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Title: Computational air traffic management
Author: Azzopardi, Marc Anthony
ISNI:       0000 0004 5355 5464
Awarding Body: Cranfield University
Current Institution: Cranfield University
Date of Award: 2015
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World air transport has been on a steady exponential rise since the 1940’s and the trend has shown remarkable resilience to external shocks. The level of air traffic has greatly exceeded the wildest expectations of the air traffic management pioneers that originally defined the basic precepts ATM that persist till today. This has stretched ATM to a point where it is starting to show signs of ineffectiveness in the face of ever increasing congestion. Delays are on the rise, costs are ballooning, flights are being elongated unnecessarily, the system is becoming increasingly susceptible to disruption, and the high environmental impact of aviation is being compounded by the inability of air traffic controllers to optimise ATM operation in real-time. If these trends are not reversed, ATM could eventually face instability. The conservative, self-preserving outlook of the ATM community has confined progress to relatively minor tweaks of a tired human-centric paradigm. However, the diverging gap between ATM performance and fundamental requirements indicates the need for a step change. In this work, the traditionally incremental approach to ATM research was broken to favour a more exploratory mindset. As a result, a new discipline called Computational Air Traffic Management has been defined to address the unique set of challenges presented by the ATM problem, by taking a more objective scientific approach. A specific embodiment of a CATM system was designed, constructed, simulated and tested and shown to be a significant step towards demonstrating the feasibility of a fully autonomous multi-agent-based air transportation system based on optimisation principles. The system offers unique advantages in terms of resilience to disruption, efficiency and future scalability. The traffic density using such a system can be realistically increased many times higher than current levels while significantly improving on the current levels of safety, operating cost, environmental impact and flight delays. This work advances the field of ATM as well as the fields of Computational Intelligence and Dynamic Optimisation of High Dimensionality Non- Convex Search Spaces.
Supervisor: Whidborne, James F. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available