通过多源数据融合，实现共享单车异常行为的可解释检测。
Towards Explainable Anomaly Detection in Shared Mobility Systems
- 结合骑行记录、天气和公交信息，构建多源数据异常检测框架。
- 站级分析揭示恶劣天气与公交停运是主要异常诱因。
- 采用可解释算法，适合运营方优化服务决策。
共享出行系统（如共享单车网络）在城市交通中起关键作用。识别系统中的异常对优化运营、提升服务可靠性和用户体验至关重要。本文提出一种可解释的异常检测框架，融合多源数据，包括共享单车出行记录、天气状况和公共交通可用性。采用孤立森林算法进行无监督异常检测，并引入基于深度的孤立森林特征重要性（DIFFI）算法实现结果可解释性。结果表明，站级分析能有效理解异常，凸显恶劣天气和公交可用性受限等外部因素的影响。研究为共享出行系统的运营决策提供支持。
原文摘要 · Abstract (English)
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.
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