Citation

Martensen H, Focant N, Diependaele K. Transp. Res. Proc. 2016; 14: 96-104.

Copyright

(Copyright © 2016, Elsevier Publications)

DOI

10.1016/j.trpro.2016.05.045

PMID

unavailable

Abstract

The monthly road traffic accident victim numbers in Belgium (2003-2014) were analyzed in latent trend time series models separately for pedestrians, cyclists, moped riders, car occupants and road user types jointly. For each road-user type the effect of a range of meteorological variables was tested. The resulting models allow a detailed view on the weather effects for different modes of transport. The strongest effects are observed for two-wheelers (motorcyclists and cyclists), with snow leading to a reduction in victim numbers while warm and sunny weather leads to an increase. The effect of rain differs according to the road user type involved.

The principles of state-space time series modelling are described along with the treatment of multicollinearity in models with several predicting variables. An outlook is given of the potential uses of the resulting models.

Language: en