Estimation of logistic transportation system performance under extreme weather condition: A data-driven approach

This is the year two effort of the 2-year project. The objective is to utilize a data-driven approach (e.g., Machine Learning and Deep Learning) to robustly, proactively, and trustworthily estimate the impact of impending extreme weather events (e.g., tropical cyclones) on key logistical infrastructure elements, including ports and highways. Beyond the first year’s research, the study team plans to broaden its objectives from focusing on the port system (multiple points) to encompassing the interconnected network of the highway system. The highway network will be conceptualized as a graph in which intersections and roads are nodes and edges, respectively. A Graph Neural Network (GNN) equipped with temporal attention will be utilized to estimate the impact of tropical cyclones on highway operational performance, leveraging its ability to process complex spatial and temporal dependencies within the highway network.

• Record URL:
  https://fersc.utk.edu/research-focus/fersc-projects-year-2/

Language

• English

Project

• Status: Active
• Funding: $326100
• Contract Numbers:

  69A3552348338

• 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:

  Center for Freight Transportation for Efficient and Resilient Supply Chain

  University of Tennessee Knoxville
  Knoxville, TN  United States  37996
• Project Managers:

  Bruner, Britain

  Kaplan, Marcella

• Performing Organizations:

  Texas A&M University, College Station

  Zachry Department of Civil Engineering
  3136 TAMU
  College Station, TX  United States  77843-3136

  University of Tennessee, Knoxville

  Department of Civil and Environmental Engineering
  John D. Tickle Building
  Knoxville, TN  United States  37886
• Principal Investigators:

  Zhang, Yunlong

  Han, Lee

  Wang, Bruce

• Start Date: 20240801
• Expected Completion Date: 20250731
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Freight transportation; Highways; Logistics; Machine learning; Neural networks; Ports; Weather conditions
• Subject Areas: Data and Information Technology; Freight Transportation; Operations and Traffic Management; Terminals and Facilities;

Filing Info

• Accession Number: 01929225
• Record Type: Research project
• Source Agency: Center for Freight Transportation for Efficient and Resilient Supply Chain
• Contract Numbers: 69A3552348338
• Files: RIP
• Created Date: Aug 29 2024 2:21PM