| Issue |
SHS Web Conf.
Volume 179, 2023
2023 6th International Conference on Humanities Education and Social Sciences (ICHESS 2023)
|
|
|---|---|---|
| Article Number | 01020 | |
| Number of page(s) | 5 | |
| Section | Social Science and Cognitive Psychoanalysis | |
| DOI | https://doi.org/10.1051/shsconf/202317901020 | |
| Published online | 14 December 2023 | |
Analysis of the Harmfulness of Abnormal Riding Behaviors of Electric Bicycles Based on Improved Multiclass Logistic Regression Model
1 Department of Traffic and Transportation Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China
2 Department of Traffic and Transportation Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China
a 122110011241@njust.edu.cn
b yingshun@njust.edu.cn
To analyze the harmfulness of abnormal riding behaviors of electric bicycles in-depth, the research focuses on the 2022 electric bicycle accident data in a specific city in China. Based on an improved multiclass logistic regression model, the relationship between different abnormal riding behaviors and the severity of electric bicycle traffic accidents is explored. Firstly, the severity of accidents is categorized into three levels as the dependent variable, while driver attributes and various hazardous driving behaviors serve as independent variables to construct the multiclass logistic regression model. Secondly, the model is optimized by eliminating irrelevant independent variables and improving the link function. Finally, the harmfulness of abnormal riding behaviors of electric bicycles is analyzed based on the results of the regression model. The results indicate that eight factors significantly influence the dependent variable, with three factors, including driving under the influence of alcohol, being more likely to lead to fatal accidents, requiring focused attention for intervention and regulation.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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