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Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models

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  • Fang Zong
  • Hongguo Xu
  • Huiyong Zhang

Abstract

The paper presents a comparison between two modeling techniques, Bayesian network and Regression models, by employing them in accident severity analysis. Three severity indicators, that is, number of fatalities, number of injuries and property damage, are investigated with the two methods, and the major contribution factors and their effects are identified. The results indicate that the goodness of fit of Bayesian network is higher than that of Regression models in accident severity modeling. This finding facilitates the improvement of accuracy for accident severity prediction. Study results can be applied to the prediction of accident severity, which is one of the essential steps in accident management process. By recognizing the key influences, this research also provides suggestions for government to take effective measures to reduce accident impacts and improve traffic safety.

Suggested Citation

  • Fang Zong & Hongguo Xu & Huiyong Zhang, 2013. "Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, October.
    • Handle: RePEc:hin:jnlmpe:475194
    DOI: 10.1155/2013/475194
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    Cited by:

    1. Ke Zhang & Yaming Guo, 2023. "Attention-Based Residual Dilated Network for Traffic Accident Prediction," Mathematics, MDPI, vol. 11(9), pages 1-15, April.
    2. Khaled Assi, 2020. "Traffic Crash Severity Prediction—A Synergy by Hybrid Principal Component Analysis and Machine Learning Models," IJERPH, MDPI, vol. 17(20), pages 1-16, October.
    3. Rachel Aldred & Susana García-Herrero & Esther Anaya & Sixto Herrera & Miguel Ángel Mariscal, 2019. "Cyclist Injury Severity in Spain: A Bayesian Analysis of Police Road Injury Data Focusing on Involved Vehicles and Route Environment," IJERPH, MDPI, vol. 17(1), pages 1-16, December.
    4. Khaled Assi & Syed Masiur Rahman & Umer Mansoor & Nedal Ratrout, 2020. "Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol," IJERPH, MDPI, vol. 17(15), pages 1-17, July.
    5. Huajing Ning & Yunyan Yu & Lu Bai, 2022. "Unsafe Behaviors Analysis of Sideswipe Collision on Urban Expressways Based on Bayesian Network," Sustainability, MDPI, vol. 14(13), pages 1-15, July.

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