Risk factors affecting crash injury severity for different groups of e-bike riders: A classification tree-based logistic regression model |
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| Institution: | 1. Key Laboratory of Highway Engineering of Ministry of Education, Changsha University of Science and Technology, Changsha 410114, China;2. School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China;1. University of Iowa Injury Prevention Research Center, 2195 Westlawn, Iowa City, IA 52242, USA;2. Department of Epidemiology, College of Public Health, University of Iowa, 145 North Riverside Drive, Iowa City, IA 52242, USA;3. Department of Biostatistics, College of Public Health, University of Iowa, 145 North Riverside Drive, Iowa City, IA 52242, USA;4. Department of Occupational and Environmental Health, College of Public Health, University of Iowa, 145 North Riverside Drive, Iowa City, IA 52242, USA;1. College of Engineering, Zhejiang Normal University, Zhejiang 321005, China;2. Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Province, Zhejiang Normal University, Zhejiang 321005, China;3. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan 611756, China;4. Sichuan Vocational and Technical College of Communications, Chengdu, Sichuan 611130, China;1. Israel National Center for Trauma and Emergency Medicine, Gertner Institute for Epidemiology and Public Health Policy, Tel-Hashomer, Israel;2. Department of Disaster Medicine, School of Public Health, Tel Aviv University, Israel;1. Center for Transportation Research, University of Tennessee, Knoxville, TN, United States;2. Department of Geography, 304 Burchfiel Geography Building, University of Tennessee, Knoxville, TN, United States;3. Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, United States;1. Department of Pediatrics, Hadassah Medical Center, Jerusalem, Israel;2. Department of General Surgery, Hadassah and Hebrew University Hospital, Jerusalem, Israel;3. Faculty of Medicine, Hebrew University, Ein Kerem, Jerusalem, Israel;4. Head, Trauma Unit, Hadassah and Hebrew University Hospital, Jerusalem, Israel;6. Department of Pediatric Emergency Medicine, Hadassah Medical Center, Jerusalem, Israel |
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| Abstract: | IntroductionAs 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.MethodThis 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.ResultsE-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 applicationBased 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. |
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| Keywords: | E-bike crash Injury severity Classification tree-based logistic regression Different riders groups |
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