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Journal Article

Citation

Chang F, Xu P, Zhou H, Lee J, Huang H. Transp. Res. F Traffic Psychol. Behav. 2019; 62: 844-854.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.trf.2019.03.010

PMID

unavailable

Abstract

Although the importance of human factors to crash occurrence has been demonstrated previously, the roles played by human factors in motorcycle killed and severely injured (KSI) crashes have remained unclear. One aim of our study is therefore to empirically determine the relative contribution of illegal behavior to motorcycle KSI crashes, conditional on real-world collisions between motorcycles and motor vehicles. Given that a crash is typically the synthetical result of human, vehicle, roadway, and environmental factors, another aim is to identify high-risk scenarios where inappropriate behavior is more likely to result in severe injuries for motorcyclists through interactions with other related factors. Based on a comprehensive dataset of 4587 police-reported crashes involving motorcycles during 2015-2017 in Hunan province, China, a data mining technique namely classification and regression tree was elaborately employed. Our results demonstrated the illegal behavior of the striking motor-vehicle drivers as one of the most dominant factors contributory to motorcycle KSI crashes, with a normalized importance value of 36.9%. We also confirmed collision object (i.e., collision with heavy or light vehicles) and helmet use of motorcyclists as determinants influencing motorcycle rider injury severities. Two types of extreme high-risk traffic scenarios were identified accordingly. A motorcycle rider was hit at weekends by a heavy motor-vehicle driver who was driving without license, driving a substantial vehicle, speeding, changing lanes illegally or driving in the wrong direction, and a motorcyclist was hit on weekdays by a heavy motor-vehicle driver aged 18-34 or 45-54, who was driving without license, driving a substantial vehicle, speeding, changing lanes illegally or driving in the wrong direction. Our findings are expected to shed more light on a deeper understanding of the illegal driving behavior as causation of motorcycle KSI crashes.


Language: en

Keywords

Data mining technique; Illegal driving behavior; Injury outcomes; Motorcycle crashes

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