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, I1 DOT 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:
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.
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.
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.
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:
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.
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 . 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 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.
Ohio DOT uses ITS data from ARTIMIS that provides the data in TMG format. ODOT also gets data from certain unused loops installed by Columbus city TMC. The data is derived from loop outputs using contact closure cards. Also, ODOT installed 44 new Roadway Weather Information Systems (RWIS) that will collect traffic data in TMG format and as well as provide real-time weather information.
Michigan DOT uses ITS data from Detroit freeways for AADT reporting. Michigan ITS (MITS) is responsible for collecting and summarizing traffic data into hourly intervals. MITS is responsible for the quality checks on the raw data. This is a relationship that has grown and been in place for the past 12 years. The ITS data also provide more control points to the ramp counting program.
Illinois uses data from toll way authorities and CATS in the Chicago area collected using a combination of loops, toll plazas etc to collect data on these high-volume roads
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.
Caltrans has a Performance Measurement System (PeMS) for the inductive loops (Choe, et al, 2002) [CD]. PeMS obtains 30-second loop detector data in real time from each Caltrans District Transportation Management Center (TMC). The data are transferred through the Caltrans wide area network (WAN) to which all districts are connected. Caltrans is working with the PeMS project team to enable transfer/sharing of data between the PeMS databases and the state highway counting program. The use of PeMS data will provide the state highway traffic-counting program with a wealth of detectors that can function either as permanent detectors or control points.
FDOT has conducted research to utilize archived ITS data for HPMS and transportation planning purposes. FDOT has developed a software system to mine ITS data from the I-4 region in Orlando. The software is used to convert the data obtained from TMC to a format usable by the quality control software (Survey Processing Software). The plan is to expand it to other TMCs. FDOT indicated that as a first step, data from ITS sources have to be available in an archive.
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.
NYSDOT uses performance-based maintenance contracts for the regular maintenance of equipment, on-call duties, and installation of new sites. These contracts are renewed annually. The scope of work for contractors provides details about the maintenance activities, turnaround times and on-time performance criteria (NYSDOT Zone 3 Contractor Specifications, 2003). [CD] The scope of work includes on-time requirements, turnaround times, and site inspection / preventive maintenance and repair visits consisting of:
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 http://www.dot.state.mn.us/guidestar/nitfinal/about.htm
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.
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