{
  "id": 7236,
  "url": "https://arxiv.org/abs/2603.14107v1",
  "title": "ST-ResGAT: Explainable Spatio-Temporal Graph Neural Network for Road Condition Prediction and Priority-Driven Maintenance",
  "summary": "Climate-vulnerable road networks require a paradigm shift from reactive, fix-on-failure repairs to predictive, decision-ready maintenance. This paper introduces ST-ResGAT, a novel Spatio-Temporal Residual Graph Attention Network that fuses residual graph-attention encoding with GRU temporal aggregation to forecast pavement deterioration. Engineered for resource-constrained deployment, the framework translates continuous Pavement Condition Index (PCI) forecasts directly into the American Society ",
  "authors": "Mohsin Mahmud Topu, Azmine Toushik Wasi, Mahfuz Ahmed Anik, MD Manjurul Ahsan",
  "category": "research",
  "topics": "transparency,environment",
  "orgs": null,
  "regions": "us",
  "published_at": "2026-03-14T20:24:32.000Z",
  "fetched_at": "2026-07-14T16:33:03.575Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7236",
  "original_url": "https://arxiv.org/abs/2603.14107v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}