In this section, you can access to the latest technical information related to the FUTURE project topic.

A data-driven trajectory optimization framework for terminal maneuvering area operations

Abstract

The contradiction between air traffic capacity and demand constrains the development of the civil aviation industry. This paper proposes a Data-Driven Trajectory Optimization Framework for Terminal Maneuvering Area operations (DDTO-TMA) to enhance the capacity of Terminal Maneuvering Area (TMA). First, a Wasserstein-distance-based spectral clustering algorithm is applied to divide historical trajectories into several groups based on flight performance. Then, based on clustering results, a set of candidate Four-Dimensional Trajectories (4DTs) is created via flyable trajectories expansion. Finally, a combined algorithm with Mahalanobis-distance-based conflict detection and Pareto frontier determination is designed to identify optimal trajectories from candidate 4DTs in an efficient way. Two validations have been performed for Guangzhou Baiyun International Airport (ZGGG) TMA. The first validation evaluates the performance of the proposed DDTO-TMA. The results show that the DDTO-TMA can generate optimal trajectories that are not only of the interest of most stakeholders but also conflict-free. Besides, with the increase in the size of the historical trajectory set, the performance of DDTO-TMA gradually increases. The second validation estimates the improvement of a TMA operation after adopting DDTO-TMA. The results illustrate that, compared with the baseline, the proposed DDTO-TMA could enhance the arrival capacity by 30% without sacrificing stakeholders' interests. Furthermore, the proposed optimal trajectory generator can be easily transferred to different airports due to its data-driven characteristics.

» Author: Xuhao Gui, Junfeng Zhang, Xinmin Tang, Jie Bao, Bin Wang

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