Data-Driven Analysis of the Chaotic Characteristics of Air Traffic Flow. (18th September 2020)

• Record Type: Journal Article
• Authors: Zhang, Zhaoyue; Zhang, An; Sun, Cong; Xiang, Shuaida; Li, Shanmei
• Other Names: Dong Chunjiao Academic Editor.
• Is Part Of: Journal of advanced transportation. Volume 2020(2020)
• Publication Date: 2020-09-18
• Subjects: Transportation -- Periodicals; 388.05
• DOI: 10.1155/2020/8830731
• Languages: English
• ISSNs: 0197-6729

Abstract

Abstract : Understanding the chaos of air traffic flow is significant to the achievement of advanced air traffic management, and trajectory data are the basic material for studying the chaotic characteristics. However, at present, there are two main obstacles to this task, namely, large amounts of noise in the measured data and the tedium of existing data processing methods. This paper improves the incorrect trajectory processing method based on ADS-B trajectory data and proposes a method by which to quickly extract the traffic flow through a certain waypoint. Currently, the commonly used theoretical analysis tools for nonlinear complex systems include the classical nonlinear dynamics analysis method and the newly developed complex network-based analysis method. The latter is currently in an exploratory stage because it has just been introduced into the study of air traffic flow. From these two perspectives, the chaotic characteristics of air traffic flow are studied in the present work. From the perspective of nonlinear dynamics, the improved C-C method is used to calculate the reliability parameters, namely, the time delay τ and embedding dimension m, of phase-space reconstruction, and the maximum Lyapunov index is calculated by using the small data volume method to prove the existence of chaos in the system. From the perspective of complex networks, the construction of a visibility graph and horizontal visibility graph is used to prove the existence of chaos in the system, and the goodness-of-fit parameters of the degree distributions of two fitting methods under different time scales are evaluated, which provides support for the air traffic flow theory.