Road Traffic Accident Risk Prediction Based on Random Forest Model

Haiyun Liu

doi:10.54254/2753-8818/2025.AD26486

Theoretical and Natural ScienceOpen access

Road Traffic Accident Risk Prediction Based on Random Forest Model

Research Article

Open Access

Road Traffic Accident Risk Prediction Based on Random Forest Model

Haiyun Liu ^1*

¹ Qingdao University

^*Corresponding author: 2023201632@qdu.edu.cn

Published on 3 September 2025

TNS Vol.134

ISSN (Print): 2753-8826

ISSN (Online): 2753-8818

ISBN (Print): 978-1-80590-343-7

ISBN (Online): 978-1-80590-344-4

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Abstract

With the increase in global car ownership, the incidence of road traffic accidents is also rising. For the prediction and research of road traffic accidents risk, this paper considers weather factors and roadside facility factors, processes missing values in the samples, balances the sampling using the SMOTE method, and determines the optimal parameters. A roadside traffic accident risk prediction model based on Random Forest is established, mainly to predict the severity of traffic accidents and the duration of the impact of accidents. It is found that in weather factors, atmospheric pressure is the dominant factor for predicting accident severity, while precipitation is the dominant factor for predicting duration. Among the roadside facility factors, the prediction of accident severity and the duration of impact are both highly correlated with the presence of intersections and junctions nearby. Finally, this paper compares the fitting effect of Random Forest algorithm, Neural Network algorithm, Adaboost algorithm, Support Vector Machine algorithm, and XGboost algorithm on the model. The R2value of the Random Forest model is 0.833, which is the highest among all models. Therefore, the Random Forest model has the best fitting effect on the research problem.

Keywords:

Traffic accident, Risk prediction, Random Forest, SMOTE method

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References

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[3]. Cheng, R., Pan, Y., Dai, J. J., Wang, T., Xie, J. C. (2023). A Review of Roadside Accident Risk Assessment and Roadside Safety Design Research on Highways. China Safety Science Journal, 33(09)214-226.

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[6]. Moosavi, S., Mohammad H. S., Srinivasan P., Rajiv R., (2019). A Countrywide Traffic Accident Dataset. arXiv preprint arXiv.

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[10]. Wang, X. (2016). Multi-Class Classification Method for Imbalanced Data Based on Tree Structure. Journal of Lvliang University, 6(02): 8-10.

References

[1]. Sun, C. Y., Dong, Q., Wang, Y. W., Ma, F. H., Xie, T. C., Li, M. (2024). A Machine Learning-Based Analysis Method for Rollover Accident Severity. Journal of Traffic Engineering, 68-77.

[2]. Dong, C. J., Wan, Y. J., Li, P. H. (2025). Multi-Category Traffic Accident Risk Assessment Based on Interpretable Random Forest. Journal of Beijing University of Technology, 1-10.

[5]. Xu, C. C., Liu, P., Wang, W., Li, Z. B. (2012). Evaluation of the impacts of traffic states on crash risks on freeways. Accident Analysis and Prevention, 47162-171.

[6]. Moosavi, S., Mohammad H. S., Srinivasan P., Rajiv R., (2019). A Countrywide Traffic Accident Dataset. arXiv preprint arXiv.

[7]. Zhang, S. Y., Wang, A. Y. (2025). Credit Risk Assessment of Small and Medium Enterprises Based on SMOTE-Boruta-LightGBM. Times Economics and Trade, 22(04): 42-48.

[10]. Wang, X. (2016). Multi-Class Classification Method for Imbalanced Data Based on Tree Structure. Journal of Lvliang University, 6(02): 8-10.

Cite this article

Liu,H. (2025). Road Traffic Accident Risk Prediction Based on Random Forest Model. Theoretical and Natural Science,134,62-72.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-APMM 2025 Symposium: Controlling Robotic Manipulator Using PWM Signals with Microcontrollers

ISBN: 978-1-80590-343-7(Print) / 978-1-80590-344-4(Online)

Editor: Marwan Omar, Mustafa Istanbullu

Conference website: https://2025.confapmm.org/adana.html

Conference date: 19 September 2025

Series: Theoretical and Natural Science

Volume number: Vol.134

ISSN: 2753-8818(Print) / 2753-8826(Online)

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