Learning-to-Dispatch: Reinforcement Learning Based Flight Planning under Emergency

Kai Zhang, Yupeng Yang, Chengtao Xu, Dahai Liu, Houbing Song

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageAmerican English
JournalIEEE Internet of Things Journal
StatePublished - Jul 10 2021

Keywords

  • Evacuation
  • reinforcement learning
  • air traffic management
  • graph theory

Disciplines

  • Aviation
  • Electrical and Computer Engineering
  • Systems and Communications

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