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Crash Risk Assessment of Off-Ramps, Based on the Gaussian Mixture Model Using Video Trajectories

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  • Ting Xu

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China
    College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Yanjun Hao

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China)

  • Shichao Cui

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China)

  • Xingqi Wu

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China)

  • Zhishun Zhang

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China)

  • Steven I-Jy Chien

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China
    Department of Civil and Environmental Engineering, New Jersey Institute of Technology, Newark, NJ 07102-1982, USA)

  • Yulong He

    (College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

Abstract

The focus of this paper is the crash risk assessment of off-ramps in Xi’an. The time-to-collision (TTC) is used for the measurement and cross-comparison of the crash risk of each location. Five sites from the urban expressway in Xi’an were selected to explore the TTC distribution. An unmanned aerial vehicle and a camera were used to collect traffic flow data for 20 min at each site. The parameters, including speed, deceleration rate, truck percentage, traffic volume, and vehicle trajectories, were extracted from video images. The TTCs were calculated for each vehicle. The Gaussian mixture model (GMM) was proposed to predict the TTC probability density functions (PDFs) and cumulative density functions (CDFs) for five sites. The Kolmogorov–Smirnov (K-S) test indicated that the samples followed the estimated GMM distribution. The relationship between the crash risk level and influencing factors was studied by an ordinal logistic regression model and a naive Bayesian model. The results showed that the naive Bayesian model had an accuracy of 86.71%, while the ordinal logistic regression model had an accuracy of 84.81%. The naive Bayesian model outperformed the ordinal logistic regression model, and it could be applied to the real-time collision warning system.

Suggested Citation

  • Ting Xu & Yanjun Hao & Shichao Cui & Xingqi Wu & Zhishun Zhang & Steven I-Jy Chien & Yulong He, 2020. "Crash Risk Assessment of Off-Ramps, Based on the Gaussian Mixture Model Using Video Trajectories," Sustainability, MDPI, vol. 12(8), pages 1-20, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3076-:d:344303
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    References listed on IDEAS

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    1. González, Rosa Marina & Marrero, Gustavo A., 2012. "Induced road traffic in Spanish regions: A dynamic panel data model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 435-445.
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