Resilient Routing: Risk-Aware Dynamic Routing in Smart Logistics via Spatiotemporal Graph Learning

2026-01-20

AI Summary

This study addresses the limitations of traditional static path planning in logistics networks, which struggles to cope with dynamic risks arising from traffic congestion and demand fluctuations. To overcome this, the authors propose a risk-aware intelligent path planning approach that first constructs a logistics topology via spatial clustering and then integrates graph convolutional networks (GCNs) with gated recurrent units (GRUs) to capture spatiotemporal features for predicting future congestion risk. Based on these predictions, edge weights are dynamically adjusted to balance efficiency and resilience. This work is the first to combine spatiotemporal graph learning with a risk-aware mechanism for logistics routing. Experimental results on the Smart Logistics Dataset 2024 demonstrate that, under high-congestion scenarios, the proposed method reduces exposure to congestion risk by 19.3% with only a 2.1% increase in travel distance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

With the rapid development of the e-commerce industry, the logistics network is experiencing unprecedented pressure. The traditional static routing strategy most time cannot tolerate the traffic congestion and fluctuating retail demand. In this paper, we propose a Risk-Aware Dynamic Routing(RADR) framework which integrates Spatiotemporal Graph Neural Networks (ST-GNN) with combinatorial optimization. We first construct a logistics topology graph by using the discrete GPS data using spatial clustering methods. Subsequently, a hybrid deep learning model combining Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU) is adopted to extract spatial correlations and temporal dependencies for predicting future congestion risks. These prediction results are then integrated into a dynamic edge weight mechanism to perform path planning. We evaluated the framework on the Smart Logistics Dataset 2024, which contains real-world Internet of Things(IoT) sensor data. The experimental results show that the RADR algorithm significantly enhances the resilience of the supply chain. Particularly in the case study of high congestion scenarios, our method reduces the potential congestion risk exposure by 19.3% while only increasing the transportation distance by 2.1%. This empirical evidence confirms that the proposed data-driven approach can effectively balance delivery efficiency and operational safety.

Problem

• resilient routing
• risk-aware
• dynamic routing
• smart logistics
• congestion risk

Innovation

• Risk-Aware Dynamic Routing
• Spatiotemporal Graph Neural Networks
• Graph Convolutional Network
• Gated Recurrent Unit
• Logistics Resilience