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

Author

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

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.

Suggested Citation

  • Andy, Lam Jun Guang & Alam, Sameer & Lilith, Nimrod & Piplani, Rajesh, 2024. "A deep reinforcement learning approach for Runway Configuration Management: A case study for Philadelphia International Airport," Journal of Air Transport Management, Elsevier, vol. 120(C).
    • Handle: RePEc:eee:jaitra:v:120:y:2024:i:c:s0969699724001376
    DOI: 10.1016/j.jairtraman.2024.102672
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