Bridging Data Gaps with Modeled Data from Generative AI: Advancing Health in Transportation Research
Transportation-related factors, such as air quality changes and exposure disparities, have significant impact on health outcome. Communities near high-traffic corridors experience elevated exposure levels, yet efforts to assess these impacts are hindered by the lack of high-resolution health and socio-demographic datasets. Traditional air quality models, such as dispersion and interpolation techniques, estimate pollutant distributions but struggle to capture localized exposure variations and real-world uncertainties due to their reliance on static assumptions. These limitations reduce the precision of transportation health impact assessments. This project addresses data gaps in air quality and health outcomes by integrating AI-generated data with traditional modeling techniques. Bridging the data gap is essential to improving exposure assessments and provide a more comprehensive understanding of transportation-related health effects. The research develops and trains generative AI models for data augmentation, using harmonized datasets to create high-fidelity modeled data that reflects real-world patterns. Furthermore, we integrate the trained AI models with air quality simulation models to estimated transportation-related air quality scenarios and assess potential health impacts. The project produces a validated generative AI model for data augmentation, generating high-resolution datasets that enhance geographic and demographic granularity in transportation health research. The application of scenario-based health impact simulations provides new insights into the relationships between air quality and health outcomes, improving the ability to evaluate transportation-related interventions. By combining AI-driven data synthesis with traditional modeling approaches, this research advances methodologies for transportation and environmental health assessments, providing more reliable data for exposure studies and policy evaluations.
Language
- English
Project
- Status: Active
- Funding: $112,500.00
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Contract Numbers:
69A3552348329
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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:
1111 Rellis Parkway
Bryan, Texas United States 77807 -
Project Managers:
Ocon, Monica
- Performing Organizations: El Paso, TX United States
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Principal Investigators:
Kotal, Anantaa
- Start Date: 20250301
- Expected Completion Date: 20260228
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
- Source Data: 02-04-UTEP
Subject/Index Terms
- TRT Terms: Air quality; Artificial intelligence; Data quality; Datasets; Public health; Research projects
- Subject Areas: Data and Information Technology; Highways; Research; Society;
Filing Info
- Accession Number: 01976242
- Record Type: Research project
- Source Agency: Center for Advancing Research in Transportation Emissions, Energy, and Health (CARTEEH)
- Contract Numbers: 69A3552348329
- Files: UTC, RIP
- Created Date: Jan 13 2026 4:10PM