International Conference on Transportation and Development 2020
Methodology for Evaluating Impact of Actuated Traffic Signal Control on Connected Vehicle Green Light Prediction
Publication: International Conference on Transportation and Development 2020
ABSTRACT
Connected vehicles (CV) that communicate with traffic signals and notify the driver of expected signal status changes have emerged in the last few years. For fixed-time operations it is a deterministic exercise to predict the signal state for a given movement. However, under actuated-coordinated operation, there are stochastic variations of phase start and end times. When traffic engineers enable additional traffic responsive logic to accommodate for changes in demand, the phase timings become more challenging to predict. This paper proposes a methodology to evaluate traffic signal prediction algorithms for vehicle-to-infrastructure CV application. Data was gathered using video footage on minor and major movement of an intersection that operated in actuated-coordination. Data was collected for 176 cycles over two days. Results showed that the CV application can predict the mean start of green within +/−2.7s. The paper concludes by recommending the evaluation methodology and graphical summaries proposed be used as tools for traffic engineers and vehicle manufactures to characterize the stochastic nature of actuated traffic signals and manage expectations of motorists and other stakeholders.
Get full access to this chapter
View all available purchase options and get full access to this chapter.
REFERENCES
Audi Newsroom. (2018). “Audi expands Traffic Light Information – now available in 10 US cities – Audi Newsroom.” <https://media.audiusa.com/en-us/releases/241>(Jul. 29, 2018).
Bang, K.-L. (1976). “Optimal Controls of Isolated Traffic Signals.” Traffic Engineering and Control, 17(7), 288–292.
Bauer, T., Ma, J., and Hatcher, K. Z. (2016). “Prediction of traffic signal state changes.” US Patent and Trademark Office, United States of America.
Bauer, T., Ma, J., and Offermann, F. (2015). “An Online Prediction System of Traffic Signal Status for Assisted Driving on Urban Streets: Pilot Experiences in the United States, China, and Germany.” ITE Journal (Institute of Transportation Engineers), 85, 37–43.
Bodenheimer, R., Brauer, A., Eckhoff, D., and German, R. (2014). Enabling GLOSA for adaptive traffic lights. Presented at the 2014 IEEE Vehicular Networking Conference (VNC), Paderborn, Germany.
Day, C., Bullock, D., Li, H., Remias, S., Hainen, A., Freije, R., Stevens, A., Sturdevant, J., and Brennan, T. (2014). Performance Measures for Traffic Signal Systems: An Outcome-Oriented Approach., Purdue University, West Lafayette, IN.
Day, C. M., and Bullock, D. M. (2011). “Computational Efficiency of Alternative Algorithms for Arterial Offset Optimization.” Transportation Research Record: Journal of the Transportation Research Board, SAGE PublicationsSage CA: Los Angeles, CA, 2259(1), 37–47.
Day, C. M., Haseman, R., Premachandra, H., Brennan, T. M., Wasson, J. S., Sturdevant, J. R., and Bullock, D. M. (2010). “Evaluation of Arterial Signal Coordination.” Transportation Research Record: Journal of the Transportation Research Board, Transportation Research Board of the National Academies, 2192(1), 37–49.
Eckhoff, D., Halmos, B., and German, R. (2013). “Potentials and limitations of Green Light Optimal Speed Advisory systems.” 2013 IEEE Vehicular Networking Conference, IEEE, 103–110.
Howard, B. (2016). “Hands on with Audi’s exciting (no, really) traffic light countdown timer.” ExtremeTech, <https://www.extremetech.com/extreme/240264-hands-audis-exciting-no-really-traffic-light-countdown-timer>(Apr. 1, 2019).
Hunt, P. B., Robertson, D. I., Bretherton, R. D., and Winton, R. I. (1981). “SCOOT: a traffic responsive method of coordinating signals.”, Transportation Research Board, Crowthorne.
Katsaros, K., Kernchen, R., Dianati, M., Rieck, D., and Zinoviou, C. (2011). “Application of vehicular communications for improving the efficiency of traffic in urban areas.” Wireless Communications and Mobile Computing, John Wiley & Sons, Ltd, 11(12), 1657–1667.
Li, H., Richardson, L. M., Day, C. M., Howard, J., and Bullock, D. M. (2017). “Scalable Dashboard for Identifying Split Failures and Heuristic for Reallocating Split Times.” Transportation Research Record: Journal of the Transportation Research Board, Transportation Research Board of the National Academies, 2620(1), 83–95.
Lowrie, P. R. (1982). “The Sydney coordinated adaptive traffic system – principles, methodology, algorithms.” International Conference on Road Traffic Signalling, Institution of Electrical Engineers, (207), 202.
Mathew, J. K., Li, H., Morgan, B., Kim, W., and Bullock, D. (2019). Probabilistic Distributions of Coordinated Traffic Signal Phase Indications for Connected Vehicle Applications. Presented at the 98th Annual Meeting of Transportation Research Board, Washington D.C.
Protschky, V., Wiesner, K., and Feit, S. (2014). “Adaptive traffic light prediction via Kalman filtering.” 2014 IEEE Intelligent Vehicles Symposium Proceedings, IEEE, 151–157.
PTB. (2017). “Benefit of DCF77.” <https://www.ptb.de/cms/en/ptb/fachabteilungen/abt4/fb-44/ag-442/dissemination-of-legal-time/dcf77/benefit-of-dcf77.html>(May 24, 2019).
SAE International. (2016). J2735D: Dedicated Short Range Communications (DSRC) Message Set DictionaryTM – SAE International. Warrendale, PA.
Smaglik, E., Sharma, A., Bullock, D., Sturdevant, J., and Duncan, G. (2007). “Event-Based Data Collection for Generating Actuated Controller Performance Measures.” Transportation Research Record: Journal of the Transportation Research Board, Transportation Research Board of the National Academies, 2035, 97–106.
Stevanovic, A. (2010). Adaptive Traffic Control Systems: Domestic and Foreign State of Practice. NCHRP Synthesis of Highway Practice No 403, National Academies Press, Washington, D.C.
Urbanik, T., Tanaka, A., Lozner, B., Lindstrom, E., Lee, K., Quayle, S., Beaird, S., Tsoi, S., Ryus, P., Gettman, D., Sunkari, S., Balke, K., and Bullock, D. (2015). “System/Coordinated Timing.” Signal Timing Manual – Second Edition, Transportation Research Board, Washington, D.C.
US Department of Transportation. (2016). “Connected Vehicles.” <https://www.pcb.its.dot.gov/eprimer/module13.aspx>(Mar. 13, 2018).
US Department of Transportation. (2018). USDOT Public Listening Summit on Automated Vehicle Policy Summary Report. Washington D.C.
US Department of Transportation. (2019). “Dynamic Mobility Applications (DMA) Program.” <https://www.its.dot.gov/research_archives/dma/bundle/mmitss_plan.htm>(Apr. 4, 2019).
Utah Department of Transportation. (2018). “Automated Traffic Signal Performance Metrics.” <https://udottraffic.utah.gov/ATSPM/Home/About>(Nov. 13, 2018).
Weisheit, T., and Hoyer, R. (2014). “Prediction of Switching Times of Traffic Actuated Signal Controls Using Support Vector Machines.” Springer, Cham, 121–129.
Wolf, J. C., Ma, J., Cisco, B., Neill, J., Moen, B., and Jarecki, C. (2019). “Deriving Signal Performance Metrics from Large-Scale Connected Vehicle System Deployment.” Transportation Research Record: Journal of the Transportation Research Board, SAGE PublicationsSage CA: Los Angeles, CA, 2673(4), 36–46.
Wu, X., and Liu, H. X. (2014). “Using high-resolution event-based data for traffic modeling and control: An overview.” Transportation Research Part C: Emerging Technologies, Pergamon, 42, 28–43.
Information & Authors
Information
Published In
International Conference on Transportation and Development 2020
Pages: 280 - 292
Editor: Guohui Zhang, Ph.D., University of Hawaii
ISBN (Online): 978-0-7844-8315-2
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Aug 31, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.