Integrating graph neural networks and LSTM for path optimization in smart port multi-modal systems

This paper addresses the challenges of dynamic environments and multimodal data fusion in multimodal transport path optimization for smart ports by proposing a GL-SSL Model that integrates Graph Neural Networks (GCN), Long Short-Term Memory (LSTM), and Self-Supervised Learning (SSL). The model fully exploits the graph-structured information of port transport networks and their temporal variations, while SSL enhances feature representation, enabling efficient optimization of path planning. Experiments were conducted on multiple public datasets, including AIS data from the Port of Rotterdam, global shipping data, and port net revenue data. Results show that the GL-SSL Model achieved significant improvements in key performance metrics. Specifically, the optimized path length reached 80 km, the transport cost was reduced to 200 cost-units (a composite metric reflecting fuel consumption, equipment wear, and labor cost), and the delay rate was maintained at 0.05 (5%), all of which are substantially better than traditional algorithms and other deep learning models. Furthermore, the model demonstrated stable performance under complex scenarios such as peak traffic, adverse weather, and equipment failures, with rapid convergence of training loss and strong robustness. These findings highlight the model's adaptability and practical application potential. Overall, this work provides effective technical support for multimodal transport path optimization in smart ports and carries important theoretical significance and broad application prospects.