Multi-scale and Collaborative Disaster Evacuation Planning Framework

When emergency occurs, no tools are available to assist the decision-making of airline planning and coordination. In this project, the research team uses big data and multi-agent modeling to integrate automatic dependent surveillance–broadcast (ADS–B) data and weather information, to optimize and visualize the airspace strategic planning during disaster, and develop a forecast and recommendation system to aid the authorities and public for optimal airline evacuation process, by using the deep reinforcement learning technique.

    Language

    • English

    Project

    • Status: Active
    • Funding: $150000
    • Sponsor Organizations:

      Center for Advanced Transportation Mobility

      North Carolina A&T State University
      1601 E. Market Street
      Greensboro, NC  United States  27411

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590
    • Project Managers:

      Liu, Dahai

    • Performing Organizations:

      Embry-Riddle Aeronautical University

      600 S. Clyde Morris Boulevard
      Daytona Beach, Fl  United States  32114
    • Principal Investigators:

      Liu, Dahai

    • Start Date: 20200401
    • Expected Completion Date: 20210831
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers Program

    Subject/Index Terms

    Filing Info

    • Accession Number: 01739390
    • Record Type: Research project
    • Source Agency: Center for Advanced Transportation Mobility
    • Files: UTC, RiP
    • Created Date: May 15 2020 11:03AM