A deep reinforcement learning approach for airport departure metering under spatial-temporal airside interactions

• Main Authors: Ali, Hasnain, Pham, Duc-Thinh, Schultz, Michael, Alam, Sameer
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering::Data::Coding and information theory; Airside Traffic Management; Airport Departure Metering
• Online Access: https://hdl.handle.net/10356/161934
• Institution: Nanyang Technological University

Airport taxi delays adversely affect airports and airlines around the world leading to airside congestion, increased Air Traffic Controllers/Pilot workload, and adverse environmental impact due to excessive fuel burn. Airport Departure Metering (DM) is an effective approach to contain taxi delays by...