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：随着轨迹数据的不断积累，其规模日益庞大，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，其直观目标是在保留尽可能多信息的同时，减少轨迹的规模，从而降低存储需求并加速查询。现有技术主要依赖手工设计的误差度量来决定简化过程中应舍弃哪些点。然而，尽管可能期望这种简化对数据后续可用性的影响微乎其微，但简化数据的可用性在很大程度上仍未得到充分探究。我们不采用间接且可能仅在一定程度上产生高可用性简化轨迹的误差度量，而是采用一种直接的简化方法，并首次提出了查询精度驱动的轨迹简化研究，其直接目标是实现一个能尽可能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，其中两个智能体协同工作，共同简化数据库中的轨迹，同时优化查询可用性。在四个真实世界轨迹数据集上的大量实验表明，该方案能在各种查询类型和动态条件下持续优于基线解决方案。