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A Model of Driver Behavior at High Speed Signalized Intersections

Author

Listed:
  • Yosef Sheffi

    (Massachusetts Institute of Technology, Cambridge, Massachusetts)

  • Hani Mahmassani

    (Massachusetts Institute of Technology, Cambridge, Massachusetts)

Abstract

Driver behavior at high speed signalized intersections is modeled in this paper as a binary decision (stop or go). A driver is assumed to stop if the time to arrive at the stop bar, at the onset of amber, is smaller than some critical time. Assuming that time perception and the critical time are normally distributed among drivers, the model's parameters are estimated using a probit calibration routine. The approach is shown to significantly reduce the sample size needed for estimating dilemma zone boundaries and therefore it represents a significant cost saving. As shown in the paper, the dilemma zone boundaries can be directly estimated (analytically) from the model without using stopping probability curves and the implied prior aggregation by speed. The efficient use of the data is a key to the reduced sample sizes needed for estimating the model.

Suggested Citation

  • Yosef Sheffi & Hani Mahmassani, 1981. "A Model of Driver Behavior at High Speed Signalized Intersections," Transportation Science, INFORMS, vol. 15(1), pages 50-61, February.
    • Handle: RePEc:inm:ortrsc:v:15:y:1981:i:1:p:50-61
    DOI: 10.1287/trsc.15.1.50
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    Citations

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    Cited by:

    1. Moon, Young J. & Coleman, Fred, 2003. "Dynamic dilemma zone based on driver behavior and car-following model at highway-rail intersections," Transportation Research Part B: Methodological, Elsevier, vol. 37(4), pages 323-344, May.
    2. Bao, Ji & Chen, Qun & Luo, Dandan & Wu, Yuli & Liang, Zuli, 2018. "Exploring the impact of signal types and adjacent vehicles on drivers’ choices after the onset of yellow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 222-236.
    3. Juan Li & Xudong Jia & Chunfu Shao, 2016. "Predicting Driver Behavior during the Yellow Interval Using Video Surveillance," IJERPH, MDPI, vol. 13(12), pages 1-15, December.
    4. Zhang, Liping & Zhou, Kun & Zhang, Wei-bin & Misener, James A., 2011. "Dynamic All-Red Extension at Signalized Intersection: Probabilistic Modeling and Algorithm," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt7kp0030b, Institute of Transportation Studies, UC Berkeley.
    5. Lu, Guangquan & Wang, Yunpeng & Wu, Xinkai & Liu, Henry X., 2015. "Analysis of yellow-light running at signalized intersections using high-resolution traffic data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 73(C), pages 39-52.
    6. Cassidy, Michael & Coifman, Benjamin, 1998. "Design Of A Machine Vision-based, Vehicle Actuated Traffic Signal Controller," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2rg0957h, Institute of Transportation Studies, UC Berkeley.
    7. Liu, Chiu & Herman, Robert & Gazis, Denos C., 1996. "A review of the yellow interval dilemma," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(5), pages 333-348, September.

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