Empirical study of air traffic dynamics using coupled network modeling and non-linear analysis
File(s)
Accepted version
Author(s)
Yang, L
Yin, S
Hu, M
Han, K
Xu, Y
Type
Conference Paper
Abstract
Air traffic is widely known as a complex, task-critical techno-social system, with numerous interactions between airspace, procedures, aircraft and air traffic controllers. In order to develop and deploy high-level operational concepts and automations scientifically and effectively, it is essential to conduct an in-depth investigation on the intrinsic human-traffic dynamics and characteristics, which is not widely seen in the literature. To fill this gap, the authors propose a coupled network to model controller and air traffic interactions. Furthermore, a set of analytical metrics including controllers’ cognitive complexity, communication load and chaotic metrics are introduced and applied in a case study of Guangzhou terminal airspace. Empirical results show the dynamics and underlying mechanisms of “ATCOs-flow” interactions are revealed and interpreted by adaptive meta-cognition strategies to cope with different phase states based on network analysis. Finally, at the system level, chaos is identified in conflict and communication behavior when system switches to the semi-stable or congested phase. This study offers analytical tools for understanding the complex human-flow interactions at potentially a broad range of air traffic systems, and underpins future developments and automation of intelligent air traffic management systems.
Date Acceptance
2017-09-29
Citation
Transportation Research Board 97th Annual Meeting
Journal / Book Title
Transportation Research Board 97th Annual Meeting
Copyright Statement
© 2018 Transportation Research Board
Source
Transportation Research Board 97th Annual Meeting
Subjects
terminal airspace
air traffic flow
air traffic controller
chaos
Publication Status
Published
Start Date
2018-01-07
Finish Date
2018-01-11
Coverage Spatial
Washington DC