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In this paper we propose a new method to predict the final destination ofvehicle trips based on their initial partial trajectories. We first review howwe obtained clustering of trajectories that describes user behaviour. Then, weexplain how we model main traffic flow patterns by a mixture of 2d Gaussiandistributions. This yielded a density based clustering of locations, whichproduces a data driven grid of similar points within each pattern. We presenthow this model can be used to predict the final destination of a new trajectorybased on their first locations using a two step procedure: We first assign thenew trajectory to the clusters it mot likely belongs. Secondly, we usecharacteristics from trajectories inside these clusters to predict the finaldestination. Finally, we present experimental results of our methods forclassification of trajectories and final destination prediction on datasets oftimestamped GPS-Location of taxi trips. We test our methods on two differentdatasets, to assess the capacity of our method to adapt automatically todifferent subsets.

Destination Prediction by Trajectory Distribution Based Model