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Predicting Severity and Duration of Road Traffic Accident

Author

Listed:
  • Fang Zong
  • Huiyong Zhang
  • Hongguo Xu
  • Xiumei Zhu
  • Lu Wang

Abstract

This paper presents a model system to predict severity and duration of traffic accidents by employing Ordered Probit model and Hazard model, respectively. The models are estimated using traffic accident data collected in Jilin province, China, in 2010. With the developed models, three severity indicators, namely, number of fatalities, number of injuries, and property damage, as well as accident duration, are predicted, and the important influences of related variables are identified. The results indicate that the goodness-of-fit of Ordered Probit model is higher than that of SVC model in severity modeling. In addition, accident severity is proven to be an important determinant of duration; that is, more fatalities and injuries in the accident lead to longer duration. Study results can be applied to predictions of accident severity and duration, which are two essential steps in accident management process. By recognizing those key influences, this study also provides suggestive results for government to take effective measures to reduce accident impacts and improve traffic safety.

Suggested Citation

  • Fang Zong & Huiyong Zhang & Hongguo Xu & Xiumei Zhu & Lu Wang, 2013. "Predicting Severity and Duration of Road Traffic Accident," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, December.
    • Handle: RePEc:hin:jnlmpe:547904
    DOI: 10.1155/2013/547904
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    Cited by:

    1. Sai Chand & Zhuolin Li & Abdulmajeed Alsultan & Vinayak V. Dixit, 2022. "Comparing and Contrasting the Impacts of Macro-Level Factors on Crash Duration and Frequency," IJERPH, MDPI, vol. 19(9), pages 1-19, May.
    2. 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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