Topological data analysis for aviation applications

Max Z. Li, Megan S. Ryerson and Hamsa Balakrishnan

Transportation Research Part E: Logistics and Transportation Review, 2019, vol. 128, issue C, 149-174

Abstract: Aviation data sets are increasingly high-dimensional and sparse. Consequently, the underlying features and interactions are not easily uncovered by traditional data analysis methods. Recent advancements in applied mathematics introduce topological methods, offering a new approach to obtain these features. This paper applies the fundamental notions underlying topological data analysis and persistent homology (TDA/PH) to aviation data analytics. We review past aviation research that leverage topological methods, and present a new computational case study exploring the topology of airport surface connectivity. In each case, we connect abstract topological features with real-world processes in aviation, and highlight potential operational and managerial insights.

Keywords: Aviation data; Topological data analysis; Persistent homology; Airline networks (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.tre.2019.05.017

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