Categorizing Flight Paths using Data Visualization and Clustering Methodologies

• URL: http://arxiv.org/abs/2310.00773v1
• Date: Sun, 1 Oct 2023 19:42:00 GMT
• Title: Categorizing Flight Paths using Data Visualization and Clustering Methodologies
• Authors: Yifan Song, Keyang Yu, Seth Young
• Abstract summary: This work leverages the U.S. Federal Aviation Administration's Traffic Flow Management System dataset and DV8 to develop clustering algorithms for categorizing air traffic by their varying flight paths. Examples of applications reveal successful, realistic clustering based on automated clustering result determination and human-in-the-loop processes.
• Score: 12.270546709771926
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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