[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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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

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

Keywords:

traffic engineering;  injury severity;  shared electric bike riders;  random parameter model;  heterogeneity in mean

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.

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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.

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