title: Investigation of time and weather effects on crash types using full Bayesian multivariate Poisson lognormal models.
abstract: Previous research shows that various weather elements have significant effects on crash occurrence and risk; however, little is known about how these elements affect different crash types. Consequently, this study investigates the impact of weather elements and sudden extreme snow or rain weather changes on crash type. Multivariate models were used for seven crash types using five years of daily weather and crash data collected for the entire City of Edmonton. In addition, the yearly trend and random variation of parameters across the years were analyzed by using four different modeling formulations. The proposed models were estimated in a full Bayesian context via Markov Chain Monte Carlo simulation. The multivariate Poisson lognormal model with yearly varying coefficients provided the best fit for the data according to Deviance Information Criteria. Overall, results showed that temperature and snowfall were statistically significant with intuitive signs (crashes decrease with increasing temperature; crashes increase as snowfall intensity increases) for all crash types, while rainfall was mostly insignificant. Previous snow showed mixed results, being statistically significant and positively related to certain crash types, while negatively related or insignificant in other cases. Maximum wind gust speed was found mostly insignificant with a few exceptions that were positively related to crash type. Major snow or rain events following a dry weather condition were highly significant and positively related to three crash types: Follow-Too-Close, Stop-Sign-Violation, and Ran-Off-Road crashes. The day-of-the-week dummy variables were statistically significant, indicating a possible weekly variation in exposure. Transportation authorities might use the above results to improve road safety by providing drivers with information regarding the risk of certain crash types for a particular weather condition.
authors.affiliation: Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada T6G 2W2. Electronic address: karim.el-basyouny@ualberta.ca.
authors.forename: Karim
authors.initials: K
authors.lastname: El-Basyouny
authors.affiliation: Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada T6G 2W2. Electronic address: barua1@ualberta.ca.
authors.forename: Sudip
authors.initials: S
authors.lastname: Barua
authors.affiliation: Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada T6G 2W2. Electronic address: mdtazul@ualberta.ca.
authors.forename: Md Tazul
authors.initials: MT
authors.lastname: Islam
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D006801:Humans; D008390:Markov Chains; D008962:Models, Theoretical; D009010:Monte Carlo Method; D011891:Rain; D012306:Risk; D012449:Safety; D012914:Snow; D014919:Wind
keywords: Full Bayesian estimation; Markov Chain Monte Carlo (MCMC); Poisson lognormal regression; Random parameters; Weather elements