Adaptive Time-Varying Graph Learning for Traffic Flow Data Based on Anomaly Moment Detection

This paper proposes an adaptive time-varying graph structure construction strategy for traffic flow data based on anomaly moment detection, which is capable of identifying anomaly moments within traffic flow signals and adaptively dividing the temporal dimension to construct corresponding graph representations for distinct traffic patterns. Specifically, we leverage a threshold determination method that incorporates the smoothness and energy of temporal difference traffic flow signals to identify anomaly moments in the traffic flow. Upon detecting these anomaly moments, we utilize them as segmentation boundaries along the timeline, adaptively partitioning the continuous time series into multiple dynamic and relatively stable subsequences. Within each subsequence, we apply a time-varying graph learning method to independently construct a graph structure that reflects the traffic flow characteristics of that specific period, thereby achieving an adaptive time-varying property in the traffic flow graph structure. Experimental results demonstrate that the proposed method exhibits reliable performance in detecting anomaly moments in traffic flow, and the adaptive time-varying graph structures accurately reflect the varying characteristics of traffic flow across different time periods.