Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning [article]

Zheng Wang, Cheng Long, Gao Cong, Yiding Liu
2020 arXiv   pre-print
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
more » ... d 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.
arXiv:2003.02542v2 fatcat:wupyxy3odremho5okuvg7ymolq