论文标题
通过深入增强学习的路线网络上的在线异常的亚区域检测
Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement Learning
论文作者
论文摘要
在许多基于位置的应用程序中，检测异常轨迹已成为重要的任务。尽管已经为这项任务提出了许多方法，但它们遭受了各种问题的困扰，包括（1）无法检测轨迹数据中较细粒度的异常异常和/或（2）非DATA驱动的异常，以及/（2）非DATA驱动的和（3）需要足够的监督标签的要求。在本文中，我们提出了一种基于新颖的增强学习解决方案，称为RL4OASD，该解决方案避免了上述方法的所有现有方法。 RL4OASD涉及两个网络，一个网络，负责道路网络和轨迹的学习功能，另一个负责基于学习的功能来检测异常的子三射，并且可以在没有标记数据的情况下对两个网络进行迭代培训。在两个实际数据集上进行了广泛的实验，结果表明，我们的解决方案可以显着胜过最先进的方法（提高了20-30％），并且有效地在线检测（处理每个新生成的数据点所需的时间少于0.1ms）。
Detecting anomalous trajectories has become an important task in many location-based applications. While many approaches have been proposed for this task, they suffer from various issues including (1) incapability of detecting anomalous subtrajectories, which are finer-grained anomalies in trajectory data, and/or (2) non-data driven, and/or (3) requirement of sufficient supervision labels which are costly to collect. In this paper, we propose a novel reinforcement learning based solution called RL4OASD, which avoids all aforementioned issues of existing approaches. RL4OASD involves two networks, one responsible for learning features of road networks and trajectories and the other responsible for detecting anomalous subtrajectories based on the learned features, and the two networks can be trained iteratively without labeled data. Extensive experiments are conducted on two real datasets, and the results show that our solution can significantly outperform the state-of-the-art methods (with 20-30% improvement) and is efficient for online detection (it takes less than 0.1ms to process each newly generated data point).
