Evidence map›Paper›PMID 38694049›Full record

ArticleHeliyon2024

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

Nian Zhou, Haotian Zeng, Runhong Xie, Tengfei Yang, Jiangwei Kong, Zhenzhu Song, Fu Zhang, Xinbiao Liao, Xinzhe Chen, Qifeng Miao and 4 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
8.2field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 4 institutions in 1 country.

Nian ZhouDepartment of Forensic Evidence Science, School of Forensic Medicine, Southern Medical University, Guangzhou, China.
Haotian ZengGuangzhou Public Security Bureau, Guangzhou, China.
Runhong XieGuangzhou Public Security Bureau, Guangzhou, China.
Tengfei YangDepartment of Forensic Evidence Science, School of Forensic Medicine, Southern Medical University, Guangzhou, China.
Jiangwei KongDepartment of Forensic Evidence Science, School of Forensic Medicine, Southern Medical University, Guangzhou, China.
Zhenzhu SongGuangzhou Public Security Bureau, Guangzhou, China.
Fu ZhangGuangdong Public Security Department, Guangzhou, China.
Xinbiao LiaoGuangdong Public Security Department, Guangzhou, China.
Xinzhe ChenSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China.
Qifeng MiaoGuangdong Province Research Center of Traffic Accident Identification Engineering Technology, Guangzhou, China.
Fengchong LanSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China.
Weidong ZhaoDepartment of Forensic Evidence Science, School of Forensic Medicine, Southern Medical University, Guangzhou, China.
Rong HanGuangzhou Public Security Bureau, Guangzhou, China.
Dongri LiDepartment of Forensic Evidence Science, School of Forensic Medicine, Southern Medical University, Guangzhou, China.
Southern Medical University · CNShanghai Public Security Bureau · CNSouth China University of Technology · CNZhejiang Provincial Public Security Department · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Accident preventionElectric bicycleInjuriesRegression analysisTraffic accidents

Identifiers

PMID38694049
PMCPMC11058882
OpenAlexW4394964701

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.