Abstract
Terminal airspace (TMA) is one of the most complex operational environments in air traffic management system. Considering the dynamical and stochastic characteristics of air traffic flow in TMAs, how to develop an adaptive capacity estimation approach to meet the operational demand has become a serious problem to be solved. In this paper, we propose an adaptive method for capacity estimation in TMA, which can adaptively recommend capacity corresponding to the dynamical operations in TMA. Firstly, we adopt a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) based trajectory clustering method to search the typical 3D routes with the trajectory’s temporal-spatial features. Then, a K-means clustering based pattern identifying method is designed to extract the similar air traffic patterns. Finally, we define the capacity as the maximum flow and derive the state transfer probability based on Markov models. The case study using one month of real-world data set collected from the TMA of Chengdu Shuangliu International Airport. Results show the promising application of the proposed approach.
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