A framework to assess the ability of automation to deliver capacity targets in European airspace

• Main Author: Tobaruela Arnedo, Gonzalo
• Other Authors: Majumdar, Arnab ; Ochieng, Washington ; Schuster, Wolfgang
• Published: Imperial College London 2015
• Subjects: 624
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.676823

The maximum number of flights that the Air Traffic Management (ATM) system can safely and efficiently control over a period of time i.e. airspace capacity, has become a limitation over the last decade, due to a rapid increase in air traffic activity. Therefore, the ATM system in developed countries is undergoing a series of modernisation initiatives to ensure that the future ATM system is able to provide sufficient capacity to safely meet future air traffic demand. As a result, there is a need to assess if the proposed changes can effectively be translated into the desired increase in capacity. This thesis addresses this issue by developing an en-route airspace capacity estimation framework, able to measure the impact of future ATM system modernisation deployments on airspace capacity. In order to do this, it identifies the key airspace capacity drivers for current and future operations, focussing on three areas: air traffic controller workload, air traffic predictability and Air Traffic Control (ATC) centre cost-efficiency. In each of the three framework areas, the research develops methodologies that overcome the deficiencies of existing capacity estimation techniques. This leads to an innovative multi-dimensional approach to airspace capacity estimation, able to reflect the different relationships of airspace capacity with the framework areas. The framework quantifies the relationship between the ATC centre planning process accuracy and airspace capacity. It estimates the effect of increased predictability on airspace capacity through the performance of the Airspace Management and Air Traffic Flow & Capacity Management functions. Finally, it computes air traffic controller workload with considerable accuracy (up to 80% of the actual workload) during medium-low workload scenarios and reflects the workload trend (Spearman's rank coefficient = 0.72) during high workload scenarios.