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Traffic Accident Severity Prediction Based on Random Forest

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  • Miaomiao Yan

    (School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
    Key Laboratory of Image Processing and Intelligent Scheduling (Huazhong University of Science and Technology), Ministry of Education, Wuhan 430074, China
    Department of Mathematics, Huzhou University, Huzhou 313000, China)

  • Yindong Shen

    (School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
    Key Laboratory of Image Processing and Intelligent Scheduling (Huazhong University of Science and Technology), Ministry of Education, Wuhan 430074, China)

Abstract

The prediction of traffic accident severity is essential for traffic safety management and control. To achieve high prediction accuracy and model interpretability, we propose a hybrid model that integrates random forest (RF) and Bayesian optimization (BO). In the proposed model, BO-RF, RF is adopted as a basic predictive model and BO is used to tune the parameters of RF. Experimental results show that BO-RF achieves higher accuracy than conventional algorithms. Moreover, BO-RF provides interpretable results by relative importance and a partial dependence plot. We can identify important influential factors for traffic accident severity by relative importance. Further, we can investigate how the influential factors affect traffic accident severity by the partial dependence plot. These results provide insights to mitigate the severity of traffic accident consequences and contribute to the sustainable development of transportation.

Suggested Citation

  • Miaomiao Yan & Yindong Shen, 2022. "Traffic Accident Severity Prediction Based on Random Forest," Sustainability, MDPI, vol. 14(3), pages 1-13, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1729-:d:740854
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    References listed on IDEAS

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    1. Gholamreza Shiran & Reza Imaninasab & Razieh Khayamim, 2021. "Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques, and Artificial Neural Network: A Modeling Comparison," Sustainability, MDPI, vol. 13(10), pages 1-23, May.
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    Cited by:

    1. Bita Etaati & Arash Jahangiri & Gabriela Fernandez & Ming-Hsiang Tsou & Sahar Ghanipoor Machiani, 2023. "Understanding Active Transportation to School Behavior in Socioeconomically Disadvantaged Communities: A Machine Learning and SHAP Analysis Approach," Sustainability, MDPI, vol. 16(1), pages 1-18, December.
    2. Sinanaj, Luan & Bedalli, Erind & Abazi Bexheti, Lejla, 2023. "A Classification Model for Predicting Road Accidents Using Web Data," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2023), Hybrid Conference, Dubrovnik, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Hybrid Conference, Dubrovnik, Croatia, 4-6 September, 2023, pages 60-71, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    3. Mireille Megnidio-Tchoukouegno & Jacob Adedayo Adedeji, 2023. "Machine Learning for Road Traffic Accident Improvement and Environmental Resource Management in the Transportation Sector," Sustainability, MDPI, vol. 15(3), pages 1-19, January.

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