TransResAI: A Compound AI System for Coastal Transportation Resilience

• URL: http://arxiv.org/abs/2606.00042v1
• Date: Tue, 28 Apr 2026 15:39:53 GMT
• Title: TransResAI: A Compound AI System for Coastal Transportation Resilience
• Abstract summary: This study presents TransResAI, a compound AI system that supports analysis of flood-aware transportation resilience via natural-language interactions.<n>The system integrates a locally deployable Large Language Model (LLM) with modules for task decomposition, secure code generation, geospatial analysis, retrieval-augmented generation, and interactive map rendering.
• Score: 1.929646837759767
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Coastal flooding increasingly threatens transportation infrastructure, yet the analytical tools needed for resilience management remain difficult for many non-specialist practitioners to use. This study presents TransResAI, a compound AI system that supports analysis of flood-aware transportation resilience via natural-language interactions. The system integrates a locally deployable Large Language Model (LLM) with modules for task decomposition, secure code generation, geospatial analysis, retrieval-augmented generation, and interactive map rendering. TransResAI links MATSim flood-scenario simulation outputs, OpenStreetMap-derived flood-risk networks, equity-focused demographic indicators, and regional documents in Hampton Roads, Virginia. A structured user study with domain experts demonstrated that TransResAI reduced task completion time by 80-88% relative to conventional GIS workflows, compressing analytical tasks from a mean of 197.1 seconds to 29.7 seconds and visualization tasks from 364.0 seconds to 46.1 seconds, while maintaining mean accuracy of 4.60/5.00 and task completion rates exceeding 94%. These findings demonstrate that compound AI architectures bridge the gap between general-purpose language models and specialized domain knowledge, as well as the quantitative rigor required for infrastructure resilience, providing transportation agencies and communities with faster, more accessible analytical tools for decision-making under growing climate uncertainty.

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