Key Laboratory of Highway Engineering of Ministry of Education, Changsha University of Science and Technology, Changsha 410114, China; School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China.
Key Laboratory of Highway Engineering of Ministry of Education, Changsha University of Science and Technology, Changsha 410114, China; School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China.
J Safety Res. 2021 Feb;76:176-183. doi: 10.1016/j.jsr.2020.12.009. Epub 2020 Dec 30.
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.
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.
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.
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.
电动自行车作为一种便捷、经济的交通工具，被不同年龄的骑行群体广泛使用，用于不同的出行目的。电动自行车骑行者遭受重伤的潜在原因在每种情况下可能不同。
本研究旨在通过分类树和逻辑回归模型相结合的方法，研究不同群体电动自行车骑行者重伤的潜在危险因素。提取了湖南省三年内发生的电动自行车事故，考虑了骑手属性、对向车辆和驾驶员属性、骑手和驾驶员不当行为、道路和环境特征等风险因素。
根据分类树分析，将电动自行车骑手分为五组，其中市区 55 岁以上非职业骑手组发生重伤的可能性最高。对每组进行逻辑回归分析表明，一些危险因素，如高速公路，对不同组别的伤害严重程度有共同的显著影响；而主要因素仅对特定组别有显著影响。
根据模型结果，为不同电动自行车骑手群体提出了缓解碰撞伤害的政策建议，主要包括加强对电动自行车危险行为的教育和执法，以及交通工程对高速公路上电动自行车使用的规范。
