Measures of Effectiveness and Validation Guidance for Adaptive Signal Control Technologies
Chapter 9. Annotated Bibliography of Representative ASCT Evaluation Studies
Alphabetical Listing by Primary Author or Agency
CCS Planning and Engineering, El Camino Real (Route 82) Adaptive Traffic Signal Coordination Project, City of Menlo Park, California, Draft Final Report dated August 29, 2003.
Study report focused on SCATS deployment in Menlo Park, California. Performance measures were stops, delay, and average speed.
Chatila, H., and Zehn, L., U.S. 95 ACS-Lite System Evaluation, Memorandum to Idaho Transportation Department, The Transpo Group, November 29, 2007.
This study conducted simulation analysis of ACS-LITE using VISSIM. The primary performance criterion was arterial travel time.
Dowling, R., Guide on the Consistent Application of Traffic Analysis Tools and Methods. November 2011. Report number FHWA-HRT-11-064, U.S. Department of Transportation, Federal Highway Administration, Washington, D.C., 2011.
Fehon, K., and Peters, J., Adaptive Traffic Signals, Comparison and Case Studies, Paper presented at the ITE Western District Annual Meeting in San Francisco, California, 2010.
This paper compared and contrasted the systems that have been successfully installed in the USA and discussed their advantages and disadvantages. The discussion includes several case studies in which the authors investigated the options available to agencies, discusses the reasons why different systems were selected for installation, and presents the results of detailed evaluations of the effectiveness of each installed system. The paper also describes the evaluation techniques that were used to provide statistically reliable evaluation results, overcoming the shortcomings that are often found in traffic engineering surveys.
Fehon, K., Adaptive Traffic Signals: Are We Missing The Boat? Paper presented at the ITE District 6 Annual Meeting, 2004.
This paper examines the reasons why the existing successful systems have failed to gain acceptance, why the newly developed systems have not yet been successfully demonstrated, and questions the validity of the reasons commonly put forward for this situation.
Gord, A., Adaptive Versus Traditional Traffic Control Systems, A Field-Based Comparative Assessment, Evaluation White Paper as part of the Pinellas Countywide ATMS Project, Gord & Associates, Inc., Financial Project ID: 408419-1-32-01, March 2007.
This study evaluated OPAC on U.S. 19 and RHODES on SR 60. The performance measures were: safety, mobility, efficiency, energy, and productivity. Mobility was measured using travel time, stops, speed and delay. Travel time, stops, speed and delay were collected with the use of GPS. Efficiency was measured as throughput. Energy was measured as fuel emissions, and productivity was measured as fuel and control delay. Extensive use of tables and charts.
Martin, P., Stevanovic, A, Vladisavljevic, I. Adaptive Signal Control IV, Evaluation of the Adaptive Traffic Control System in Park City, Utah. University Transportation Centers Program, 2006.
The goal of the project was to assess the effectiveness of the future UDOT ATCS relative to existing traffic control system, and to transfer ATCS expertise from the Vendor to UDOT staff. MOEs were stopped delay, corridor travel time, average speed, stops, cycle length, and traffic demand.
Data collection was stopped-time delay studies on 12 intersections. Turning movement counts were used to relate relevant stopped delay studies with concurrent traffic demand, and input in simulation. The CORSIM and VISSIM simulation models were connected to SCOOT.
Research objectives were to define the traffic efficiency criteria that Utah’s first ATCS should meet, define MOE’s to assess the criteria, conduct a before and after study, and “with” and “without” evaluations, and to assess operator’s acceptance of ATCS technology. The “with” and “without” evaluation used field data measurement (intersection delay and travel time), and network simulation.
Nandam, K.L., and Hess, D., Dynamic Change of Left Turn Phase Sequence between Time-of-Day Patterns – Operational and Safety Impacts. Institute of Transportation Engineers 2000 Annual Conference, Nashville, Tennessee, August 2000.
This report used vehicle stops, delay, and travel time as the primary performance measure, but also looked at crash data. The result of the study was that changing signal phasing by time of day did not affect safety.
NCHRP – National Cooperative Highway Research Program. Quantifying Congestion. Volume 1 – Final Report. NCHRP 398. Transportation Research Board, Washington, D.C., 1997.
Pesti, G., Byrd, P.S., Kruse, M., and McCoy, P.T., Demonstration of State-of-the-Art Integrated Traffic Management System: Evaluation of the SPOT Adaptive Traffic Control System, University of Nebraska-Lincoln, Report No. 97068, May 1999.
This study evaluated the effectiveness of the PEEK SPOT adaptive system on an arterial street that experienced large, sudden, and unpredictable changes in traffic flow in Omaha, NE. Performance measures studied were: stopped-time delay and travel-time delay conducted in a “before and after” condition. The study showed that the SPOT system increased travel time, delay, and stops. The report contains several valuable examples of performance visualization approaches.
Peters, J., McCoy, J., Bertini, R., Evaluating an Adaptive Signal Control System in Gresham, ITE District 6 Annual Meeting, Portland, Oregon, July 2007.
A limited study of the SCATS deployment in Gresham, Oregon which looked at travel time, stops, delay and fuel consumption. The goal of the deployment was to reduce travel time through the corridor and implement a user-friendly system. By all accounts the authors report a successful project; although there was limited field data reported. The report was premature in that data collection was underway at the time of reporting. No follow up study results could be located.
Robertson, H.D., J.E. Hummer, and D.C. Nelson. Manual of Transportation Engineering Studies. Institute of Transportation Engineers. Prentice-Hall, Englewood Cliffs, New Jersey. 1994.
This manual provides the basis for conducting travel time delay studies.
Selinger, M., and Schmidt, L., Adaptive Traffic Control Systems in the United States: Updated Summary and Comparison. HDR, 2010.
Survey findings indicate that the definition of Adaptive traffic control is: “an advanced traffic signal control system that updates traffic signal timing in some automated way.” Operational objectives were to improve: arterial travel time, arterial delay, number of stops, intersection delay, queue lengths, and average speed.
Smaglik, E., Bullock, D., Gettman, D., Day, C., Premachandra, H., Comparison of Alternative Real-Time Performance Measures for Measuring Signal Phase Utilization and Identifying Oversaturation. TRB Paper 11-0931. Presented at the 89th Annual Meeting of the Transportation Research Board, Washington, D.C., 2011.
This study analyzed the difference between calculated GOR and V/C ratios over different detection zone lengths and differing vehicle speeds, compares these values to a calculated delay metric, and observes the effectiveness of GOR as an indicator of oversaturation.
Smaglik, E., Bullock, D., and Sharma, A., Pilot Study on Real-Time Calculation of Arrival Type for Assessment of Arterial Performance, Published in the Journal of Transportation Engineering (July 2007), 133(7): 415-422.
Good study that identified that a tool is needed to quantify progression quality. The preferred tool would use phase status and time-stamped vehicle arrivals to determine arrival types. Common traffic controllers currently bin data in sizes between 5- to 10-minute intervals, but there is no consistency. This is an easy metric to calculate but the authors identify that automated means to collect the data are needed.
Stevanovic, A., Kergaye, C., and Martin, P., SCOOT and SCATS: A Closer Look into Their Operations. Presented at the 88th Annual Meeting of the Transportation Research Board, Washington, D.C., 2009.
This study compared SCOOT and SCATS signal timing using VISSIM. The study of the SCATS system used real-world data; whereas the SCOOT data was derived through the use of simulation. Study looked at cycle lengths, offsets, and splits and compared those factors to segment performance measures of total intersection delay, throughput, and quality of progression between adjacent intersections. The evaluation was limited to pairs of intersections.
Stevanovic, A., Stevanovic, J., Jolovic, D., Nallamothu, V., Retiming Traffic Signals to Minimize Surrogate Safety Measures on Signalized Road Networks. Presented at the 91st Annual Meeting of the Transportation Research Board, Washington, D.C., 2012.
Study used VISSIM, SSAM, and VISGAOST for optimizing signal timing to reduce surrogate safety measures and thus reduce risk of potential real-world crashes.
Tarnoff, P. (2009a) Blue Sky Thinking in Thinking Highways, Vol. 4 No. 1.
Tarnoff, P. (2009b) Made to Measure in Traffic Technology International, April/May 2009.
Volling, M.T., Arterial Travel Time Using Magnetic Signature Reidentification Theory of Application and ITS deployments in San Diego, Presentation at ITS America, 2009.
Tindale-Oliver & Associates, Inc., Martin County Advanced Traffic Management Systems (ATMS) Assessment. Final Report prepared for Martin County, Florida, 2008.
The purpose of this project was to estimate the benefits and costs associated with deploying an ATMS for the Martin County region. Study looked at deployment on five corridors. Existing condition was MarcNX closed-loop system. Report identified the benefits of the ACS-Lite installation in Bradenton, Florida, SCATS in Broward County and Pasco, Florida, SCOOT in Collier County, and RHODES and OPAC in Pinellas County, Florida. The study considered delay, stops and travel time performance measures. The study was focused on MOEs and cost of deployment.
TJKM Transportation Consultants, Evaluation of Main Street Adaptive Traffic Signal Systems, Technical Report prepared for the City of South San Francisco, May 11, 2011.
Study of MOEs for InSync deployment.
Turner, S., Eisele, W., Benz, R., and Holdener, D., Travel Time Data Collection Handbook, Texas Transportation Institute Research Report 07470-1F, Federal Highway Administration, FHWA-PL-98-035, Washington, D.C., 1998.
FHWA and TTI study on data collection. Chapter 3 identifies test vehicles.
Wang, J., Robinson, B., Shelby, S., Cox, K., Townsend, W., Evaluation of ACS Lite Adaptive Control using Sensys Arterial Travel Time Data, Presented at ITS America 20th Annual Meeting and Exposition in Houston, Texas, 2010.
This paper describes the deployment of an ACS Lite adaptive control system on an arterial in Atlanta, Georgia, and presents a performance evaluation including arterial travel time measures obtained using a vehicle reidentification system. The results of this study showed that the deployed ACS Lite system substantially reduced arterial travel time and side-street queue lengths during peak traffic flow periods. The performance measures were travel time and queue lengths on side-streets. Queue length data collected manually using staff of 5 stationed at each intersection to record maximum number of vehicles in queue at onset of each green cycle. Arterial travel time data was collected by Sensys system.
Wetzel, C., and Dickson, C., SynchroGreen Real-Time Adaptive Traffic Control System, Seminole County SR 436 Deployment. White Paper by Seminole County Public Works/Traffic Engineering Division, Seminole County, Florida, 2011.
This paper evaluated SynchroGreen deployed in Seminole County, Florida. The study evaluated vehicle and pedestrian traffic. Traditional performance measures were considered, those being: travel time, delay, and stops. In addition, the study considered side-street delay and pedestrian delay. The authors used “on-off” technique to measure the effectiveness. Arterial travel time, delay and stop data was collected with the use of a GPS receiver. Side-street delay was collected based on the 2010 HCM procedures at two intersections over a 30-minute period in 15-second count intervals. Pedestrian delay was measured in the field with a stop watch at three intersections. Emission data was simulated using MICRO2, which was included in the GPS software.