IDEAS home Printed from https://ideas.repec.org/a/spr/eurjtl/v8y2019i5d10.1007_s13676-018-0132-0.html
   My bibliography  Save this article

Unsupervised prototype reduction for data exploration and an application to air traffic management initiatives

Author

Listed:
  • Alexander Estes

    (University of Maryland)

  • David J. Lovell

    (University of Maryland)

  • Michael O. Ball

    (University of Maryland)

Abstract

We discuss a new approach to unsupervised learning and data exploration that involves summarizing a large data set using a small set of “representative” elements. These representatives may be presented to a user in order to provide intuition regarding the distribution of observations. Alternatively, these representatives can be used as cases for more detailed analysis. We call the problem of selecting the representatives the unsupervised prototype reduction problem. We discuss the KC-UPR method for this problem and compare it to other existing methods that may be applied to this problem. We propose a new type of distance measure that allows for more interpretable presentation of results from the KC-UPR method. We demonstrate how solutions from the unsupervised prototype reduction problem may be used to provide decision support for the planning of air traffic management initiatives, and we produce computational results that compare the effectiveness of several methods in this application. We also provide an example of how the KC-UPR method can be used for data exploration, using data from air traffic management initiatives at Newark Liberty International Airport.

Suggested Citation

  • Alexander Estes & David J. Lovell & Michael O. Ball, 2019. "Unsupervised prototype reduction for data exploration and an application to air traffic management initiatives," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 467-510, December.
    • Handle: RePEc:spr:eurjtl:v:8:y:2019:i:5:d:10.1007_s13676-018-0132-0
    DOI: 10.1007/s13676-018-0132-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13676-018-0132-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13676-018-0132-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lee, Wing Yee & Goodwin, Paul & Fildes, Robert & Nikolopoulos, Konstantinos & Lawrence, Michael, 2007. "Providing support for the use of analogies in demand forecasting tasks," International Journal of Forecasting, Elsevier, vol. 23(3), pages 377-390.
    2. Hassin, Refael & Levin, Asaf & Morad, Dana, 2003. "Lexicographic local search and the p-center problem," European Journal of Operational Research, Elsevier, vol. 151(2), pages 265-279, December.
    3. Nahmmacher, Paul & Schmid, Eva & Hirth, Lion & Knopf, Brigitte, 2016. "Carpe diem: A novel approach to select representative days for long-term power system modeling," Energy, Elsevier, vol. 112(C), pages 430-442.
    4. Sourour Elloumi & Martine Labbé & Yves Pochet, 2004. "A New Formulation and Resolution Method for the p-Center Problem," INFORMS Journal on Computing, INFORMS, vol. 16(1), pages 84-94, February.
    5. Burman, Prabir & Polonik, Wolfgang, 2009. "Multivariate mode hunting: Data analytic tools with measures of significance," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1198-1218, July.
    6. Avijit Mukherjee & Mark Hansen, 2007. "A Dynamic Stochastic Model for the Single Airport Ground Holding Problem," Transportation Science, INFORMS, vol. 41(4), pages 444-456, November.
    7. Dimitris Bertsimas & Guglielmo Lulli & Amedeo Odoni, 2011. "An Integer Optimization Approach to Large-Scale Air Traffic Flow Management," Operations Research, INFORMS, vol. 59(1), pages 211-227, February.
    8. Michael O. Ball & Robert Hoffman & Amedeo R. Odoni & Ryan Rifkin, 2003. "A Stochastic Integer Program with Dual Network Structure and Its Application to the Ground-Holding Problem," Operations Research, INFORMS, vol. 51(1), pages 167-171, February.
    9. Dorit S. Hochbaum & David B. Shmoys, 1985. "A Best Possible Heuristic for the k -Center Problem," Mathematics of Operations Research, INFORMS, vol. 10(2), pages 180-184, May.
    10. Michael O. Ball & Robert Hoffman & Avijit Mukherjee, 2010. "Ground Delay Program Planning Under Uncertainty Based on the Ration-by-Distance Principle," Transportation Science, INFORMS, vol. 44(1), pages 1-14, February.
    11. Dan Lovallo & Carmina Clarke & Colin Camerer, 2012. "Robust analogizing and the outside view: two empirical tests of case‐based decision making," Strategic Management Journal, Wiley Blackwell, vol. 33(5), pages 496-512, May.
    12. Balázs Kotnyek & Octavio Richetta, 2006. "Equitable Models for the Stochastic Ground-Holding Problem Under Collaborative Decision Making," Transportation Science, INFORMS, vol. 40(2), pages 133-146, May.
    13. Caruso, C. & Colorni, A. & Aloi, L., 2003. "Dominant, an algorithm for the p-center problem," European Journal of Operational Research, Elsevier, vol. 149(1), pages 53-64, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alexander S. Estes & Michael O. Ball, 2020. "Equity and Strength in Stochastic Integer Programming Models for the Dynamic Single Airport Ground-Holding Problem," Transportation Science, INFORMS, vol. 54(4), pages 944-955, July.
    2. Andreatta, Giovanni & Dell'Olmo, Paolo & Lulli, Guglielmo, 2011. "An aggregate stochastic programming model for air traffic flow management," European Journal of Operational Research, Elsevier, vol. 215(3), pages 697-704, December.
    3. Yi Liu & Mark Hansen, 2016. "Incorporating Predictability Into Cost Optimization for Ground Delay Programs," Transportation Science, INFORMS, vol. 50(1), pages 132-149, February.
    4. Avijit Mukherjee & Mark Hansen & Shon Grabbe, 2012. "Ground delay program planning under uncertainty in airport capacity," Transportation Planning and Technology, Taylor & Francis Journals, vol. 35(6), pages 611-628, June.
    5. Alexander S. Estes & Michael O. Ball, 2021. "Monge Properties, Optimal Greedy Policies, and Policy Improvement for the Dynamic Stochastic Transportation Problem," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 785-807, May.
    6. Samà, Marcella & D’Ariano, Andrea & D’Ariano, Paolo & Pacciarelli, Dario, 2017. "Scheduling models for optimal aircraft traffic control at busy airports: Tardiness, priorities, equity and violations considerations," Omega, Elsevier, vol. 67(C), pages 81-98.
    7. Guo, Yechenfeng & Hu, Minghua & Zou, Bo & Hansen, Mark & Zhang, Ying & Xie, Hua, 2022. "Air Traffic Flow Management Integrating Separation Management and Ground Holding: An Efficiency-Equity Bi-objective Perspective," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 394-423.
    8. Karsu, Özlem & Morton, Alec, 2015. "Inequity averse optimization in operational research," European Journal of Operational Research, Elsevier, vol. 245(2), pages 343-359.
    9. Dixit, Aasheesh & Jakhar, Suresh Kumar, 2021. "Airport capacity management: A review and bibliometric analysis," Journal of Air Transport Management, Elsevier, vol. 91(C).
    10. Xiangning Dong & Xuhao Zhu & Minghua Hu & Jie Bao, 2023. "A Methodology for Predicting Ground Delay Program Incidence through Machine Learning," Sustainability, MDPI, vol. 15(8), pages 1-19, April.
    11. Michael O. Ball & Robert Hoffman & Avijit Mukherjee, 2010. "Ground Delay Program Planning Under Uncertainty Based on the Ration-by-Distance Principle," Transportation Science, INFORMS, vol. 44(1), pages 1-14, February.
    12. Liu, Yulin & Liu, Yi & Hansen, Mark & Pozdnukhov, Alexey & Zhang, Danqing, 2019. "Using machine learning to analyze air traffic management actions: Ground delay program case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 80-95.
    13. Cynthia Barnhart & Dimitris Bertsimas & Constantine Caramanis & Douglas Fearing, 2012. "Equitable and Efficient Coordination in Traffic Flow Management," Transportation Science, INFORMS, vol. 46(2), pages 262-280, May.
    14. Agustı´n, A. & Alonso-Ayuso, A. & Escudero, L.F. & Pizarro, C., 2012. "On air traffic flow management with rerouting. Part II: Stochastic case," European Journal of Operational Research, Elsevier, vol. 219(1), pages 167-177.
    15. Mukherjee, Avijit & Hansen, Mark, 2009. "A dynamic rerouting model for air traffic flow management," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 159-171, January.
    16. Chen, J. & Chen, L. & Sun, D., 2017. "Air traffic flow management under uncertainty using chance-constrained optimization," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 124-141.
    17. Liu, Pei-chen Barry & Hansen, Mark & Mukherjee, Avijit, 2008. "Scenario-based air traffic flow management: From theory to practice," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 685-702, August.
    18. Murça, Mayara Condé Rocha, 2018. "Collaborative air traffic flow management: Incorporating airline preferences in rerouting decisions," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 97-107.
    19. Guglielmo Lulli & Amedeo Odoni, 2007. "The European Air Traffic Flow Management Problem," Transportation Science, INFORMS, vol. 41(4), pages 431-443, November.
    20. Chandra Ade Irawan & Said Salhi & Zvi Drezner, 2016. "Hybrid meta-heuristics with VNS and exact methods: application to large unconditional and conditional vertex $$p$$ p -centre problems," Journal of Heuristics, Springer, vol. 22(4), pages 507-537, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:eurjtl:v:8:y:2019:i:5:d:10.1007_s13676-018-0132-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.