Citation

Xu J, Sun L. J. Southeast Univ. Engl. Ed. 2014; 30(1): 96-100.

Copyright

(Copyright © 2014, Southeast University - Nanjing)

DOI

10.3969/j.issn.1003-7985.2014.01.018

PMID

unavailable

Abstract

In order to improve crash occurrence models to account for the influence of various contributing factors, a conditional autoregressive negative binomial (CAR-NB) model is employed to allow for overdispersion (tackled by the NB component), unobserved heterogeneity and spatial autocorrelation (captured by the CAR process), using Markov chain Monte Carlo methods and the Gibbs sampler. Statistical tests suggest that the CAR-NB model is preferred over the CAR-Poisson, NB, zero-inflated Poisson, zero-inflated NB models, due to its lower prediction errors and more robust parameter inference. The study results show that crash frequency and fatalities are positively associated with the number of lanes, curve length, annual average daily traffic (AADT) per lane, as well as rainfall. Speed limit and the distances to the nearest hospitals have negative associations with segment-based crash counts but positive associations with fatality counts, presumably as a result of worsened collision impacts at higher speed and time loss during transporting crash victims. Copyright.

Language: en