Equitable Flood Impact Analysis Integrating GeoAI and Digital Twin Modeling

This research proposal aims to explore the interdependencies between water and transportation infrastructures in urban areas. The flood risks and their impacts on infrastructure and livelihoods such as transportation mobility and accessibility will be assessed with a focus on social equity; for example, low-income households are more likely to live in high-risk zones, face higher damages, and are less able to recover. The project will use a data-driven approach to quantify the impacts at two scales: (1) macroscopic scale (such as a town or city) using GeoAI tools, and (2) microscopic scale (such as a census tract) using digital twin model. GeoAI tools integrate geospatial analysis with machine learning methods. In this project, the tool will integrate time-series-based traffic, precipitation, and hydrology data with the publicly available datasets on mobility and disadvantage indices. The City of Greensboro in North Carolina will be used as a testbed for the application of the GeoAI tool. For the microscopic scale of analysis, a census tract with the highest flood risk will be adopted for digital twin modeling. Digital twin models will provide a high-fidelity representation of real-world systems by integrating diverse datasets, such as topography, hydrology, and transportation infrastructure. Key outputs include an integrated GeoAI-based traffic-water infrastructure analysis, a case study using the digital twin model for microscopic impacts of flooding, and shared geographic information system (GIS) tools for mitigation strategies and policies. The research outcome will be useful in providing managerial and operational guidance on ways to address inequity issues during such events. The project aligns well with the U.S. Department of Transportation's (USDOT’s) sustainability goal by facilitating agency collaboration and addressing climate resilience.

• Record URL:
  https://c2smarter.engineering.nyu.edu/equitable-flood-impact-analysis-integrating-geoai-and-digital-twin-modeling

Language

• English

Project

• Status: Active
• Funding: $75000
• Contract Numbers:

  69A3551747124

• 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:

  Connected Communities for Smart Mobility Towards Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER)

  New York University
  Brooklyn, New York  United States  11201
• Project Managers:

  Pohl, Lizzie

• Performing Organizations:

  Connected Communities for Smart Mobility Towards Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER)

  New York University
  Brooklyn, New York  United States  11201

  North Carolina A&T State University

  1601 E. Market Street
  Greensboro, NC  United States  27411
• Principal Investigators:

  Pandey, Venktesh

  Jha, Manoj

  Chowdhury, Shuva

  Park, Hyoshin

• Start Date: 20231001
• Expected Completion Date: 20240930
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Data analysis; Equity; Floods; Geospatial data; Impact studies; Machine learning; Urban areas
• Geographic Terms: Greensboro (North Carolina)
• Subject Areas: Data and Information Technology; Environment; Hydraulics and Hydrology; Planning and Forecasting; Transportation (General);

Filing Info

• Accession Number: 01897933
• Record Type: Research project
• Source Agency: Connected Communities for Smart Mobility Towards Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER)
• Contract Numbers: 69A3551747124
• Files: UTC, RIP
• Created Date: Oct 30 2023 11:02PM