Data-Driven Analysis of the Chaotic Characteristics of Air Traffic Flow

Zhaoyue Zhang,An Zhang,Cong Sun,Shuaida Xiang,Shanmei Li

doi:10.1155/2020/8830731

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.

Highlights

With the growth of China’s national economy, its civil aviation industry has been greatly improved
Air traffic transportation is an important component of the modern transportation system, and the study of air traffic flow characteristics has gradually become a crucial task
China’s air traffic flow is characterized by rapid growth and very uneven distribution. e dynamics of air traffic flow depend on the number and the length of air routes, the number of airports, the number of takeoffs and landings, etc

Summary

Introduction

With the growth of China’s national economy, its civil aviation industry has been greatly improved. E time series of air traffic flow based on measured data is an effective method by which its nonlinear characteristics can be studied. Li et al proposed an improved maximum Lyapunov exponential algorithm based on the small data volume method and wavelet noise reduction theory, identified the chaotic characteristics in flight conflict time series, and proved the feasibility of the application of chaos theory to flight conflict prediction [8]. It presents an improvement of the efficient identification and processing method of incorrect trajectory data, and the results of clustering and nonclustering when extracting the air traffic flow passing through a waypoint are compared.

Processing of ADS-B Trajectory Data

Extraction of Air Traffic Flow Time Series on Different Time Scales

Findings

Conclusions e innovations of the present work are as follows: