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We describe our first-place solution to the ECML/PKDD discovery challenge ontaxi destination prediction. The task consisted in predicting the destinationof a taxi based on the beginning of its trajectory, represented as avariable-length sequence of GPS points, and diverse associatedmeta-information, such as the departure time, the driver id and clientinformation. Contrary to most published competitor approaches, we used analmost fully automated approach based on neural networks and we ranked firstout of 381 teams. The architectures we tried use multi-layer perceptrons,bidirectional recurrent neural networks and models inspired from recentlyintroduced memory networks. Our approach could easily be adapted to otherapplications in which the goal is to predict a fixed-length output from avariable-length sequence.

Artificial Neural Networks Applied to Taxi Destination Prediction