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

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 (AI) 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. Expected outputs include a Human-AI Agentic Workflow methodology; an open-source software pipeline that converts enriched OpenStreetMap and related geospatial data into building-level travel demand inputs; a curated, version-controlled database of discoverable open-data sources with source-provenance records; validation procedures and accuracy metrics for handling conflicting sources; a pilot Digital Forum that serves as an educational resource and practitioner-facing decision-support tool; at least one peer-reviewed publication; and training or curriculum materials for students learning to manage artificial intelligence ecosystems in transportation planning.

Language

• English

Project

• Status: Active
• Funding: $247,894.00
• Contract Numbers:

  69A3552344815

  69A3552348320

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  University of Texas at Austin

  Austin, TX  United States  78712
• Performing Organizations:

  Georgia Institute of Technology, Atlanta

  790 Atlantic Drive
  Atlanta, GA  United States  30332-0355
• Principal Investigators:

  Laval, Jorge

• Start Date: 20260601
• Expected Completion Date: 20270531
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Data fusion; Geospatial data; Transportation planning; Travel demand
• Subject Areas: Data and Information Technology; Planning and Forecasting; Transportation (General);

Filing Info

• Accession Number: 02001772
• Record Type: Research project
• Source Agency: Center for Understanding Future of Travel Behavior and Demand
• Contract Numbers: 69A3552344815, 69A3552348320
• Files: UTC, RIP
• Created Date: Sep 4 2026 3:25PM