  Motivated by the increasing availability of vehicle trajectory data, we
propose learn-to-route, a comprehensive trajectory-based routing solution.
Specifically, we first construct a graph-like structure from trajectories as
the routing infrastructure. Second, we enable trajectory-based routing given an
arbitrary (source, destination) pair.
  In the first step, given a road network and a collection of trajectories, we
propose a trajectory-based clustering method that identifies regions in a road
network. If a pair of regions are connected by trajectories, we maintain the
paths used by these trajectories and learn a routing preference for travel
between the regions. As trajectories are skewed and sparse, many region pairs
are not connected by trajectories. We thus transfer routing preferences from
region pairs with sufficient trajectories to such region pairs and then use the
transferred preferences to identify paths between the regions. In the second
step, we exploit the above graph-like structure to achieve a comprehensive
trajectory-based routing solution. Empirical studies with two substantial
trajectory data sets offer insight into the proposed solution, indicating that
it is practical. A comparison with a leading routing service offers evidence
that the paper's proposal is able to enhance routing quality.
  This is an extended version of "Learning to Route with Sparse Trajectory
Sets" [1], to appear in IEEE ICDE 2018.
