A deep reinforcement learning approach for Runway Configuration Management: A case study for Philadelphia International Airport

Lam Jun Guang Andy, Sameer Alam, Nimrod Lilith and Rajesh Piplani

Journal of Air Transport Management, 2024, vol. 120, issue C

Abstract: Airports featuring multiple runways have the capability to operate in diverse runway configurations, each with its unique setup. Presently, Air Traffic Controllers (ATCOs) heavily rely on their operational experience and predefined procedures (†playbooks†) to plan the utilization of runway configurations. These ’playbooks’ however lack the capacity to comprehensively address the intricacies of a dynamic runway system under increasing weather uncertainties.

Keywords: Runway configuration management; Deep reinforcement learning; Proximal policy optimization; Air traffic management (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jaitra:v:120:y:2024:i:c:s0969699724001376

DOI: 10.1016/j.jairtraman.2024.102672

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