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Journal of Advanced Transportation
Volume 2017, Article ID 5391054, 8 pages
https://doi.org/10.1155/2017/5391054
Research Article

Bayesian Hierarchical Modeling Monthly Crash Counts on Freeway Segments with Temporal Correlation

School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong 510641, China

Correspondence should be addressed to Huiying Wen; nc.ude.tucs@newyh

Received 26 June 2017; Accepted 11 September 2017; Published 24 October 2017

Academic Editor: Francesco Bella

Copyright © 2017 Qiang Zeng et al. 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.

Abstract

As the basis of traffic safety management, crash prediction models have long been a prominent focus in the field of freeway safety research. Studies usually take years or seasons as the observed time units, which may result in heterogeneity in crash frequency. To eliminate that heterogeneity, this study analyzes monthly crash counts and develops Bayesian hierarchical models with random effects, lag-1 autoregression (AR-1), and both (REAR-1) to accommodate the multilevel structure and temporal correlation in crash data. The candidate models are estimated and evaluated in the freeware WinBUGS using a crash dataset obtained from the Kaiyang Freeway in Guangdong Province, China. Significant temporal effects are found in the three models, and Deviance Information Criteria (DIC) results show that taking temporal correlation into account considerably improves the model fit compared with the Poisson model. The hierarchical models also avoid any misidentification of the factors with significant safety effects, because their variances are greater than in the Poisson model. The DIC value of the AR-1 model is substantially lower than that of the random effect model and equivalent to that of the REAR-1 model, which indicates the superiority of the lag-1 autoregressive structure in accounting for the temporal effects in crash frequency.