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Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories

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Andreas ZüfleAuthors Info & Claims

SIGSPATIAL '24: Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems

Pages 350 - 360

https://doi.org/10.1145/3678717.3691324

Published: 22 November 2024 Publication History

Abstract

Semantic trajectories, which enrich spatial-temporal data with textual information such as trip purposes or location activities, are key for identifying outlier behaviors critical to healthcare, social security, and urban planning. Traditional outlier detection relies on heuristic rules, which requires domain knowledge and limits its ability to identify unseen outliers. Besides, there lacks a comprehensive approach that can jointly consider multi-modal data across spatial, temporal, and textual dimensions. Addressing the need for a domain-agnostic model, we propose the Transferable Outlier Detection for Human Semantic Trajectories (TOD4Traj) framework. TOD4Traj first introduces a modality feature unification module to align diverse data feature representations, enabling the integration of multi-modal information and enhancing transferability across different datasets. A contrastive learning module is further proposed for identifying regular mobility patterns both temporally and across populations, allowing for a joint detection of outliers based on individual consistency and group majority patterns. Our experimental results have shown TOD4Traj's superior performance over existing models, demonstrating its effectiveness and adaptability in detecting human trajectory outliers across various datasets.

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Cited By

Kong RAmiri HLiu YKennedy LGupta MKim JZüfle A(2024)Human Mobility Challenge: Are Transformers Effective for Human Mobility Prediction?Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Human Mobility Prediction Challenge10.1145/3681771.3700130(60-63)Online publication date: 16-Dec-2024
https://doi.org/10.1145/3681771.3700130
Amiri HYang RZüfle A(2024)GeoLife+: Large-Scale Simulated Trajectory Datasets Calibrated to the GeoLife DatasetProceedings of the 7th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation10.1145/3681770.3698573(25-28)Online publication date: 29-Oct-2024
https://doi.org/10.1145/3681770.3698573
Zhang ZAmiri HLiu ZZhao LZuefle A(2024)Large Language Models for Spatial Trajectory Patterns MiningProceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection10.1145/3681765.3698467(52-55)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3681765.3698467
Show More Cited By

Index Terms

Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems
      2. Location based services

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cover image ACM Conferences

SIGSPATIAL '24: Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems

October 2024

743 pages

ISBN:9798400711077

DOI:10.1145/3678717

Copyright © 2024 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Published: 22 November 2024

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SIGSPATIAL '24: The 32nd ACM International Conference on Advances in Geographic Information Systems

October 29 - November 1, 2024

GA, Atlanta, USA

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SIGSPATIAL '24 Paper Acceptance Rate 37 of 122 submissions, 30%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

Kong RAmiri HLiu YKennedy LGupta MKim JZüfle A(2024)Human Mobility Challenge: Are Transformers Effective for Human Mobility Prediction?Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Human Mobility Prediction Challenge10.1145/3681771.3700130(60-63)Online publication date: 16-Dec-2024
https://doi.org/10.1145/3681771.3700130
Amiri HYang RZüfle A(2024)GeoLife+: Large-Scale Simulated Trajectory Datasets Calibrated to the GeoLife DatasetProceedings of the 7th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation10.1145/3681770.3698573(25-28)Online publication date: 29-Oct-2024
https://doi.org/10.1145/3681770.3698573
Zhang ZAmiri HLiu ZZhao LZuefle A(2024)Large Language Models for Spatial Trajectory Patterns MiningProceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection10.1145/3681765.3698467(52-55)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3681765.3698467
Liu YKennedy LAmiri HZüfle A(2024)Neural Collaborative Filtering to Detect Anomalies in Human Semantic TrajectoriesProceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection10.1145/3681765.3698463(79-89)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3681765.3698463
Amiri HKong RZüfle A(2024)Urban Anomalies: A Simulated Human Mobility Dataset with Injected AnomaliesProceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection10.1145/3681765.3698459(1-11)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3681765.3698459
Amiri HKohn WRuan SKim JKavak HCrooks APfoser DWenk CZüfle A(2024)The Patterns of Life Human Mobility SimulationProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691319(653-656)Online publication date: 22-Nov-2024
https://doi.org/10.1145/3678717.3691319

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