Citation

Barua S, El-Basyouny K, Islam MT. Anal. Meth. Accid. Res. 2015; 5-6: 28-42.

Copyright

(Copyright © 2015, Elsevier Publishing)

DOI

10.1016/j.amar.2015.02.001

PMID

unavailable

Abstract

This study investigated the inclusion of spatial correlation in random parameters collision count-data models. Three different modeling formulations were applied to measure the effects of spatial correlation in random parameters models using three years of collision data collected from two cities, Richmond and Vancouver (British Columbia, Canada). The proposed models were estimated in a Full Bayesian (FB) context using a Markov Chain Monte Carlo (MCMC) simulation. The Deviance Information Criteria (DIC) values and chi-square statistics indicated that all the models were comparable to one another. According to the parameter estimates, a variety of traffic and road geometric covariates were found to significantly influence collision frequencies. For the Richmond dataset, only 38.3% of the total variability was explained by spatial correlation under model with both heterogeneous effects and spatial correlation (Model C), as most of the variations were likely captured by heterogeneous effects and site variation. For the Vancouver dataset, the effects of spatial correlation were much clearer, with a high percentage of the total variability (83.8%) explained by spatial correlation under Model C. Moreover, model estimation results showed that the precision of parameter estimates slightly improved by inclusion of spatial correlation when the sample size was small. However, parameter estimations did not change significantly and goodness of fit did not improve which indicate that it cannot be substantiated with the current datasets that the random parameters model with both heterogeneous effects and spatial correlation is better than other models investigated. Therefore, further studies with different datasets are needed to get more clear understanding of the added benefits of incorporating spatial correlation in random parameters model.

Language: en