LLM-Grounded Explainability for Port Congestion Prediction via Temporal Graph Attention Networks

arXiv:2603.04818v1 Announce Type: new Abstract: Port congestion at major maritime hubs disrupts global supply chains, yet existing prediction systems typically prioritize forecasting accuracy without providing operationally interpretable explanations. This paper proposes AIS-TGNN, an evidence-grounded framework that jointly performs congestion-escalation prediction and faithful natural-language explanation by coupling a Temporal Graph Attention Network (TGAT) with a structured large language mo