Categorizing Flight Paths using Data Visualization and Clustering Methodologies

TL;DR

This paper compares two clustering methods for categorizing flight paths using FAA data and DV8 visualization, demonstrating their effectiveness in different flight segments and enhancing air traffic analysis.

Contribution

It introduces and evaluates spatial and vector-based clustering algorithms for air traffic path categorization using interactive visualization tools.

Findings

Geographic distance clustering performs better for enroute paths.

Cosine similarity clustering is more effective for terminal operations.

Point extraction improves computational efficiency.

Abstract

This work leverages the U.S. Federal Aviation Administration's Traffic Flow Management System dataset and DV8, a recently developed tool for highly interactive visualization of air traffic data, to develop clustering algorithms for categorizing air traffic by their varying flight paths. Two clustering methodologies, a spatial-based geographic distance model, and a vector-based cosine similarity model, are demonstrated and compared for their clustering effectiveness. Examples of their applications reveal successful, realistic clustering based on automated clustering result determination and human-in-the-loop processes, with geographic distance algorithms performing better for enroute portions of flight paths and cosine similarity algorithms performing better for near-terminal operations, such as arrival paths. A point extraction technique is applied to improve computation efficiency.