as axle and volume traffic counters using road tubes or inductive loops. Improving performance of these detectors is primarily achieved through a combination of installation, calibration, and maintenance practices as well as through technical improvements.
Examples of Use in States
Accuracy of Counters
The accuracy of counters declines in high-volume conditions, especially using pneumatic road tubes. The accuracy of classifiers also declines in congested or especially in stop-and-go conditions. The following are potential solutions to the problem and illustrated by examples.
Maintenance, Calibration, and Testing
Pneumatic tubes are a stable technology and are the mainstay of short-term equipment in many states. States interviewed are comfortable in using this technology, while recognizing its limitations. In order to increase the efficiency of road tubes, states require staff and contractors to select appropriate locations to minimize some common problems (e.g., stop-and-go traffic, parking on road tubes, pavement surface deterioration), secure the tubes to the roadway, and check the settings on the counter.
The use of high-quality surge suppressors and adequate equipment ground on-site minimizes the risk of damage to pneumatic road tubes due to lightening. Also, the use of gas-discharge tubes for primary protection of phone lines.
In order to reduce the risk of premature loop failure due to pavement rutting or other pavement factors, avoid the use of inductive loops in thin pavements (less than 4 inches thick) or in pavements that need rehabilitation. Their installation in such pavements will often induce even more problems. Improve pavement maintenance and use deeper saw cuts to allow milling as needed. The use of high quality loop detector wire with a thick PE or PVC tube such as IMSA Spec 51-5 and twist loop lead-in wire at least 6 turns per foot to reduce cross talk is recommended.
Figure 3.1: Independent Array Installation of Road-tubes (Virginia DOT)
In Virginia, trained operators check equipment for accuracy during the initial setup operation in all cases. All equipment currently in use has a visual display with real-time results. Each new count setup requires an evaluation of performance before continuing on to the next count. Road-tubes are checked before each setup and replaced as needed. Advanced loop logic functions provide information when piezo-sensors begin to fail so that preventive maintenance can be planned. Equipment performance is continuously reviewed, and hardware and firmware upgrades are added as needed. In-house software is used to examine all data collected to determine the performance of equipment and sensors. New rules and parameters are added to the review process as needed. Any performance issues are addressed by making calibration changes to the detectors setup. Any changes in performance are addressed immediately. Locations with extreme stop-and-go traffic are avoided.
Technology Improvements
One of the breakthroughs, which enhance vehicle detector output by utilizing inductive loop signatures, is now available in the Peek ADR-6000. The software enhancement techniques involve several algorithms designed for use in roadside vehicle detection equipment and which may apply to vehicle classification, toll applications, and incident detection. Recent tests by the TTI indicated that the Peek ADR-6000 was very accurate as a classifier, counter, and speed detection device and as a generator of simultaneous contact closure output. However, its recent introduction into the U.S. market and being adapted from a toll application are factors in its need for further refinement. The classification result for a dataset of 1,923 vehicles indicated only 21 errors and resulted in a classification accuracy of 99 percent (ignoring Class 2 and 3 discrepancies). This data sample occurred during a peak period and included some stop-and-go traffic. For count accuracy, the Peek in this same dataset only missed one vehicle (it accurately accounts for vehicles changing lanes) (Middleton and Parker, 2002).
Additional Information on CD
Description
Non-intrusive sensors require less exposure of workers to traffic hazards and are sufficiently accurate for traffic volume monitoring applications except in very congested and stop and go conditions. The use of non-intrusive data collection equipment for traffic data collection has been investigated by various states primarily to realize two major advantages: relative ease of installation and improved safety of traffic personnel. Non-intrusive traffic detection technologies include infrared-, microwave-, laser-, acoustic-, and video-based sensors.
While some of the states are experimenting and testing some types of non-intrusive equipment, other states are now beginning to review that option. The following sections summarize state practices and experiences with non-intrusive equipment.
The Detector Evaluation and Testing Team (DETT) of the California Department of Transportation has recently tested two non-intrusive detectors, RTMS and Wavetronix SmartSensor. Results indicate that overall count accuracy was almost always within 95 percent of true counts and within 98 percent on some lanes. Speeds were also within 95 percent. One difference between the Wavetronix and the RTMS X3 detectors was the difficulty of setup and calibration. The Wavetronix only required 15 to 20 minutes total to set up, whereas the factory representative took about one hour per lane for the RTMS (Middleton et al., 2004).
The Traffic Monitoring Unit of the NYSDOT has successfully developed a permanent acoustic traffic monitoring site. This site was developed in-house to support non-intrusive sensor technology with applications in data collection and ITS activities. Further details are presented in Chapter 4 of this report.
In addition to using the acoustic sensors as permanent stations, NYSDOT also has four mobile platforms equipped with the sensor for portable counts including coverage counts, special counts, and some ITS design applications. Each is used to collect volume data on high-speed, high-volume, multi-lane facilities where typical collection methods cannot be used due to safety concerns or equipment limitations.
Sources of further information
The Vehicle Detection Clearinghouse, a multi-state, pooled-fund project managed by the Southwest Technology Development Institute (SWTDI) at New Mexico State University (NMSU) and sponsored in cooperation with the U.S. DOT FHWA, is a valuable resource for o documentation about technology, evaluation and testing results, and details on use of technologies by states. On the Internet, the clearinghouse is located at www.nmsu.edu/~traffic.
FHWA sponsored Field Test of Monitoring of Urban Vehicle Operations Using Non-Intrusive Technologies (FHWA-PL-97-018). The final report of the evaluation is available in html format at http://www.dot.state.mn.us/guidestar/nitfinal/about.htm
Additional Information on CD
Issues Addressed
Description
A primary concern in the monitoring of high-volume routes is the safety of data collection crews. Various states have developed strategies/guidelines to ensure safety of the agency personnel and the traveling public. Some of the strategies include setting of safety zones, training, and guidelines for field personnel.
Examples of Use by States
 |
Source: Interviews with WsDOT, 2003
Figure 3.2: Washington DOT Zones for Data Collection
These procedures are also reinforced through a video about safety included in the handbook.
Additional Information on CD
Issues Addressed
Description
Ramp balancing using counts on on/off ramps combined with control counts on the main line are used in locations with high traffic volumes where it is not possible to conduct mainline counts safely. The TMG defines ramp counting as the process of counting traffic volumes on all entrance/exit ramps between two established mainline counters, such as permanent ATRs or other installations, and then reconciling the count data to estimate mainline AADT. A limitation of the ramp-counting approach to estimate mainline volume is that, travel-lane volumes cannot be estimated because traffic entering the road cannot be allocated to lanes. This limitation is not a concern for data collected to meet the specifications of the HPMS, but it may have implications for other programs that depend on lane-specific traffic volume information.
Examples of Use in States
California, Florida, Georgia, Michigan, Ohio, Texas, and Washington use ramp-balancing approaches that were developed based on the guidelines and recommendations of the TMG.
Additional Information on CD
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Freeway ramp balancing is performed to calculate mainline Annual Average Daily Traffic (AADT) between 2 control stations. This process also calculates Ramp AADTs. The latest LRI/MADT and daily reports for ramps will be needed. The following are instructions for filling out the Freeway ramp balancing computation worksheet: The instruction number corresponds to the number identified on the sheet.
Notes:
All ramps must be accounted for. If a ramp is not counted you can either estimate the volume or use the last count. If the last count was already adjusted post it in the adjusted column. |
Source: Joe Avis, Caltrans, "Ramp Balancing Process,
Computational Spreadsheet."
Figure 3.3: California Ramp Balancing Guidelines
Issues Addressed
Description
A noticeable trend in traffic monitoring is contracting data collection activities to private contractors or other agencies. Under such arrangements, private contractors are responsible for data collection activities, with the DOT playing a supervisory role. Performance criteria is increasingly becoming popular with state DOTs as a means of ensuring data quality from the contractors.There also has been an increased interest in using county and local personnel in traffic-counting programs by providing county and local agencies equipment and training to collect and report data to the state DOTs.Examples of Use in StatesMaintenance and Performance Contracts
VDOT has established performance-based lease criteria for payment of data collection services. Contractor compensation is based on the amount of acceptable data being submitted by the contractor. Furthermore, VDOT requires a certain quantity of acceptable data from each site to be able to use that site for traffic factor creation. The list below summarizes some key elements of the agreement:[3]
There will be full payment for all Automatic Traffic Recorders (ATRs) and modems at sites with 25 or more days of useable classification and volume data (for factor creation) during a calendar month.
Use of Counties to Collect Data (Resource Sharing)
Additional Information on CD
Issues Addressed
Description
The use of ITS data for traffic operation applications has the advantages of non-intrusion, continuous counts, and wider coverage. It also minimizes safety concerns associated with data collection on high-volume routes. Traffic data from ITS sources is of great interest to traffic monitoring programs and HPMS in particular. As stated earlier, the bulk of HPMS volume data is from short counts of 24 to 48 hours in duration. ITS sensors, while still not capable of serving as permanent counters, can efficiently provide at least a day's or two worth of data.Examples of Use in StatesFHWA conducted a survey to assess the use of ITS detectors for HPMS data reporting (Gillmann, 2002). Some of the findings from the 43 respondents were:
On the question of whether ITS data can be used for AADT reporting, most states responded with a "qualified" yes except New Jersey, which said no. The three main concerns with the use of ITS-generated data are (i) validation of data, (ii) requirements of 24-hour continuous hourly data on all lanes, and (iii) vehicle classification data.Some of the major initiatives and successes with the use ITS data by state DOTs are described below:
Sources of further information:
Additional Information on CD
D1. Data Processing and Quality Control Procedures
Issues Addressed
Description
Data processing to assess accuracy, completeness, and validity of traffic data from continuous count stations is carried out using either in-house software packages or legacy mainframe programs. Typically, a software package is used to flag potentially erroneous data for further review. Similarly, data from short-term counts are processed with in-house software packages or one supplied with the equipment. Data validity and completeness are checked using a combination of business rules and criteria.
Examples of Use by States
Processing Software Validity Rules
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Editing Traffic Counts: What to look for: a. Completeness of data b. Hourly volume vs Next/Prior day- check consistency c. Hourly volume vs recent Max/Min - count too low or too high d. Hourly percent distributions by direction- are peaks where they should be. e. Zero volume for an hour- is it common. f. Consecutive hourly zero volumes- should not happen g. Consecutive hours with same non-zero volume h. Daily volume vs recent Max/Min - count too low or too high i. Daily directional splits |
Figure 3.4: California's Checklist for Editing Traffic Counts[5]
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1) VDOT Traffic Monitoring System Data Quality Codes
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Figure 3.5: Virginia's Quality Flags and Error Messages from the Information System [6]
Sources of further information
Additional Information on CD
D2. Adjustment Factors and Growth Factors Calculation
Issues Addressed
Description
Adjustment factors are used to convert short-term volume counts to AADT. These factors include seasonal factors which account for daily, monthly, weekly variations in data; axle correction factors use when axles instead of vehicles are counted; and growth factors when counts are not available. Most states interviewed indicated that estimating these adjustment factors are based on the recommendations of the TMG. Some states have detailed documentation of the methods used to calculate these factors. It was observed what while adjustment factors were calculated based on factor groups, these groups were mostly determined by functional classifications rather than by traffic volumes. There is no difference in the procedures for calculating the adjustment factors based on traffic volumes.
Examples of Use by States
The L factor measures the level of traffic by the day of the week. The seven-day average equals 1.00. The factors typically range from 0.80 to 1.20. The daily traffic volumes are related to AADT by L (level) factor. The L factor is calculated by the following formula:
Where: 7- day counts are taken for 4, 8, or 12 months on a symmetrical basis in a year.
The R factor measures the Range of fluctuation between average summer and average winter traffic. This factor is calculated by day of week as well as a 7- day average. The factors typically vary from 0.00 to 0.70. For a few control stations that have higher traffic in the winter than in the summer, the factor is negative. There are a few control stations with extreme summer/winter fluctuations causing the factor to be higher than 0.70. The R factor is calculated by the following formula:
Where: N = the number
of months counted.
7-day counts are taken
for 4, 8, or 12 months on a symmetrical basis in a year.
The I factor measures the Incremental changes in the R factor from month to month in the fluctuation from summer to winter. The factors typically vary from 0.00 to +/- 10.00. If the R factor is very close to 0.00 the I factor is larger. How much a month is "R" differs from the Average "R". This is needed to adjust the specific day profile counts R factor. The I factor is calculated by the following formula:
Where:
V = Monthly average daily traffic.
A
= Annual average daily traffic.
R
= 7-day R factor.
These factors are recomputed every year.
The station AADT is then calculated by dividing Profile Count Volumes (counts for which one day of complete data is available) by the average L factor for back and ahead traffic stations (ATRs) for the same day of week, plus average R factor for back and ahead ATRs for the same day of week, multiplied by the incremental regional factor, I, for back traffic station.
Additional Information on CD
The purpose of this chapter is to further describe intrusive and non-intrusive data collection equipment used by state DOTs. The discussion identifies the limitations, advantages, and evaluation results of the various data collection equipment. The descriptions are intended to provide a basic guide to technology selection.
Equipment used to count traffic volumes and classify vehicles is very similar. In many cases, the only differences are the layout of the sensors on the roadway and user-selectable inputs in the data collection electronics unit. The following sections identify intrusive and non-intrusive detection technologies that agencies typically use to count and classify vehicles. For HPMS purposes, there must be not only a count of total vehicles but a classification of vehicles according to the prescribed classification scheme. Perhaps the most common scenario for states is to maintain continuous count stations that provide year-round counts from automated systems and apply factors from short-term classification counts to estimate the number of vehicles by type.
Agencies typically use portable traffic volume counters for short-term data collection where a single-axle sensor will suffice. These devices can count all traffic on a roadway or an individual lane, depending on how the installer configures the sensors. The road component may consist of pneumatic tubes or other types of sensors (i.e., piezoelectric film or cable, tape switches, inductive loops, and magnetometers).
For the most part, vehicle classification systems currently fit the "intrusive" category, and they can be either permanent or portable. They typically utilize inductive loops, piezoelectric sensors, or a combination of the two sensor types (AASHTO, 1992). In any case, a minimum of two sensors sends detections to a data collection and storage unit at the roadside. Most classifier systems generate their most accurate data by using a combination of both piezoelectric (or other axle sensor) and inductive loop detectors. This means either two piezoelectric sensors and one inductive loop (preferred) or two inductive loops and one piezoelectric sensor. The standard FHWA classification scheme (Scheme F) measures axle spacing, which requires an axle sensor, with inductive loops providing vehicle presence. Automatic vehicle classification (AVC) sites store vehicle classification information for specific lanes (e.g., Long Term Pavement Performance [LTPP] sites) or for each lane of an entire roadway.All states interviewed rely on a combination of intrusive permanent counting equipment (primarily loops plus piezoelectric sensors) and pneumatic road tubes for short-term counts. The primary method for short-term data collection is road tubes and inductive loops for permanent counts.
All the states interviewed have similar issues with using road tubes on high-volume locations, including safety of data collection crew, securing road tubes, and classification errors.
The following are the common problems identified by the states for traffic data collection on high-volume routes:Safety concerns with installing traffic collection equipmentSensor problems due to rutting and pavement deterioration
Communication problems with the traffic counters, including failures, cross-talk, chattering among loops
Pneumatic tubes are hollow rubber tubes stretched across the portion of the roadway for collecting vehicle count and/or speed data. One end of the tube connects to a traffic counter/ classifier with the other end plugged to prevent air leakage as a vehicle crosses the tube. As a vehicle passes over the tube, its tires compress the tube, actuating an air pressure transducer on the classifier. This means that pneumatic tubes operate in pulse mode only.
Although there are several problems associated with them, these tubes are the most common device used by states for short-term counts. Tubes are relatively inexpensive, and installation is quick and easy. These tubes, typically 0.5 inch in diameter, are relatively accurate for light traffic flows, but they damage easily. The safety of traffic personnel installing road-tubes in high-volume roads is also a concern.
The inductive loop consists of one or more turns of insulated loop wire installed in a shallow slot that is sawed in the pavement, a lead-in cable, and a detector electronic unit. Electrical induction consists of a detector unit that passes a current through the stranded loop wire, thereby creating an electromagnetic field around the wire. Moving a conductive metal object, such as a vehicle, through this field disturbs the electromagnetic field, producing a change in energy level. As the vehicle enters the electromagnetic field of the loop, it causes a decrease in the inductance of the loop and an increase in the oscillation frequency. The inductive loop detector, which was introduced in the 1960s, continues today as the most commonly used form of detector, even though its weaknesses are widely recognized.
Proper installation of the loop in the road surface is important to ensure the reliability of the system. Some pavement surfaces, such as bridge decks, preclude the saw cutting necessary to install permanent inductive loop detectors. A primary disadvantage of inductive loop detectors is the expense of relocating or repairing loops after installation. This procedure requires extensive traffic control and results in congestion and motorist delay (Tyburski, 1989). Detector "cross-talk" and increased pavement stress are two additional disadvantages of inductive loop detector systems. There are also several adverse conditions that affect the operation of inductive loops, including high voltage power lines under the pavement, a pavement subsurface with a high iron content, and unstable pavement conditions. Underground wires, conduit, and pull boxes are susceptible to being damaged by utility work. Modern detection electronics can overcome the first two conditions, but changing or unstable pavement conditions result in increased inductive loop maintenance costs (TTI, 1992). One advantage of inductive loop systems over some of the non-intrusive alternatives is their ability to maintain accuracy in all weather and lighting conditions (ITE, 1991).
Opinions differ on the reliability of inductive loop systems. Some agencies believe that inductive loop technology is the best available, while others have experienced high failure rates (TTI, 1992). Studies on inductive loops revealed that several installation processes needed revision to improve the inductive loop detectors' reliability. Improper saw-cutting techniques, loop-wire splicing, and inadequate loop-sealant bonding resulted in loop wire breakage (Labell and May, 1990).
Given the widespread use of inductive loops throughout the United States, it is logical to fully utilize their capabilities and even to further enhance these capabilities. Inductive loops detect "presence" of vehicles. In its typical use, the inductive loop is basically an on-off device, or a contact closure, indicating that a vehicle is either present or not. In conjunction with its companion electronics, a single loop can provide vehicle counts and occupancies, whereas dual loops (often referred to as "traps") can provide speeds and vehicle classification (by length). However, other useful information is available from inductive loops by adding the appropriate hardware and software. These new concepts need to be considered because they add a new dimension to a state or local agency's capabilities in traffic monitoring.
The previous two sub-sections discussed traffic-detection equipment. Another component of traffic detection relates to the classifiers used to translate axle-presence detection to vehicle volumes and classes. There are many different classifiers in the market today that use the spacing between axle hits to determine classification based on previously determined class tables.
The Georgia Tech Research Institute and Georgia DOT performed a series of field tests on several vehicle classification devices that are currently used in order to determine accuracy and adequacy of the equipment. The field test location was on IH-20 in the metropolitan Atlanta area, and the test included two 48-hour tests for detailed vehicle-by-vehicle analysis and one seven-day test for longer term accuracy statistics (Harvey and Champion, 1996).
Published results were in a format that provided anonymity to participating companies and to specific equipment to avoid the appearance of competitiveness (Harvey and Champion, 1996).Documentation of results compared actual vehicle classification to system classification and the overall classification accuracy. The analysis of results found that the most common classification errors involved the differentiation of class 2 (Passenger Cars) and class 3 (Other Two-Axle, Four-Tire, Single Unit Vehicles) vehicles by test equipment. The results also found that the most accurately classified vehicles were large trucks, which comprise classes 8 through 12. The test team also found that there is a strong correlation between the accuracy of a classifier and the reliability of the axle sensor used to collect the data, and that axle-sensor error accounts for a large number of the overall classification errors. The increased accuracy regarding trucks is attributed to the distinct separation in the number and spacing of truck axles (Harvey and Champion, 1996).
Virginia DOT uses the following equipment and strategies:
A magnetometer typically consists of an intrusive sensor about the size and shape of a small can, a lead-in cable, and an amplifier. The cylinder portion of the magnetometer contains sensor coils that operate similarly to inductive loops. These coils are installed in a small circular hole in the center of each lane and communicate with the roadside by wires or radio link. Magnetometers function by detecting increased density of vertical flux lines of the earth's magnetic field caused by the passage of a mass of ferrous metals, such as a motorized vehicle. They operate in either presence or pulse modes and are embedded in the pavement. Magnetometers require less cutting of the pavement than inductive loop sensors, are easier to install, and can be installed underneath bridge decks without damage to the deck. The disadvantages of magnetometers are similar to those of inductive loop detector systems, in that they sometimes double count trucks and are less likely to detect motorcycles due to the vehicle's small detection zone (Labell and May, 1990).
Illinois DOT has had great success in using Numetric Hi-Star sensors. These sensors use Vehicle Magnetic Imaging (VMI) technology and are capable of the volume, speed, and length classification of vehicles plus road surface temperature, wet/dry surface condition, and roadway occupancy. IDOT finds these sensors easy to install and found them to be excellent for traffic volume data for highways carrying less than 75,000 AADT. While high-volume routes exist in Illinois (especially in the Chicago area), IDOT does not use these sensors in such locations but gets the data from the Chicago Area Transportation Study (CATS). This equipment also performs well for length based classification which Illinois is a big proponent of.
The 3M system consisted of three components: Canoga Model 702 Non-Invasive microloop probes, Canoga C800 series vehicle detectors, and 3M ITS Link Suite application software. The microloop probes can monitor traffic from a three-inch non-metallic conduit 18 to 36 inches below the road surface or from underneath a bridge structure. Installers must use a magnetometer underneath bridges to determine proper placement of the probes; otherwise, optimum performance requires trial-and-error. Probes installed in a "lead" and "lag" configuration under pavements or bridges can monitor speeds by creating speed traps in each lane. One of the requirements of this system is that the probes remain relatively vertical, so keeping the horizontal bores straight is critical. Probes placed in a non-vertical orientation can lead to speed errors. MnDOT tests under pavement indicated excellent volume and speed results. The absolute percent volume difference between sensor and baseline was under 2.5 percent, which is within the accuracy capability of the baseline loop system. For speeds, the test system generated 24-hour test data with absolute percent difference of average speed between baseline and test system from 1.4 to 4.8 percent for all three lanes (Minnesota DOT, 2002).
At a relatively low-to-moderate volume site in College Station, Texas, TTI found that, for a six-day count period, 3M microloops were almost always within 5 percent of baseline counts. In the right lane, all except two 15-minute intervals out of the 330 total intervals were within 5 percent of baseline counts. The remaining two were within 10 percent of baseline counts. Therefore, microloop counts were within 5 percent of baseline counts 99.4 percent of the time in the right lane (dual probes). In the left lane (single probes), 94.5 percent of the 15-minute intervals were within 5 percent, 4.5 percent were between 5 and 10 percent, and 1.0 percent were more than 10 percent from the baseline (Middleton and Parker, 2000).
NYSDOT tested 3M Microloops for bridge deck applications. NYSDOT also tested SAS-1 Acoustic sensors due to their advantages of low-power requirements and low cost. The main advantage stated by New York is the safety of traffic personnel.
A number of non-intrusive technologies also can be used for counting traffic volumes and for classifying vehicles. The use of non-intrusive data collection equipment for traffic data collection has been investigated by various states. While some of the states are experimenting and testing some types of non-intrusive equipment, other states are now beginning to review that option. This category of vehicle detectors includes active and passive infrared sensing systems, passive acoustic detectors, ultrasonic detectors, microwave and radar detection systems, automatic vehicle identification systems, and video detection systems. Some of the potential advantages of non-intrusive devices include ease of repair and ability to do so off the roadway. Several potential disadvantages were identified, including:
Illinois DOT is a strong proponent of length-based classification and has worked with FHWA to report length-based classification for HPMS. The use of length-based classifications encourages the use of non-intrusive detectors. Often the inability of such devices to classify vehicles into 13 vehicle categories is mentioned as a major impediment to their increased use.
The following paragraphs describe each of these systems and discuss advantages and disadvantages of system equipment.
Active infrared sensors operate by focusing a narrow beam of energy and either measuring the reflected energy or measuring the direct energy disruption by an infrared-sensitive cell. In the first case, one device both sends and receives energy, and interprets the reflected pattern. In the second, energy disruption represents vehicle presence so that detections occur when vehicles pass through the beam and interrupt the signal. The infrared beam can be transmitted from overhead or from one side of the road to the other. Infrared systems can provide information on vehicle height, width, and length, in addition to simple passage of vehicles.
Preliminary testing of active infrared detectors by public agencies indicates very promising results for monitoring vehicle speeds and classifications. TTI tested the Autosense II by Schwartz Electro-Optics (SEO) and found it to operate during day/night transitions and other lighting conditions without significant problems. However, its cost of $10,000 per lane may be a deterrent to its use. A second disadvantage of this sensor as compared to most other non-intrusive sensors is the requirement to be placed directly over each lane. This requires lane closure to install and remove the sensor element. Advantages include its ease of setup and generation of data protocols for interpreting its output. Also, it was more accurate in its classification accuracy (based on vehicle dimensions) than another non-intrusive sensors tested (Middleton et al. 1997). Based on information from others, weather conditions that appear to be problematic for this device are heavy fog, heavy dust, and heavy rain. England uses infrared detectors extensively for both pedestrian crosswalks and signal control. The San Francisco-Oakland Bay Bridge uses infrared detection systems to detect presence of vehicles across all five lanes of the upper deck of the bridge (ITE, 1991).
In contrast to the SEO ASII, which monitors and measures vehicle dimensions, the Autosense IIA counts axles. Installation of the IIA is above and to the side of each lane being monitored so that its field of scan includes a side view of the vehicle and its axles. Early testing by the vendor in November 1998 and during the first quarter of 1999 indicates axle-counting accuracy of 95 percent. The manufacturer anticipates further refinement of system algorithms based on "real world" data and improvement of classification accuracy to the design goal of 99.5 percent. The design used by SEO for this detector allows its firmware to execute the axle-counting algorithm without a dedicated computer to perform post-processing. Vehicle classification and axle count are reported within 25 milliseconds of vehicle passage. The release date for the Autosense IIA to be available to the general public was scheduled for April 1999. The Autosense IIA is the only non-intrusive detector identified by the authors that can classify according to the standard FHWA classification scheme using number of axles and axle spacings.
As noted earlier, ODOT uses EIS RTMS units in five locations to collect traffic volume data. ODOT also owns four Off Road Axle Detection Sensors (ORADS) developed and constructed as part of a research project. In addition, ODOT provided funding for an Ohio University research project on Improved Work Zone Design Guidelines. As part of this study, they will be purchasing 16 mobile trailer units equipped with non-intrusive sensors. ODOT will receive these units once the study is complete. ODOT also has tested video (Autoscope) and acoustic. ODOT feels that the main disadvantages are no classification information and difficult set-up.
Virginia DOT is actively researching several non-intrusive technology devices. To date, only the RTMS sidefire radar has been approved for use. It can be used as a portable detector and has the required accuracy needed. Virginia DOT has reviewed other non-intrusive products, but none has met their current needs. For example,
Caltrans tested RTMS extensively but did not obtain favorable results, including long set-up times and occlusion problems. However, Caltrans recognizes that these technologies have improved since and has developed guidelines/requirements for non-intrusive detectors. The draft guidelines are intended to help California personnel make educated estimates of whether microwave sensors can fulfill their requirements. The document contains checklists of requirements that must be met, test results of various microwave models, technology descriptions, and installation overviews.
VDOT uses a portable customized side fire RTMS device for high-volume freeway. The device needs some training to set up and calibrate but works well for volume counts. TTI tested the accuracy of RTMS at a site on the I-35 in Texas. This site does have stop-and-go traffic sometimes during the peak periods so it provides a good test for non-intrusive sensors. It was noted that the RTMS has to be located a minimum of about 18-ft from the nearest traffic lane to be effective. Detectors located less than 6-ft from the nearest lane did not yield reasonable results for that lane. The results indicate that, RTMS accuracy ranges 0 to 5 percent and that occlusion reduces accuracy (both counts and speeds). Also, slow speeds compromise RTMS accuracy. With regards to setup time, it was observed that it takes about an hour per lane even with trained personnel.
The SmarTek SAS-1 is a passive acoustic detector that monitors vehicular noise (primarily tire noise) as vehicles pass the detection area. The detector can monitor as many as five lanes and the SAS-1 must be oriented in a sidefire position. Precise alignment is not critical because the sensor can cover a wide area. Heights recommended by the vendor range from 25 feet to 40 feet, and the recommended offset range is 10 feet to 20 feet. Higher mounting positions can reduce the effects of occlusion in multiple lane applications.
TTI research found that the SAS-1 predominantly undercounted in both peak and off-peak conditions. The SAS-1 speed estimates were within 5 to 10 mph of baseline during some peak periods but as much as 20 to 25 mph different in others. Free-flow speed estimates were usually within 5 mph of baseline speeds (Middleton and Parker, 2002). TTI has not tested the accuracy of the SAS-1 vehicle classification algorithm.
The Traffic Monitoring Unit of the New York State Department of Transportation has successfully developed a permanent acoustic traffic monitoring site. This type of site was developed in-house by NYSDOT personnel to support non-intrusive sensor technology with applications in data collection and ITS activities. The conceptual priority for use of this type of site was installation on facilities where the cost of in-pavement sensors was not justified due to roadway and traffic conditions that greatly limited sensor service life. Use of this type of site greatly reduces data collection costs, but still meets the needs of the Department. Each site consists of a Smartek SAS-1 acoustic sensor mounted on an existing light pole or sign structure at a height of 30 to 40 feet, structure dependant. A small cabinet mounted at the base houses Smartek electronic and communication interfaces as well as power management electronics. The platform is supported by a 12 volt electrical system with one 50 watt Kyocera solar module charging two 75 Ah deep-cycle batteries to supply power. A Trafinfo.com Trafmate digital pager is used to download archived data via telemetry.
In addition to using the acoustic sensors as permanent stations, NYSDOT also has four mobile platforms equipped with the sensor for portable counts including coverage counts, special counts and some ITS design applications. Four Mobile Traffic Monitoring Platforms have been built to date. Each is used to collect volume data on high-speed, high-volume multi-lane facilities where safety concerns or equipment limitations prevent use of typical collection methods. Each platform supports a Smartek SAS-1 acoustic sensor extended on a 35-foot telescoping mast. The platform weighs approximately 1000 pounds, is easily transportable, and can be erected outside the traveled way and operational in approximately 30 minutes.
The cost of each platform fully outfitted with solar power, deep-cycle batteries, a telescoping 35-foot mast, acoustic sensor, and supporting electronics is approximately $7,000. A somewhat similar commercial version of the platform is available for approximately $28,000. However, that setup uses a different type of sensor with a high power consumption rate. It requires generator-supplied power and has no communications capability. The in-house research, development, and construction of this project represent an initial cost savings to NYSDOT of approximately $21,000 for each platform. The anticipated life span of the clean, maintenance-free, solar cell-charged deep-cycle batteries is five years with no additional fuel costs. The batteries are recycled at the end of their useful life. The average cost of construction of one three-to-six-lane count site with loop sensors that is typically used for only a few weeks during the life span of the loops is approximately $30,000. Each count taken utilizing the platform at each location will save the Department $30,000 each time. Assuming two trailers will be used to take a minimum of ten scheduled counts each year on facilities with three or more lanes, the benefit cost ratio for such a device was estimated to be 21:4.
A video image detection (VID) system consists of one or more cameras providing a clear view of the area, a microprocessor-based system to process the video image, and a module to interpret the processed images. Advanced VID systems can collect, analyze, and record traditional traffic data; detect and verify incidents; classify vehicles by length; and monitor intersections. The ability of VID systems to classify vehicles is generally limited to daylight hours unless street lighting is bright enough for the VID's daytime algorithm. Their nighttime detection algorithms depend on detection of headlights, and the systems cannot distinguish between the various headlights of individual vehicle classes. It should also be noted that video systems on the market today provide only three to five vehicle length classifications. Therefore, these systems cannot be used to classify by axles as required by the FHWA classification scheme unless approved by FHWA. The most recent Texas Transportation Institute (TTI) tests indicate some very promising features of one VID system, the Autoscope Solo Pro, but its classification accuracy was not included in the tests.
While there have been rapid advances in vehicle detection technology, inductive loops and piezo electric sensors are considered by states as the most efficient way to collect traffic data. Improvements in loop installations and vehicle counters have greatly reduced the problems associated with inductive loops. Advanced vehicle counters with loop signatures-based detection and classifications promise to build upon the improvements. However, the use of loops continues to be cumbersome due to its inherent requirements such as pavement cutting, traffic control and lane closures, and maintenance problems. Pneumatic tubes are the preferred technology for short-term counts.
Non-intrusive detectors provide an alternative to minimize or eliminate some of the safety and maintenance issues with loops and tubes. These technologies include infrared-, acoustic-, microwave-, and video-based sensors. Various tests have shown that these sensors currently meet requirements as far as volume monitoring is concerned but fall short on classification of vehicles.
The art and practice of traffic data gathering and processing has been well established over the years. Each state DOT follows a set of procedures, chooses, and uses equipment that best meets their specific needs. The guidelines presented in this chapter acknowledge the existence of these state-specific practices and procedures. These guidelines are intended to help enhance the process and improve the quality of traffic data collection and processing on high-volume routes especially. The guidelines are not intended as a set of uniform standards that all states must follow, neither are they intended to replace existing successful practices. Instead, these 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 primary objective is to improve the quality of traffic data on high-volume routes.
The guidelines are grouped into four broad categories - data collection, data processing and quality assurance, use of ITS data, and equipment. These are based on best or common practices and equipment descriptions presented in chapters 3 and 4 of this report. The guidelines are presented with examples and hyperlinks to further detailed information on the accompanying CD.
Data collection for HPMS reporting will continue to be based on short-term counts and permanent count stations. The following steps are considered useful for traffic monitoring on high-volume routes.
The first step is to define what constitutes high-traffic volume. While most states tend to define high-traffic volume routes in terms of the ability to install data collection equipment safely, such perception can be translated into traffic volume. The definition of high-volume routes in terms of AADT is believed to provide a standard way of identifying routes that carry traffic volumes that are high enough to endanger the safety of data collection crew. It is probable that the traffic threshold value may not be the same across all states. In some states, AADT of 50,000 may be considered high, while 100,000 may be the threshold in other states. For example, IlDOT uses 70,000 AADT while NYSDOT uses 80,000 AADT to define high volume routes.
However, analysis of AADT data to determine which states to interview indicate that invariably, the top 10 states based of the mileages of roadway carrying traffic volumes satisfying the three thresholds (50,000, 75,000, and 100,000 AADT) are the same. The ranking of the states however vary depending on the threshold. As a guide, therefore, it is recommended that high-volume routes can be defined as those carrying traffic in excess of 50,000 AADT.
The next step is to identify routes carrying traffic volume that satisfy the threshold value. Safety of traffic personnel during installation of traffic sensors was the primary concern expressed by the states interviewed. Therefore, it is important that state DOTs identify locations where safety is a concern due to traffic volumes, geometry, or other reasons. This step also involves identifying locations where data collection is difficult due to technological limitations caused by congestion and stop-and-go traffic. Once such locations have been identified, it becomes easy to identify appropriate data collection strategies regarding
Washington state uses color coded safety zones to identify locations for data collection. These zones were not identified strictly based on traffic volume but a combination of traffic and roadway characteristics and identify personnel and installation time requirements for locations. Details of this approach are provided in "Safety Zones for Traffic Monitoring", (WsDOT) [CD].
Strategies suitable for the high-volume locations are intended to improve the data collection process and address the problems and challenges associated with high-volume routes as discussed in the previous chapter. Following are some recommended strategies and approaches:
5.1.1.1 Provide
training and guidelines
A strategy to improve data collection practices on high volume
routes is to provide training, including safety guidelines for all field
personnel and additional safety procedures to follow in equipment installation
and retrieval. The use of safety guidelines or operational manuals that
include safety requirements should be encouraged. Useful examples include the
following.
5.1.1.2 Coordinate Equipment
Installation with Construction and Maintenance
It is recommended that states plan the installation and maintain
data collection equipment (e.g., inductive loops) to coincide with pavement
construction and maintenance activities (e.g., in California). This ensures
safety to data collection personnel and allows equipment installation,
inspection, and maintenance under controlled traffic conditions. Also, it is
recommended that equipment installation is carried out during off-peak hours.
Inductive loops and piezo sensors are the preferred equipment for ATRs. However, some states (e.g., Florida) are trying to install loops and conduits on multilane facilities and then use them for short-term counts by connecting a traffic counter when required. A properly installed loop and conduit can provide good quality data when a traffic detector is connected without compromising safety of the traffic personnel. The installation of such equipment is better accomplished when coordinated with construction and maintenance operations.
5.1.1.3 Use ramp-balancing
techniques
On limited access facilities with high-volume traffic, ramp
balancing is suggested if permanent count stations are not available for
sections of the mainline. The Traffic Monitoring Guide [CD] provides guidelines on ramp counting. In locations where ramp-balancing
approaches are used, attempts should be made to automate the data reduction
steps, especially in calculating mainline volumes from ramp counts, in converting
volume counts to AADTs, and in converting segment volumes to HPMS section
volumes.
California, Florida, Georgia, Michigan, Ohio, Texas, and Washington use of ramp-balancing approaches that were developed based on the guidelines and recommendations of the Traffic Monitoring Guide. The following examples serve as guides in the use of ramp balancing technique.
5.1.1.4 Use of Techniques
for Better Classification and Lane-by-Lane Detection
One of the reported problems of traffic monitoring on
high-volume routes is miscounting and misclassification of vehicles due to
multiple hits, phantom hits on multi-lane facilities. It is recommended that on
such facilities, technologies and techniques that improve lane-by-lane
detection and classification of vehicles be used. The following examples
illustrate successful techniques:
5.1.3.5 Use data and resource
sharing agreements with local agencies
The Urban Transportation Monitor (April 16, 2004)
survey referenced earlier indicated that 79 percent of the responding cities (
i.e., 98 out of 124) do not have any agreements among local agencies that
coordinate traffic collection activities, resulting in waste of funding, duplication
of efforts, and inability to share resources.
Data and resource sharing agreements codify the roles, expectations, and responsibilities among the parties providing and using traffic data. Such agreements can conceivably occur between public entities, entirely between private entities, or between private and public entities. Data-sharing agreements typically discuss such items as security and confidentiality, liability, frequency of data transmittals, to which the data may be disseminated, and fees. For example, NYSDOT uses counties for traffic data collection.
5.1.3.6 Use contractors for data collection
The use of private contractors to collect traffic data is
increasing in states. This is especially true for short-term count data. It is
suggested that the quality of data and requirements for system operation be
included as a standard in specifications. The following are examples of
successful contracts with private data providers.
Traffic data for high-volume routes is currently processed in the same manner as for other traffic locations. In reporting AADT values required for HPMS, two related steps are involved - data processing to verify validity and completeness, and calculation of adjustment factors. These and other data quality assurance guidelines are presented below.
Data processing to verify validity and completeness is carried out using either in-house software packages or legacy mainframe programs by all states interviewed. For HPMS and traffic monitoring, all states interviewed use software to flag potentially erroneous data for further review by DOT personnel who have extensive local knowledge and experience. Most of the states DOTs interviewed do not use data processing software to process short-term count data except in cases where vendor-provided software is used to download data from the device. Some states have in-house software packages to process short-count data (e.g., Florida uses a software product called "Survey Processing Software"; Washington State uses an in-house program; New Jersey uses TRADAS, a commercially available system and legacy mainframe software that was developed in-house).
A recent Urban Transportation Monitor (April 16, 2004) survey of traffic engineers in the U.S. and Canada reported that about 36 percent of the respondents (i.e., 45 out of 124) did not use any quality control software in processing data. The survey indicated that the software used for quality control of traffic counts is mostly from the manufacturer (56 percent), with some third-party (8 percent) and in-house software (11 percent). It is recommended that all agencies collecting data assess the quality of data, especially for high-volume routes. It is important that in the absence of third-party or in-house software, agencies should at least require vendors to provide software with equipment that would allow data-validity checks based on common or published criteria, especially for short-term counts. Several states have recently updated their traffic-processing software to more recent relational database-driven applications.
Several states are in the process of developing comprehensive database systems to store, process, and query all their traffic data. These database systems are also expected to have rigorous quality control and assessment procedures. For example, Texas is developing the Statewide Traffic and Recording System (STARS), Ohio is developing Traffic Keeper-Ohio (TKO), and Georgia is updating their QC/QA system. California is already using a relational database system called the Transportation Systems Network (TSN).
Documentation and user guides for some of the software used by states are provided on the CD. These include:
FHWA initiated a pooled fund study with Minnesota, Wisconsin, South Dakota, Indiana, New York, Connecticut, North Carolina, South Carolina, Georgia, Florida, New Mexico, California, Idaho, and Montana to develop a system for consistent traffic data quality edits. Although concluded before all its intended objectives were met, the study compiled a list of all data-screening tools used by one or more of the participating states as they are applied to short or continuous volume, vehicle classification, and/or WIM data for the selected data products. The report included a set of logically consistent, state-of-the-practice rules for traffic-data screening derived from five, multiple-day knowledge-engineering sessions attended by more than 60 traffic-data screening experts. The report also included traffic-data screening algorithms, definitions, and pseudo-code statements to support the development of rule-based testing software (MnDOT, 1997).
Adjustment factors based on TMG [CD] recommendations are needed to convert short-term volume counts to AADTs by accounting for seasonal, monthly, and daily variations. TMG recommends that counts missed because of equipment failures, bad weather, or other reasons should be made up during the year. Partial counts of less than 24 hours should, as a general rule, be retaken.
Most states interviewed indicated that they calculate seasonal factors based on rolling averages of ATR data based on TMG guidelines and factor groups. The following are examples of other approaches in use by some states. These are described in detail in Chapter 3 of this report.
Several states interviewed noted that, concerns about the quality of data obtained from external sources preclude their extensive use. Currently, there is no accepted method to assess traffic data quality from different sources and applications. A framework for assessing the quality of traffic data was developed that provides a valuable tool for agencies involved in data collection[8]. The framework provides methodology for calculating six recommended fundamental measures of traffic data quality. The methodologies presented in the framework are applicable to both ITS and non-ITS generated traffic data. The framework is expected to provide guidance to states on how to assess the quality of traffic data. The fundamental traffic data quality measures are defined below:
Depending on the application, not all six measures will be required. For purposes of HPMS reporting, accuracy, completeness, validity, and coverage appear to be the most important data quality measures.
As noted earlier (Chapter 3), all states interviewed conduct some limited quality control checks to at least identify potentially erroneous data. All states interviewed use validity criteria or data processing rules to assess the quality of the data. Data processing rules used by the states interviewed are based on AASHTO and TMG guidelines and included range checks, completeness of data, and lane-distribution splits. For example, California uses a relational database system called the Transportation Systems Network (TSN). Virginia uses a detailed quality assessment procedure that includes six different categories of quality.
However, none of the states interviewed uses a comprehensive data quality assessment procedure compared to the data quality assessment framework referenced above. States are encouraged to review the Traffic Data Quality Measurement Framework, Draft Report ( Battelle, 2004) [CD] for use in assessing the quality of traffic data from different sources and for different applications.
ITS data offer a valuable source of traffic data especially to the HPMS program. Many of the states interviewed view the ITS data as a potential source for some of their data. Two major issues are quality of the data and the inability to provide classification data. Some state DOTs already 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. The difference in quality of data from these sensors is directly related to the differing requirements of the operations and traffic monitoring groups. While it is acknowledged that many of the ITS sensor locations suffer from quality concerns such as missing and inaccurate data, no classification, and frequent and extended downtimes, it is still possible to collect useable data from ITS data sources, especially in lieu of short-term counts. The following sections describe some potential approaches to encourage the use of ITS data for HPMS volume reporting.
Merging ITS field infrastructure (like inductive loops and sensors) with traditional traffic counting devices would allow the use of the traffic counters/ classifiers alongside ITS devices. The Detector Isolation Assembly (DIA) approach used in California is a good example. The DIA approach allows the use of existing infrastructure on high-volume routes and enhances the safety of the traffic personnel. Caltrans is in the process of developing sensor-sharing technology to use the existing infrastructure of loops, cabinets, and power supplies to collect planning data. Caltrans' DIA device also provides total isolation between the traffic recording and the traffic control functions. The DIA device is housed in the same cabinet as the traffic controller and senses the electronic switch closure produced by the detector and passes the signal to the traffic recorder. This technology offers great potential for using existing infrastructure to obtain planning data and is of immediate use at high-volume locations with traffic controllers and ITS detectors (Triplett and Avis, 2002). California does not use ITS data yet for HPMS reporting. However, Caltran's counting program has about 219 locations where detector infrastructure on signals and ramps is shared.
Similarly, Ohio DOT, working on the same principle of detector sharing, uses loops currently not used for operational analysis by the ITS groups for its traffic data collection.
Both ITS and traffic monitoring groups collect similar traffic data. More often, the equipment used by the two groups is incompatible. It is suggested that agencies investigate the use of compatible equipment or sensor-sharing arrangements where the signals from the in-road sensors are split into two devices. For example, certain equipment in certain locations would allow data to be polled at short intervals of time as required for operations and would also have enough storage for daily downloads by the traffic monitoring groups.
Some early efforts in this area already exist. For example, the Division of Planning in Kentucky invested in equipment they like and trust and ARTIMIS (the TMC in the Cincinnati area) identified modifications to these devices so that they also can be used for ITS applications by the TMC.
The key to the success of the approaches presented above (i.e., resource sharing and compatible equipment) is the identification of locations where these strategies can be implemented. Also, locating ITS sensors strategically would allow the sensors to be maintained jointly by the traffic monitoring and ITS groups. These locations should be identified by the traffic monitoring group as important components of the traffic monitoring program either due to high volumes or for other reasons. Cooperation can range from informal technical assistance to formal data-sharing agreements and personnel support. The following are some examples.
Increasing use of data from ITS data archives could supplement HPMS and traffic monitoring programs. However, the use of ITS data archives is being limited by concerns about quality of data and the effort needed to successfully process and integrate these sources into the remainder of the traffic monitoring program. Examples of data archive projects are outlined below. Other states (e.g., Ohio, Illinois, Michigan notably) also use ITS data in archival form to supplement their data collection needs.
Chapter 4 of this report provides detailed descriptions of the various types of traffic data collection equipment. It is acknowledged that all states employ data collection equipment by different manufacturers. The selection of equipment is based on individual state experiences, needs, and conditions. Invariably, inductive loops are the primary choice with permanently installed equipment used for continuous and short-term counts while pneumatic tubes are used for short-term counts. The equipment from different manufacturers, although designed to perform identical tasks, may have different characteristics in terms of reliability, accuracy, robustness, and durability, among others. The following are highlights of advances in data collection technology, both traditional and non-intrusive. These are designed to guide the selection of equipment and technologies for data collection.
There are some recent advances in detection technology directed at improving traffic volume and vehicle classification on high-volume routes especially in congested and stop-and-go traffic conditions. Improvements in loop installations and vehicle counters have reduced greatly the problems with inductive loops. Advanced vehicle counters with loop signatures-based detection and classifications promise to build upon the improvements. Inductive loop signatures, a technology that involves several algorithms designed for use in roadside vehicle detection equipment, may apply to vehicle classification, toll applications, and incident detection. For example, recent tests on the loop-signature technology conducted by the TTI indicated that the technology was very accurate as a classifier, counter, and speed-detection device and as a generator of simultaneous contact closure output (Middleton and Parker, 2002).
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, which are used by Washington, Virginia, and Georgia. In Washington, tube counters are set and validated prior to every count. A manual count (100 axles or 5 minutes of traffic, whichever comes first) is performed and compared to the data from the traffic counters. Similarly, each of the continuous count sites is validated once a year by a manual traffic count (three hours duration). In Virginia, trained operators check equipment for accuracy during the initial setup operation in all cases. All equipment currently in use has a visual display with real-time results. Advanced loop logic functions are included to provide warning signs when piezo-sensors begin to fail so that preventive maintenance can be planned. Georgia DOT randomly tests ATRs for accuracy using video logs, which are then compared to the collected data. GDOT allows a tolerance level of 5 percent variance from the ground truth that all equipment are expected to meet. Ohio DOT provides guidelines for testing and acceptance of traffic counters. Details can be found in "Warranty, Service and Acceptance Requirements", Ohio DOT, 2004 [CD].
The use of maintenance contracts for rapid restoration of ATRs is a strategy being considered by some states interviewed. The ability to restore an ATR in the least possible time is critical for state DOTs because of the importance of these sites to traffic monitoring programs. Tasks for such contracts include performing regular maintenance of equipment, on-call duties, and installation of new sites. Some states, including Ohio, New York, and Maryland, have used on-call contractors for maintaining and installing permanent count stations. Other states also have expressed interest in task-order-based maintenance contracts including Texas, Florida, and Maryland (Fekpe et al. 2003). The following are some examples.
Many states are considering the use of non-intrusive equipment. Out of 13 states interviewed, 10 indicated they either use or are testing non-intrusive detection equipment. These devices are being tested through small pilot tests and programs. In order for state DOTs to appreciate the capabilities of non-intrusive equipment and to meet individual state requirements, it is suggested that states develop specifications or criteria that non-intrusive detectors must satisfy. These specifications or criteria would include, at a minimum, information on:
These specifications or criteria would be useful to both state DOTs and equipment vendors. For example, Caltrans has developed guidelines/requirements for non-intrusive detectors (Microwave Vehicle Detection Systems Guidelines, 2003) [CD]. The draft guidelines are intended to help personnel in California to make educated estimates of whether microwave sensors can fulfill their requirements. The document contains checklists of requirements that must be met, test results of various microwave models, technology descriptions, and installation overviews.Also, FHWA sponsored a Field Test of Monitoring of Urban Vehicle Operations Using Non-Intrusive Technologies (FHWA-PL-97-018). The final report of the evaluation is available in html format at www.dot.state.mn.us/guidestar/1996_2000/nit1/NIT_REPORT.pdf
The rapid improvements in detection technology have resulted in various products being tried by the state DOTs. Sharing information about the capabilities or experiences with certain 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, pooled-fund project managed by the Southwest Technology Development Institute (SWTDI) at New Mexico State University (NMSU) (www.nmsu.edu/~traffic) and sponsored in cooperation with the U.S. DOT FHWA, is a valuable resource for information on technology, evaluation, testing results, and level of use by states.
FHWA in conjunction with VDC produced a summary of vehicle detection and surveillance technologies in 2000 ("A Summary of Vehicle Detection and Surveillance Technologies used in Intelligent Transportation Systems") [CD]. The document describes the common types of vehicle detection and surveillance technologies in terms of theory of operation, installation methods, advantages and disadvantages, summary information about performance in clear and inclement weather, as well as their relative costs. The descriptions also include vendor-provided information about specific sensor models, their functions and applications, users, and installation and maintenance costs. Martin et al., (2003) [CD].also conducted a comprehensive evaluation of vehicle detector technologies.
The practices and guidelines presented in this report are intended as a reference document 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. This report and the accompanying CD are intended to serve as a resource to state DOTs by providing information on best and common practices as well as a library of additional documents produced by state DOTs. While many of the practices are common and widely known, it is expected that this compendium assembles the various approaches being used to improve HPMS traffic data collection activities especially on high-volume routes. Following are general conclusions from this examination of current data collection and processing practices.
The definition of high-volume traffic routes varies from agency to agency. In fact, no state has a definition based solely on traffic volume. Rather high-volume locations are defined in terms of the difficulty in installing data-collecting equipment with safety of traffic personnel mentioned as the primary concern. It is recommended that states have adopted several practices to improve data collection, processing, and reporting for such routes. The practices are grouped into four major categories: (i) general, (ii) data collection equipment (iii) data collection, and (iv) data processing, quality control, and quality assurance. Descriptions of these practices are provided in Chapter 3 and illustrate how states address the issues and challenges, and include sources of further information and contacts.
Training and guidelines for field personnel involved in installing and removing equipment was identified as crucial by many states. Approaches like ramp balancing on limited access freeways, coordinating equipment installation with construction activities, and use of safety procedures are some strategies used by state DOTs to improve their data collection efforts. It was also noted that data and resource sharing is becoming an increasingly common practice among state agencies.
The use of ITS generated data for HPMS reporting is increasing. 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. While quality concerns exist, ITS data have great potential especially to supplement short-term counts.
Inductive loops and pneumatic road-tubes are the prevalent equipment of choice among the state DOTs. Equipment problems were common to all states interviewed, regardless of the type of equipment and traffic conditions. Several strategies are identified to improve data collection including the use of maintenance contracts, installation guidelines, the use of advanced technologies and techniques.
Non-intrusive equipment are being tested by many states for data collection. Various technologies ranging from microwave, acoustic, laser, etc have been investigated by 10 of the 13 states interviewed. Descriptions of non-intrusive data collection equipment identify the limitations, advantages, and evaluation results and provide a guide to technology selection.
AASHTO Standard Practice for Determination of International Roughness Index for Quantifying Roughness of Pavements, AASHTO PP 37-02.
AASHTO, Joint Task Force on Traffic Monitoring Standards of the AASHTO Highway Subcommittee on Traffic Engineering. AASHTO Guidelines for Traffic Data Programs. American Association of State Highway and Transportation Officials, Washington, D.C., 1992.
American Society for Testing and Materials (ASTM), Standard Specification and Test Methods for Highway Traffic Monitoring Devices, Review Copy: Version C for E17.52, Draft December 2002.Avis, J., Transportation System Network, Presentation at NATMEC 2002.
Cunagin, W.D., A.B. Grubbs, and D.J. Vitello Jr. Development of an Overhead Vehicle Sensor System. Research Report 426-1F. Texas Transportation Institute, Texas A&M University System, College Station, TX, October 1987.
Cunagin, W.D., S.O. Majdi, and H.Y. Yeom. Development of Low Cost Piezoelectric Film WIM System, Research Report 1220-1F, Texas Transportation Institute, Texas A&M University System, College Station, TX, 1991.
Federal Highway Administration (FHWA), HPMS Field Manual, 2001b, https://www.fhwa.dot.gov/ohim/hpmsmanl/hpms.htm.
Federal Highway Administration (FHWA), Study Tour for European Traffic Monitoring Programs and Technologies, FHWA's Scanning Program, U.S. Department of Transportation, Federal Highway Administration, Washington D.C., August 1997.
Federal Highway Administration (FHWA), Traffic Monitoring Guide, 2001, https://www.fhwa.dot.gov/ohim/tmguide/.
Fekpe, E., Gopalakrishna, D., Margiotta, R., Turner, S., Middleton, D., Traffic Data Quality Workshop Proceedings and Action Plan, Report to FHWA Office of Highway Policy Information, September 2003.
Florida DOT, Project Forecasting Handbook, 2002.
Florida DOT, Traffic Monitoring Handbook, Transportation Statistics Office, 2002.Gillmann, R., Status of ITS Data for HPMS, Memo for FHWA, 2002.
Harvey, B.A., and G.H. Champion. "Classification Algorithms/Vehicle Classification Accuracy," presented at NATDAC96 hosted by Alliance for Transportation Research, Federal Highway Administration, and the New Mexico State Highway and Transportation Department, Albuquerque, NM, May 5-6, 1996.
Heltebridle, L., Pennsylvania's HPMS Quality Review, Presented at the HPMS Issues Workshop, Chicago, August 2002
Institute of Transportation Engineers (ITE), The Traffic Detector Handbook, Second Edition, Washington, D.C., 1991.
Labell, L.N., and A.D. May. Detectors for Freeway Surveillance and Control: Final Report. Institute of Transportation Studies. University of California at Berkeley, Berkeley, CA, 1990.
Labi, S., and Fricker, J.D., "Assessing and Updating INDOT's Traffic Monitoring System for Highways", FHWA/IN/JTRP-98/12, 2001. Available at http://rebar.ecn.purdue.edu/JTRP_Completed_Project_Documents/HPR%5F0438/FinalReport/Hpr_0438_final_Prelude.pdf
Margiotta, R., State of the Practice for Traffic Data Quality, White Paper for FHWA Office of Policy, http://www.itsdocs.fhwa.dot.gov/JPODOCS/REPTS_TE/13768.html, 2003.
Martin.P.,, Feng.Y., Wang.X., Detector Technology Evaluation, Department of Civil and Environmental Engineering, University of Utah Traffic Lab, November 2003.
Middleton, D., R. White, and J. Crawford. Initial Evaluation for Traffic Monitoring Equipment Evaluation Facility. Report FHWA/TX-04/0-4664-1, Texas Transportation Institute, Texas A&M University, College Station, TX, May 2004.
Middleton, D. and R. Parker. Initial Evaluation of Selected Detectors to Replace Inductive Loops on Freeways, Research Report FHWA/TX1439-7, Texas Transportation Institute, College Station, TX, April 2000.
Middleton, D., and Parker, R., Evaluation of Promising Vehicle Detection Systems, Research Report FHWA/TX-03/2119-1, Draft, Texas Transportation Institute, College Station, TX, October 2002.
Middleton, D., Jasek, D., Charara, H., and Morris, D. Evaluation of Innovative Methods to Reduce Stops to Trucks at Isolated Intersections, Study No. 7-2972, Research Report TX 97/2972-1S, Sponsored by the Texas Department of Transportation, Austin, TX, August 1997.
Minnesota DOT (MnDOT), Phase II Evaluation of Non-Intrusive Technologies for Traffic Detection, Final Report, St. Paul, MN, September 2002.
NYSDOT, Highway Data Services Bureau, Loop/Piezo Automatic Traffic Recorder specification, September 2001
Texas Transportation Institute (TTI), Texas Traffic Signal Detector Manual. Texas Transportation Institute Report 1163-1. Texas A&M University, Texas A&M University System, College Station, TX, July 1992.
Triplett, R. and Avis, J., Sensor Sharing Project, Presentation at NATMEC 2002.
Tyburski, R.M. "A Review of Road Sensor Technology for Monitoring Vehicle Traffic," Institute of Transportation Engineers Journal, Volume 59, Number 8, Institute of Transportation Engineers, Washington D.C., August 1989.
Volpe National Transportation Systems Center, An Overview of Traffic Monitoring Programs in Large Urban Areas, Report for FHWA Office of Information Management, July 1997.
Volpe National Transportation Systems Center, Case Studies of Traffic Monitoring Programs in Large Urban Areas, Report for FHWA Office of Information Management, July 1997b.
Washington DOT, Short Term Count Factoring Guide, 2003.
Woods, D.L., J.D. Blaschke, and H.G. Hawkins. A Short Course on Traffic Signal Design. Texas Transportation Institute, Texas A&M University System, College Station, TX, November 1986.
Wright, T., Hu, P.S., et al. Variability in Traffic Monitoring Data, Oak Ridge National Laboratory, August 1997.
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Joe
Avis
Chief Traffic Data and Photolog Unit
Division of Traffic Operations
California Department of
Transportation (Caltrans)
joe.avis@dot.ca.gov
Ph: (916) 654-3072
Brian Domsic
Division of Transportation
System Information
California Department of
Transportation (Caltrans)
Ph: (916) 653-3272
Fax: (916) 654-6583
brian.domsic@dot.ca.gov
Jim Neidigh
Transportation Planning & Programming Division
Texas Department of Transportation (TxDOT)
Ph: (512) 486-5137
jneidigh@dot.state.tx.us
Rob Robinson
Data Management Unit Chief
Illinois Department of
Transportation (IDOT)
Ph: (217) 785-2353
Fax: (217) 524-6251
robinsonre@nt.dot.state.il.us
Harshad Desai(formerly)
Manager, Traffic Data Section
Florida DOT (FDOT)
Ph: (850) 414-4718
harshad.desai@dot.state.fl.us(now)
Office of Highway Policy
Information
Federal Highway Administration
(202) 366-5047
harshad.desai@fhwa.dot.gov
Nabeel Akhtar
Florida Department of Transportation (FDOT)
nabeel.akhtar@dot.state.fl.us
David Gardner
Manager, Traffic Monitoring
Section
Ohio Department of Transportation
Ph: (614) 752-5740
dgardner@dot.state.oh.us
Tony Manch
Engineer, Office of Technical Services
Ohio Department of Transportation (ODOT)
amanch@dot.state.oh.us
Ph: (614) 466-3075
Mike Baxter
Assistant Division Chief, HISD
Database Management, Traffic
Monitoring, HPMS, Road Inventory
Maryland State Highway
Administration
Ph: (410) 545-5511
Fax: (410) 209-5033
mbaxter@sha.state.md.us
Mike Walimaki
Supervisor,
Travel Information Unit
Data Collection SectionAsset Management Division
Michigan Department of Transportation (MDOT)
Ph: (517) 335-2914
walimakim@michigan.gov
Tom Schinkel
Virginia DOT, Planning
Ph: (804) 225-3123
Fax: (804) 371-0190
Tom.Schinkel@VirginiaDOT.org
John Rosen
Highway Usage Branch Manager
Washington DOT (WsDOT)
RosenJ@wsdot.wa.gov
David Adams
Office of Transportation Data
Georgia Department of Transportation
Ed.Adams@dot.state.ga.us
Ph: (770) 986-1364
Todd Westhuis
NYSDOT Highway Data Services
Bureau
Traffic Monitoring Section
Supervisor
Ph: (518) 457-7203
Twesthuis@dot.state.ny.us
Louis Whiteley
Section Chief, Traffic and Technology Section
New Jersey DOT (NJDOT)
Louis.Whiteley@dot.state.nj.us
Philip HughesContracts/Agreements Administrator
Massachusetts Highway Department
Philip.hughes@MHD.state.ma.us
Ph: (617) 973-7330
Annual Average Daily Traffic
(AADT)
The estimate of typical daily
traffic on a road segment for all days of the week, Sunday through Saturday,
over the period of one year.
Average Daily Traffic (ADT)
The total traffic volume during
a given time period (more than a day and less than a year) divided by the
number of days in that time period.
Automatic Traffic Recorder
(ATR)
A device that records the
continuous passage of vehicles across a given section of roadway by hours of
the day, days of the week or months of the year.
ATR Counts
Base traffic counts recorded at
an automatic traffic recorder.
Automatic Vehicle Classifier
AVC)
A device that works in
conjunction with computerized electronic equipment that counts and classifies
vehicles by type and axle configuration.
Axle Correction Factor
The factor developed to adjust
vehicle-axle sensor-base data for the incidence of vehicles with more than two
axles, or the estimate of total axles based on automatic vehicle classification
data divided by the total number of vehicles counted.
Count
The data collected as a result
of measuring and recording traffic characteristics such as vehicle volume,
classification, speed, weight, or a combination of these characteristics.
Count Period
The beginning and ending date
and time of traffic characteristic measurement.
Count Type
The traffic characteristic being
measured, the measurement device, and time period.
Coverage Count
A traffic count taken as part of
the requirement for system-level estimates of traffic. The count is typically
short-term, and may be volume, classification, or Weigh-in-Motion.
Functional Classification
The grouping of streets and
highways into classes, or systems, according to the character of service they
are intended to provide. The recognition that individual roads do not serve
travel independently and most travel involves movement through a network of
roads is basic to functional classification.
Geographic Information System
GIS)
A method of storing, analyzing,
and displaying spatial data.
Highway Performance
Monitoring System (HPMS)
A federally mandated data
reporting system for all roads except local.
Intelligent Transportation
System (ITS)
A system that employs
electronics, communications, and/or information processing to improve the
efficiency of surface transportation operations and provide real-time
information about travel options.
Loop Detector
A detector that senses changes
in inductance, of its inductive loop sensor, caused by the passage or presence
of a vehicle near the sensor.
Manual Counts
Measurement of traffic
characteristics based on human observation, which may or may not be
electronically recorded.
Metropolitan Planning
Organization (MPO)
Regional agency responsible for
urbanized area transportation planning.
National Highway System (NHS)
A designated system of highways
of National Significance mandated under the Intermodal Surface Transportation
Efficiency Act of 1991. The purpose of the NHS is to provide an interconnected
system of principal arterial routes to serve major population centers, airports
and public transportation facilities, to meet national defense requirements and
to serve interstate and interregional travel.
Permanent Count Stations
ATRs that are permanently placed
at specific locations throughout the region to record the distribution and
variation of traffic flow by hours of the day, days of the week, and months of
the year from year to year.
Seasonal Factors
Parameters used to adjust base
counts that consider travel behavior fluctuations by day of the week and month
of the year.
Strategic Highway Research
Program (SHRP)
A five-year program for pavement
and operations research funded by Congress and managed through the National
Academy of Sciences. One of the four research areas, Long-term Pavement
Performance, is planned as a 20-year program.
Traffic Management Center (TMC)
Also known as Traffic Operations Center, it serves as the nerve center for a traffic management system. Data
on traffic conditions collected in real time by any of a variety of means are
transmitted to the TMC where traffic engineers, assisted by computer, monitor
traffic flow and respond to congestion in a variety of ways, such as adjusting
traffic signal timing or transmitting information on current conditions to
motorists via changeable message signs.
Traffic Monitoring Guide
(TMG)
Document that provides FHWA's
recommended approach to the monitoring of traffic characteristics. The guide provides
direction for persons interested in conducting a statistically based monitoring
of traffic counting, vehicle classification, and truck weighing.
Traffic Program
The collection, editing,
summarization, reporting, and analysis of traffic volume, classification, and
weight data.
Vehicle Classification
The measurement, summarization,
and reporting of traffic volume by vehicle type and axle configuration.
Vehicle Miles Traveled (VMT)
Average Sunday through Saturday
vehicle movement on a specific road segment multiplied by the length of the
road segment, reported in the form of daily and annual VMT.
Weigh-in-Motion (WIM)
The process of estimating a
moving vehicle's static gross weight and the portion of that weight that is
carried by each wheel, axle, or axle group or combination thereof, by
measurement and analysis of dynamic forces applied by its tires to a measuring
device.
Guide to "HPMS High-Volume Best Practices and Guidelines" CDHold Control (CTRL) Key and Click on Hyperlink to go to the DocumentThis Guide accompanies the HPMS High-Volume Best Practices and Guidelines Final Report. The final report includes references to the documents hyperlinked below.To reference individual documents, please use the hyperlinks below.
California Department of Transportation, Traffic Operations, Microwave Vehicle Detection Systems (MVDS) Guidelines, DRAFT, 2003
Summary: This document provides draft guidelines for the installation and operations of Microwave Vehicle Detection System (MVDS). The document is intended to aid Caltrans personnel when making decisions on where and when to effectively deploy MVDS. Particularly, this guide is to help the designer to understand what the MVDS solution can do and how to use it as well as to help the personnel know what to watch out for in taking delivery of this equipment from the Contractor.
Contact Joe Avis
New York State Department of Transportation, (i) Permanent (ii) Mobile Platform Acoustic Site Summaries, and (iii) LOOP / PIEZO based Automatic Traffic Recorder Specifications
Summary: The first two documents from NYSDOT provide specifications for permanent, mobile acoustic sensors with a focus on the benefit costs of using such technologies. The third document provides the specifications for a loop/piezo based automatic traffic recorder.
Contact: Todd Westhuis
U.S. DOT, Federal Highway Administration, A Summary of Vehicle Detection and Surveillance Technologies used in Intelligent Transportation Systems, produced by the Vehicle Detector Clearinghouse (VDC) for FHWA ITS Joint Program Office, Fall 2000
Summary: This summary document was developed to assist in the selection of vehicle detection and surveillance technologies that support traffic management and traveler information services. Included are descriptions of common types of vehicle detection and surveillance technologies in terms of theory of operation, installation methods, advantages and disadvantages, and summary information about performance in clear and inclement weather and relative cost. Following each technology description is vendor-provided information about specific sensor models, their functions and applications, users, and installation and maintenance costs.
Contact: www.nmsu.edu/traffic
New York State Department of Transportation, Highway Data Services Bureau, Zone 3 contractor specifications, June 15 2003
Summary: This document provides the statement of work for acquiring the services of a private contractor for a particular zone within NYSDOT. The document lists the nature of the services required and the quality levels expected from the contractor.
Contact: Todd Westhuis
Ohio Department of Transportation, Traffic Keeper-Ohio (TKO) Traffic Edit Guidelines, Service, Acceptance and Warranty Requirements
Summary: The former document describes the proposed traffic count editing guidelines for the Ohio DOT's new count processing software - TKO. The latter document describes the service, acceptance testing and the warranty requirements for equipment for Ohio DOT
Contact: David Gardner
Virginia Department of Transportation, Guide to Installing Road-Tubes in Virginia
Summary: A pocket guide from VDOT describing the installation of road tubes. The pamphlet discusses different configurations, settings and road tube specifications and care.
Contact: Tom Schinkel
New Jersey Department of Transportation, Traffic Monitoring Standards, January 2000
Summary: The purpose of these standards is to improve and ensure the quality of the traffic information which is used to support decisions at all levels of highway management in the state of New Jersey. These standards apply to all short-term traffic monitoring activities conducted by or for the New Jersey Department of Transportation (NJDOT).
Contact: Louis Whiteley
Gillmann, R., Status of ITS Data for HPMS, Memo for FHWA, 2002
Summary: The memo documents the status of ITS data use for HPMS by states based on a 3 question survey. Responses are available for 43 states.
Fekpe et al., Traffic Data Quality Workshop and Action Plan, prepared for U.S DOT, FHWA Office of Policy, 2003, available at the U.S DOT ITS/JPO Electronic Document Library (#13839) http://www.itsdocs.fhwa.dot.gov/JPODOCS/REPTS_TE/13839.html
Summary: The quality of the traffic data and the information produced from the data are critical factors that affect the abilities of transportation agencies to ensure the security of transportation and the management of the nation's transportation resources. The focus of data quality is on establishing a consistent methodology for ensuring that data are managed so that a measure of reliability is sustained. The report defines an action plan to address traffic data quality issues including work items that can be executed through the U.S. Department of Transportation (DOT), stakeholder organizations (e.g., American Association of State Highway Transportation Officials [AASHTO], ITS America), and state DOTs.
U.S. Department of Transportation, Office of Policy, Traffic Monitoring Guide, 2001
Summary: The TMG offers suggestions to help improve and advance current programs with a view towards the future of traffic monitoring. A basic program structure for traffic monitoring is presented. The guide provides specific examples of how statewide data collection programs should be structured, describes the analytical logic behind that structure, and provides the information highway agencies need to optimize the framework for their particular organizational, financial, and political structures.
Mergel, J., Case Studies of Traffic Monitoring Programs in Large Urban Areas, prepared by Volpe National Transportation Systems Center, July 1997
Summary: The paper documents a series of examples of urban trafficmonitoring data collection programs in order to support the development ofurban traffic monitoring databases and promote the upgrading of urban traffic monitoring programs. Examples include - Philadelphia, Portland, Minneapolis-St. Paul and the Tampa metropolitan area.
Mergel, J., An Overview of Traffic Monitoring Programs in Large Urban Areas, prepared by Volpe National Transportation Systems Center, July 1997
Summary: The document provides an overview of traffic monitoring programs with a focus on case studies, or model approaches on urban traffic monitoring data collection programs in large urban areas. This report documents the status of traffic monitoring data collection and program activities found in urbanized areas, including cost, staffing, organization, institutional arrangements, equipment used, sharing of data, uses of the data, problems encountered, etc based on a survey of local traffic data collection personnel.
Choe, T., Skabordonis, A., Variya, P., Freeway Performance Measurement System (PeMS): An operational analysis tool, for presentation and publication in the 81st TRB Annual Meeting, 2002
Summary: Performance Measurement Systems (PeMS) is a freeway performance measurement system for loop detector data for all of California. The paper describes the use of PeMS in conducting operational analysis, planning and research studies. The advantages of PeMS over conventional study approaches is demonstrated from case studies on conducting freeway operational analyses, bottleneck identification, Level of Service determination, assessment of incident impacts, and evaluation of advanced control strategies.
Contact: PEMS Website
New York State Department of Transportation, Highway Data Services Bureau, Traffic Count Editor: User Manual and System Documentation, February 2003
Summary: This document provides user and technical documentation for the Traffic Count Editor, the software program used by New York State DOT. The document also lists the business rules or the validity checks provided by the software.
Contact: Todd Westhuis
Washington Department of Transportation, Safety Zones for Traffic Monitoring, Regions: Eastern, North Central, North Western, South Central, South Western, Olympia
Summary: Washington State identified safety zone maps for installation of data collection equipment. Zones are differentiated based on crew requirements and time-of-day constraints. These zones were not identified strictly based on traffic volume but a combination of traffic and roadway characteristics.
Contact: John Rosen
Washington Department of Transportation, Short Count Factoring Guide, June 2004
Summary: This guide was created to promote good practice and uniformity in techniques being used for traffic counting and the estimation of Annual Average Daily Traffic (AADT) figures from short duration count data. It is an informational guide to encourage high standards and uniform practices among traffic counting programs for accurate representation of traffic on our public roadways is available to all interested parties.
Contact: John Rosen
California Department of Transportation, HPMS Workbook, 2002
Summary: The California Department of Transportation, Division of Transportation System Information, Highway Performance Branch, in cooperation with the U.S. Department of Transportation, Federal Highway Administration, prepared this workbook as a guide for reporting the federally mandated HPMS data.
Contact: Brian Domsic
Peter Martin et al, Detector Technology Evaluation, November 2003
Summary: This paper reports on the present status of detector technologies and on development trends in these technologies. This report designs a systematic selection method suitable for permanent applications. The selection method considers factors including data type, data accuracy (in different environmental and traffic conditions), ease of installation and calibration, costs, reliability, and maintenance. A variety of detector technologies and devices are compared. This report provides comparison matrixes based on detector technology and specific devices in this field of technology. The technology matrixes offer general information about each detector technology. The device matrixes give specific information regarding each particular detector device. Selecting an appropriate device is more important than choosing a specific technology. The matrixes must be continuously updated to reflect changes in the detector market.
Maryland State Highway Administration, Specification for Consulting Services for the collection of Manual Traffic and Portable Machine Counts and On-Site Traffic Engineering and Highway Engineering Assistance, 09/2004
Summary: The document describes the scope of work, requirements and qualifications for contractors to perform traffic counts in the state of Maryland as part of a task-order contract.
Contact: Mike Baxter
Battelle, Traffic Data Quality Measurement Framework, prepared for U.S DOT FHWA Office of Highway Policy Information, 2004, DRAFT
Summary: The report describes methods and tools to enable traffic data collectors and users to determine the quality of traffic data they are providing, sharing, and using. This report presents the framework that provides methodologies for calculating the data quality metrics for different applications and illustrates them with case study examples. The report also presents guidelines and standards for calculating data quality measures that are intended to address the following key traffic data quality issues:
California Department of Transportation, Ramp Balancing Process, Computational Worksheet
Summary: The document from Caltrans provide more information about the processing of traffic data with some sample spreadsheets used for ramp balancing by the district offices.
Contact: Joe Avis
Pennsylvania Department of Transportation, PennDOT Quality Reviews, 2002.
Summary: The PowerPoint presentation discusses Pennsylvania DOT's HPMS Quality review approach. The purposes of the review are to ascertain the current state of HPMS data quality, ensure that errors found are corrected, and identify training needs and institutional issues. The presentation provides an example of an approach to train and ensure good quality data from MPOs, city and local agencies involved in data collection and reporting
Contact: Laine Heltebridle
Florida Department of Transportation, Transportation Statistics Office, Traffic Monitoring Handbook, October 2002
Summary: The traffic monitoring handbook is Florida DOT's comprehensive document on all traffic monitoring related issues including data collection and processing guidelines. The handbook contains many useful videos about traffic monitoring.
Contact: Nabeel Akhtar
Florida Department of Transportation, Safety Video for Field Personnel
Summary: The short video provides guidance on safely installing traffic detectors.
Florida Department of Transportation, Standardization of Count and Classification equipment set-up and configuration process, prepared by PB Farradyne, 1995
Summary: The report outlines the steps needed to set-up and configure Florida DOT's traffic monitoring equipment to ensure uniform, complete and consistent outputs.
Florida Department of Transportation, Survey Processing Software (SPS) User Manual, June 2001
Summary: The report provides a user manual for Survey Processing Software (SPS), Version 3.2 which is used by Florida DOT to process short-term count data. The software was developed to provide the Florida DOT District Offices with software that can transfer data from a variety of highway traffic counters to PCs, perform standards editing, and then transfer summarized classification and count data statistics from their PC to the FDOT mainframe. The software will also download the station inventory from the mainframe to the District PC.
Chief Traffic Data and Photolog
Unit
Division of Traffic Operations
California Department of
Transportation (Caltrans)
joe.avis@dot.ca.gov
Ph: (916) 654-3072
Division of Transportation
System Information
California Department of
Transportation (Caltrans)
h: (916) 653-3272
Fax: (916) 654-6583
brian.domsic@dot.ca.gov
Florida Department of Transportation (FDOT)
nabeel.akhtar@dot.state.fl.us
Manager, Traffic Monitoring
Section
Ohio Department of Transportation
Ph: (614) 752-5740
dgardner@dot.state.oh.us
Assistant Division Chief, HISD
Database Management, Traffic
Monitoring, HPMS, Road Inventory
Maryland State Highway
Administration
Ph: (410) 545-5511
Fax: (410) 209-5033
mbaxter@sha.state.md.us
Supervisor, Travel Information
Unit
Data Collection SectionAsset Management Division
Michigan Department of Transportation (MDOT)
Ph: (517) 335-2914
walimakim@michigan.gov
Virginia DOT, Planning
Ph: (804) 225-3123
Fax: (804) 371-0190
Tom.Schinkel@VirginiaDOT.org
Highway Usage Branch Manager
Washington DOT (WsDOT)
RosenJ@wsdot.wa.gov
NYSDOT Highway Data Services
Bureau
Traffic Monitoring Section
Supervisor
Ph: (518) 457-7203
Twesthuis@dot.state.ny.us
Contracts/Agreements Administrator
Massachusetts Highway Department
Philip.hughes@MHD.state.ma.us
Ph: (617) 973-7330
Section Chief, Traffic and Technology Section
New Jersey DOT (NJDOT)
Louis.Whiteley@dot.state.nj.us.
[1] Traffic Data Quality Measurement, Battelle for FHWA, Office of Highway Policy Information, 2004
[2] The Urban Transportation Monitor. Vol. 18, No. 7, April 16, 2004.
3 Interview with Tom Schinkel, Virginia Department of Transportation for FHWA's Traffic Data Quality Workshop project, October 1, 2002
4 NYSDOT, Zone 3 Contractor Specifications, June 2003.
[5] California Department of Transportation (Caltrans), Guide for Staff to review traffic data, from Joe Avis, Chief, Traffic Data and Photolog Unit, Division of Traffic Operations.
[6] Virginia DOT, Average Daily Traffic volumes on Interstate, Arterial and Primary Routes, Glossary of Terms, 2001, available at http://www.virginiadot.org/projects/resources/(IAP)AADT.pdf
[7] Information provided by Joe Avis, Caltrans.
[8] Traffic Data Quality Measurement, Battelle for FHWA's Office of Highway Policy Information, 2004.
