IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v19y2019i4d10.1007_s12351-018-00449-x.html
   My bibliography  Save this article

Where will the next ski injury occur? A system for visual and predictive analytics of ski injuries

Author

Listed:
  • Sandro Radovanovic

    (University of Belgrade)

  • Boris Delibasic

    (University of Belgrade)

  • Milija Suknovic

    (University of Belgrade)

  • Dajana Matovic

    (University of Belgrade)

Abstract

Ski injury is a rare event with 2‰ rate (2 injuries per 1000 skier days expected). Additionally, injuries are dispersed over a ski resort spatially and temporally, making it harder to predict where the injury will occur. In order to inspect ski-related injuries, we have developed a visual system which allows global and spatial inspection of ski lift transportation RFID data. To enrich the visual environment, we have embedded a predictive lasso regression model which predicts injury occurrence spatially and temporally over a ski resort with an AUC performance of 0.766. We propose the model which allows decision makers to make real-time decisions on allocation of rescue service capacities at a ski resort. Predictive model improves the models existing in literature as it works for various locations at a ski resort, and makes predictions of occurring injuries on an hourly basis.

Suggested Citation

  • Sandro Radovanovic & Boris Delibasic & Milija Suknovic & Dajana Matovic, 2019. "Where will the next ski injury occur? A system for visual and predictive analytics of ski injuries," Operational Research, Springer, vol. 19(4), pages 973-992, December.
  • Handle: RePEc:spr:operea:v:19:y:2019:i:4:d:10.1007_s12351-018-00449-x
    DOI: 10.1007/s12351-018-00449-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-018-00449-x
    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/s12351-018-00449-x?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. Wolff, François-Charles, 2014. "Lift ticket prices and quality in French ski resorts: Insights from a non-parametric analysis," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1155-1164.
    2. Robert Tibshirani, 2011. "Regression shrinkage and selection via the lasso: a retrospective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 273-282, June.
    3. François-Charles Wolff, 2014. "Lift ticket prices and quality in French ski resorts: Insights from a non-parametric analysis," Working Papers hal-00952999, HAL.
    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. François-Charles Wolff, 2016. "Bargaining powers of buyers and sellers on the online diamond market: a double perspective non-parametric analysis," Annals of Operations Research, Springer, vol. 244(2), pages 697-718, September.
    2. Lucian Belascu & Alexandra Horobet & Georgiana Vrinceanu & Consuela Popescu, 2021. "Performance Dissimilarities in European Union Manufacturing: The Effect of Ownership and Technological Intensity," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    3. Alberti, Federica & Mantilla, César, 2020. "Provision of noxious facilities using a market-like mechanism: A simple implementation in the lab," Working papers 35, Red Investigadores de Economía.
    4. Zhang, Guike & Gao, Zengan & Dong, June & Mei, Dexiang, 2023. "Machine learning approaches for constructing the national anti-money laundering index," Finance Research Letters, Elsevier, vol. 52(C).
    5. Lee Anthony & Caron Francois & Doucet Arnaud & Holmes Chris, 2012. "Bayesian Sparsity-Path-Analysis of Genetic Association Signal using Generalized t Priors," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(2), pages 1-31, January.
    6. Sokbae Lee & Myung Hwan Seo & Youngki Shin, 2016. "The lasso for high dimensional regression with a possible change point," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 193-210, January.
    7. Nikolaus Hautsch & Ostap Okhrin & Alexander Ristig, 2014. "Efficient Iterative Maximum Likelihood Estimation of High-Parameterized Time Series Models," SFB 649 Discussion Papers SFB649DP2014-010, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    8. Jin, Shaobo & Moustaki, Irini & Yang-Wallentin, Fan, 2018. "Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case," LSE Research Online Documents on Economics 88118, London School of Economics and Political Science, LSE Library.
    9. Hettihewa, Samanthala & Saha, Shrabani & Zhang, Hanxiong, 2018. "Does an aging population influence stock markets? Evidence from New Zealand," Economic Modelling, Elsevier, vol. 75(C), pages 142-158.
    10. Shao, Hu & Lam, William H.K. & Sumalee, Agachai & Chen, Anthony & Hazelton, Martin L., 2014. "Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 52-75.
    11. Andrés Gómez & Oleg A. Prokopyev, 2021. "A Mixed-Integer Fractional Optimization Approach to Best Subset Selection," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 551-565, May.
    12. Lee, Kuo-Jung & Chen, Ray-Bing & Wu, Ying Nian, 2016. "Bayesian variable selection for finite mixture model of linear regressions," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 1-16.
    13. Lu, Xuefei & Baraldi, Piero & Zio, Enrico, 2020. "A data-driven framework for identifying important components in complex systems," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    14. Negm, L.M. & Youssef, M.A. & Chescheir, G.M. & Skaggs, R.W., 2016. "DRAINMOD-based tools for quantifying reductions in annual drainage flow and nitrate losses resulting from drainage water management on croplands in eastern North Carolina," Agricultural Water Management, Elsevier, vol. 166(C), pages 86-100.
    15. Gang Zhou & Manyi Cui & Junhong Wan & Shiqiang Zhang, 2021. "A Review on Snowmelt Models: Progress and Prospect," Sustainability, MDPI, vol. 13(20), pages 1-27, October.
    16. Yanfang Zhang & Chuanhua Wei & Xiaolin Liu, 2022. "Group Logistic Regression Models with l p,q Regularization," Mathematics, MDPI, vol. 10(13), pages 1-15, June.
    17. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    18. Tuantuan Zhang & Xingwen Jiang & Song Yang & Junwen Chen & Zhenning Li, 2022. "A predictable prospect of the South Asian summer monsoon," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    19. Chen, Ya & Tsionas, Mike G. & Zelenyuk, Valentin, 2021. "LASSO+DEA for small and big wide data," Omega, Elsevier, vol. 102(C).
    20. Kong, Nancy & Dulleck, Uwe & Jaffe, Adam B. & Sun, Shupeng & Vajjala, Sowmya, 2023. "Linguistic metrics for patent disclosure: Evidence from university versus corporate patents," Research Policy, Elsevier, vol. 52(2).

    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:operea:v:19:y:2019:i:4:d:10.1007_s12351-018-00449-x. 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.