ATADA: Adaptive Time Aware Anomaly Detection Approach for Real-Time Intelligent Transportation Systems.

Accurately detecting passenger traffic flow in public transportation, e.g., for the purpose of identifying congested stations, and detecting anomalies in that flow, are both important for enhancing passenger satisfaction and safety. However, current methods for traffic flow detection and prediction train their models in offline modes using potentially outdated data, which can’t be refreshed with new data. In this paper, we introduce a two-step, continuously updating framework aimed at real-time prediction of anomalies within transportation networks, which we name Adaptive Time-Aware Anomaly Detection Approach (ATADA). Specifically, the first step filters out regular traffic patterns and balances the data set, while the second step employs a sequence-to-sequence attention model, a type of deep learning model, to further detect the traffic anomalies. We propose a dynamic online time-aware learning mechanism which enables our models to continuously train on incoming data and to adapt predictive strategies based on the most recent traffic patterns. The proposed method is validated using real subway AFC data from Suzhou, China and Hangzhou, China. Experimental results demonstrate that our framework significantly improves efficiency while maintaining high accuracy in real-time traffic anomaly detection.