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Severity Predictions for Intercity Bus Crashes on Highway Using a Random Parameter Ordered Probit Model

Author

Listed:
  • Kanghyun Kim

    (Department of Urban Planning and Transportation Engineering, Keimyung University, Daegu 42601, Republic of Korea)

  • Jungyeol Hong

    (Department of Transportation Engineering, Keimyung University, Daegu 42601, Republic of Korea)

Abstract

As intercity buses are a mode that moves large-scale occupancy between regions, it accounts for the mode share-means for mid- to long-distance movement in South Korea. However, the study of intercity bus safety needs to be more extensive, and safety policies are carried out based on traditional probability models without considering the data characteristics of bus accidents. Therefore, in this study, the Random Parameter Ordered Logit model was applied to derive fixed parameter factors that have the same effect on the severity of intercity bus accidents and Random Parameters that consider the heterogeneity of unique attributes by accident. It also analyzed the marginal effect of intercity bus accident severity. As a result of this study, the influencing factors that reflect heterogeneity with random parameters were driver’s condition: drowsiness, vehicle size: medium, crash type: vehicle–pedestrian accident, road condition: wet pavement, and log form of AADT. The random parameter ordered logit model was traditionally found to be more suitable than the ordinal logit model, which only reflects fixed factors and more reliable predictions considering the heterogeneity of accident characteristics for each observation.

Suggested Citation

  • Kanghyun Kim & Jungyeol Hong, 2023. "Severity Predictions for Intercity Bus Crashes on Highway Using a Random Parameter Ordered Probit Model," Sustainability, MDPI, vol. 15(17), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:13131-:d:1230313
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    References listed on IDEAS

    as
    1. Arshad Jamal & Waleed Umer, 2020. "Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network," IJERPH, MDPI, vol. 17(20), pages 1-22, October.
    2. Xiaojun Shao & Xiaoxiang Ma & Feng Chen & Mingtao Song & Xiaodong Pan & Kesi You, 2020. "A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions," IJERPH, MDPI, vol. 17(2), pages 1-18, January.
    3. Tong Zhu & Zishuo Zhu & Jie Zhang & Chenxuan Yang, 2021. "Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances," IJERPH, MDPI, vol. 18(21), pages 1-19, October.
    4. Feng Chen & Mingtao Song & Xiaoxiang Ma, 2019. "Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model," IJERPH, MDPI, vol. 16(14), pages 1-12, July.
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