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Int. J. Environ. Res. Public Health 2019, 16(2), 219; https://doi.org/10.3390/ijerph16020219

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

1
School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
2
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University Road #2, Nanjing 211189, China
3
Department of Civil, Environment and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA
4
State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Received: 29 November 2018 / Revised: 4 January 2019 / Accepted: 4 January 2019 / Published: 14 January 2019
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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. View Full-Text
Keywords: spatial autocorrelation; spatial spillover effects; conditional autoregressive prior; freeway crash frequency spatial autocorrelation; spatial spillover effects; conditional autoregressive prior; freeway crash frequency
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wen, H.; Zhang, X.; Zeng, Q.; Lee, J.; Yuan, Q. Investigating Spatial Autocorrelation and Spillover Effects in Freeway Crash-Frequency Data. Int. J. Environ. Res. Public Health 2019, 16, 219.

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