<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper proposes a flight data-driven model to identify air traffic flow patterns based on multi-layer cluster analysis for identifying and characterizing traffic flow patterns in the airspace of the Beijing-Tianjin-Hebei multi-airport system. The spatial and temporal trends of flight data are mined to capture the air traffic flow characteristics, and the busyness of waypoints help air traffic managers to understand the reuse status of runways and airspace for capacity planning decision support in the complex airspace of Beijing-Tianjin-Hebei.</p>
</abstract>

This paper proposes a flight data-driven model to identify air traffic flow patterns based on multi-layer cluster analysis for identifying and characterizing traffic flow patterns in the airspace of the Beijing-Tianjin-Hebei multi-airport system. The spatial and temporal trends of flight data are mined to capture the air traffic flow characteristics, and the busyness of waypoints help air traffic managers to understand the reuse status of runways and airspace for capacity planning decision support in the complex airspace of Beijing-Tianjin-Hebei.

Traffic Flow Pattern Recognition in Beijing-Tianjin-Hebei Multi-Airport Airspace

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Traffic Flow Pattern Recognition in Beijing-Tianjin-Hebei Multi-Airport Airspace"}

This paper proposes a flight data-driven model to identify air traffic flow patterns based on multi-layer cluster analysis for identifying and characterizing...

Traffic Flow Pattern Recognition in Beijing-Tianjin-Hebei Multi-Airport Airspace

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 02. Jan. 2024

Akzeptiert: 08. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0328

Schlüsselwörter
Air traffic management, Machine learning, Traffic flow pattern recognition, Trajectory clustering, Traffic feature recognition

© 2024 Zhijian Ye et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Traffic Flow Pattern Recognition in Beijing-Tianjin-Hebei Multi-Airport Airspace

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 02. Jan. 2024

Akzeptiert: 08. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0328

SchlüsselwörterAir traffic management, Machine learning, Traffic flow pattern recognition, Trajectory clustering, Traffic feature recognition

© 2024 Zhijian Ye et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Air traffic management, Machine learning, Traffic flow pattern recognition, Trajectory clustering, Traffic feature recognition