Enhanced Traffic Prediction Using Spatial–Temporal Graph Neural Networks

Abstract

The ability to forecast traffic conditions in urban environments is essential for intelligent transport systems because it provides proactive congestion management, traffic control and informed urban mobility planning. However, due to the extreme spatial and temporal volatility of traffic flow patterns, conventional statistical, machine learning, and sequence-based deep learning approaches either fail to account for the spatial relationships between highway segments or fail to sufficiently model long-range temporal dynamics. To address these gaps, the present study uses a Spatial-Temporal Graph Neural Network (STGNN) framework that can learn a combination of spatial and temporal relationships in road networks and changing time-varying patterns in traffic flow. The model has a graph-based architecture where graph-convolutional layers are combined with gated recurrent units and transformer-based attention units, thus creating a hybrid architecture that is capable of multi-scale spatio-temporal dependencies. The METR-LA dataset was used in experiments, and it was observed that STGNN had smaller Mean Absolute Percentage Error(MAPE), Mean Absolute Error(MAE), and Root Mean Squared Error(RMSE) at 15, 30, and 60 minute horizons of prediction than the baseline and the implemented models: Sequence to Sequence (Seq2Seq) and Temporal Graph Convolutional Network (T-GCN). The model achieved the best results for the 15-minute interval with an MAE of 2.71, an RMSE of 5.17, and a MAPE of 7.08. The results imply that adaptive spatial learning, together with the temporal sequence modeling, can produce much better forecasting stability, which underlines the potential STGNN-based traffic prediction systems have to contribute to real-time traffic control.