  We present analysis techniques for large trajectory data sets that aim to
provide a semantic understanding of trajectories reaching beyond them being
point sequences in time and space. The presented techniques use a driving
preference model w.r.t. road segment traversal costs, e.g., travel time and
distance, to analyze and explain trajectories.
  In particular, we present trajectory mining techniques that can (a) find
interesting points within a trajectory indicating, e.g., a via-point, and (b)
recover the driving preferences of a driver based on their chosen trajectory.
We evaluate our techniques on the tasks of via-point identification and
personalized routing using a data set of more than 1 million vehicle
trajectories collected throughout Denmark during a 3-year period. Our
techniques can be implemented efficiently and are highly parallelizable,
allowing them to scale to millions or billions of trajectories.
