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

arXiv: https://arxiv.org/abs/2601.13632 · HTML: https://mlsanity.com/paper/2601.13632/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Builds on (most cited first, 4 of 4)

- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case](https://mlsanity.com/paper/1910.07421/) (2019)
- [CT-PatchTST: Channel-Time Patch Time-Series Transformer for Long-Term Renewable Energy Forecasting](https://mlsanity.com/paper/2501.08620/) (2025)
- [Enhancing Customer Contact Efficiency with Graph Neural Networks in Credit Card Fraud Detection Workflow](https://mlsanity.com/paper/2504.02275/) (2025)

## Built on by (most cited first, 12 of 12)

- [Sustainable Hybrid Document-Routed Retrieval for Financial RAG: Resolving the Robustness-Precision Trade-off](https://mlsanity.com/paper/2603.26815/) (2026)
- [The Efficiency Frontier: A Unified Framework for Cost-Performance Optimization in LLM Context Management](https://mlsanity.com/paper/2605.23071/) (2026)
- [Tiny-Critic RAG: Empowering Agentic Fallback with Parameter-Efficient Small Language Models](https://mlsanity.com/paper/2603.00846/) (2026)
- [DynaRAG: Bridging Static and Dynamic Knowledge in Retrieval-Augmented Generation](https://mlsanity.com/paper/2603.18012/) (2026)
- [EAGLE: Edge-Aware Graph Learning for Proactive Delivery Delay Prediction in Smart Logistics Networks](https://mlsanity.com/paper/2604.05254/) (2026)
- [TA-Mem: Tool-Augmented Autonomous Memory Retrieval for LLM in Long-Term Conversational QA](https://mlsanity.com/paper/2603.09297/) (2026)
- [LLM-Grounded Explainable AI for Supply Chain Risk Early Warning via Temporal Graph Attention Networks](https://mlsanity.com/paper/2603.04818/) (2026)
- [Adaptive Serverless Resource Management via Slot-Survival Prediction and Event-Driven Lifecycle Control](https://mlsanity.com/paper/2604.05465/) (2026)
- [Hypergraph and Latent ODE Learning for Multimodal Root Cause Localization in Microservices](https://mlsanity.com/paper/2605.00351/) (2026)
- [SemHash-LLM: A Multi-Granularity Semantic Hashing Framework for Document Deduplication](https://mlsanity.com/paper/2607.01601/) (2026)
- [ARCANA: A Reflective Multi-Agent Program Synthesis Framework for ARC-AGI-2 Reasoning](https://mlsanity.com/paper/2607.09059/) (2026)
- [Dependency-Aware Chain-of-Thought Compression for Financial Reasoning](https://mlsanity.com/paper/2609.00413/) (2026)

## Abstract (arXiv)

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.