Analysis of the Causes of Traffic Accidents and Identification of Accident-Prone Points in Long Downhill Tunnel of Mountain Expressways Based on Data Mining

Fu Wang, Jing Wang, Xianfeng Zhang, Dengjun Gu, Yang Yang and Hongbin Zhu
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Fu Wang: School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China
Jing Wang: School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China
Xianfeng Zhang: Wuhan Transportation Planning & Design Co., Ltd., Wuhan 430010, China
Dengjun Gu: School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China
Yang Yang: School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China
Hongbin Zhu: School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China

Sustainability, 2022, vol. 14, issue 14, 1-22

Abstract: China has a large vehicle base, uneven road conditions, and the highest rate of traffic accidents in the world. Particularly on the long downhill sections of expressway tunnels in mountainous areas with harsh geographical conditions, traffic accidents are densely distributed, and once a traffic accident occurs, the consequences are serious, which poses a large threat to people’s lives and property. This paper mined and analyzed the traffic accident data collected by the project on the Baoding section of Zhangshi Expressway. SPSS software was used to analyze the traffic accident data characteristics of the long downhill tunnel of the mountain expressways. The time, space, accident form, vehicle type, and road alignment distribution characteristics of the traffic accident in the long downhill tunnel section of mountain expressways were obtained. The decision tree algorithm was used to construct the cause analysis model of traffic accidents in the long downhill tunnel of mountain expressways, and the five primary influencing factors were obtained: horizontal curve radius, week, slope length, time, and cart ratio. The improved cumulative frequency curve method was used to study the accident-prone points of mountain expressways, and the accident-prone points and potential accident-prone points were obtained.

Keywords: mountain expressways; long downhill tunnels; traffic accidents; accident-prone points; data mining (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (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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