Improve air traffic flow under uncertain environment

• Main Author: Kai, Liang Tan
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Final Year Project
• Language: English
• Published: 2014
• Subjects: DRNTU::Engineering::Aeronautical engineering::Aviation
• Online Access: http://hdl.handle.net/10356/60421
• Institution: Nanyang Technological University

Amidst rising popularity of air travel, air traffic congestion and delays have become increasingly pressing. Air Traffic Flow Management (ATFM) has since been identified as a potential solution to alleviate air traffic congestions. ATFM is most often characterised by mathematical optimisation models, whereby optimal routing decisions are sought to improve air traffic flow. The projects done by previous researches in this area mainly investigate the computational performance of optimisation models. Large, macroscopic sample cases were also presented. However, no in depth analyses on air traffic flow behaviour had been provided using these models. In this paper, a deterministic flight-by-flight model was first constructed, with various modifications made. One such modification was the linearisation of a super-linear constraint in the original model. Air traffic scenarios were simulated to investigate air traffic flow behaviours on a microscopic scale. Then, a probabilistic chance constraint was introduced to model uncertainties such as weather disruptions. Logical and reasonable results were obtained for all the simulations. It was found that improving capacity at sectors with vast connectivity is beneficial to the air traffic flow. Chance constraints were also found to show great potential to be applied to airport capacity. A joint chance constraint for departure and arrival capacity of an airport could be considered by future researches. The sample case can also be expanded to include more airports and sectors in order to better emulate a real world air traffic problem.