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

Ting Xu, Yanjun Hao, Shichao Cui, Xingqi Wu, Zhishun Zhang, Steven I-Jy Chien and Yulong He
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Ting Xu: College of Transportation Engineering, Chang’an University, Xi’an 710064, 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
Yulong He: College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China

Sustainability, 2020, vol. 12, issue 8, 1-20

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.

Keywords: time-to-collision; Gaussian mixture model; risk assessment; E-M algorithm; ordinal logistic regression model; naive Bayesian (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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