  Similar trajectory search is a fundamental problem and has been well studied
over the past two decades. However, the similar subtrajectory search (SimSub)
problem, aiming to return a portion of a trajectory (i.e., a subtrajectory)
which is the most similar to a query trajectory, has been mostly disregarded
despite that it could capture trajectory similarity in a finer-grained way and
many applications take subtrajectories as basic units for analysis. In this
paper, we study the SimSub problem and develop a suite of algorithms including
both exact and approximate ones. Among those approximate algorithms, two that
are based on deep reinforcement learning stand out and outperform those
non-learning based algorithms in terms of effectiveness and efficiency. We
conduct experiments on real-world trajectory datasets, which verify the
effectiveness and efficiency of the proposed algorithms.
