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A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions

Author

Listed:
  • Xiaojun Shao

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education Tongji University, Shanghai 201804, China)

  • Xiaoxiang Ma

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education Tongji University, Shanghai 201804, China)

  • Feng Chen

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education Tongji University, Shanghai 201804, China)

  • Mingtao Song

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education Tongji University, Shanghai 201804, China)

  • Xiaodong Pan

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education Tongji University, Shanghai 201804, China)

  • Kesi You

    (Shanghai Municipal Engineering Design Institute (Group) Co., Ltd., Shanghai 200092, China)

Abstract

Social and economic burdens caused by truck-involved rear-end collisions are of great concern to public health and the environment. However, few efforts focused on identifying the difference of impacting factors on injury severity between car-strike-truck and truck-strike-car in rear-end collisions. In light of the above, this study focuses on illustrating the impact of variables associated with injury severity in truck-related rear-end crashes. To this end, truck involved rear-end crashes between 2006 and 2015 in the U.S. were obtained. Three random parameters ordered probit models were developed: two separate models for the car-strike-truck crashes and the truck-strike-car crashes, respectively, and one for the combined dataset. The likelihood ratio test was conducted to evaluate the significance of the difference between the models. The results show that there is a significant difference between car-strike-truck and truck-strike-car crashes in terms of contributing factors towards injury severity. In addition, indicators reflecting male, truck, starting or stopped in the road before a crash, and other vehicles stopped in lane show a mixed impact on injury severity. Corresponding implications were discussed according to the findings to reduce the possibility of severe injury in truck-involved rear-end collisions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:2:p:395-:d:306025
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    References listed on IDEAS

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    1. Stefan Boes & Rainer Winkelmann, 2006. "Ordered Response Models," Springer Books, in: Olaf Hübler & Jachim Frohn (ed.), Modern Econometric Analysis, chapter 12, pages 167-181, Springer.
    2. 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.
    3. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
    4. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    5. Abay, Kibrom A. & Paleti, Rajesh & Bhat, Chandra R., 2013. "The joint analysis of injury severity of drivers in two-vehicle crashes accommodating seat belt use endogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 50(C), pages 74-89.
    6. Bhat, Chandra R., 2003. "Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 837-855, November.
    7. Feng Chen & Suren Chen & Xiaoxiang Ma, 2016. "Crash Frequency Modeling Using Real-Time Environmental and Traffic Data and Unbalanced Panel Data Models," IJERPH, MDPI, vol. 13(6), pages 1-16, June.
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    Cited by:

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    7. 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.
    8. Ming Lv & Xiaojun Shao & Chimou Li & Feng Chen, 2022. "Driving Performance Evaluation of Shuttle Buses: A Case Study of Hong Kong–Zhuhai–Macau Bridge," IJERPH, MDPI, vol. 19(3), pages 1-13, January.
    9. Konstantinou, Theodora & Gkritza, Konstantina, 2023. "Are we getting close to truck electrification? U.S. truck fleet managers’ stated intentions to electrify their fleets," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).

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