[2603.04818] LLM-Grounded Explainability for Port Congestion Prediction via Temporal Graph Attention Networks

Computer Science > Artificial Intelligence arXiv:2603.04818 (cs) [Submitted on 5 Mar 2026] Title:LLM-Grounded Explainability for Port Congestion Prediction via Temporal Graph Attention Networks Authors:Zhiming Xue, Yujue Wang View a PDF of the paper titled LLM-Grounded Explainability for Port Congestion Prediction via Temporal Graph Attention Networks, by Zhiming Xue and 1 other authors View PDF HTML (experimental) 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 th...