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

• المؤلفون الرئيسيون: Lam, Andy Jun Guang, Alam, Sameer, Lilith, Nimrod, Piplani, Rajesh
• مؤلفون آخرون: School of Mechanical and Aerospace Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2024
• الموضوعات: Engineering; Runway configuration management; Deep reinforcement learning
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/180894

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...