  Increasing and massive volumes of trajectory data are being accumulated that
may serve a variety of applications, such as mining popular routes or
identifying ridesharing candidates. As storing and querying massive trajectory
data is costly, trajectory simplification techniques have been introduced that
intuitively aim to reduce the sizes of trajectories, thus reducing storage and
speeding up querying, while preserving as much information as possible.
Existing techniques rely mainly on hand-crafted error measures when deciding
which point to drop when simplifying a trajectory. While the hope may be that
such simplification affects the subsequent usability of the data only
minimally, the usability of the simplified data remains largely unexplored.
Instead of using error measures that indirectly may to some extent yield
simplified trajectories with high usability, we adopt a direct approach to
simplification and present the first study of query accuracy driven trajectory
simplification, where the direct objective is to achieve a simplified
trajectory database that preserves the query accuracy of the original database
as much as possible. Specifically, we propose a multi-agent reinforcement
learning based solution with two agents working cooperatively to collectively
simplify trajectories in a database while optimizing query usability. Extensive
experiments on four real-world trajectory datasets show that the solution is
capable of consistently outperforming baseline solutions over various query
types and dynamics.
