Forecasting traffic accidents using disaggregated data

Traffic accidents, measured monthly, present different characteristics when the aggregate is compared to its individual components. When disaggregated data are used, the effects of policy variables, calendar events, and different seasonal behaviors should be clearly understood and their coefficients properly estimated. In this paper, we compare the empirical performance of various models in assessing the effects of policy variables, legal changes, and traffic security campaigns. In addition, aggregated versus disaggregated forecasts of the main accident variables are compared in order to examine the robustness of the forecasting improvement from using disaggregated data. In particular, we test the robustness of this improvement against the specification of the model, information set, type of measure of forecasting accuracy, and forecast year. Overall, we conclude that forecast combinations based on disaggregated models display better performance.