Submitting Campus
Daytona Beach
Department
Electrical Engineering and Computer Science
Document Type
Article
Publication/Presentation Date
7-10-2021
Abstract/Description
The effectiveness of resource allocation under emergencies especially hurricane disasters is crucial. However, most researchers focus on emergency resource allocation in a ground transportation system. In this paper, we propose Learning-to- Dispatch (L2D), a reinforcement learning (RL) based air route dispatching system, that aims to add additional flights for hurricane evacuation while minimizing the airspace’s complexity and air traffic controller’s workload. Given a bipartite graph with weights that are learned from the historical flight data using RL in consideration of short- and long-term gains, we formulate the flight dispatch as an online maximum weight matching problem. Different from the conventional order dispatch problem, there is no actual or estimated index that can evaluate how the additional evacuation flights influence the air traffic complexity. Then we propose a multivariate reward function in the learning phase and compare it with other univariate reward designs to show its superior performance. The experiments using the real world dataset for Hurricane Irma demonstrate the efficacy and efficiency of our proposed schema.
Publication Title
IEEE Internet of Things Journal
Publisher
Institute of Electrical and Electronics Engineers
Scholarly Commons Citation
Zhang, K., Yang, Y., Xu, C., Liu, D., & Song, H. (2021). Learning-to-Dispatch: Reinforcement Learning Based Flight Planning under Emergency. IEEE Internet of Things Journal, (). Retrieved from https://commons.erau.edu/publication/1770
