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Comparison of injury severity between E-bikes-related and other two-wheelers-related accidents: Based on an accident dataset

事故(哲学) 运输工程 速度限制 有序概率单位 Probit模型 毒物控制 普罗比特 伤害预防 工程类 碰撞 职业安全与健康 人为因素与人体工程学 法律工程学 计算机安全 环境卫生 计算机科学 医学 统计 数学 哲学 认识论 病理
作者
Qian Qian,Jing Shi
出处
期刊:Accident Analysis & Prevention [Elsevier]
卷期号:190: 107189-107189 被引量:11
标识
DOI:10.1016/j.aap.2023.107189
摘要

This study aims to compare the accident injury severity of e-bikes with that of other types of two-wheelers based on accident data and to analyze the factors influencing them. Using 1015 police accident records from Zhangjiakou City in 2020 and 2021, the accident injury severity of e-bikes was firstly compared with that of other two-wheelers based on five levels of accident injury severity classified according to the records. Two ordered Probit regression models were secondly used to compare the factors influencing the accident injury severity of e-bikes with that of other two-wheelers and the magnitude of their effects. At the same time, the contributions of each influential factor to the degree of accident injury of two-wheelers were estimated with the assistance of classification trees. Results show that e-bikes are closer to bicycles than motorcycles in terms of injury severities and the factors influencing them, in which the factors “accident configuration,” “division of responsibility for the accident,” and “collision with a heavy vehicle or four-wheeled vehicle” are significant. Based on the findings, potential measures are suggested to reduce e-bike accident casualties, such as improving rider education, ensuring speed limit enforcement, promoting safety equipment wearing, and making road design friendly to non-motorized and elderly riders. The results of this study can provide an essential reference for traffic management and rider education measures on e-bikes.
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