Exploring Dynamic Characteristics of Multi-State Air Traffic Flow: A Time Series Approach

Shanmei Li,Jing Wang,Chao Wang

doi:10.1109/access.2020.2984510

Shanmei Li, Jing Wang + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.2984510

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Abstract

Understanding the dynamics of air traffic flow is important to achieving advanced air traffic management. This work explores the dynamic evolution and fluctuation characteristics of multistate air traffic time series from a complex network perspective, which is essential for understanding the nature of an air traffic system. With the application of the fundamental diagram (FD), we discover that the relative velocity, flight distance and trajectory similarity are the three key variables for interpreting the arrival traffic flow states of the Xiamen Gaoqi International Airport. According to these three variables, time series are classified into four traffic states based on the K-means algorithm: free flow (FF), transitional flow (TF), slightly congested flow (SCF) and heavily congested flow (HCF). The extracted time series in different states are converted into complex networks using the visibility graph method. We analyze and compare the statistical features of the networks in the four states in terms of indexes, such as the degree distribution and network structure. The results indicate that the complex network characteristics can be used to distinguish air traffic states from the original traffic flow. Our work may be helpful for scholars and engineers to better understand the intrinsic nature of air traffic and for the development of intelligent assistant decision-making systems for air traffic management.

Highlights

Air traffic congestion increases flight delays and causes environmental pollution and large economic losses
To date, a limited number of studies have been conducted on the dynamic evolution of air traffic flow, there are a large number of studies on air traffic flow optimization and management in the air traffic management community
This work explores the dynamic evolution and fluctuation characteristics of air traffic time series from the perspective of complex networks, which is essential for understanding the nature of air traffic systems

Summary

INTRODUCTION

Air traffic congestion increases flight delays and causes environmental pollution and large economic losses. To further explore the dynamics of air traffic flow in different states, we classify traffic flow data into several clusters by using the K-means method based on the time series of AFD, ARV and ATS. Two nodes in the complex network corresponding to traffic flow time series have visibility lines only if these nodes have connections in the time series of the average flight distance, the average relative flight speed and the average similarity.

STATISTICAL CHARACTERISTICS OF AIR TRAFFIC FLOW

DISTRIBUTION OF THE CLUSTER COEFFICIENT TO BETWEENNESS

CONCLUSIONS