5. PLANNING APPLICATIONS

            The essence of the transportation planning process is the development of a comprehensive blueprint for the development and improvement of mass transit, highway, airport, seaport, railroad, bicycle and pedestrian facilities. Specifically, state and metropolitan planning organizations develop their blueprints by assessing their current conditions, system performance, and potential impacts of development/improvement alternatives. The assessments are based on the predicted future implications of these alternatives with regard to system capacity, travel demand, system condition, safety, economic conditions, population, and land use.

            Therefore, accurate, timely and representative data are crucial to estimating current travel demand (passenger and freight movements combined), forecasting future demand, evaluating and projecting the societal and environmental consequences of various developments and projects, and monitoring the performance of the system(s). Roadway and traffic data are the focus of this Chapter from the perspectives of archiving and using archived ITS-generated data.

5.1 STATE-OF-THE-PRACTICE REVIEW

5.1.1 Archiving and Sharing ITS-Generated Roadway/Traffic Data

            ITS-generated traffic and roadway data that are pertinent to transportation planning applications are: traffic volume, speed, and vehicle classification. Specifically, the data collected from the traffic surveillance component of Freeway Management Systems and Arterial Management Systems. Also, data used for electronic screening in the Commercial Vehicle Information Systems and Networks (CVISN) can supplement traffic surveillance data to better estimate vehicle classification data.

            One hundred and six agencies responded to the Freeway Management Survey in both 1999 and 2000. Almost one in three responding agencies in 1999 did not collect any freeway data while this percentage increased to almost 40% in 2000 (Table 5.1). Data collected from this Freeway Management survey range from traffic volume, to information related to intermodal connections. By far the most commonly collected freeway data are: traffic volume, and information on scheduled work zones (Figure 5.1). The most common technique used to collect traffic data is loop detectors, followed by video imaging detectors. Figure 5.2 depicts the prominence of different techniques used to collect traffic data. It is obvious from this figure that less intrusive technologies are becoming popular in collecting traffic data. Traffic volume and vehicle classification data are the two data elements that are most likely to be archived. Eighty-seven percent of the agencies in 1999 that collected traffic volume data also archive them. And, seventy-six percent of the agencies that collect vehicle classification data also archive them (Table 5.1). The most noteworthy observation is the fewer number of agencies that generate and archive lane occupancy data, declining from 39 agencies in the year 1999 to 6 in year 2000. We speculate that data reporting errors led to the downward trend from 1999 to 2000 in terms of fewer data elements being generated and/or archived.

Table 5.1 Number of Agencies that Generate and/or Archive Traffic Data

1999 and 2000 ITS Deployment Tracking Surveys

            *These questions were not asked in 1999

            **These questions were not asked in 2000

Figure 5.1 Data Generated and Archived from Freeway Management Systems

(Out of 74 Agencies Reported Data Generation and Archiving in 1999

Out of 66 Agencies Reported Data Generation and Archiving in 2000)

            Tables 5.2 and 5.3 show the propensity for data archiving in 1999 and 2000, with a significant downward trend. No agencies collect all seventeen data elements identified on the survey questionnaire. In 1999, one agency collected 14 of out the 17 data elements and it archived every data element collected. Overall, 31 of the 78 agencies in 1999 archived all of the traffic data collected (cells on the diagonal line) while the corresponding numbers are 19 out of 66 agencies in year 2000. Ten agencies in 1999 reportedly collected traffic data but did not archive any (the “0“ column).

* Excludes agencies that archived more data elements than they actually collected.

5.1.2 Usage and Users of the Archived Freeway Data

            Based on the tracking survey results, the media is the largest archived data requester/user, followed by state Departments of Transportation (Figure 5.3). The archived traffic data are primarily used for traffic analysis and planning (Figure 5.4). However, the specific applications of the analysis are unclear from the surveys. It is also unclear which of the users use which of the archived data and for what purpose.

5.1.3 Barriers to Data Archiving

            To avoid reporting burden, only a limited number of agencies were contacted for questions more specifically related to data archiving. For those agencies that did not archive any data collected, the reasons given were: (1) lack of need to archive, and (2) lack of necessary hardware, storage capacity, and resources (either human or monetary) to archive. Lack of articulated needs was the overarching reason for those agencies that archived part, but not all, of the data elements.

5.2 CASE STUDIES OF PLANNING APPLICATIONS

            The purpose of the case studies was to:

            ●    gain better insight into why archived ITS-generated data are, and are not, used in terms of technological and institutional situations and barriers,

            ●    identify additional opportunities for archiving and using ITS-generated data, and

            ●    identify successful practices in archiving ITS data for transportation decision-making.

Specifically, the case studies addressed the following questions:

            1.   What data do ITS deployments generate?

            2.   How are data generated and archived?

            3.   Are the ITS-generated data shared?

            4.   If so, how, with whom and for what purposes?

            5.   Were there barriers to archiving and sharing the data? If so, what were they and how were they overcome?

            It should be re-emphasized that this assessment only focuses on ITS deployments that have generated data that have the potential of being used in applications other than what was originally intended. This assessment has not attempted to identify ITS deployments that have not, but that could have, generated and archived data such as the I-95 Corridor FleetForward project, and the in-vehicle signing system for school buses at railroad-highway grade crossings. Specifically, the prerequisite for being selected to be a case study in this project was that an ITS system has to have been generating and archiving data.

            ITS projects in two metropolitan areas were examined - Atlanta and Los Angeles. The rationale for selecting these two locations was because of their data archiving activities and the level of collaboration among local agencies and jurisdictions. However, this by no means suggests that no other areas are archiving and sharing ITS-generated data as extensively as these two areas. In fact, an increasing number of agencies and researchers have been using archived ITS-generated freeway data in the past two years to monitor their system performance.

5.2.1 Georgia Department of Transportation's NaviGAtor in Atlanta

            NaviGAtor is designed to gather information from a variety of sources: a video monitoring and detection system, Highway Emergency Response Operators (HEROs) and the public. NaviGAtor links the Transportation Management Center (TMC) to the Transportation Control Centers (TCCs) of five surrounding counties (Cobb, Gwinnett, Clayton, Fulton and Dekalb), the City of Atlanta, and the Metropolitan Atlanta Rapid Transit Authority (MARTA), creating an intelligent transportation network spanning more than 220 miles.

5.2.1.1 System Description

            The current level of coverage is about 220 miles of instrumented roadway including freeway and arterial streets monitored by the central TMC and satellite TCCs. The central TMC currently has 42 miles of instrumented freeway along Interstates 75 and 85 inside the Interstate 285 beltway. Additional 14 miles of instrumentation were deployed along the northern arc of the 285 beltway in July, 2001.

            Atlanta’s NaviGAtor system (which is operated by Georgia DOT) is highly integrated with other agencies. The central TMC is currently linked to the Traffic Control Centers (TCC) of 5 other counties: Clayton, Cobb, DeKalb, Fulton, and Gwinnette counties, plus the City of Atlanta and the Metropolitan Atlanta Rapid Transit Authority (MARTA). These county and city TCCs are used to manage arterial road systems, while the NaviGAtor TMC manages the freeways. All monitored data are shared between the centers so that the arterial TCCs can see the traffic conditions on the freeways, and conversely, the freeway TMC can see traffic conditions on the arterial streets. The NaviGAtor TMC acts as the main warehouse for stored data. However, it is not clear whether the city and county TCCs archived any of their data.

Some of the agencies that are currently integrated into the system are:

            ●    Georgia Emergency Management Agency,

            ●    Atlanta City Police Department,

            ●    911,

            ●    Metropolitan Atlanta Rapid Transit Authority, and

            ●    Georgia State Patrol, which is located in the same compound as the TMC. (Although not currently connected to the system, it will be connected in the near future.)

            NaviGAtor uses the video monitoring and detection system (i.e., a camera-based system) to provide real-time images of road conditions. It serves as an incident verification tool. Installed on Interstates 75 and 85 are more than 317 fixed black-and-white Autoscope detector cameras that are spaced 1/3 of a mile apart, and 67 pan, zoom and tilt full color surveillance cameras spaced 2/3 of a mile apart. Information on average speed, traffic volume, lane occupancy and vehicle classification is collected from the Autoscope cameras. Video monitoring from the surveillance cameras allows the operators at the TMC to verify incidents, thus reducing response time, speeding up removal of incidents and minimizing congestion. The newly instrumented northern arc of I-285 added 114 additional fixed black and white cameras, and 36 color cameras. This brings the total to 103 pan, zoom, and tilt full-color cameras and 431 fixed black and white cameras.

            Of these two types of camera, only data generated from the fixed black and white Autoscope cameras are archived. The color surveillance cameras provide views of the traffic for the authorities as well as for the public at large via the Internet. No information from the color surveillance cameras is archived.

            A gyroscopic camera mounted on a helicopter is used for aerial monitoring. This aerial camera provides live video within a 50-mile radius of Atlanta, vastly increasing NaviGAtor's area of coverage.

            The County and City TCCs use primarily loop detectors for traffic signal optimization on arterial streets. There are some surveillance cameras installed in order to view incidents but no data is generated from these cameras that can be archived.

5.2.1.2 Data Archiving

            The Autoscope cameras gather data on a 20 second polling period. These data are then aggregated into 15-minute logs, which are stored onto an internal server for 30 days. Each month, the data are compressed, written and archived to a CD. When uncompressed, this system generates 4.6 megabytes of data on a daily basis.^5.1 Data have been archived for the past 4 years.

            The Autoscope camera images are interpreted by software at the TMC. Speed and vehicle counts are interpreted directly while the other data elements are derived using mathematical algorithms. The following is a list of data elements that are generated by the NaviGAtor freeway system for each lane of the freeway:

            ●    Speed,

            ●    Vehicle Counts,

            ●    Lane Occupancy,

            ●    Vehicle Classification

                  ►    Auto – Less than 25 feet,

                  ►    Light Truck – 25 to 49 feet, and

                  ►    Tractor Trailer  – ≥ 50 feet.

            ●    Headway: a measurement of the average gap between vehicles,

            ●    Flow Rate: expressed as number of vehicles per lane per hour, reflecting lane capacity,

            ●    Level of Service: Graded A through F, where A= light traffic, and F= gridlock,

            ●    Presence of stopped vehicles, and

            ●    Wrong way vehicles

Georgia DOT has the ownership of the data stored at the TMC. Of these data elements, only data on traffic volume, speed and lane occupancy are archived on a fifteen-second interval. Figure 5.5 illustrates an example of these archived data. These archived freeway data are stored in a format which facilities use for further analyses.

            Although the information disseminated by Variable Message Signs (Figure 5.6) is not archived at the present time, the availability of these data in real time on the Internet suggests that it can easily be archived. The usefulness of this information, by itself, is probably of little value. However, if integrated with sensor, roadway and weather information, it could provide valuable insights in the nature of locale-specific congestion and delay, and the effectiveness of different control strategies.

            Unfortunately, there is no convenient way for sharing archived data. When archived data are requested, Georgia DOT burns a CD that contains the data in compressed .txt format. The data are uncleaned and contain time stamps and camera ID numbers, along with their associated data elements.

        Accommodating data requests can be extremely time consuming, which has prompted Georgia DOT to consider several options:

            ●    Make the data available online for the public to query using some sort of relational database.  This idea is under serious consideration and is likely to happen in the near future.

            ●    Another option being considered is a data broker who could take the entire data management responsibility off Georgia DOT. This option is still in its infancy and faces many institutional barriers such as how to use taxpayer money to contract out to a private company. One possible solution might be a mutually beneficial arrangement where control of the data management is given to a company or an organization, but no funds are passed from Georgia DOT to the data broker. The data broker can then provide value added to the data and sell it.

5.2.1.4 Barriers and Lessons Learned

            Although freeway data are archived and shared with the planning communities, Georgia DOT speculates that the archived data have not been fully utilized because of incompatible data format and data aggregation.

            Initially, there was much concern and debate over the development of an integrated system with shared control of equipment, especially the pan, tilt, zoom, color surveillance cameras. Many were concerned that sharing the control of cameras might conflict with the different objectives of different agencies. In reality, this concern has not been substantiated. Nonetheless, a camera control hierarchy has been developed.

            Although the surveillance cameras can be controlled by many different groups in the NaviGAtor system, Atlanta City Police have priority control of these cameras. This policy intends that if there are other users on the system trying to adjust the same camera simultaneously, the system defers priority control to the City Police. Otherwise, anyone in the NaviGAtor network with a set of controls can adjust the cameras. After the City Police, the NaviGAtor TMC has priority over their freeway cameras, and the various TCCs have priority over their arterial cameras. The diagram depicting the control hierarchy is illustrated in Figure 5.7.

            Data archiving and sharing of the archived data transpire despite the fact that ADUS has yet to be part of the NaviGAtor’s system architecture. At the time of the interview, a plan was underway to use NaviGAtor data to meet reporting needs required by the Highway Performance Monitoring System (HPMS).

            The following case studies focus on traffic operations and transportation planning in the Los Angeles metropolitan area. Experience and plans in archiving ITS-generated data and using the archived data in three agencies were analyzed: (1) the City of Los Angeles’ Department of Transportation (LADOT); (2) Los Angeles Metropolitan Transportation Agency (LAMTA), and (3) California Department of Transportation (CalTrans). The rationales to include these agencies in our case study are that: (1) they each appear to have different perspectives on data archiving and sharing data, and (2) there is, or appears to be, an ongoing plan to eventually integrate almost all ITS systems in the southern California areas into an integrated system. The extent of archiving data and use of archived data varies from one agency to the next. For example, although there were plans to implement ADUS, little ADUS was actually implemented in LAMTA.^5.2 Nonetheless, there was consensus among all agencies visited that ADUS has significant benefits.

5.2.2 Automated Traffic Surveillance and Control (ATSAC) System

5.2.2.1 System Description

            The City of Los Angeles Department of Transportation (LADOT) has centralized authority over the planning and operation of the City's street system - which consists of 6,400 street miles, 1,400 major and secondary miles, 5,000 collector and local miles, 40,000 intersections and 160 freeway miles. Among its many other responsibilities, LADOT is responsible for the installation and maintenance of traffic signals, and other traffic control devices; and administers the City's transit programs. LADOT develops and uses the Automated Traffic Surveillance and Control (ATSAC) System to optimize the capacity of its existing highway system by reducing delay and minimizing traffic congestion. It is estimated that ATSAC reduces travel time of recurrent traffic by 12%, intersection delay by 32%, and intersection stops by 30%.^5.3

            The Automated Traffic Surveillance and Control (ATSAC) system, a computer-based traffic signal control system, is used by the City of LA to optimize the capacity of its existing arterial systems (Figure5.8). ATSAC monitors traffic conditions and system performance. Loop detectors imbedded in the arterial roads detect the passage of vehicles, vehicle speed, and the level of congestion. If required, the signal timing is either automatically changed by the ATSAC computers or manually changed by the operator to achieve better traffic flow.

            To date, ATSAC operates 11,000 loop detectors and has been deployed in 2,449 of the 4,285 signalized intersections. Funding has been committed to instrument additional intersections, leaving only 25% of the city’s intersections without instrumentation. To supplement the information from loop detectors, closed-circuit television (CCTV) surveillance equipment has been installed at critical locations throughout the city. At the time of the site visit (February 2000), there were 150 CCTV installed.

ATSAC-Generated Data

            Loop detectors generate data on vehicle counts, speed, and lane occupancy. Real-time traffic conditions are posted on the web.^5.4 Congestion level is determined by speed thresholds. For example, congestion is considered “heavy” when the average vehicle speed fails below 10 miles per hour (mph), “moderate” between 10 and 20 mph, and “none” above 20 mph. No data are generated from the CCTVs. Although no incident information is generated from ATSAC, the California Highway Patrol (CHP) posts incident information on its’ Traffic Incident Information Page.^5.5 This page contains all incidents to which CHP responded. Hot spots, the most serious incidents, are identified on real-time basis. Information generated for each incident includes: the area, type (e.g. “Traffic Collision-Ambulance Responding”), location, time, additional details, and CHP’s responses of the incident. Figure 5.9 illustrates a few examples of the incident report. These incidents are geo-coded on a GIS platform (Figure 5.10). Although incident data are not archived at the present time, it could easily be done especially with the “text” format of the data.

Figure 5.9 Examples of Incident Report Posted on the Web

Figure 5.10 An Example of CHP Traffic Incident Web Page

Source: http://cad.chp.ca.gov/

5.2.2.2 Archiving, Sharing and Uses of Archived ATSAC Data

            Traffic data generated from ATSAC are archived only for one week. The system automatically over-writes the old data after a week. Requests on archived data are addressed on an individual basis. For example, Southern California Association of Governments (SCAG) uses ATSAC’s traffic volume information to validate results from its travel forecast models. A contract was put in place for LADOT to archive and reformat the traffic data.

            In addition to outside users (e.g., SCAG), LADOT uses the archived ATSAC data to identify typical traffic patterns (“signatures”). Traffic surveillance data collected from surface streets impose a challenge different from those brought about by the freeway traffic data. Unlike freeway systems, no loop detectors are placed on curve lanes, thereby underestimating traffic volume. Although not a barrier to archiving traffic data, this is definitely a challenge for the proper use of the data.

5.2.2.3 Barriers, Lessons Learned, and Potential of Archived ATSAC-Generated Data

            ATSAC officials believe that any long-term data archiving needs first to overcome storage limitations. To date, ATSAC data amount to 76MB per day. Their second concern about data archiving is the challenge to make data accessible in a reasonable time frame. To make ATSAC more useful for planning purposes, ATSAC data should be integrated with data from other sources (e.g., weather).

            Although little is occurring right now at ATSAC on data archiving and data sharing, the potential of the archived data is considerable. For example, the LADOT, in collaboration with the Los Angeles County Metropolitan Transportation Authority (MTA), is implementing an advanced transit project - the Transit Priority System (TPS).^5.6 This project is designed to improve the on-time performance of metro buses by adjusting the signal timing at intersections for buses as they approach the intersection. A transponder mounted underneath a bus communicates with the ATSAC loop detectors imbedded in the roadway. Traffic signal priority is provided to a particular bus only if the bus is running behind schedule. If data generated from this project are archived, they can provide the information needed to develop plans for transit operations without impacting street traffic.

5.2.3 CalTrans District 7

5.2.3.1 System Description

            CalTrans District 7 is the state transportation department that constructs and maintains the California state highway and freeway system in Los Angeles and Ventura Counties (Figure 5.11). Specifically, it manages 909 freeway and highway miles in LA County, and 273 freeway and highway miles in Ventura County. On a typical day, these miles carry an average of 90 million vehicle miles traveled (VMT) per day.

            Loop detectors are installed every ½ mile. Data on V(olume), O(ccupancy), and S(peed) are generated. The 30-second data are archived for thirteen months. In addition to the loop detectors, more than 20,000 CCTVs are installed. However, no video data are archived.

5.2.3.2 Archiving, Sharing and Uses of Archived Data

            Lane-specific VOS data that are archived on a stand-alone server are accessible by University of California, Berkeley to perform the California Transportation System Performance Measures Project (PeMs).^5.7 This project assists Caltrans Traffic Operations and Traffic Operational Centers to manage traffic operations. Caltrans’ primary goal in using these archived data is to measure the performance of the freeway system and to identify recurrent congestion. Parameters such as Vehicle Miles Traveled (VMT) and Vehicle Hours Traveled (VHT) are calculated based on data collected from each loop detector. These are archived in PATH’s website.

            Furthermore, CalTrans uses archived traffic volume data for special event planning, and archived speed profiles for incident detection. The latter is still somewhat ad hoc. CalTrans staff recommend the use of Common Object Request Broker Architecture (CORBA) to facilitate data sharing. No video images are archived due to the privacy concern. Archived ITS-generated data could be more widely used and many parts of the agency could benefit from ADUS.

5.2.4 Michigan ITS Center and Michigan Department of Transportation

5.2.4.1 System Description

            The Michigan Intelligent Transportation Systems Center (MITS Center) oversees a traffic monitoring system that covers 180 miles of Detroit area freeways. The daily traffic volumes on this system range from 80,000 to 190,000 vehicles on 6 to 12 lane facilities. These conditions make mainline traffic counting nearly impossible.

            This system includes 24 television monitors, 155 CCTV cameras, 57 changeable message signs, 60 ramp metering locations, 2,600 inductive vehicle detectors, and a communications system consisting of fiber optics, microwave, spread spectrum radio, and coaxial cable.

5.2.4.2 Data Archived

            About half of the vehicle detectors are at one-third mile spacing with one detector for each lane. The remaining detectors were installed with 2 detectors for each lane with a two-mile spacing. Data collected these detectors include: traffic volume, speed, lane occupancy percentages, and equipment failure rates. These data are collected in minute increments for each lane and archived by the Center. Lane-specific volume data are then summarized into hourly totals. This information for each location is then consolidated into a daily spreadsheet file which is electronically transmitted to Transportation Planning Bureau on a monthly basis.

5.2.4.3 Use of Archived Data

            The Transportation Planning Bureau uses the archived volume data to produce Average Annual Daily Traffic (AADT) estimates for the Detroit area. Data are also used to analyze trends for future planning.

5.2.5 Other ADUS Applications

            In addition to the aforementioned case studies, there are currently a significant number of ADUS planning applications. For example, archived traffic volume data are generated by Maryland’s Department of Public Works and Transportation. These data are then archived and used by Maryland’s Department of Park and Planning for traffic modeling to forecast future traffic volumes. Furthermore, these data are also being used to produce network volume maps, model calibration databases, and alternative network testing. Another example is the Mitretek work that extracts real-time traffic condition data from various websites every five minutes. It uses these data to assess the on-time reliability impacts of Advanced Traveler Information Services (ATIS). Finally, researchers at the Washington State Transportation Center use data archived from multiple sources (e.g., CVISN truck tags, GPS devices, freeway loops, other traffic counters, and transit vehicle information) to create a complete picture of congestion and of its effects on trucking movements in the Seattle metropolitan area. It should be emphasized that these examples are not by any means an exhaustive list. .

5.3 BARRIERS AND SOLUTIONS

            The institutional and other barriers associated with implementing ADUS are largely the same among the different ITS applications. These common barriers such as cost, proprietary rights, data issues, politics, and a lack of understanding or knowledge about ADUS are covered in further detail in Chapter 2.

            Beyond these common barriers, the archiving of roadway and traffic data is being held back by a difference in perspective between data producers (in this case, the TMCs) and data users (in this case, the planners). People working in traffic management centers are forced to think in terms of hours and minutes, while metropolitan planners think in terms of months and years. This difference in perspective makes it more difficult to implement ADUS planning applications. One possible solution to bridge this gap is to demonstrate the value of using archived data to improve operations. For example, an analysis of historical traffic patterns and signal timing can help develop proactive signal timing strategies that are sensitive to the temporal patterns of the traffic (detailed in Section 4.4). A number of new initiatives by the Federal Highway Administration have recognized the need for additional data to improve operations.

5.4 POTENTIAL OF ARCHIVED DATA

            The potential is immense for using archived data to meet planning data needs. However, it has been emphasized in the literature that the greatest benefit of ITS-generated data is perhaps when they are integrated with traditional data, for example, to broaden geographic coverage and to increase information reliability. An example of such data integration is the Washington State Transportation Center’s study on measuring truck performance. That said, the question of how to integrate archived data and traditional data becomes considerably more imperative than the questions of how to archive data and how to use the archived data. Our attempt to identify potential uses for archived data does not deal with the challenges of data integration. As such, our assessments could appear to be overly optimistic and premature at this point (Table 5.4). Also included in Table 5.4 are the potential of archived data to meet federal reporting requirements.

            In addition to the general potential opportunities outlined in Table 5.4, more specific ones are identified. These opportunities are, or appear to be, practically feasible, can be quickly deployed, and are most likely to produce immediate benefits/results. The rationale for identifying these “low-hanging fruits” is that the sooner that quantifiable benefits of using ITS-generated data for operations improvement are demonstrated and disseminated, the sooner additional deployments will be stimulated.

Table 5.4 Potential of Archived Freeway and Arterial Traffic Data

5.4.1 ADUS for Urban Travel

            Monitoring urban travel has been an immense challenge. Our previous study has demonstrated two specific benefits of using the archived ITS-generated traffic data in helping address this challenge.^5.8 First, ITS data can supplement the traditional data so that more reliable traffic estimates can be developed. Second, ITS data can be used to calculate adjustment factors that are more reliable than those calculated based on a limited number of continuous counters. Based on these findings, it is conceivable that the ITS-generated traffic counts data can significantly improve the traffic monitoring programs in urban areas.

5.4.2 ADUS for Data Reporting

            Many of the planning organizations have the responsibility of meeting mandatory data reporting requirements. The task could be extremely tedious and sometime suffers from the lack of sufficient data. The ability of ADUS in helping meet these requirements seems obvious. However, the success of accomplishing this task depends on the integration of ITS generated data and non-ITS generated data, the extraction of needed information from ITS deployments, and the conversion of this extracted information to conform to existing software and reporting formats.

5.4.3 ADUS for Spatial Movements of Travel Demand

            The assignment of both passenger and freight movements to transportation networks is an essential element of the transportation planning process. It helps evaluate the social, environmental, and economic impacts of various policies and programs. Information on the trip origins and destinations has been extremely difficult to collect. Data from the on-board GPS units in conjunction with the archived traffic surveillance data and electronic toll data have the potential to meet this data need. That said, a feasibility study is imperative.

5.4.4 ADUS for Performance Measures

            The interest in the development and use of performance measures and performance-based planning and program development has increased dramatically to meet customer needs under different conditions. Performance measures have been used at several levels, ranging from day-to-day operations to long-term capital planning that enhances system operations. Link travel times, duration of congestion, reliability, level of service (LOS), seasonal road closures, recurring and non-recurring delays are some of the typical performance measures used to improve highway operation performance. As part of the rationales for a NCHRP report,^5.9 “...evaluating and improving system operations through performance measures can be challenging. Data collection and analysis demands can be overwhelming....” Archived ITS-generated data on traffic, speed, incidents, weather, and work zone schedules offer unique opportunities to fill this data collection concern. However, it should be emphasized that ADUS can best be used in developing facility-specific performance measures due to the current limited number of traffic surveillance deployments.

ENDNOTES:

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