A Distributed Hierarchical Spatio-Temporal Edge-Enhanced Graph Neural Network for City-Scale Dynamic Logistics Routing
DOI:
https://doi.org/10.66238/fsrma89Keywords:
Distributed GNN, Logistics Routing, Dynamic Path Planning, Spatio-Temporal Graph Learning, Intelligent Transportation SystemsAbstract
City-scale logistics routing has become increasingly challenging as metropolitan road networks expand to tens of millions of edges and traffic conditions evolve rapidly under high-volume mobility demands. Conventional centralized routing algorithms and monolithic graph neural network (GNN) models face significant limitations in scalability, latency, and real-time adaptability, restricting their effectiveness in large-scale urban logistics systems. To address these challenges, this paper proposes a Distributed Hierarchical Spatio-Temporal Edge-Enhanced Graph Neural Network (HSTE-GNN) for dynamic routing over ultra-large road networks. The proposed framework partitions the city-scale road graph into regional subgraphs that are processed in parallel across distributed computing nodes, enabling efficient learning of localized traffic dynamics. Within each partition, an edge-enhanced spatio-temporal module jointly captures node states, dynamic edge attributes, and short-term temporal dependencies. To ensure global routing coherence, a hierarchical coordination layer aggregates cross-region representations via an asynchronous parameter-server mechanism, allowing timely global route updates under high-frequency traffic changes. This distributed and hierarchical design balances local responsiveness with global consistency, significantly improving scalability and inference efficiency. Experiments conducted on real-world large-scale traffic datasets from Beijing and New York validate the effectiveness of the proposed approach. Compared with strong spatio-temporal baselines such as ST-GRAPH, the distributed HSTE-GNN achieves 34.9% lower routing delay, 14.7% lower MAPE, and 11.8% lower RMSE, while improving global route consistency by 7.3%. These results demonstrate that the proposed framework provides a scalable, adaptive, and efficient solution for next-generation intelligent transportation systems, particularly for large-scale logistics platforms requiring rapid and reliable routing decisions
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