Analyzing the Causes of Traffic Accidents of Online Ride-Hailing Cars Using the Bayesian Network

doi:10.12068/j.issn.1005-3026.2023.01.020

Abstract

Abstract: To mitigate the traffic accidents of online ride-hailing cars， the accident causes were studied by taking the ride-hailing drivers as the research object. A self-reported questionnaire survey was conducted to collect information about self characteristics， work intensity， work stress， risky driving behaviors， and accident history for 2458 ride-hailing drivers. After sorting of data， the Bayesian network method was used to establish the prediction model of accident frequency. The accuracy of the model was calibrated using the confusion matrix and receiver operating characteristic curve based on the ten-fold cross-validation. The results show that the model has a good prediction ability. The model found 11 influencing factors directly related to accident frequency and identified 16 categories of unfavorable states leading to an increased probability of high-frequency accidents. Also， nonlinear amplification and superposition effects of the combination of multiple unfavorable states on accident frequency were confirmed. The conclusions of the study help the management department to make prevention countermeasures to reduce the accident frequency of online ride-hailing cars.

Key words: traffic safety; online ride-hailing car accidents; Bayesian network (BN); interactions; accident causal mechanism

CLC Number:

U491.3

PENG Zhi-peng， PAN Heng-yan， WANG Yong-gang. Analyzing the Causes of Traffic Accidents of Online Ride-Hailing Cars Using the Bayesian Network[J]. Journal of Northeastern University(Natural Science), 2023, 44(1): 145-152.

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