Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2003.02542v2
• Date: Tue, 14 Jul 2020 07:34:08 GMT
• Title: Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning
• Authors: Zheng Wang, Cheng Long, Gao Cong, Yiding Liu
• Abstract summary: 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.
• Score: 39.40996178862515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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