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A Bayesian semi-parametric model to estimate relationships between crash counts and roadway characteristics

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  • Shively, Thomas S.
  • Kockelman, Kara
  • Damien, Paul

Abstract

This paper uses a semi-parametric Poisson-gamma model to estimate the relationships between crash counts and various roadway characteristics, including curvature, traffic levels, speed limit and surface width. A Bayesian nonparametric estimation procedure is employed for the model's link function, substantially reducing the risk of a mis-specified model. It is shown via simulation that little is lost in terms of estimation quality if the nonparametric estimation procedure is used when standard parametric assumptions (e.g., linear functional forms) are satisfied, but there is significant gain if the parametric assumptions are violated. It is also shown that imposing appropriate monotonicity constraints on the relationships provides better function estimates. Results suggest that key factors for explaining crash rate variability across roadways are the amount and density of traffic, presence and degree of a horizontal curve, and road classification. Issues related to count forecasting on individual roadway segments and out-of-sample validation measures also are discussed.

Suggested Citation

  • Shively, Thomas S. & Kockelman, Kara & Damien, Paul, 2010. "A Bayesian semi-parametric model to estimate relationships between crash counts and roadway characteristics," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 699-715, June.
    • Handle: RePEc:eee:transb:v:44:y:2010:i:5:p:699-715
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    References listed on IDEAS

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    Cited by:

    1. Parry, Katharina & Hazelton, Martin L., 2013. "Bayesian inference for day-to-day dynamic traffic models," Transportation Research Part B: Methodological, Elsevier, vol. 50(C), pages 104-115.
    2. 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.
    3. Wang, Zhengli & Jiang, Hai, 2019. "Simultaneous correction of the time and location bias associated with a reported crash by exploiting the spatiotemporal evolution of travel speed," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 199-223.
    4. Laura Cáceres & Miguel A. Fernández & Alfonso Gordaliza & Aquilino Molinero, 2021. "Detection of Geometric Risk Factors Affecting Head-On Collisions through Multiple Logistic Regression: Improving Two-Way Rural Road Design via 2+1 Road Adaptation," IJERPH, MDPI, vol. 18(12), pages 1-13, June.

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