Applying a Bayesian multivariate spatio-temporal interaction model based approach to rank sites with promise using severity-weighted decision parameters

Copyright © 2021. Published by Elsevier Ltd..

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.

Media Type:

Electronic Article

Year of Publication:

2021

Contained In:

Accident; analysis and prevention - Vol. 157 (2021), p. 106190

Language:

English

Contributors:

Zeng, Qiang
Xu, Pengpeng
Wang, Xuesong
Wen, Huiying
Hao, Wei

Links:

Volltext

Keywords:

*Accidents, Traffic
*Models, Statistical
Bayes Theorem
Bayesian ranking
China
Crash severity
Humans
Journal Article
Multivariate spatio-temporal interaction model
Ranking criterion
Safety
Site with promise

Notes:

Date Completed 24.06.2021

Date Revised 24.06.2021

published: Print-Electronic

Citation Status MEDLINE

Copyright: From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Physical Description:

Online-Ressource

doi:

10.1016/j.aap.2021.106190

PMID:

34020182

PPN (Catalogue-ID):

NLM326842810