ATCEM: A synthetic model for evaluating air traffic complexity

Air traffic complexity, which measures the disorder of air traffic distribution, has become the critical indicator to reflect air traffic controller workload in air traffic management (ATM) system. However, it is hard to assess the system accurately because there are too many correlated factors, which make the air traffic complexity nonlinear. This paper presents an air traffic complexity evaluation model with integrated classification using computational intelligence (ATCEM). To avoid redundant factors, critical factors contributing to complexity are analyzed and selected from numerous factors in the ATCEM. Subsequently, to construct the mapping relationship between selected factors and air traffic complexity, an integrated classifier is built in ATCEM. With efficient training and learning based on aviation domain knowledge, the integrated classifier can effectively and stably reflect the mapping relationship between selected factors and the category of air traffic complexity to ensure the precision of the evaluation. Empirical studies using real data of the southwest airspace of China show that the ATCEM outperforms a number of state-of-the-art models. Moreover, using the critical complexity factors selected in ATCEM, the air traffic complexity could be effectively estimated.