Towards Explainable Anomaly Detection in Shared Mobility Systems

TL;DR

This paper introduces an interpretable anomaly detection framework for shared mobility systems that combines multi-source data and employs Isolation Forest with DIFFI for better understanding of anomalies, aiding operational decisions.

Contribution

It presents a novel, interpretable anomaly detection approach integrating multiple data sources and using DIFFI for enhanced interpretability in shared mobility systems.

Findings

Station-level analysis improves anomaly understanding.

External factors like weather significantly influence anomalies.

The framework enhances decision-making in mobility operations.

Abstract

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.