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[1] Zhang Xiaolong, Huang Jianling, Bian Yang, et al. Analysis of factors affecting injury severityof shared electric bike riders [J]. Journal of Southeast University (English Edition), 2023, 39 (3): 284-291. [doi:10.3969/j.issn.1003-7985.2023.03.009]
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Analysis of factors affecting injury severityof shared electric bike riders()
共享电动自行车骑行者事故伤害严重程度影响因素分析
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
39
Issue:
2023 3
Page:
284-291
Research Field:
Traffic and Transportation Engineering
Publishing date:
2023-09-20

Info

Title:
Analysis of factors affecting injury severityof shared electric bike riders
共享电动自行车骑行者事故伤害严重程度影响因素分析
Author(s):
Zhang Xiaolong1;  Huang Jianling1;  2;  Bian Yang1;  Zhao Xiaohua1;  Li Jia1
1Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
2Beijing Intelligent Transportation Development Center, Beijing 100073, China
张晓龙1;  黄建玲1;  2;  边扬1;  赵晓华1;  李佳1
1北京工业大学城市建设学部, 北京 100124; 2北京市智慧交通发展中心, 北京 100073
Keywords:
traffic engineering;  injury severity;  shared electric bike riders;  random parameter model;  heterogeneity in mean
交通工程;  事故伤害严重程度;  共享电动自行车骑行者;  随机参数模型;  均值异质性
PACS:
U491
DOI:
10.3969/j.issn.1003-7985.2023.03.009
Abstract:
This study investigated the potential factors affecting the injury severity of shared electric bike(e-bike)riders and analyzed potential heterogeneity using a dataset comprising of 1 343 shared e-bike insurance accidents recorded by a shared e-bike company as the research object. The injury severity was categorized into two levels: not injured and injured. Twelve independent variables were selected based on six aspects involving attributes of shared e-bike rider, vehicle, road, environment, time, and accident. The effects of different factors on the injury severity of shared e-bike riders were assessed using the random parameter logit model with heterogeneity in means. Results indicate that the variable “other traffic participants at fault” in the accident scenarios featured a random parameter that adhered to a normal distribution and exhibited mean heterogeneity. This increased the likelihood of injury among shared e-bike riders. However, the probability of injury decreased when the scenario involved both the variable “other traffic participants at fault” and component damage. The variables female, intact road surface, dry road pavement, nighttime, single-vehicle accidents, and both at-fault accidents could increase the injury probability among shared electric bike riders to varying degrees. The findings of this research provide a theoretical basis for the development of traffic safety strategies targeted at shared electric bike riders.
以某共享电动自行车企业的1 343起出保险事故为研究对象, 分析影响共享电动自行车骑行者事故伤害严重程度的潜在因素及可能存在的异质性.将伤害严重程度分为未受伤和受伤2个等级, 从共享电动自行车骑行者属性、车辆属性、道路属性和环境属性等因素中选取12个自变量进行分析.通过构建均值异质性随机参数Logit模型, 探究了不同因素对共享电动自行车骑行者伤害严重程度的影响.结果表明:事故责任中的变量“其他交通参与者责任”具有服从正态分布的随机参数特征, 且具有均值异质性, 表现为事故责任是其他交通参与者的事故会增加共享电动自行车骑行者受伤的可能性, 机件故障则能减少其增加幅度;女性、路面平整度良好、路表状态干燥、夜间、单车事故、双方责任事故会不同程度地增加共享电动自行车骑行者受伤的概率.研究结果为制定保障共享电动自行车驾驶者交通安全的策略提供理论依据.

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Memo

Memo:
Biographies: Zhang Xiaolong(1996—), male, Ph. D. candidate; Bian Yang(corresponding author), female, doctor, professor, bianyang@bjut.edu.cn.
Foundation item: The National Natural Science Foundation of China(No. 52072012).
Citation: Zhang Xiaolong, Huang Jianling, Bian Yang, et al. Analysis of factors affecting injury severity of shared electric bike riders[J].Journal of Southeast University(English Edition), 2023, 39(3):284-291.DOI:10.3969/j.issn.1003-7985.2023.03.009.
Last Update: 2023-09-20