Multi-agent deep reinforcement learning for mix-mode runway sequencing

• Main Authors: Shi, Limin, Pham, Duc-Thinh, Alam, Sameer
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence; Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling; Reinforcement Learning; Runway Sequencing; Airport Optimization; Multi-Agent Approach
• Online Access: https://hdl.handle.net/10356/162775
• Institution: Nanyang Technological University

In mixed-mode operation, arrivals and departures are allowed to land and depart on the same runway. An appropriate strategy from air traffic controllers for arrivals and departures sequencing would boost the runway throughput significantly. On the other hand, safety is still the most crucial feature...