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

• URL: http://arxiv.org/abs/2603.04818v1
• Date: Thu, 05 Mar 2026 05:07:40 GMT
• Title: LLM-Grounded Explainability for Port Congestion Prediction via Temporal Graph Attention Networks
• Authors: Zhiming Xue, Yujue Wang,
• Abstract summary: This paper proposes an evidence-grounded framework that jointly performs congestion-escalation prediction and faithful natural-language explanation.<n>The framework provides a practical pathway toward operationally deployable AI for maritime congestion monitoring and supply-chain risk management.
• Score: 0.6353525052246608
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 model (LLM) reasoning module. Daily spatial graphs are constructed from Automatic Identification System (AIS) broadcasts, where each grid cell represents localized vessel activity and inter-cell interactions are modeled through attention-based message passing. The TGAT predictor captures spatiotemporal congestion dynamics, while model-internal evidence, including feature z-scores and attention-derived neighbor influence, is transformed into structured prompts that constrain LLM reasoning to verifiable model outputs. To evaluate explanatory reliability, we introduce a directional-consistency validation protocol that quantitatively measures agreement between generated narratives and underlying statistical evidence. Experiments on six months of AIS data from the Port of Los Angeles and Long Beach demonstrate that the proposed framework outperforms both LR and GCN baselines, achieving a test AUC of 0.761, AP of 0.344, and recall of 0.504 under a strict chronological split while producing explanations with 99.6% directional consistency. Results show that grounding LLM generation in graph-model evidence enables interpretable and auditable risk reporting without sacrificing predictive performance. The framework provides a practical pathway toward operationally deployable explainable AI for maritime congestion monitoring and supply-chain risk management.

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