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A Comprehensive Analysis of Multi-Vehicle Crashes on Expressways: A Double Hurdle Approach

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  • Jungyeol Hong

    (Department of Transportation Engineering, University of Seoul, Seoul 02504, Korea)

  • Reuben Tamakloe

    (Department of Transportation Engineering, University of Seoul, Seoul 02504, Korea)

  • Dongjoo Park

    (Department of Transportation Engineering, University of Seoul, Seoul 02504, Korea)

Abstract

To maintain safe expressways, it is necessary to investigate the causes of severe traffic accidents and establish a strategy. This study aims to analyze crashes and identify the influence of crash-risk factors on multi-vehicle (MV) crashes. Crashes involving three types of vehicles namely passenger cars, buses, and freight trucks were analyzed using a seven-year data spanning 2011 to 2017 which consists of crashes that occurred on expressways in South Korea. We applied a double hurdle approach in which a model consists of two estimators: The first estimation, which is a binary logit model selects MV crashes from the dataset; and the second estimation which is a truncated regression model estimates the number of vehicles involved in the MV crash. We found that driver traffic violations such as the improper distance between vehicles, reversing and passing increases the probability of MV crashes occurring. MV crashes in tunnels and mainlines were found to be positively correlated with the number of vehicles involved in the crash, whereas fewer vehicles were involved in MV crashes at ramps and toll-booths. Further, we found that the hurdle model with an exponential form of conditional mean of the latent variable provides better estimation parameters.

Suggested Citation

  • Jungyeol Hong & Reuben Tamakloe & Dongjoo Park, 2019. "A Comprehensive Analysis of Multi-Vehicle Crashes on Expressways: A Double Hurdle Approach," Sustainability, MDPI, vol. 11(10), pages 1-22, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2782-:d:231387
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    References listed on IDEAS

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    Cited by:

    1. Maosheng Li & Hui Xie & Panpan Shu, 2021. "Study on the Impact of Traffic Accidents in Key Areas of Rural Roads," Sustainability, MDPI, vol. 13(14), pages 1-17, July.
    2. Feifeng Jiang & Kwok Kit Richard Yuen & Eric Wai Ming Lee & Jun Ma, 2020. "Analysis of Run-Off-Road Accidents by Association Rule Mining and Geographic Information System Techniques on Imbalanced Datasets," Sustainability, MDPI, vol. 12(12), pages 1-32, June.
    3. Hyunho Chang & Dongjoo Park, 2020. "Potentialities of Vehicle Trajectory Big Data for Monitoring Potentially Fatigued Drivers and Explaining Vehicle Crashes on Motorway Sections," Sustainability, MDPI, vol. 12(15), pages 1-16, July.
    4. 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.
    5. Xiuguang Song & Rendong Pi & Yu Zhang & Jianqing Wu & Yuhuan Dong & Han Zhang & Xinyuan Zhu, 2021. "Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes," IJERPH, MDPI, vol. 18(10), pages 1-16, May.

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