Urban Traffic Congestion Level Prediction Using a Fusion-Based Graph Convolutional Network

In an urban environment, the accurate prediction of congestion levels is a prerequisite for formulating traffic demand management strategies reasonably. Current traffic forecasting studies mostly focus on the road topological network and assume that the spatial linkages of road segments is fixed, thus ignoring associated congestion level changes between road segments. This study introduces a fusion-based graph convolutional network called the F-GCN. The model aims to capture the dynamic correlations of the congestion levels among road segments while constructing the static topology of the road network. In particular, the entropy-based grey relation analysis method is first implemented in the dynamic graph convolutional network (GCN) module to measure the potential correlations among the observed congestion levels. Then, spatio-temporal blocks that integrate GCN layers, spatial attention mechanisms, and long short-term memory layers are built for both the static and dynamic GCN modules. Finally, the F-GCN model is developed by fusing the static GCN and dynamic GCN module. The numerical experiments on Beijing taxies demonstrated that the proposed F-GCN model achieved a significant 5.47%, 5.64%, and 8.14% improvements for the 15-, 30-, and 45-min prediction tasks in the weighted mean absolute percentage error compared to the state-of-the-art baseline models. This improvement highlights the effectiveness of the model in predicting congestion levels, demonstrating its superiority and potential in capturing the dynamic potential correlations among the congestion levels of road segments.