Traffic Congestion Prediction Using Graph Convolution Networks

Abstract

Traffic congestion is a critical issue in urban transportation systems, leading to increased travel times, fuel consumption, and air pollution. Furthermore, accurate prediction of traffic conditions is essential for effective traffic management and route planning. However, traditional approaches often fail to capture the complex spatiotemporal dependencies inherent in road networks. This study compares the performance of Graph Convolutional Networks (GCNs) for traffic congestion prediction using Amsterdam sensor location datasets from 13 locations (01-10-2023 to 31-10-2023) and 18 locations (01-01-2024 to 26-01-2024). The GCN model achieves an accuracy above 0.5, with a peak accuracy of 0.6 for the 18-location dataset and 0.55 for the 13-location dataset. Precision ranges from 0.5 to 0.8, while recall oscillates between 0.5 and 0.6. Also, the F1-score reaches 0.6 for the 18-location dataset and remains above 0.4 for the 13-location dataset. The results demonstrate the GCN's effectiveness in capturing spatial dependencies and achieving high-performance metrics, with better performance observed for larger datasets. Moreover, the findings contribute to the development of intelligent schemes for GCNs and the Internet of Vehicles in Intelligent Transportation Systems (ITS), advancing traffic congestion prediction capabilities.