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

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.

Keywords

• air traffic management
• machine learning
• traffic flow pattern recognition
• trajectory clustering
• traffic feature recognition
• 97u80