Traffic Assignment of Urban Road Based on Heterogeneous Graph Neural Networks

Traffic assignment is crucial for urban traffic regulation and management. Based on this background, this study proposes a heterogeneous graph neural network that integrates Transformer-based multi-head self-attention for traffic assignment in urban road networks. The model builds a heterogeneous graph with both physical road links and virtual origin–destination links. It features a dual-encoder structure: the V-Encoder and the R-Encoder. The V-Encoder employs Transformer multi-head self-attention to capture long-range spatial relationships between origin and destination nodes. In contrast, the R-Encoder aggregates local topological features to characterize the transmission of flow across road segments. A combined loss function that includes flow conservation constraints is designed to ensure predictions are both accurate and physically realistic. Experiments on the Sioux Falls and EMA networks demonstrate that the method outperforms baseline models under various congestion conditions, exhibiting high accuracy and efficiency. Ablation tests show that Transformer multi-head self-attention is vital for performance enhancement. The approach also remains robust under abnormal conditions, such as in the case of incomplete OD demands, making it a practical solution for efficient, low-carbon, and sustainable traffic management.