Literature DB >> 32297809

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

Lin Hu1,2, Xinting Hu1,2, Jie Wang3,4, Aiwu Kuang4, Wei Hao4, Miao Lin5.   

Abstract

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.

Entities:  

Keywords:  E-bike accident; age; fatality risk; impact speed

Year:  2020        PMID: 32297809     DOI: 10.1080/15389588.2020.1747614

Source DB:  PubMed          Journal:  Traffic Inj Prev        ISSN: 1538-9588            Impact factor:   1.491


  5 in total

1.  Demographics of road injuries and micromobility injuries among China, India, Japan, and the United States population: evidence from an age-period-cohort analysis.

Authors:  Yudi Zhao; Jinhong Cao; Yudiyang Ma; Sumaira Mubarik; Jianjun Bai; Donghui Yang; Kai Wang; Chuanhua Yu
Journal:  BMC Public Health       Date:  2022-04-14       Impact factor: 4.135

2.  The Real-World Effects of Route Familiarity on Drivers' Eye Fixations at Urban Intersections in Changsha, China.

Authors:  Lin Hu; Guangtao Guo; Jing Huang; Xianhui Wu; Kai Chen
Journal:  Int J Environ Res Public Health       Date:  2022-08-03       Impact factor: 4.614

3.  Injuries to Users of Single-Track Vehicles.

Authors:  Piotr Konrad Leszczyński; Justyna Kalinowska; Krzysztof Mitura; Daryna Sholokhova
Journal:  Int J Environ Res Public Health       Date:  2022-09-24       Impact factor: 4.614

4.  Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents.

Authors:  Fang Wang; Zhen Wang; Lin Hu; Hongzhen Xu; Chao Yu; Fan Li
Journal:  Front Bioeng Biotechnol       Date:  2021-06-29

5.  Impact of Helmet-Wearing Policy on E-Bike Safety Riding Behavior: A Bivariate Ordered Probit Analysis in Ningbo, China.

Authors:  Jibiao Zhou; Tao Zheng; Sheng Dong; Xinhua Mao; Changxi Ma
Journal:  Int J Environ Res Public Health       Date:  2022-02-28       Impact factor: 3.390

  5 in total

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