Resilient Routing: Risk-Aware Dynamic Routing in Smart Logistics via Spatiotemporal Graph Learning
Zhiming Xue, Sichen Zhao, Yalun Qi, Xianling Zeng, Zihan Yu

TL;DR
This paper introduces a risk-aware dynamic routing framework using spatiotemporal graph neural networks to improve logistics resilience amid traffic congestion and demand fluctuations, validated on real IoT data.
Contribution
It develops a novel RADR framework combining GCN and GRU for congestion prediction and dynamic path planning in smart logistics, addressing limitations of static routing strategies.
Findings
Reduces congestion risk exposure by 19.3% in high congestion scenarios.
Increases transportation distance by only 2.1%.
Enhances supply chain resilience through data-driven routing.
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…
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Taxonomy
TopicsVehicle Routing Optimization Methods · Advanced Technologies in Various Fields · Supply Chain Resilience and Risk Management
