Risk factors affecting crash injury severity for different groups of e-bike riders: A classification tree-based logistic regression model

Wang Zhengwu; Huang Shuai; Wang Jie; Sulaj Denisa; Hao Wei; Kuang Aiwu

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Risk factors affecting crash injury severity for different groups of e-bike riders: A classification tree-based logistic regression model

机译：影响不同群体电子自行车骑手的碰撞伤害严重程度的危险因素：基于分类的树木逻辑回归模型

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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. (C) 2020 National Safety Council and Elsevier Ltd. All rights reserved.

机译：介绍：作为一种方便而实惠的运输方式，E-Bike被不同年龄骑士团体广泛使用，并用于不同的旅行目的。在每种情况下，患有严重损伤的患者的底层骑车者的潜在原因可能是不同的。方法：本研究旨在通过使用组合方法，集成分类树和逻辑回归模型来检查不同群体的严重伤害的潜在风险因素。在湖南省发生的三年e-bike崩溃是提取的，并且危险因素包括骑士属性，对手车辆和驾驶员的属性，骑手和司机，道路和环境特征的不合适行为。结果：基于分类树分析，E-Bike Rideers分为五组，城市地区55岁以上的非职业车手组与五组严重伤害的可能性最高。每组的物流分析表明，高速道路等几种风险因素对不同群体的损伤严重程度具有显着影响;虽然主要因素仅对特定群体产生重大影响。实际应用：根据模型结果，建议对缓解不同电子自行车骑手组的撞击伤害的政策影响，主要包括加强电子自行车危险行为以及交通工程来规范电子自行车的使用高速道路。（c）2020国家安全委员会和elestvier有限公司保留所有权利。

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来源
《Journal of Safety Research》 |2021年第2期|176-183|共8页
作者
Wang Zhengwu; Huang Shuai; Wang Jie; Sulaj Denisa; Hao Wei; Kuang Aiwu;
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作者单位

Changsha Univ Sci & Technol Key Lab Highway Engn Minist Educ Changsha 410114 Peoples R China|Changsha Univ Sci Sch Traff & Transportat Engn Changsha 410114 Peoples R China;

Changsha Univ Sci & Technol Key Lab Highway Engn Minist Educ Changsha 410114 Peoples R China|Changsha Univ Sci Sch Traff & Transportat Engn Changsha 410114 Peoples R China;

Changsha Univ Sci & Technol Key Lab Highway Engn Minist Educ Changsha 410114 Peoples R China|Changsha Univ Sci Sch Traff & Transportat Engn Changsha 410114 Peoples R China;

Changsha Univ Sci Sch Traff & Transportat Engn Changsha 410114 Peoples R China;

Changsha Univ Sci Sch Traff & Transportat Engn Changsha 410114 Peoples R China;

Changsha Univ Sci Sch Traff & Transportat Engn Changsha 410114 Peoples R China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《化学文摘》(CA);
原文格式 PDF
正文语种 eng
中图分类
关键词
E-bike crash; Injury severity; Classification tree-based logistic regression; Different riders groups;

机译：e-bike崩溃;伤害严重程度;基于树的分类逻辑回归;不同的骑手组;

Risk factors affecting crash injury severity for different groups of e-bike riders: A classification tree-based logistic regression model

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