Towards Building-Level Modeling: Human-Driven Agentic Workflows for Multi-Source Data Synthesis

Term Start:

June 1, 2026

Term End:

May 31, 2027

Budget:

$247,894

Keywords:

Artificial Intelligence, Data Fusion, Travel Behavior

Thrust Area(s):

Data Collection Mechanisms, Data Modeling and Analytic Tools

University Lead:

Georgia Institute of Technology

Researcher(s):

Jorge Laval

Travel demand modeling is limited by the aggregation problem: data are often synthesized at census-tract or traffic-zone scales that obscure building-level mobility patterns. This project develops a human-directed agentic workflow in which a transportation planner orchestrates specialized artificial intelligence agents that discover, evaluate, and integrate open geospatial data from sources such as OpenStreetMap, building footprint databases, census repositories, municipal portals, and selected sensor feeds. To address risks from plausible but incorrect agent outputs, the pilot will use a bounded proof-of-concept geography, source-provenance tracking, human review, conflict checks, and explicit accuracy metrics against benchmark data. The work will demonstrate the workflow through an open Digital Forum where practitioners and students can access tools, data, and examples for building-level demand modeling.

Project scope

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