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|Title: ||Predicting road crashes using calendar data|
|Authors: ||VAN DEN BOSSCHE, Filip|
|Issue Date: ||2006|
|Citation: ||TRB 2006, Washington, U.S.A..|
|Abstract: ||In road safety, macroscopic models are developed to support the quantitative targets in safety
programmes. Targets are based on estimated numbers of fatalities and crashes that are
typically derived from models. When constructing these models, typical problems are the
lack of relevant data, the limited time horizon and the availability of future values for
As a solution to these restrictions, we suggest the use of calendar data. These include a trend,
a trading day pattern, dummy variables for the months and a heavy traffic measure. In this
paper, we test the relevance of calendar data for the explanation and prediction of road safety.
ARIMA models and regression models with ARMA errors and calendar variables are built.
Predictions are made by both models and the quality of the predictions is compared.
We use Belgian monthly crash data (1990-2002) 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 the outcomes related to killed or seriously injured persons, 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
|Type: ||Conference Material|
|Appears in Collections: ||Research publications|
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