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Topological data analysis for aviation applications

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

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.

Suggested Citation

  • Li, Max Z. & Ryerson, Megan S. & Balakrishnan, Hamsa, 2019. "Topological data analysis for aviation applications," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 149-174.
    • Handle: RePEc:eee:transe:v:128:y:2019:i:c:p:149-174
    DOI: 10.1016/j.tre.2019.05.017
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    References listed on IDEAS

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    1. Li, Max Z. & Ryerson, Megan S., 2019. "Reviewing the DATAS of aviation research data: Diversity, availability, tractability, applicability, and sources," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 111-130.
    2. Guépet, Julien & Briant, Olivier & Gayon, Jean-Philippe & Acuna-Agost, Rodrigo, 2017. "Integration of aircraft ground movements and runway operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 104(C), pages 131-149.
    3. Belkoura, Seddik & Cook, Andrew & Peña, José Maria & Zanin, Massimiliano, 2016. "On the multi-dimensionality and sampling of air transport networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 94(C), pages 95-109.
    4. Zhou, Andu & Maletić, Slobodan & Zhao, Yi, 2018. "Robustness and percolation of holes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 459-468.
    5. Du, Wen-Bo & Zhang, Ming-Yuan & Zhang, Yu & Cao, Xian-Bin & Zhang, Jun, 2018. "Delay causality network in air transport systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 466-476.
    6. Yu, Bin & Guo, Zhen & Asian, Sobhan & Wang, Huaizhu & Chen, Gang, 2019. "Flight delay prediction for commercial air transport: A deep learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 203-221.
    7. Du, Wen-Bo & Zhou, Xing-Lian & Lordan, Oriol & Wang, Zhen & Zhao, Chen & Zhu, Yan-Bo, 2016. "Analysis of the Chinese Airline Network as multi-layer networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 89(C), pages 108-116.
    8. Cook, Andrew & Blom, Henk A.P. & Lillo, Fabrizio & Mantegna, Rosario Nunzio & Miccichè, Salvatore & Rivas, Damián & Vázquez, Rafael & Zanin, Massimiliano, 2015. "Applying complexity science to air traffic management," Journal of Air Transport Management, Elsevier, vol. 42(C), pages 149-158.
    9. Zhou, Yaoming & Wang, Junwei & Huang, George Q., 2019. "Efficiency and robustness of weighted air transport networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 14-26.
    10. Wei, P. & Chen, L. & Sun, D., 2014. "Algebraic connectivity maximization of an air transportation network: The flight routes’ addition/deletion problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 61(C), pages 13-27.
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    Cited by:

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    3. Rajendran, Suchithra & Srinivas, Sharan, 2020. "Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).

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