The Use of Panel Data Analysis Techniques in Airspace Capacity Estimation

摘要

Air traffic in Europe is increasing at a rapid rate and traffic patterns no longer display pronounced daily peaks but instead exhibit peak spreading. This paper considers the factors that affect controller workload throughout the whole day. It provides a framework for using cross-sectional time-series analysis with simulated data to derive a model that describes a functional relationship between the workload and the factors that influence it for en-route airspace. Simulation studies are presented for two contrasting regions of European airspace to determine robustness and transferability of the model. An important feature of the analysis was the use of controller input, via interviews, to create variables that were reliable indicators of workload. The results indicate that a sub-set of traffic and sector variables and their parameter estimates can be used to predict controller workload, and hence capacity, in any sector of the two regions simulated in any given hour.