A Distributed Hierarchical Spatio-Temporal Edge-Enhanced Graph Neural Network for City-Scale Dynamic Logistics Routing

Abstract

City-scale logistics routing has become increasingly challenging as metropolitan road networks grow 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 suffer from limited scalability, high latency, and poor real-time adaptability, which restricts their effectiveness in large 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 framework partitions the city-scale graph into regional subgraphs processed in parallel across distributed computing nodes, enabling efficient learning of localized traffic dynamics. Within each region, an edge-enhanced spatio-temporal module jointly models node states, dynamic edge attributes, and short-term temporal dependencies. A hierarchical coordination layer further aggregates cross-region representations through an asynchronous parameter-server mechanism, ensuring global routing coherence under high-frequency traffic updates. This distributed hierarchical design balances local responsiveness with global consistency, significantly improving scalability and inference efficiency. Experiments on real-world large-scale traffic datasets from Beijing and New York demonstrate that HSTE-GNN outperforms strong spatio-temporal baselines such as ST-GRAPH, achieving 34.9% lower routing delay, 14.7% lower MAPE, and 11.8% lower RMSE, while improving global route consistency by 7.3%. These results confirm that the proposed framework provides a scalable, adaptive, and efficient solution for next-generation intelligent transportation systems and large-scale logistics platforms.