Towards Explainable Anomaly Detection in Shared Mobility Systems
This work addresses operational optimization and reliability for shared mobility systems, but it is incremental as it applies existing methods to a specific domain.
The paper tackles anomaly detection in bike-sharing systems by developing an interpretable framework that integrates trip records, weather, and transit data, using Isolation Forest with DIFFI for interpretability, finding station-level analysis effectively identifies anomalies influenced by external factors like weather and transit availability.
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.