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Investigating Spatial Autocorrelation and Spillover Effects in Freeway Crash-Frequency Data

Author

Listed:
  • Huiying Wen

    (School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University Road #2, Nanjing 211189, China)

  • Xuan Zhang

    (School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University Road #2, Nanjing 211189, China)

  • Qiang Zeng

    (School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University Road #2, Nanjing 211189, China)

  • Jaeyoung Lee

    (Department of Civil, Environment and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA)

  • Quan Yuan

    (State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China)

Abstract

This study attempts to investigate spatial autocorrelation and spillover effects in micro traffic safety analysis. To achieve the objective, a Poisson-based count regression with consideration of these spatial effects is proposed for modeling crash frequency on freeway segments. In the proposed hybrid model, the spatial autocorrelation and the spillover effects are formulated as the conditional autoregressive (CAR) prior and the exogenous variables of adjacent segments, respectively. The proposed model is demonstrated and compared to the models with only one kind of spatial effect, using one-year crash data collected from Kaiyang Freeway, China. The results of Bayesian estimation conducted in WinBUGS show that significant spatial autocorrelation and spillover effects simultaneously exist in the freeway crash-frequency data. The lower value of deviance information criterion (DIC) and more significant exogenous variables for the hybrid model compared to the other alternatives, indicate the strength of accounting for both spatial autocorrelation and spillover effects on improving model fit and identifying crash contributing factors. Moreover, the model results highlight the importance of daily vehicle kilometers traveled, and horizontal and vertical alignments of targeted segments and adjacent segments on freeway crash occurrences.

Suggested Citation

  • Huiying Wen & Xuan Zhang & Qiang Zeng & Jaeyoung Lee & Quan Yuan, 2019. "Investigating Spatial Autocorrelation and Spillover Effects in Freeway Crash-Frequency Data," IJERPH, MDPI, vol. 16(2), pages 1-12, January.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:2:p:219-:d:197546
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    References listed on IDEAS

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    1. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    4. Huang, Helai & Song, Bo & Xu, Pengpeng & Zeng, Qiang & Lee, Jaeyoung & Abdel-Aty, Mohamed, 2016. "Macro and micro models for zonal crash prediction with application in hot zones identification," Journal of Transport Geography, Elsevier, vol. 54(C), pages 248-256.
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    Cited by:

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    4. Zheng, Qikang & Sharmeen, Fariya & Xu, Chengcheng & Liu, Pan, 2024. "Assessing regional road traffic safety in Sweden through dynamic panel data analysis: Influence of the planned innovative policies and the unplanned COVID-19 pandemic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).

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