Context-Aware Trajectory Anomaly Detection

Haoji Hu, Jina Kim, Jinwei Zhou, Sofia Kirsanova, Jang Hyeon Lee, Yao Yi Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationGEOANOMALIES 2024 - Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection
EditorsYao-Yi Chiang, Khurram Shafique, Carola Wenk, Andreas Zufle, Jack Cooper, Joon-Seok Kim, Enrico Mattei
PublisherAssociation for Computing Machinery, Inc
Pages12-15
Number of pages4
ISBN (Electronic)9798400711442
DOIs
StatePublished - Oct 29 2024
Event1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection, GEOANOMALIES 2024 - Atlanta, United States
Duration: Oct 29 2024 → …

Publication series

NameGEOANOMALIES 2024 - Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection

Conference

Conference1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection, GEOANOMALIES 2024
Country/TerritoryUnited States
CityAtlanta
Period10/29/24 → …

Bibliographical note

Publisher Copyright:
© 2024 Copyright held by the owner/author(s).

Keywords

  • contextual information
  • trajectory anomaly detection
  • variational autoencoder

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