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A Random-Parameter Negative Binomial Model for Assessing Freeway Crash Frequency by Injury Severity: Daytime versus Nighttime

Author

Listed:
  • Ping Zhang

    (School of Engineering, Tibet University, No. 36 Jiangsu, Lhasa 850000, China)

  • Chenzhu Wang

    (School of Transportation, Southeast University, 2 Sipailou, Nanjing 210096, China)

  • Fei Chen

    (School of Transportation, Southeast University, 2 Sipailou, Nanjing 210096, China)

  • Suping Cui

    (School of Engineering, Tibet University, No. 36 Jiangsu, Lhasa 850000, China)

  • Jianchuan Cheng

    (School of Transportation, Southeast University, 2 Sipailou, Nanjing 210096, China)

  • Wu Bo

    (School of Engineering, Tibet University, No. 36 Jiangsu, Lhasa 850000, China)

Abstract

This study explored the effects of contributing factors on crash frequency, by injury severity of all, daytime, and nighttime crashes that occurred on freeways. With three injury severity outcomes classified as light injury, minor injury, and severe injury, the effects of the explanatory variables affecting the crash frequency were examined in terms of the crash, traffic, speed, geometric, and sight characteristics. Regarding the model estimations, the lowest AIC and BIC values (2263.87 and 2379.22, respectively) showed the superiority of the random-parameter multivariate negative binomial (RPMNB) model in terms of the goodness-of-fit measure. Additionally, the RPMNB model indicated the highest R 2 (0.25) and predictive accuracy, along with a significantly positive α parameter. Moreover, transferability tests were conducted to confirm the rationality of separating the daytime and nighttime crashes. Based on the RPMNB models, several explanatory variables were observed to exhibit relatively stable effects whereas other variables presented obvious variations. This study can be of certain value in guiding highway design and policies and developing effective safety countermeasures.

Suggested Citation

  • Ping Zhang & Chenzhu Wang & Fei Chen & Suping Cui & Jianchuan Cheng & Wu Bo, 2022. "A Random-Parameter Negative Binomial Model for Assessing Freeway Crash Frequency by Injury Severity: Daytime versus Nighttime," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9061-:d:870290
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    References listed on IDEAS

    as
    1. Rainer Winkelmann, 2008. "Econometric Analysis of Count Data," Springer Books, Springer, edition 0, number 978-3-540-78389-3, December.
    2. Xiong, Yingge & Tobias, Justin L. & Mannering, Fred L., 2014. "The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 109-128.
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    6. Rainer Winkelmann, 2000. "Seemingly Unrelated Negative Binomial Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 62(4), pages 553-560, September.
    7. Chenzhu Wang & Fei Chen & Jianchuan Cheng & Wu Bo & Ping Zhang & Mingyu Hou & Feng Xiao, 2020. "Random-Parameter Multivariate Negative Binomial Regression for Modeling Impacts of Contributing Factors on the Crash Frequency by Crash Types," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-13, November.
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    Cited by:

    1. Xu Sun & Hanxiao Hu & Shuo Ma & Kun Lin & Jianyu Wang & Huapu Lu, 2022. "Study on the Impact of Road Traffic Accident Duration Based on Statistical Analysis and Spatial Distribution Characteristics: An Empirical Analysis of Houston," Sustainability, MDPI, vol. 14(22), pages 1-14, November.

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