Dynamic pricing analysis of redundant time of sports culture hall based on big data platform

  • Rui Jiang
  • Yingping LiEmail author
Original Article


Sports play an important role in people’s physical health and mental regulation. The choice of sports venues is closely related to people’s age, income, and leisure time and consumption level. Traditional sports cultural halls have some drawbacks in operation time and pricing mode, which lead to waste of space resources, unbalanced net profit, and weak consumption intention. Based on operation mode of traditional sports cultural hall, this paper analyzes the game relationship between profit and consumption intention of sports cultural hall. By constructing a large data platform of the pricing model and using mathematical statistics and support vector machine, the dynamic pricing strategy of the spare time of sports cultural hall is formulated. By comparing different pricing strategies, the overall profit of sports cultural hall has been greatly improved. And the optimal strategy of adjusting pricing dynamically with time is obtained, which provides an effective method for research of the dynamic pricing strategy.


Sports cultural hall Big data Dynamic pricing Support vector machine 



  1. 1.
    Miguel P, Amado D (2017) Foreword of the special issue on motivation in physical education, sport and physical activity and health. J Hum Kinet 59(59):3–4CrossRefGoogle Scholar
  2. 2.
    Mastnak W (2017) Sports and martial arts activities for public health purposes: the musician’s risk profiles and exercise-based health care as a model. J Public Health 25(3):231–241CrossRefGoogle Scholar
  3. 3.
    Staurowsky EJ, Murray K, Puzio M et al (2013) Revisiting James Madison University: a case analysis of program restructuring following so called ‘Title IX’ cuts. J Intercollegiate Sport 1:96–119CrossRefGoogle Scholar
  4. 4.
    Ye XS, Tang HT, Xiong W et al (2017) Simulation research on the income system of the existing sports venues PPP project: considering transaction costs. J Chengdu Sport Univ 43(3):22–29Google Scholar
  5. 5.
    Du X (2014) Analysis and design of sports cultural hall based on operation mode of virtual. Electronic Test 41(01):60–64Google Scholar
  6. 6.
    Ming W, Dongjie S, Lai KK, et al (2009) Risk control of providing a kind of quasi-public goods under China’s current pricing mechanism case of energy industry. International Joint Conference on Computational Sciences & Optimization. IEEE, 10, pp 966–969Google Scholar
  7. 7.
    Hodge GA, Greve C (2016) Public–private partnerships: an international performance review. Public Adm Rev 67(3):545–558CrossRefGoogle Scholar
  8. 8.
    Camerini L, Giacobazzi M, Boneschi M et al (2011) Design and implementation of a web-based tailored sports cultural hall to enhance self-management of fibromyalgia. User Model User-Adap Inter 21(4–5):485–511CrossRefGoogle Scholar
  9. 9.
    Zhang GX (2011) Risk assessment of large-scale gyms and stadiums based on BOT mode. International Conference on Information Science & Engineering 25(2):12–15Google Scholar
  10. 10.
    Jain A (2017) Weapons of math destruction: how big data increases inequality and threatens democracy. Bus Econ 52:1):1–1):3CrossRefGoogle Scholar
  11. 11.
    Vallerand RJ, Blanchard C, Mageau GA et al (2003) Les passions de l’ame: on obsessive and harmonious passion. J Pers Soc Psychol 85(4):756CrossRefGoogle Scholar
  12. 12.
    Vallerand RJ (2008) On the psychology of passion: in search of what makes people’s lives most worth living. Can Psychol/Psychologie canadienne 49(1):1–13CrossRefGoogle Scholar
  13. 13.
    Lafrenière MA, Jowett S, Vallerand RJ et al (2008) Passion in sport: on the quality of the coach-athlete relationship. J Sport Exerc Psychol 30(5):541–560CrossRefGoogle Scholar
  14. 14.
    Back KJ, Lee CK, Stinchfield R (2011) Gambling motivation and passion: a comparison study of recreational and pathological gamblers. J Gambl Stud 27(3):355–370CrossRefGoogle Scholar
  15. 15.
    Philippe FL, Vallerand RJ, Houlfort N et al (2010) Passion for an activity and quality of interpersonal relationships: the mediating role of emotions. J Pers Soc Psychol 98(6):917–932CrossRefGoogle Scholar
  16. 16.
    Ding Q, Wang JH, Lu L et al (2015) Journal of Nanjing Institute of Physical Education (Social Sciences Edition) 23(2):64–69Google Scholar
  17. 17.
    Lulic T, Maciukiewicz JM, Gonzalez DA et al (2018) The effect of aging and contextual information on manual asymmetry in tool use. Exp Brain Res 236(8):2347–2362CrossRefGoogle Scholar
  18. 18.
    Chen Y, He S, Hou F et al (2018) An efficient incentive mechanism for device-to-device multicast communication in cellular networks. IEEE Trans Wirel Commun 17(12):7922–7935CrossRefGoogle Scholar
  19. 19.
    Wu C, Li K, Shi T (2017) Supply chain coordination with two-part tariffs under information asymmetry. Int J Prod Res 55(9):2575–2589CrossRefGoogle Scholar
  20. 20.
    Barucca P, Caldarelli G, Squartini T (2017) Tackling information asymmetry in networks: a new entropy-based ranking index. J Stat Phys:1–17Google Scholar
  21. 21.
    Gao J, Zhou T, Lab CX et al (2016) Big data reveal the status of economic development. J Univ Electron Sci Technol China 45(4):625–633Google Scholar
  22. 22.
    Bai B, Chen J, Wang M et al (2017) Application research on big data in energy conservation and emission reduction of transportation industry. IOP Conf Ser Earth Environ Sci 69(1):012029CrossRefGoogle Scholar
  23. 23.
    Fengquan (2016) How to build the management mode for sports cultural halls in ordinary universities in China. Sports Sci 9(4):226–231Google Scholar
  24. 24.
    Yin Z (2016) GroRec: a group-centric intelligent recommender system integrating social, mobile and big data technologies. IEEE Trans Serv Comput 9(5):786–795CrossRefGoogle Scholar
  25. 25.
    Zhang Q, Lei G (2014) Research on current management status and countermeasures of public stadium. Appl Mech Mater 644(3):5795–5798MathSciNetCrossRefGoogle Scholar
  26. 26.
    Kumaresan A (2015) Framework for building a big data platform for publishing industry. Lecture Notes in Business Information Processing 224(1):377–388CrossRefGoogle Scholar
  27. 27.
    Choi TM, Chan HK, Yue X (2016) Recent development in big data analytics for business operations and risk management. IEEE Trans Cybern 47(1):81–92CrossRefGoogle Scholar
  28. 28.
    Bisdikian C (2001) An overview of the Bluetooth wireless technology. IEEE Commun Mag 39(12):86–94CrossRefGoogle Scholar
  29. 29.
    Bao W (2001) Application of Windows socket technique to communication process of the train diagram network system based on client/server structure. J Modern Transportation 9(2):115–121Google Scholar
  30. 30.
    Hu B, Deng C, Ye J (2011) Design and implementation of visual electronic commerce based on browser/server architecture. Adv Mater Res 271(3):336–339CrossRefGoogle Scholar
  31. 31.
    Bellman R (1956) Dynamic programming and Lagrange multipliers. Proc Natl Acad Sci U S A 42(10):767–769MathSciNetCrossRefGoogle Scholar
  32. 32.
    Diwan T, Sathe SR (2017) A generalized strategy for parallelization of non-serial polyadic dynamic programming on multicore and manycore. Adv Sci Lett 23(4):3802–3807CrossRefGoogle Scholar
  33. 33.
    Yamada KD (2018) Derivative-free neural network for optimizing the scoring functions associated with dynamic programming of pairwise-profile alignment. Algorithms Mol Biol 13(1):5CrossRefGoogle Scholar
  34. 34.
    Sutadian AD, Muttil N, Yilmaz AG et al (2017) Using the analytic hierarchy process to identify parameter weights for developing a water quality index. Ecol Indic 75:220–233CrossRefGoogle Scholar
  35. 35.
    Abdelazim AI, Ibrahim AM, Aboulzahab EM (2017) Development of an energy efficiency rating system for existing buildings using analytic hierarchy process – the case of Egypt. Renew Sustain Energy Rev 71(7):414–425CrossRefGoogle Scholar
  36. 36.
    Zhang Z, Pedrycz W (2018) Intuitionistic multiplicative group analytic hierarchy process and its use in multicriteria group decision-making. IEEE Trans Cybern 48(7):1950–1962CrossRefGoogle Scholar
  37. 37.
    Zhang YD, Yang ZJ, Lu HM et al (2017) Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation. IEEE Access 4(99):8375–8385Google Scholar
  38. 38.
    Guo J, Chen Z, Ban YL et al (2017) Precise enumeration of circulating tumor cells using support vector machine algorithm on a microfluidic sensor. IEEE Trans Emerg Top Comput 5(4):518–525CrossRefGoogle Scholar
  39. 39.
    Jie S, Fujita H, Peng C et al (2017) Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble. Knowl-Based Syst 120(C):4–14Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2020

Authors and Affiliations

  1. 1.School of Physical EducationJiujiang UniversityJiujiangChina
  2. 2.School of Economics and ManagementJiujiang UniversityJiujiangChina

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