A deep reinforcement learning approach for runway configuration management: a case study for Philadelphia International Airport

• Main Authors: Lam, Andy Jun Guang, Alam, Sameer, Lilith, Nimrod, Piplani, Rajesh
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering; Runway configuration management; Deep reinforcement learning
• Online Access: https://hdl.handle.net/10356/180894
• Institution: Nanyang Technological University

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 configura...