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: Completed
- Funding: $150000
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Contract Numbers:
69A3551747125
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Sponsor Organizations:
Center for Advanced Transportation Mobility
North Carolina Agricultural and Technical State University
Greensboro, NC United States 27411Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Project Managers:
Liu, Dahai
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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: 20210831
- 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
- Contract Numbers: 69A3551747125
- Files: UTC, RIP
- Created Date: May 15 2020 11:03AM