Multi‐scale and Collaborative Disaster Evacuation Planning Framework

Abstract

When an emergency occurs, no tools are available to assist the decision‐making of airline planning and coordination. In this project, we use big data and multi‐agent modeling to integrate ADS‐B data and weather information, to optimize and visualize the airspace strategic planning during a 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. 

CATM Research Affiliates:
Dahai Liu (ERAU: Lead)
Houbing Song (ERAU)