Enhancing air traffic conflict resolution through machine learning, conformal automation, and flow-centric paradigms

• Main Author: Guleria, Yash
• Other Authors: Sameer Alam
• Format: Thesis-Doctor of Philosophy
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Engineering; Air traffic conflict resolution; ATCO-automation conformance; Machine learning; Supervised learning; Reinforcement learning; Flow centric operations; Air traffic controllers
• Online Access: https://hdl.handle.net/10356/177541
• Institution: Nanyang Technological University

Air traffic conflict resolution is a dynamic, time-sensitive, and safety-critical aspect of air traffic control, which involves a complex interaction of humans, machines, and procedures. In current sector-based operations where the airspace is subdivided into smaller geographical regions, ensuring...