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Analysis and Prediction of Pedestrians’ Violation Behavior at the Intersection Based on a Markov Chain

Author

Listed:
  • Chengyuan Mao

    (Road and Traffic Engineering Institute, Zhejiang Normal University, Jinhua 321004, China)

  • Lewen Bao

    (Road and Traffic Engineering Institute, Zhejiang Normal University, Jinhua 321004, China)

  • Shengde Yang

    (Road and Traffic Engineering Institute, Zhejiang Normal University, Jinhua 321004, China)

  • Wenjiao Xu

    (Road and Traffic Engineering Institute, Zhejiang Normal University, Jinhua 321004, China)

  • Qin Wang

    (Road and Traffic Engineering Institute, Zhejiang Normal University, Jinhua 321004, China)

Abstract

Pedestrian violations pose a danger to themselves and other road users. Most previous studies predict pedestrian violation behaviors based only on pedestrians’ demographic characteristics. In practice, in addition to demographic characteristics, other factors may also impact pedestrian violation behaviors. Therefore, this study aims to predict pedestrian crossing violations based on pedestrian attributes, traffic conditions, road geometry, and environmental conditions. Data on the pedestrian crossing, both in compliance and in violation, were collected from 10 signalized intersections in the city of Jinhua, China. We propose an illegal pedestrian crossing behavior prediction approach that consists of a logistic regression model and a Markov Chain model. The former calculates the likelihood that the first pedestrian who decides to cross the intersection illegally within each signal cycle, while the latter computes the probability that the subsequent pedestrians who decides to follow the violation. The proposed approach was validated using data gathered from an additional signalized intersection in Jinhua city. The results show that the proposed approach has a robust ability in pedestrian violation behavior prediction. The findings can provide theoretical references for pedestrian signal timing, crossing facility optimization, and warning system design.

Suggested Citation

  • Chengyuan Mao & Lewen Bao & Shengde Yang & Wenjiao Xu & Qin Wang, 2021. "Analysis and Prediction of Pedestrians’ Violation Behavior at the Intersection Based on a Markov Chain," Sustainability, MDPI, vol. 13(10), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:10:p:5690-:d:557636
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    References listed on IDEAS

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    4. Chiara Gruden & Irena Ištoka Otković & Matjaž Šraml, 2020. "Neural Networks Applied to Microsimulation: A Prediction Model for Pedestrian Crossing Time," Sustainability, MDPI, vol. 12(13), pages 1-22, July.
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