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Predicting Driver Behavior during the Yellow Interval Using Video Surveillance

Author

Listed:
  • Juan Li

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

  • Xudong Jia

    (Civil Engineering Department, California State Polytechnic University, Pomona, CA 91768, USA)

  • Chunfu Shao

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

Abstract

At a signalized intersection, drivers must make a stop/go decision at the onset of the yellow signal. Incorrect decisions would lead to red light running (RLR) violations or crashes. This study aims to predict drivers’ stop/go decisions and RLR violations during yellow intervals. Traffic data such as vehicle approaching speed, acceleration, distance to the intersection, and occurrence of RLR violations are gathered by a Vehicle Data Collection System (VDCS). An enhanced Gaussian Mixture Model (GMM) is used to extract moving vehicles from target lanes, and the Kalman Filter (KF) algorithm is utilized to acquire vehicle trajectories. The data collected from the VDCS are further analyzed by a sequential logit model, and the relationship between drivers’ stop/go decisions and RLR violations is identified. The results indicate that the distance of vehicles to the stop line at the onset of the yellow signal is an important predictor for both drivers’ stop/go decisions and RLR violations. In addition, vehicle approaching speed is a contributing factor for stop/go decisions. Furthermore, the accelerations of vehicles after the onset of the yellow signal are positively related to RLR violations. The findings of this study can be used to predict the probability of drivers’ RLR violations and improve traffic safety at signalized intersections.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:12:p:1213-:d:84553
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
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