The risk of moose Alces alces collision : a predictive logistic model for moose-train accidents

We used logistic models to estimate the risk of moose-train collisions for the Rorosbanen railway in Norway. During 1990-1997, a total of 13.506 tr departures were registered along Rorosbanen duringthe months when the risk of collision was highest (December to March). The statistical model selected to predict the risk of moose-train collisions included train r oute,time of day, lunar phaseand average train speed, as well as two climatic covariables i.e. snow depth and temperature. Trains running at night, in the mor ning or in the eveningexperienced a higher risk of collision with moose Alces alces than day trains. The probability of collision was also higher during nights of full moons than during nights of half orno moon. As observed previously with trains in Norway moose-kills increased with increasing snow depth and decreasing temperatures. To test the predictability of the model, we used a logistic model based on train departures during 1990-1996 to predict the number of moose-train accidents during winter 1996/97. Although the model had asatisfactorily high predictability, the best models would probably bethose based on a combinat