EAGLE: Edge-Aware Graph Learning for Proactive Delivery Delay Prediction in Smart Logistics Networks

📰 ArXiv cs.AI

EAGLE is a graph learning approach for proactive delivery delay prediction in smart logistics networks

advanced Published 8 Apr 2026

Action Steps

Model logistics networks as graphs with edge-aware learning
Integrate operational data streams from warehouses and transportation lanes
Train EAGLE model for proactive delivery delay prediction
Evaluate and refine EAGLE model using real-world logistics data

Who Needs to Know This

Data scientists and logistics professionals can benefit from EAGLE as it enables proactive prediction of delivery delays, allowing for more efficient supply chain management

Key Insight

💡 EAGLE's edge-aware graph learning approach can effectively capture spatial dependencies in logistics networks for proactive delay prediction

Key Takeaways

EAGLE is a graph learning approach for proactive delivery delay prediction in smart logistics networks

Full Article

Title: EAGLE: Edge-Aware Graph Learning for Proactive Delivery Delay Prediction in Smart Logistics Networks

Abstract:
arXiv:2604.05254v1 Announce Type: new Abstract: Modern logistics networks generate rich operational data streams at every warehouse node and transportation lane -- from order timestamps and routing records to shipping manifests -- yet predicting delivery delays remains predominantly reactive. Existing predictive approaches typically treat this problem either as a tabular classification task, ignoring network topology, or as a time-series anomaly detection task, overlooking the spatial dependenci

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