01519659

Component

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 developed for seven crash types using five years of daily weather and crash data gathered for the entire City of Edmonton. In addition, the time trend and random variation of parameters across the years were analyzed by developing 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 time 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, snow on the ground and daylight hours 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 and number of registered vehicles were 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 and season-of-the-year dummy variables were statistically significant, indicating a possible weekly and seasonal variation in exposure.

This paper was sponsored by TRB committee ANB20 Safety Data, Analysis and Evaluation.

01503729

14-2133

English

21p

2014

Transportation Research Board 93rd Annual Meeting

Digital/other

Figures; References; Tables

Bayes' theorem; Markov chains; Monte Carlo method; Moving violations; Periods of the day; Poisson distributions; Ran off road crashes; Tailgating; Traffic crashes; Weather conditions

Lognormal distributions

Edmonton (Canada)

Environment; Highways; Planning and Forecasting; Safety and Human Factors; I15: Environment; I72: Traffic and Transport Planning; I80: Accident Studies

Transportation Research Board Annual Meeting 2014 Paper #14-2133

TRIS, TRB, ATRI

Jan 27 2014 2:45PM