Multi dynamic temporal representation graph convolutional network for traffic flow prediction

Abstract

Traffic flow prediction is fundamental to the dynamic control and application of Intelligent Transportation Systems (ITS), which play a crucial role in alleviating road congestion. However, existing approaches have not fully exploited the inherent dynamic and multifaceted spatiotemporal features within traffic data, posing significant challenges in achieving accurate traffic flow predictions. To address this issue, we propose a novel Multi Dynamic Temporal Representation Graph Convolutional Network (MDTRGCN). Specifically, we introduce a dynamic graph construction method that learns the time‒space dependencies specific to road segments. On the basis of this method, we develop a dynamic graph convolution module that aggregates the hidden states of neighboring nodes to a focal node by propagating messages across a dynamic adjacency matrix. Moreover, a multiaspect fusion module is presented, which combines auxiliary hidden states learned from traffic volume with primary hidden states derived from traffic speed. Finally, we propose a temporal representation module that infers the content of masked subsequences from small portions of unmasked subsequences and their temporal context. The experimental results on real-world datasets demonstrate that the proposed method not only achieves state-of-the-art predictive performance but also provides clear and interpretable insights into the dynamic spatial relationships of road segments.