Analysis of factors affecting riders injury severity in E-bike-motor vehicle collision accidents based on CIDAS data

Peng Wang; Miao Lin; XiaoHu Li; WenXia Wang; Wen Wei

doi:10.1117/12.2645678

23 November 2022 Analysis of factors affecting riders injury severity in E-bike-motor vehicle collision accidents based on CIDAS data

Peng Wang, Miao Lin, XiaoHu Li, WenXia Wang, Wen Wei

Author Affiliations +

Proceedings Volume 12302, Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022); 123022S (2022) https://doi.org/10.1117/12.2645678
Event: Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022), 2022, Guangzhou, China

Abstract

To analyze influencing factors of rider injury severity in E-bike-motor vehicle collision accidents, we have analyzed 1,246 accidents in the China In-depth Accident Study (CIDAS) dataset using the Light Gradient Boosting Machine (LightGBM) model, which is used to further explore primary factors affecting the injury of riders. Experimental results show that severity of injury is highly correlated with traffic environment, characteristics of electric vehicles and riders. Meanwhile, we find that the throwing distance of riders has an obvious threshold effect on death, and accidents occurred outside urban areas are more likely to lead to death of riders. Finally, suggestions are made for the causes of accident injuries. This research is beneficial to the understanding of complex nonlinear relationships between accident injuries and influencing factors. The experimental results and analysis provide more guidelines to investigate injury prevention measures.

Citation Download Citation

Peng Wang, Miao Lin, XiaoHu Li, WenXia Wang, and Wen Wei "Analysis of factors affecting riders injury severity in E-bike-motor vehicle collision accidents based on CIDAS data", Proc. SPIE 12302, Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022), 123022S (23 November 2022); https://doi.org/10.1117/12.2645678

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KEYWORDS

Injuries

Roads

Data modeling

Analytical research

Factor analysis

Statistical modeling

Performance modeling

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