학술논문

Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement Learning
Document Type
Conference
Source
2023 IEEE 39th International Conference on Data Engineering (ICDE) ICDE Data Engineering (ICDE), 2023 IEEE 39th International Conference on. :246-258 Apr, 2023
Subject
Computing and Processing
Deep learning
Roads
Reinforcement learning
Feature extraction
Data engineering
Trajectory
Task analysis
trajectory data
anomalous subtrajectory detection
road networks
deep reinforcement learning
Language
ISSN
2375-026X
Abstract
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).