A Vessel-Aware Graph Neural Network Framework for Port Motorway Traffic State Estimation

Abstract

Accurate traffic state estimation (TSE) for port motorway networks is critical for mitigating congestion and improving operational efficiency in port cities. Port motorways have their specific traffic patterns, such as surges in container truck demand triggered by vessel arrival events. Considering this characteristic, we propose a novel TSE method named HGCN-VA, enabling a real-time inductive inference based on a graph neural network (GNN). The framework constructs a heterogenous graph comprising both road sensors and ports, while introducing an influence weight learning module based on attention mechanisms to adaptively align vessel events with their traffic consequences. A hierarchical spatial module is further designed, where initial heterogeneous diffuse graph convolution layers explicitly model cross-domain interactions between port and sensor nodes, followed by standard diffusion graph convolution layers to capture traffic propagation patterns. Extensive experiments on a simulation port dataset demonstrate that HGCN-VA outperforms baselines in TSE accuracy. Ablation studies further validate the effectiveness of incorporating vessel information and the proposed heterogeneous spatial modeling strategy.