Studying the effect of weather conditions on daily crash counts using a discrete time-series model.
In previous research, significant effects of weather conditions on car crashes have been found. However, most studies use monthly or yearly data and only few studies are available analyzing the impact of weather conditions on daily car crash counts. Furthermore, the studies that are available on a daily level do not explicitly model the data in a time-series context, hereby ignoring the temporal serial correlation that may be present in the data. In this paper, we introduce an integer autoregressive model for modelling count data with time interdependencies. The model is applied to daily car crash data, metereological data and traffic exposure data from the Netherlands aiming at examining the risk impact of weather conditions on the observed counts. The results show that several assumptions related to the effect of weather conditions on crash counts are found to be significant in the data and that if serial temporal correlation is not accounted for in the model, this may produce biased results.
Brijs
Tom
T
Transportation Research Institute, Hasselt University, Wetenschapspark 5 - gebouw 6, B-3590 Diepenbeek, Belgium. tom.brijs@uhasselt.be
Karlis
Dimitris
D
Wets
Geert
G
D000056:Accident Prevention / Q000379:methods*; D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D002980:Climate; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D016012:Poisson Distribution; D012043:Regression, Psychology; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D013996:Time and Motion Studies