Categorizing Flight Paths using Data Visualization and Clustering
  Methodologies
Yifan Song Keyang Yu Seth Young
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.