Key Factors Analysis of Severity of Automobile to Two-Wheeler Traffic Accidents Based on Bayesian Network

Lining Liu, Xiaofei Ye, Tao Wang, Xingchen Yan, Jun Chen and Bin Ran
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Lining Liu: Faculty of Maritime and Transportation, Ningbo University, Fenghua Road 818#, Ningbo 315211, China
Xiaofei Ye: Faculty of Maritime and Transportation, Ningbo University, Fenghua Road 818#, Ningbo 315211, China
Tao Wang: School of Architecture and Transportation, Guilin University of Electronic Technology, Lingjinji Road 1#, Guilin 541004, China
Xingchen Yan: College of Automobile and Traffic Engineering, Nanjing Forestry University, Longpan Road 159#, Nanjing 210037, China
Jun Chen: School of Transportation, Southeast University, Si Pai Lou 2#, Nanjing 211189, China
Bin Ran: Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA

IJERPH, 2022, vol. 19, issue 10, 1-17

Abstract: The purpose of this paper is to analyze the complex coupling relationships among accident factors contributing to the automobile and two-wheeler traffic accidents by establishing the Bayesian network (BN) model of the severity of traffic accidents, so as to minimize the negative impact of automobile to two-wheeler traffic accidents. According to the attribution of primary responsibility, traffic accidents were divided to two categories: the automobile and two-wheeler traffic as the primary responsible party. Two BN accident severity analysis models for different primary responsible parties were proposed by innovatively combining the Kendall correlation analysis method with the BN model. A database of 1560 accidents involving an automobile and two-wheeler in Guilin, Guangxi province, were applied to calibrate the model parameters and validate the effectiveness of the models. The result shows that the BN models could reflect the real relationships among the influential factors of the two types of traffic accidents. For traffic accidents of automobiles and two-wheelers as the primary responsible party, respectively, the biggest influential factors leading to fatality were weather and visibility, and the corresponding fluctuations in the probability of occurrence were 32.20% and 27.23%, respectively. Moreover, based on multi-factor cross-over analysis, the most influential factors leading to fatality were: {Off-Peak Period → Driver of Two-Wheeler: The elderly → Driving Behavior of Two-Wheeler: Parking} and {Drunk Driving Two-Wheeler → Having a License of Automobiles → Visibility: 50 m~100 m}, respectively. The results provide a theoretical basis for reducing the severity of automobile to two-wheeler traffic accidents.

Keywords: big data and traffic safety; severity of accidents; automobile to two-wheeler traffic accidents; Kendall rank correlation; Bayesian network (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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