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Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks

Author

Listed:
  • Lian Zhu

    (Transportation Research Center, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Linjun Lu

    (Transportation Research Center, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Wenying Zhang

    (Transportation Research Center, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Yurou Zhao

    (Transportation Research Center, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Meining Song

    (Transportation Research Center, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

Crashes that occur on curved roadways are often more severe than straight road accidents. Previously, most studies focused on the associations between curved sections and roadway geometric characteristics. In this study, significant factors such as driver behavior, roadway features, vehicle factors, and environmental characteristics are identified and involved in analyzing traffic accident severity. Bayesian network analysis was conducted to deal with data, to explore the associations between variables, and to make predictions using these relationships. The results indicated that factors including point of impact, site of location, accident side of road, alcohol/drugs condition, etc., are relatively critical in crashes on horizontal curves. Accident severity increases when crashes occur on bridges. The sensitivity of accident severity to vehicle use, traffic control, point of impact, and alcohol/drugs condition is relatively high. Moreover, a combination of negative factors will aggravate accident severities. The results also proposed some suggestions regarding the design of vehicles, as well as the construction and improvement of curved roadways.

Suggested Citation

  • Lian Zhu & Linjun Lu & Wenying Zhang & Yurou Zhao & Meining Song, 2019. "Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks," Sustainability, MDPI, vol. 11(8), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:8:p:2223-:d:222388
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    References listed on IDEAS

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    Cited by:

    1. Xiao Zhang & Xiaofeng Hu & Yiping Bai & Jiansong Wu, 2020. "Risk Assessment of Gas Leakage from School Laboratories Based on the Bayesian Network," IJERPH, MDPI, vol. 17(2), pages 1-18, January.
    2. Spasoje Mićić & Radoje Vujadinović & Goran Amidžić & Milanko Damjanović & Boško Matović, 2022. "Accident Frequency Prediction Model for Flat Rural Roads in Serbia," Sustainability, MDPI, vol. 14(13), pages 1-14, June.
    3. Nopadon Kronprasert & Katesirint Boontan & Patipat Kanha, 2021. "Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand," Sustainability, MDPI, vol. 13(16), pages 1-18, August.

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