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- 5.3.1 Project 10 – UTDF
- 5.4 Intersection Performance Evaluation
- 5.4.1 Project 11 – External Intersection Performance Evaluation
- 5.4.2 Project 12 – Internal Intersection Performance Evaluation
- 6 Conclusions
- 6.1 Extended Signal Timing Manual (Project 8)
- 6.2 Short Count Procedures (Project 1)
- 6.3 Estimate Turning Movements from Detectors (Project 3)
5.3 Data StructureWhile the development of NTCIP in large part has been a task spearheaded by the public sector, there have been other developments in the private section that provide a common denominator among the various simulation and optimization programs. One of the most important of these is the Universal Traffic Data Format currently used by the Trafficware Corporation. This significant recent development that not only has expedited data input to the models; but also has facilitated transferring the optimized results to the traffic control systems. The Universal Traffic Data Format (UTDF) is an open standard data format specification for traffic signal and traffic related data for intersections that has been developed and promoted by Trafficware, the developers of Synchro and SimTraffic. UTDF can be used to efficiently transfer data between traffic software packages. UTDF can also be used to share data between software and traffic signal controller hardware. UTDF contains the ability to store multiple volume counts and timing plans for multiple intersections. This allows for a structured method of storing large amounts of traffic data, and a significant reduction in data entry of signal timing parameters. UTDF allows data to be shared between otherwise incompatible software packages. It is anticipated that many software developers will support UTDF. In this scenario data is entered once and then used by all the software together. It is possible for planning departments to store traffic counts for various scenarios and use them for capacity analysis as well as other purposes. With UTDF-compatible software it could be possible for planners to completely automate traffic impact studies for future development and roadway improvements. Text files are easy for end users to edit with any text editor such as Windows NotepadTM. The column-aligned format is provided for compatibility with Turning Movement Count (TMC) files and for easy editing with text editors. The comma-delimited text files (CSV) can also easily be viewed and edited by spreadsheets such or Microsoft Excel. The user or software developer is free to choose the most convenient format. All of these systems support multiple traffic signal plans that can be called by time of day and by traffic flow measures. All of these systems support the capability of measuring the traffic flow rates from sensors installed in or over the roadway. By combining these two features with the interface to Synchro, one can claim a true “closed loop” system. It works like this. Data are collected for a particular period by the system. These data are then electronically transferred to Synchro using the UTDF format. Synchro is executed and optimum timing parameters are generated. These parameters are converted to system input parameters and are electronically transferred from Synchro back to the traffic control system. This flow of information from the street, to the optimization model, back to the system is called 1 ½ Generation Traffic Control. This capability is available with most system currently deployed. Although this capability exists, it is not often used. One reason is that few systems have enough instrumentation to actually derive new timing plan data. Another reason is that although the capability is inherent in the system design, few vendors are promoting this capability. While there is considerable promise to improve the signal timing process in this general area of parameter conversion, the most significant advances have been made by the private sector responding to competitive pressures. This area is very difficult to address because it is basically a linkage between two packages that are in the private sector, Synchro and QuicNet/4, for example. There are other examples that we could cite that are comprised of a linkage between a public sector program (Transyt-7F or Passer II) and a private sector system, ACTRA for example. Perhaps the best contribution to be made in this area is to support training programs that encourage better use of the capabilities of systems. 5.3.1 Project 10 – UTDFThe objective of this proposed project are two-fold: to investigate the use of UTDF to support developing controllers based on using NTCIP and the results of the ATC program, and to investigate the use of UTDF to support existing controllers, at least those currently deployed in significant numbers throughout the United States. 5.4 Intersection Performance EvaluationThere are no existing manual or automatic tools available for use by the Traffic Engineer to evaluate the performance of a signalized intersection in real-time. The Engineer can stand on the corner and observe, or the Engineer can estimate the performance using one of the simulation tools available. “Controller in the Loop” simulation is one approach that has emerged in recent years that helps to bridge the gap between the real-time world and the simulation world. With this approach the software simulation model generates vehicles, which activate simulated detector calls that are sent to the controller. The controller then uses this information to decide the signal phase of the intersection and sends this information back to the software model. The software model displays the current signals on the screen along with the vehicles in the network, which stop and go according to the signal phase. Meanwhile the software calculates MOE's such as vehicle delay time, queue measurements, speed, and volume. Once the real-time simulation is completed, MOE data compiled by the simulation software can be analyzed. In recent years, microscopic traffic simulation has become an integral part of transportation and traffic planning, evaluation, and research. This technology has advanced greatly over the past decade but there remains a gap between traffic simulation and real traffic operation. Software-generated traffic simulations can never replicate real traffic conditions exactly. A clear reason for this inaccuracy is that the emulated traffic signal control logic in the simulation model in many cases is unable to replicate real traffic signal control exactly. The concern, therefore, is to be able to refine existing methods, or develop new methods to evaluate intersection performance in real-time. Two projects address this issue. One considers the problem from the perspective of evaluating the intersection performance from an external perspective. This is, an observer (or machine) would measure performance independently of the intersection controller. The second considers the problem from an internal perspective. The intersection performance would be evaluated using data that is (or could be) available to the controller. 5.4.1 Project 11 – External Intersection Performance EvaluationThe criterion used initially to diagnose the problem is arbitrary and relies on the experience of the Signal Timing Engineer to make the correct decision to rectify the problem. There is a need to better define the diagnostic process to enable a more consistent performance in determining the extent of the problem. This need extends not only to the initial identification of the problem, but also to the evaluation of the adjustments made to solve the problem. Once the adjustments are completed, the existing process still relies on the experience of the Signal Timing Engineer to judge that the adjustments are an improvement (“Looks OK”). The need is to formalize this evaluation to enable a more consistent performance by non-expert personnel. One approach would be to extend the Expert System approach defined in Project 9 to include the evaluation phase. Another approach would be to identify specific points in the signal timing process where objective criterion can be employed to reduce the subjectivity to a minimum. This improvement requires clearly defined steps that are performed manually (adjust and observe), so that new practitioners have a set of guidelines to follow. This improvement would focus on the documentation (recording timing plan changes) and determine ways to improve this activity. 5.4.2 Project 12 – Internal Intersection Performance EvaluationAs noted above, evaluating intersection performance is more often than not very arbitrary. What looks OK to one engineer may very well not look OK to another. One feasible alternative way to evaluate signal timing performance is simulation. While most simulation models provide the same measures of effectiveness, their values and interpretation frequently differ from model to model given identical inputs. This is not an unexpected result since the models use different assumptions and different algorithms to derive the estimates. During the last few years, researchers have compared the models to each other and to ground truth to try to determine which provides the most accurate estimates. Mystkowski and Khan18 compared the queue length estimates based on several models and field results. The models considered were CORSIM, version 4.01; Passer II-90, version 2.0; Synchro, version 3.0; SIGNAL94, version 1.22; Transyt-7F. This paper documented the methods used to estimate queue lengths and provides clarification on the definitions used for the different models. Seeking new measures of effectiveness to be able to accurately evaluate intersection performance is another goal of many researchers. Husch’s Intersection Capacity Utilization19 is one such measure. The Intersection Capacity Utilization provides a straightforward method to calculate an intersection's level of service. The method simply compares a sum of the critical movement’s volume to saturation flow rates, based on minimum green time required for each movement. In general, the trend in recent years is to use simulation to evaluate intersection performance. For example, Transyt-7F can be used to generate optimum signal settings. Transyt-7F can also be used to evaluate existing signal settings. The model can be executed with the signal settings frozen and it will produce measures of effectiveness based on the existing settings. The model can be executed again and allowed to seek an optimum. The measures of effectiveness from the optimized settings can be compared to the measures of effectiveness from the original settings to get a quantified estimate of the probable improvement. This, however, requires a lot of work, generally more than the typical engineer is willing to do to retime a traffic signal. While simulation offers some hope, even with the controller in the loop, it still leaves a lot to be desired. This project offers a slightly different approach. The focus in this proposed project is to carefully examine the data that is available at the controller to determine if a method can be developed that could automatically and continuously evaluate the performance of an intersection using the information available at the local controller. This information includes: the duration of the signal phase (traffic movement); the demand as measured by the detector(s) for that phase; the cycle length; demand on competing phases; the time of day and day of week; and additional detector measures (occupancy, speed). This effort extends the scope of this study into the real-time control arena; if successful, the analysis would likely be carried to fruition by a different agency. However, this project could provide the initial analysis and examination to form the foundation for the efforts that would follow. The product of this project is an analysis of how intersection performance can be objectively analyzed using data that are available to the local controller. Implicit is the need for the analysis (algorithm) to be one that could be implemented in an intersection controller. Ideally, it would be simple enough that it could be implemented in legacy controllers 6 ConclusionsThe initial conclusion that may be drawn as a result of this effort is that there has been much progress during the last several decades in the area of traffic signal timing optimization. This has resulted in providing the Traffic Engineer with several very powerful alternatives to use to optimize signal settings. Because of this progress, it was concluded that the proposed research efforts should concentrate on areas other than optimization. As noted in the previous section, 12 projects were identified that offered the potential to improve the traffic signal timing process. Obviously, some of these projects offer more promise than others. The three projects in priority order that offer the most potential are note below with a brief discussion of the selection. 6.1 Extended Signal Timing Manual (Project 8)When we evaluated the literature with respect to the overall signal timing process, we were surprised when we found that there was no nationally accepted document that described that entire signal timing process. Several states produce a signal timing manual that defines the suggested approach for that state. We feel that this project is a high priority since many Cities and States could benefit from a well-written, well-illustrated, Signal Timing Manual. We feel that the Manual should contain not only the signal timing procedures, but also the evaluation procedures that we described in Project 9 – Signal Timing Field Adjustment Techniques. This combination would place the relevant information concerning the signal timing process in one document. 6.2 Short Count Procedures (Project 1)We selected this project as another high priority, second only to the development of the Signal Timing Manual. Virtually all discussions regarding signal timing eventually evolve into a discussion of the costs related to collecting turning movement data. One obvious way to minimize this cost is to minimize the time required to obtain this data. This is the purpose of this project. The objective of this project is to develop and prove the optimum technique to obtain estimates of peak period traffic flows using short-term observations. The specific techniques would be based on procedures that can be followed by a single person to obtain accurate estimates of all intersection movements. A critical issue is to determine how many approaches a single person can observe simultaneously. Obviously, at low volume intersections, a single observer can count all traffic movements. At high volume intersections, this is not possible. The developed procedure, therefore, must allow for a single observer to count one or more traffic movements in sequence. 6.3 Estimate Turning Movements from Detectors (Project 3)This project is closely related to the Short Count Project since it also addresses the issue of data collection. All current signal systems have the ability to capture detector data, and many systems have the ability to export these data to optimization programs and import the resulting timing data. However, there is not defined process (supported by research) that describes the detector-to-signal parameter transformation.A research conducted by Martin developed and evaluated a model, Turning Movement Estimation in Real Time (TMERT), that infers unknown traffic flows (intersection turning movements) from measured volumes in sparsely detectorized networks. The model has shown its ability to accurately estimate turning movements. This project would expand on the work conducted by Martin et. al. and determine if the process can be simplified from a complex Linear Programming research model, to a practical application that can be interfaced to systems typically deployed in the United States. 1 “Survey of Traffic Software Use” www.trafficware.com/survey2000.htm, Trafficware Corporation, June 2000. 2 MnDOT Traffic Signal Timing and Coordination Manual, June 2002. 3 Webster, F.V., “Traffic Signal Settings Road Research Paper Number 39, Scientific and Industrial Research, HMSO, London, 1958. 4 Newell, G.F. and Osuma, E.E., “Properties of Vehicle Actuated Signals,” Transportation Science, Volume 3, Number 2, May 1969. 5 Staunton, M.M., “Vehicle Actuated Signal Controls for Isolated Locations,” The National Institute for Planning and Construction Research, No. RT-159, Dublin, Ireland, 1976. 6 Skabardonis, A., “Determination of Timings in Signal Systems With Traffic-Actuated Controllers”, Transportation Research Board, 1996. 7 Orcutt, FL., “Understanding Controller Settings”, ITE Journal, June 1995. 8 Change, EC-P, “Guidelines for Actuated Controllers in Coordinated Systems”, Transportation Research Board, 1996. 9 Bonneson, J. Lee, S., “Actuated Controller Settings for the Diamond Interchange with Three-Phase Operation”, Texas Transportation Institute, September 2000. 10 Mystkowski, C. Khan, S., “Estimating Queue Lengths Using SIGNAL94, SYNCHRO3, TRANSYT-7F, PASSER II-90, and CORSIM”. November 1998. 11 Husch, D. “Intersection Capacity Utilization 2000: A Procedure for Evaluating Signilized Intersections”, Trafficware Corporation, 2000. 12 Gerken, Jeff, “A practical Approach to Management Traffic Data for Large Scale Studies”, Mid-Continent Transportation Symposium, May, 2000. 13 Martin, P., “Turning Movement Estimates”, ITS-IDEA Project 53; Final Report, June 2000. 14 Nihan, NL and Davis, GA, “Application of Prediction-Error Minimization and Maximum Likelihood to Estimate Intersection O-D Matrices From Traffic Counts”, Transportation Science, May 1989. 15 Bass, PL., Williams, TA. Dresser, GB, “Tranplan Corridor Analysis: Procedures Guide. Final Report”, FHWA/TX-95/1235-16F; Report 1235-16F; TTI: 0-1235, August 1994. 16 Davis, FA. Lan, C-J., “Estimating Intersection Turning Movement Proportions From Less-Than-Complete Sets of Traffic Counts”, Transportation Research Record. June 1995. 17 Martin, P., et.al. “Turning Movement Estimates”, ITS-IDEA Project 53; Final Report, June 2000. 18 Mystkowski, C. Khan, S., “Estimating Queue Lengths Using SIGNAL94, SYNCHRO3, TRANSYT-7F, PASSER II-90, and CORSIM”. November 1998. 19 Husch, D. “Intersection Capacity Utilization 2000: A Procedure for Evaluating Signilized Intersections”, Trafficware Corporation, 2000. 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