Journal article
Characteristics of media-reported road traffic crashes involving professional delivery e-bike riders versus other e-bike riders in China
Journal of safety research, Vol.96, pp.55-63
02/2026
DOI: 10.1016/j.jsr.2025.11.008
Abstract
•Media-reported crashes help uncover overlooked risks among professional delivery e-bike riders in China.•Delivery rider crashes were more likely to involve non-motorized vehicles, road infrastructure and pedestrian crossings.•Delivery rider crashes showed higher rates of dangerous riding behaviors such as distracted riding and speeding .•Delivery and non-delivery rider crashes differed in the distribution of vehicle- and road-related contributing factors.
Introduction: With the rapid expansion of food delivery services, crashes involving professional delivery e-bike riders have emerged as a critical traffic safety issue in China. However, limited research has systematically examined the epidemiological characteristics of such crashes. Methods: We analyzed media-reported crashes from 2009-2023 collected in the Automated Road Traffic Crash Data Platform (ARTCDP), which uses natural language processing (NLP) and BERT algorithms to extract data from publicly-available media reports. Differences in crash characteristics, geographic distribution, and contributing factors were compared between professional delivery and other e-bike riders. Results: A total of 4,417 professional delivery e-bike rider crashes and 17,025 other e-bike rider crashes were included. Pedestrians were the most common collision object (37%). Delivery riders were less likely to collide with motor vehicles (29% vs. 42%) and more likely to hit non-motorized vehicles (22% vs. 12%) and infrastructure (12% vs. 9%) than non-delivery riders. Intersections were the top crash site, but crashes involving delivery riders occurred more often at pedestrian crossings (21%) and bus stops (11%). The crashes demonstrated inconsistent geographic distributions. About 40% of all crashes had two or more contributing factors. Compared with crashes among other e-bike riders, those involving professional delivery e-bike riders had a higher proportion of distracted riding (27% vs. 17%), speeding (18% vs. 12%), riding in the opposite lane (15% vs. 6%), and illegal parking (12% vs. 4%). Malfunctioning tires, a lack of a barrier between opposite lanes, and impeded view by obstacles also occurred more frequently in crashes involving professional delivery e-bike riders than those involving other e-bike riders (40% vs. 20%, 35% vs. 22%, and 35% vs. 18%, respectively). Conclusions: Crashes involving professional delivery e-bike riders in China show distinct patterns linked to occupational exposure, underscoring the need for targeted, multifaceted road safety interventions. Practical Applications: Findings support tailored interventions for delivery riders, including vehicle checks, rider training, and infrastructure improvements near pedestrian crossings and bus stops.
Details
- Title: Subtitle
- Characteristics of media-reported road traffic crashes involving professional delivery e-bike riders versus other e-bike riders in China
- Creators
- Na Zhang - Central South UniversityPeixia Cheng - Capital Medical UniversityDavid C. Schwebel - University of Alabama at BirminghamWangxin Xiao - Central South UniversityLi Li - Central South UniversityLei Yang - Central South UniversityMin Zhao - Central South UniversityShuying Zhao - Central South UniversityZhenzhen Rao - Central South UniversityPeishan Ning - Central South UniversityGuoqing Hu - Central South University
- Resource Type
- Journal article
- Publication Details
- Journal of safety research, Vol.96, pp.55-63
- DOI
- 10.1016/j.jsr.2025.11.008
- ISSN
- 0022-4375
- eISSN
- 1879-1247
- Publisher
- Elsevier Ltd
- Number of pages
- 9
- Grant note
- National Natural Science Foundation of China: 82073672, 82273743 Graduate Innovation Project of University: 1053320222179
This study was funded by the National Natural Science Foundation of China (grant numbers 82073672 and 82273743) and supported by the Graduate Innovation Project of University (Project No. 1053320222179) .
- Language
- English
- Electronic publication date
- 11/27/2025
- Date published
- 02/2026
- Academic Unit
- Research Administration; Psychological and Brain Sciences
- Record Identifier
- 9985090634902771
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