Citation

Zeng Q, Xu P, Wang X, Wen H, Hao W. Accid. Anal. Prev. 2021; 157: 106190.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.aap.2021.106190

PMID

unavailable

Abstract

Ranking sites with promise is an essential step for cost-effective engineering improvement on roadway traffic safety. This study proposes a Bayesian multivariate spatio-temporal interaction model based approach for ranking sites. The severity-weighted crash frequency and crash rate are used as the decision parameters. The posterior expected rank and posterior mean of the decision parameters are adopted as the statistical criteria. The proposed approach is applied to rank road segments on Kaiyang Freeway in China, which is conducted via programming in the freeware WinBUGS. The results of Bayesian estimation and assessment indicate that incorporating spatio-temporal correlations and interactions into the crash frequency model significantly improves the overall goodness-of-fit performance and affects the identified crash-contributing factors and the estimated safety effects for each severity level. With respect to the ranking results, significant differences are found between those generated from the proposed approach and those generated from the naïve ranking approach and a Bayesian approach based on the multivariate Poisson-lognormal model. Besides, the ranks under the posterior mean criterion are found generally consistent with those under the posterior expected rank criterion.

Language: en

Keywords

Crash severity; Bayesian ranking; Multivariate spatio-temporal interaction model; Ranking criterion; Site with promise