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

• TRT Terms: Cooperation; Data fusion; Decision making; Disaster preparedness; Evacuation; Optimization; Strategic planning; Weather
• Identifier Terms: Automatic Dependent Surveillance-Broadcast (ADS-B)
• Subject Areas: Aviation; Data and Information Technology; Operations and Traffic Management; Planning and Forecasting; Security and Emergencies;

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