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.

翻译：随着轨迹数据日益增长并大规模积累，这些数据可服务于多种应用，例如挖掘热门路径或识别拼车候选者。由于存储和查询大规模轨迹数据成本高昂，轨迹简化技术应运而生，其直观目标是在尽可能保留信息的同时减小轨迹尺寸，从而降低存储开销并加速查询。现有技术主要依赖手工设计的误差度量来决定简化轨迹时需删除的轨迹点。尽管人们希望这种简化对后续数据可用性的影响微乎其微，但简化后数据的可用性仍未得到充分探索。本文摒弃了可能间接产生较高可用性简化轨迹的误差度量方法，转而采用一种直接的简化途径，首次提出基于查询精度驱动的轨迹简化研究，其直接目标是获得一个能最大程度保留原始数据库查询精度的简化轨迹数据库。具体而言，我们提出了一种基于多智能体强化学习的解决方案，通过两个智能体协同工作来集体简化数据库中的轨迹，同时优化查询可用性。在四个真实轨迹数据集上的大量实验表明，该解决方案能够在多种查询类型和动态场景下持续优于基线方案。