Related Experiment Video
Updated: Jul 5, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Studying the effect of weather conditions on daily crash counts using a discrete time-series model
Tom Brijs1, Dimitris Karlis, Geert Wets
1Transportation Research Institute, Hasselt University, Wetenschapspark 5 - gebouw 6, B-3590 Diepenbeek, Belgium. tom.brijs@uhasselt.be
Abstract:
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.
Related Concept Videos
What is Weather?
Precipitation Processes
Precipitation and Co-precipitation
Statistical Methods for Analyzing Epidemiological Data
Survival Tree
Building a Survival Tree
Constructing a survival tree begins...
Steps in Outbreak Investigation