Towards Explainable Anomaly Detection in Shared Mobility Systems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915402728079360 |
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| author | Isgandarov, Elnur Cederle, Matteo Chiariotti, Federico Susto, Gian Antonio |
| author_facet | Isgandarov, Elnur Cederle, Matteo Chiariotti, Federico Susto, Gian Antonio |
| contents | Shared mobility systems, such as bike-sharing networks, play a crucial role in urban transportation. Identifying anomalies in these systems is essential for optimizing operations, improving service reliability, and enhancing user experience. This paper presents an interpretable anomaly detection framework that integrates multi-source data, including bike-sharing trip records, weather conditions, and public transit availability. The Isolation Forest algorithm is employed for unsupervised anomaly detection, along with the Depth-based Isolation Forest Feature Importance (DIFFI) algorithm providing interpretability. Results show that station-level analysis offers a robust understanding of anomalies, highlighting the influence of external factors such as adverse weather and limited transit availability. Our findings contribute to improving decision-making in shared mobility operations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15643 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Explainable Anomaly Detection in Shared Mobility Systems Isgandarov, Elnur Cederle, Matteo Chiariotti, Federico Susto, Gian Antonio Machine Learning Artificial Intelligence Shared mobility systems, such as bike-sharing networks, play a crucial role in urban transportation. Identifying anomalies in these systems is essential for optimizing operations, improving service reliability, and enhancing user experience. This paper presents an interpretable anomaly detection framework that integrates multi-source data, including bike-sharing trip records, weather conditions, and public transit availability. The Isolation Forest algorithm is employed for unsupervised anomaly detection, along with the Depth-based Isolation Forest Feature Importance (DIFFI) algorithm providing interpretability. Results show that station-level analysis offers a robust understanding of anomalies, highlighting the influence of external factors such as adverse weather and limited transit availability. Our findings contribute to improving decision-making in shared mobility operations. |
| title | Towards Explainable Anomaly Detection in Shared Mobility Systems |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.15643 |