# Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2003.02542 · HTML: https://mlsanity.com/paper/2003.02542/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [LightPath: Lightweight and Scalable Path Representation Learning](https://mlsanity.com/paper/2307.10171/) (2023)
- [Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement Learning](https://mlsanity.com/paper/2211.08415/) (2022)
- [A Reinforcement Learning Based R-Tree for Spatial Data Indexing in Dynamic Environments](https://mlsanity.com/paper/2103.04541/) (2021)

## Abstract (arXiv)

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.