Establishing the Correlation Between Complexity and Performance for Arrival Operations
Air traffic complexity indicators play an essential role in measuring operational performance and control-ler workload. However, current studies mainly depend on the manual scoring method to scale performance or workload. This paper focuses on arrival operations and presents a data-driven strategy to establish the correla-tion between complexity and performance to avoid the subjectivity of the currently used manual scoring method. Firstly, we present twenty-six indicators for describing air traffic complexity and two indicators for arrival op-erational performance. Secondly, the clustering method distinguishes peak and off-peak situations for arrival operation. Moreover, clustering results are compared to investigate the correlation between complexity and per-formance initially. Thirdly, the classification method is adopted to determine such correlation further. In addi-tion, we also identify the affecting factors which could influence operational performance. Finally, trajectories of arrival aircraft landing at Guangzhou Baiyun Inter-national Airport (ZGGG) are used for case validation. The results indicate that there is a strong correlation be-tween complexity and performance. The accuracy and precision of classification are approximately 90%. Fur-thermore, the number of aircraft significantly impacts the arrival operational performance within TMA.
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