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