Equitable Flood Impact Analysis Integrating GeoAI and Digital Twin Modeling

Details

• Creators:
  Tasnia, Rifa ; Alaka, Oladimeji Basit ; Noori, Mustafa ; Pandey, Venktesh ; Park, Hyoshin ; Chowdhury, Shuva ; Jha, Manoj K.
• Corporate Creators:
  Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC)
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  Artificial Intelligence Data Analysis Equity (Justice) Geospatial Data Impact Studies Machine Learning Urban Areas
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC)
• Abstract:
  Flooding poses a significant threat to transportation infrastructure, yet research on the effects of dynamic flood levels on road networks remains limited. Increasing body of literature demonstrates that agencies can leverage digital data on travel demand, infrastructure, and water levels to improve integrated flood management and promote social equity. However, traditional traffic models do not consider terrain and elevation, limiting their ability to assess flood risks. For state-level planning, this omission affects resource allocation and long-term resilience strategies. To address these gaps, our study captures the interdependence between water and transportation infrastructures, evaluating flood risks and their impact on road networks. We develop an interoperable digital twin framework integrating road networks with simulations of rising water levels, focusing on rerouting strategies during floods to assess infrastructure vulnerabilities in North Carolina focusing on Hyde County (rural) and Wilmington (urban). Using SUMO, Python, and QGIS, along with elevation datasets, we simulate flooding scenarios at varying severity levels and demand conditions to identify high-risk roads and intersections and quantify their impact on traffic networks. We demonstrate that open-source tools can effectively model submerged roads, represented as lane closures in traffic simulators, and enable dynamic rerouting decisions. Additionally, interoperable flood and water infrastructure models facilitate the prompt identification of at-risk roads, improving disaster preparedness. To further analyze network efficiency under different flooding conditions, we model the interaction of demand and supply using four-step planning model. These findings highlight the congestion impacts of increasing flood severity and thresholds on network performance, emphasizing the necessity for adaptive infrastructure planning and emergency response strategies.
• Format:
  PDF
• Funding:
  69A3552348326
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:af2ef3e5729d09e8c108f0605905707af18d2008b9136238dc201fe83af0827fe381fcca7894c21ea4ec8646113c27a6e9ed17d795e97c7121a41269614d8306
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/86441/dot_86441_DS1.pdf
• File Type:
  [PDF - 13.32 MB ]