Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model

Hasan. A. H. Naji, Qingji Xue, Nengchao Lyu, Chaozhong Wu and Ke Zheng
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Hasan. A. H. Naji: School of Computer and Information Engineering, Nanyang Institute of Technology, Chang Jiang Road No 80, Nanyang 473004, China
Qingji Xue: School of Computer and Information Engineering, Nanyang Institute of Technology, Chang Jiang Road No 80, Nanyang 473004, China
Nengchao Lyu: Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
Chaozhong Wu: Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
Ke Zheng: School of Computer Science and Technology, Wuhan University of Technology, Heping Road, Wuchang District, Wuhan 430063, China

Sustainability, 2018, vol. 10, issue 8, 1-20

Abstract: With the considerable increase in ownership of motor vehicles, traffic crashes have become a challenge. This paper presents a study of naturalistic driving conducted to collect driving data. The experiments were performed on different road types in the city of Wuhan in China. The collected driving data were used to develop a near-crash database, which covers driving behavior, near-crash factors, driving environment, time, demographics, and experience. A new definition of near-crash events is also proposed. The new definition considers potential risks in driving behavior, such as braking pressure, time headway, and deceleration. A clustering analysis was carried out through a K-means algorithm to classify near-crash events based on their risk level. In addition, a mixed-ordered logit model was used to examine the contributing factors associated with the driving risk of near-crash events. The results indicate that ten factors significantly affect the driving risk of near-crash events: deceleration average, vehicle kinetic energy, near-crash causes, congestion on roads, time of day, driving miles, road types, weekend, age, and experience. The findings may be used by transportation planners to understand the factors that influence driving risk and may provide valuable insights and helpful suggestions for improving transportation rules and reducing traffic collisions thus making roads safer.

Keywords: K-means; near-crash events; driving risk evaluation; mixed-ordered logit model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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