Highlights Investigate impact of weather elements and sudden weather changes on crash type. Analysis used multivariate Poisson lognormal model with random parameters. Models were estimated using citywide daily crash data by type from 2006 to 2010. Sudden weather changes were significant and positively related to three out of seven crash types. Results showed significant with intuitive relationships between weather elements and crash types.

    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.


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    Titel :

    Investigation of time and weather effects on crash types using full Bayesian multivariate Poisson lognormal models


    Beteiligte:
    El-Basyouny, Karim (Autor:in) / Barua, Sudip (Autor:in) / Islam, Md Tazul (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2014-08-16


    Format / Umfang :

    9 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch