Graph Neural Networks for Predicting Urban Traffic Congestion in Smart Cities

Urban traffic congestion is a serious issue for all smart cities in terms of sustainability and efficiency. Existing traffic prediction models do not properly account for the spatial and temporal dependencies in roads within the urban road network. This paper proposes a new methodology for modeling urban traffic congestion using a graph neural network (GNN) to design the road network as a defined dynamic graph, with the addition of multi-view traffic data (i.e. speed, volume, and occupancy), temporal sequences, and external contextual factors such as weather conditions and public events. Enhanced GNN architecture is utilized with attention and hierarchical graph pooling to learn local and universal traffic patterns. The model is validated and evaluated on a dataset collected over six months for a medium smart city, alongside three baseline models (LSTM, Xgboost and Static GCN). The proposed GNN model outperformed the three baseline models RMSE is 3.42 km/h MAE is 2.78, and -2 R2 score 0.915 with a F1 score of 0.89 in congested hours classification. The results of the proposed methodology confirm the suitability of spatial-temporal graph learning approaches for traffic forecasting methods. To conclude, this methodology demonstrates a strong scalable approach to computing predictive traffic models as part of critical smart city infrastructure.