Abstract
Trajectory anomaly detection is crucial for effective decision-making in urban and human mobility management. Existing methods of trajectory anomaly detection generally focus on training a trajectory generative model and evaluating the likelihood of reconstructing a given trajectory. However, previous work often lacks important contextual information on the trajectory, such as the agent’s information (e.g., agent ID) or geographic information (e.g., Points of Interest (POI)), which could provide additional information on accurately capturing anomalous behaviors. To fill this gap, we propose a context-aware anomaly detection approach that models contextual information related to trajectories. The proposed method is based on a trajectory reconstruction framework guided by contextual factors such as agent ID and contextual POI embedding. The injection of contextual information aims to improve the performance of anomaly detection. We conducted experiments in two cities and demonstrated that the proposed approach significantly outperformed existing methods by effectively modeling contextual information. Overall, this paper paves a new direction for advancing trajectory anomaly detection.
Original language | English (US) |
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Title of host publication | GEOANOMALIES 2024 - Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection |
Editors | Yao-Yi Chiang, Khurram Shafique, Carola Wenk, Andreas Zufle, Jack Cooper, Joon-Seok Kim, Enrico Mattei |
Publisher | Association for Computing Machinery, Inc |
Pages | 12-15 |
Number of pages | 4 |
ISBN (Electronic) | 9798400711442 |
DOIs | |
State | Published - Oct 29 2024 |
Event | 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection, GEOANOMALIES 2024 - Atlanta, United States Duration: Oct 29 2024 → … |
Publication series
Name | GEOANOMALIES 2024 - Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection |
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Conference
Conference | 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection, GEOANOMALIES 2024 |
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Country/Territory | United States |
City | Atlanta |
Period | 10/29/24 → … |
Bibliographical note
Publisher Copyright:© 2024 Copyright held by the owner/author(s).
Keywords
- contextual information
- trajectory anomaly detection
- variational autoencoder