Journal of safety research · 2020
Risk factors affecting crash injury severity for different groups of e-bike riders: A classification tree-based logistic regression model.
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Wang Zhengwu, Huang Shuai, Wang Jie, Sulaj Denisa, Hao Wei, Kuang Aiwu
Аннотация
INTRODUCTION: As a convenient and affordable means of transportation, the e-bike is widely used by different age rider groups and for different travel purposes. The underlying reasons for e-bike riders suffering from severe injury may be different in each case.
METHOD: This study aims to examine the underlying risk factors of severe injury for different groups of e-bike riders by using a combined method, integration of a classification tree and a logistic regression model. Three-year of e-bike crashes occurring in Hunan province are extracted, and risk factor including rider's attribute, opponent vehicle and driver's attribute, improper behaviors of riders and drivers, road, and environment characteristics are considered for this analysis.
RESULTS: E-bike riders are segmented into five groups based on the classification tree analysis, and the group of non-occupational riders aged over 55 in urban regions is associated with the highest likelihood of severe injury among the five groups. The logistics analysis for each group shows that several risk factors such as high-speed roads have commonly significant effects on injury severity for different groups; while major factors only have significant effects for specific groups.
PRACTICAL APPLICATION: Based on model results, policy implications to alleviate the crash injury for different e-bike riders groups are recommended, which mainly include enhanced education and enforcement for e-bike risky behaviors, and traffic engineering to regulate the use of e-bikes on high speed roads.
METHOD: This study aims to examine the underlying risk factors of severe injury for different groups of e-bike riders by using a combined method, integration of a classification tree and a logistic regression model. Three-year of e-bike crashes occurring in Hunan province are extracted, and risk factor including rider's attribute, opponent vehicle and driver's attribute, improper behaviors of riders and drivers, road, and environment characteristics are considered for this analysis.
RESULTS: E-bike riders are segmented into five groups based on the classification tree analysis, and the group of non-occupational riders aged over 55 in urban regions is associated with the highest likelihood of severe injury among the five groups. The logistics analysis for each group shows that several risk factors such as high-speed roads have commonly significant effects on injury severity for different groups; while major factors only have significant effects for specific groups.
PRACTICAL APPLICATION: Based on model results, policy implications to alleviate the crash injury for different e-bike riders groups are recommended, which mainly include enhanced education and enforcement for e-bike risky behaviors, and traffic engineering to regulate the use of e-bikes on high speed roads.