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Programming and Computer Software

, Volume 44, Issue 3, pp 181–189 | Cite as

Towards a Cloud Computing Paradigm for Big Data Analysis in Smart Cities

  • R. Massobrio
  • S. Nesmachnow
  • A. Tchernykh
  • A. Avetisyan
  • G. Radchenko
Article
  • 36 Downloads

Abstract

In this paper, we present a Big Data analysis paradigm related to smart cities using cloud computing infrastructures. The proposed architecture follows the MapReduce parallel model implemented using the Hadoop framework. We analyse two case studies: a quality-of-service assessment of public transportation system using historical bus location data, and a passenger-mobility estimation using ticket sales data from smartcards. Both case studies use real data from the transportation system of Montevideo, Uruguay. The experimental evaluation demonstrates that the proposed model allows processing large volumes of data efficiently.

Keywords

cloud computing big data smart cities intelligent transportation systems 

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References

  1. 1.
    Deakin, M. and Al Waer, H., From intelligent to smart cities, Intell. Build. Int., 2011, vol. 3, no. 3, pp. 140–152.CrossRefGoogle Scholar
  2. 2.
    Grava, S., Urban Transportation Systems, Choices for communities, 2003.Google Scholar
  3. 3.
    Chen, C., Ma, J., Susilo, Y., Liu, Y., and Wang, M., The promises of big data and small data for travel behavior (aka human mobility) analysis, Transportation Res., Part C: Emerging Technol., 2016, vol. 68, pp. 285–299.CrossRefGoogle Scholar
  4. 4.
    Sussman, J.S., Perspectives on Intelligent Transportation Systems (ITS), Springer, 2008.Google Scholar
  5. 5.
    Figueiredo, L., Jesus, I., Machado, J.T., Ferreira, J., and de Carvalho, J.M., Towards the development of intelligent transportation systems, Intell. Transp. Syst., 2001, vol. 88, pp. 1206–1211.Google Scholar
  6. 6.
    Foster, I., Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering, Boston, MA, USA: Addison-Wesley, 1995.MATHGoogle Scholar
  7. 7.
    White, T., Hadoop: The Definitive Guide, O’Reilly Media, 2009.Google Scholar
  8. 8.
    Attiya, H. and Welch, J., Distributed Computing: Fundamentals, Simulations and Advanced Topics, Wiley, 2004.CrossRefMATHGoogle Scholar
  9. 9.
    Buyya, R., Broberg, J., and Goscinski, A.M., Cloud Computing Principles and Paradigms, Wiley, 2011.CrossRefGoogle Scholar
  10. 10.
    Dean, J. and Ghemawat, S., MapReduce: Simplified data processing on large clusters, Commun. ACM, 2008, vol. 1, pp. 107–113. 51CrossRefGoogle Scholar
  11. 11.
    Shafer, J., Rixner, S., and Cox, A.L., The hadoop distributed filesystem: Balancing portability and performance, IEEE International Symposium on Performance Analysis of Systems and Software, 2010, pp. 122–133Google Scholar
  12. 12.
    Zheng, X., Chen, W., Wang, P., Shen, D., Chen, S., Wang, X., and Yang, L., Big data for social transportation, IEEE Trans. Intell. Transp. Syst., 2016, vol. 17, no. 3, pp. 620–630.CrossRefGoogle Scholar
  13. 13.
    Oh, S., Byon, Y.J., and Yeo, H., Improvement of search strategy with K-nearest neighbors approach for traffic state prediction, IEEE Trans. Intell. Transp. Syst., 2016, vol. 17, no. 4, pp. 1146–1156.CrossRefGoogle Scholar
  14. 14.
    Shi, Q. and Abdel-Aty, M., Big data applications in real-time traffic operation and safety monitoring and improvement on urban expressways, Transp. Res., Part C: Emerging Technol., 2015, vol. 58, pp. 380–394.CrossRefGoogle Scholar
  15. 15.
    Ahn, J., Ko, E., and Kim, E.Y., Highway traffic flow prediction using support vector regression and Bayesian classifier, International Conference on Big Data and Smart Computing (BigComp), 2016, pp. 239–244CrossRefGoogle Scholar
  16. 16.
    Chen, X.Y., Pao, H.K., and Lee, Y.J., Efficient traffic speed forecasting based on massive heterogenous historical data, IEEE International Conference on Big Data (Big Data), 2014, pp. 10–17CrossRefGoogle Scholar
  17. 17.
    Xia, D., Wang, B., Li, H., Li, Y., and Zhang, Z., A distributed spatial–temporal weighted model on MapReduce for short-term traffic flow forecasting, Neurocomputing, 2016, vol. 179, pp. 246–263.CrossRefGoogle Scholar
  18. 18.
    Nesmachnow, S., Computacion cientifica de alto desempeno en la Facultad de Ingenieria, Universidad de la Republica, Revista de la Asociacion de Ingenieros del Uruguay, 2010, vol. 61, no. 1, pp. 12–15.Google Scholar
  19. 19.
    Yang, H., Sasaki, T., Iida, Y., and Asakura, Y., Estimation of origin-destination matrices from link traffic counts on congested networks, Transp. Res., Part B: Methodol., 1992, vol. 26, no. 6, pp. 417–434.CrossRefGoogle Scholar
  20. 20.
    Trepanier, M., Tranchant, N., and Chapleau, R., Individual trip destination estimation in a transit smart card automated fare collection system, J. Intell. Transp. Syst., 2007, vol. 11, no. 1, pp. 1–14.CrossRefGoogle Scholar
  21. 21.
    Wang, W., Attanucci, J.P., and Wilson, N.H., Bus passenger origin-destination estimation and related analyses using automated data collection systems, J. Public Transp., 2011, vol. 14, no. 4, pp. 131–150.CrossRefGoogle Scholar
  22. 22.
    Munizaga, M.A. and Palma, C., Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile, Transp. Res., Part C: Emerging Technol., 2012, vol. 24, pp. 9–18.CrossRefGoogle Scholar
  23. 23.
    Pena, D., Tchernykh, A., Nesmachnow, S., Massobrio, S., Drozdov, A.Y., and Garichev, S.N., Multiobjective vehicle type and size scheduling problem in urban public transport using MOCell, IEEE International Conference Engineering and Telecommunications, Moscow, Russia, 2016, pp. 110–113Google Scholar
  24. 24.
    Massobrio, R., Pias, A., Vazquez, N., and Nesmachnow, S., Map-Reduce for Processing GPS Data from Public Transport in Montevideo, 2do Simposio Argentino de Grandes Datos, Uruguay, 2016.Google Scholar
  25. 25.
    Fabbiani, E., Vidal, P., Massobrio, R., and Nesmachnow, S., Distributed Big Data analysis for mobility estimation in Intelligent Transportation Systems, Latin American High Performance Computing Conference, 2016, pp. 146–160Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  • R. Massobrio
    • 1
  • S. Nesmachnow
    • 1
  • A. Tchernykh
    • 2
    • 3
    • 4
    • 6
  • A. Avetisyan
    • 3
    • 5
    • 6
  • G. Radchenko
    • 4
  1. 1.Universidad de la RepublicaMontevideoUruguay
  2. 2.CICESE Research Center, Carretera Tijuana-Ensenada 3918Fraccionamiento Zona PlayitasEnsenadaMexico
  3. 3.Institute for System Programming of the RASMoscowRussia
  4. 4.South Ural State UniversityChelyabinskRussia
  5. 5.Lomonosov Moscow State UniversityMoscowRussia
  6. 6.Moscow Institute of Physics and TechnologyDolgoprudny, Moscow oblastRussia

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