Abstract
A trajectory is a sequence of timestamped point locations that captures the movement of an object such as a vehicle. Such trajectories encode complex spatial and temporal patterns and provide rich information about object mobility and the underlying infrastructures, typically road networks, within which the movements occur. A trajectory dataset is evolving when new trajectories are included continuously. The ability to detect anomalous trajectories in online fashion in this setting is fundamental and challenging functionality that has many applications, e.g., location-based services. State-of-the-art solutions determine anomalies based on the shapes or routes of trajectories, ignoring potential anomalies caused by different sampling rates or time offsets. We propose a multi-scale model, termed MST-OATD, for anomalous streaming trajectory detection that considers both the spatial and temporal aspects of trajectories. The model's multi-scale capabilities aim to enable extraction of trajectory features at multiple scales. In addition, to improve model evolvability and to contend with changes in trajectory patterns, the model is equipped with a learned ranking model that updates the training set as new trajectories are included. Experiments on real datasets offer evidence that the model can outperform state-of-the-art solutions and is capable of real-time anomaly detection. Further, the learned ranking model achieves promising results when updating the training set with newly arrived trajectories.
Original language | English (US) |
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Title of host publication | KDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining |
Publisher | Association for Computing Machinery |
Pages | 2980-2990 |
Number of pages | 11 |
ISBN (Electronic) | 9798400704901 |
DOIs | |
State | Published - Aug 24 2024 |
Event | 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024 - Barcelona, Spain Duration: Aug 25 2024 → Aug 29 2024 |
Publication series
Name | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
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ISSN (Print) | 2154-817X |
Conference
Conference | 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024 |
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Country/Territory | Spain |
City | Barcelona |
Period | 08/25/24 → 08/29/24 |
Bibliographical note
Publisher Copyright:© 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Keywords
- anomalous trajectory detection
- evolving datasets
- spatio-temporal
ASJC Scopus subject areas
- Software
- Information Systems