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Modeling Driver Behavior near Intersections in Hidden Markov Model

Author

Listed:
  • Juan Li

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Qinglian He

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Hang Zhou

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Yunlin Guan

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Wei Dai

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Intersections are one of the major locations where safety is a big concern to drivers. Inappropriate driver behaviors in response to frequent changes when approaching intersections often lead to intersection-related crashes or collisions. Thus to better understand driver behaviors at intersections, especially in the dilemma zone, a Hidden Markov Model (HMM) is utilized in this study. With the discrete data processing, the observed dynamic data of vehicles are used for the inference of the Hidden Markov Model. The Baum-Welch (B-W) estimation algorithm is applied to calculate the vehicle state transition probability matrix and the observation probability matrix. When combined with the Forward algorithm, the most likely state of the driver can be obtained. Thus the model can be used to measure the stability and risk of driver behavior. It is found that drivers’ behaviors in the dilemma zone are of lower stability and higher risk compared with those in other regions around intersections. In addition to the B-W estimation algorithm, the Viterbi Algorithm is utilized to predict the potential dangers of vehicles. The results can be applied to driving assistance systems to warn drivers to avoid possible accidents.

Suggested Citation

  • Juan Li & Qinglian He & Hang Zhou & Yunlin Guan & Wei Dai, 2016. "Modeling Driver Behavior near Intersections in Hidden Markov Model," IJERPH, MDPI, vol. 13(12), pages 1-15, December.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:12:p:1265-:d:85813
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    References listed on IDEAS

    as
    1. Xi Zou & David Levinson, 2006. "Modeling Pipeline Driving Behaviors: A Hidden Markov Model Approach," Working Papers 200607, University of Minnesota: Nexus Research Group.
    2. Bougler, Bénédicte & Cody, Delphine & Nowakowski, Christopher, 2008. "California Intersection Decision Support: A Driver-Centered Approach to Left-Turn Collision Avoidance System Design," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt5nz512bt, Institute of Transportation Studies, UC Berkeley.
    3. Denos Gazis & Robert Herman & Alexei Maradudin, 1960. "The Problem of the Amber Signal Light in Traffic Flow," Operations Research, INFORMS, vol. 8(1), pages 112-132, February.
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    Cited by:

    1. Yaqi Liu & Xiaoyuan Wang, 2020. "Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions," IJERPH, MDPI, vol. 17(19), pages 1-22, September.

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