Casualty risk of e-bike rider struck by passenger vehicle using China in-depth accident data

被引:64
|
作者
Hu, Lin [1 ,2 ]
Hu, Xinting [1 ,2 ]
Wang, Jie [3 ,4 ]
Kuang, Aiwu [4 ]
Hao, Wei [4 ]
Lin, Miao [5 ]
机构
[1] Changsha Univ Sci & Technol, Automot & Mech Engn, Changsha, Peoples R China
[2] Changsha Univ Sci & Technol, Hunan Prov Key Lab Safety Design & Reliabil Techn, Changsha, Peoples R China
[3] Changsha Univ Sci & Technol, Key Lab Highway Engn, Minist Educ, Changsha 410114, Peoples R China
[4] Changsha Univ Sci & Technol, Sch Traff & Transportat Engn, Changsha, Peoples R China
[5] China Automot Technol & Res Ctr, Tianjin, Peoples R China
基金
中国国家自然科学基金;
关键词
E-bike accident; fatality risk; impact speed; age; INJURY SEVERITY; CYCLISTS; SPEED; BEHAVIOR; CRASHES; DRIVERS;
D O I
10.1080/15389588.2020.1747614
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Objective: Traffic deaths involving e-bike (electric bike) riders are increasing in China. This study aims to quantitatively investigate the association between e-bike rider casualty and impact speed in electric bike-passenger vehicle collisions based on China in-depth accident study data. Methods: According to the collision location and driving direction of the e-bike and the vehicle, electric bike-passenger vehicle collisions are divided into five collision types: frontal collision, e-bike side collision, vehicle side collision, scrape collision and rear-end collision. Since e-bike side collision (the side of e-bike impacted with the front of vehicle) is the leading type and has the highest likelihood of severe or fatal injury in all collision types, e-bike side collisions are further selected to build the casualty risk functions of e-bike rider in relation to the rider age and the impact speed (vehicle impact speed and e-bike impact speed). Results: The analysis results show that, as for e-bike side collisions and e-bike impact speed is 20 km/h, the fatality risk of riders is approximately 2.9% at vehicle impact speed of 30 km/h, 23% at 50 km/h, 50% at 60 km/h, and 90% at 80 km/h. Rider age is also significantly associated with a higher risk of severe and fatality injury. The e-bike impact speed is not significantly associated with the severe and fatality risk in e-bike side collisions. Conclusions: The findings of this study provide meaningful insights to formulate effective policies especially for speed limit management to improve the safety of e-bikes.
引用
收藏
页码:283 / 287
页数:5
相关论文
共 8 条
  • [1] Risk prediction and factor analysis of rider's injury severity in passenger car and E-bike accidents based on interpretable machine learning
    Wei, Tianzheng
    Zhu, Tong
    Liu, Haoxue
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, 2024, 238 (01) : 172 - 186
  • [2] The influence of passenger car front shape on pedestrian injury risk observed from German in-depth accident data
    Li, Guibing
    Lyons, Mathew
    Wang, Bingyu
    Yang, Jikuang
    Otte, Dietmar
    Simms, Ciaran
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2017, 101 : 11 - 21
  • [3] Effects of passenger car front profile and human factors on pedestrian lower extremity injury risk using German in-depth accident data
    Wang, Bingyu
    Wang, Fang
    Otte, Dietmar
    Han, Yong
    Peng, Qian
    [J]. INTERNATIONAL JOURNAL OF CRASHWORTHINESS, 2019, 24 (02) : 163 - 170
  • [4] Optimizing vehicle Front-End structure for e-bike rider Safety: An advanced Multi-Objective approach using injury prediction models
    Wang, Qiang
    Yu, Boxuan
    Liu, Yu
    Fei, Jing
    Liu, Zhuling
    Zhang, Guanjun
    Guo, Yage
    Bai, Zhonghao
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2024, 207
  • [5] A study on the cyclist lower extremity injury risk by using German in-depth accident data
    Wang, Bingyu
    Zou, Jun
    Meng, Xiange
    Han, Yong
    Wu, He
    Otte, Dietmar
    Peng, Qian
    [J]. INTERNATIONAL JOURNAL OF CRASHWORTHINESS, 2024, 29 (05) : 856 - 864
  • [6] Injury severity analysis of e-bike riders in China based on the in-vehicle recording video crash data: a random parameter ordered logit model
    Wang, Changshuai
    Shao, Yongcheng
    Ye, Fei
    Zhu, Tong
    [J]. INTERNATIONAL JOURNAL OF INJURY CONTROL AND SAFETY PROMOTION, 2024,
  • [7] A study of head brain injuries in car-to-pedestrian crashes with reconstructions using in-depth accident data in China
    Li, F.
    Yang, J. K.
    [J]. INTERNATIONAL JOURNAL OF CRASHWORTHINESS, 2010, 15 (02) : 117 - 124
  • [8] The effects of Vehicle Front Design Variables and Impact Speed on Lower Extremity Injury in Pedestrian collisions using In-depth Accident Data
    Wang, Bingyu
    Yang, Jikuang
    Otte, Dietmar
    [J]. PROCEEDINGS 2016 EIGHTH INTERNATIONAL CONFERENCE ON MEASURING TECHNOLOGY AND MECHATRONICS AUTOMATION ICMTMA 2016, 2016, : 768 - 772