Analysis of road traffic accidents and casualties associated with electric bikes and bicycles in Guangzhou, China: A retrospective descriptive analysis

被引:5
作者
Zhou, Nian [1 ]
Zeng, Haotian [2 ]
Xie, Runhong [2 ]
Yang, Tengfei [1 ]
Kong, Jiangwei [1 ]
Song, Zhenzhu [2 ]
Zhang, Fu [3 ]
Liao, Xinbiao [3 ]
Chen, Xinzhe [4 ]
Miao, Qifeng [5 ]
Lan, Fengchong [4 ]
Zhao, Weidong [1 ]
Han, Rong [2 ]
Li, Dongri [1 ]
机构
[1] Southern Med Univ, Sch Forens Med, Dept Forens Evidence Sci, Guangzhou, Peoples R China
[2] Guangzhou Publ Secur Bur, Guangzhou, Peoples R China
[3] Guangdong Publ Secur Dept, Guangzhou, Peoples R China
[4] South China Univ Technol, Sch Mech & Automot Engn, Guangzhou, Peoples R China
[5] Guangdong Prov Res Ctr Traff Accid Identificat Eng, Guangzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
Electric bicycle; Accident prevention; Regression analysis; Traffic accidents; Injuries; RIDERS; BEHAVIORS; SAFETY; SEVERITY; CRASHES; RISK;
D O I
10.1016/j.heliyon.2024.e29961
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Introduction: Electric bicycles (e-bikes) and bicycles in large Chinese cities have recently witnessed substantial growth in ridership. According to related accident trends, this study analyzed characteristics and spatial distribution in the period when e-bike-related accidents rapidly increased to propose priority measures to reduce accident casualties. Methods: For e-bike- and bicycle-related accident data from the Guangzhou Public Security Traffic Management Integrated System, linear regression was used to examine the trends in the number of accidents and age-adjusted road traffic casualties from 2011 to 2021. Then, for the period when e-bike-related accidents rapidly increased, descriptive statistics were computed regarding rider characteristics, illegal behaviors, road types, collision objects and their accident liability. One-way analysis of variance (ANOVA) followed by Bonferroni's multiple comparison test. P < 0.05 was considered statistically significant. Finally, the density distribution of accidents was presented, and Moran's I (MI) was used for assessing spatial autocorrelation. Hotspots were identified based on an optimized hotspot analysis tool. Results: Between 2011 and 2021, the number of accidents and casualty rate (per 100,000 population) increased for e-bikes but decreased for bicycles. After 2018, e-bike-related accidents increased rapidly, and bicycle-related accidents plateaued. Accident hotspots were concentrated in central city areas and suburban areas close to the former. Three-quarters of accidents occurred in motorized vehicle lanes. Most occurred on roads without physically segregated nonmotorized vehicle lanes. More than three-fifths of the accidents involved motor vehicles with at least four wheels. The prevalence (per 100 people) of casualties among e-bike rider victims and cyclist victims accounted for 92.0 % and 96.5 %, respectively. A total of 71.6 % of e-bike-related accidents involved migrant workers. Riding in motorized vehicle lanes was the most common illegal behavior. Conclusions: Although e-bike-related and bicycle-related accidents presented similar characteristics, the sharp increase in e-bike-related accidents requires attention. To improve e-bike safety, governments should develop appropriate countermeasures to prevent riders from riding on motorways, such as improving road infrastructure, adjusting the driver's license system and addressing priority control areas.
引用
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页数:12
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