  Two-thirds of the people who buy a new car prefer to use a substitute instead
of the built-in navigation system. However, for many applications, knowledge
about a user's intended destination and route is crucial. For example,
suggestions for available parking spots close to the destination can be made or
ride-sharing opportunities along the route are facilitated. Our approach
predicts probable destinations and routes of a vehicle, based on the most
recent partial trajectory and additional contextual data. The approach follows
a three-step procedure: First, a $k$-d tree-based space discretization is
performed, mapping GPS locations to discrete regions. Secondly, a recurrent
neural network is trained to predict the destination based on partial sequences
of trajectories. The neural network produces destination scores, signifying the
probability of each region being the destination. Finally, the routes to the
most probable destinations are calculated. To evaluate the method, we compare
multiple neural architectures and present the experimental results of the
destination prediction. The experiments are based on two public datasets of
non-personalized, timestamped GPS locations of taxi trips. The best performing
models were able to predict the destination of a vehicle with a mean error of
1.3 km and 1.43 km respectively.
