Predicting road crashes using calendar data

Abstract

In road safety, macroscopic models are developed to support the quantitative safety targets. These are based on models for the estimated numbers of fatalities and crashes. Typical problems hereby are the lack of relevant data, the limited time horizon ad the availability of future values for explanatory variables. As a solution to these restrictions, we suggest the use of calendar data for the prediction of road safety. These include a trend, a trading day pattern, dummy variables for the months and a heavy traffic measure. ARIMA models and regression models with ARMA errors and calendar variables are built. Predictions are made with both models and the quality of the predictions is compared. Belgian monthly crash data (19990-2002) are used to develop models for the number of persons killed or seriously injured, the number of persons lightly injured and the corresponding number of crashes. The regression models fit better than the pure ARIMA models. The trend and trading day variables are significant for killed or seriously injured outcomes, while the heavy traffic measure is significant in all models. The predictions made by the regression models are better than those from the ARIMA models, especially for the lightly injured outcomes.