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


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.

This is a preview of subscription content, log in to check access.


  1. 1.

    Deakin, M. and Al Waer, H., From intelligent to smart cities, Intell. Build. Int., 2011, vol. 3, no. 3, pp. 140–152.

    Article  Google 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.

    Article  Google 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.

    Google 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.

    Google Scholar 

  9. 9.

    Buyya, R., Broberg, J., and Goscinski, A.M., Cloud Computing Principles and Paradigms, Wiley, 2011.

    Google Scholar 

  10. 10.

    Dean, J. and Ghemawat, S., MapReduce: Simplified data processing on large clusters, Commun. ACM, 2008, vol. 1, pp. 107–113. 51

    Article  Google 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. 122–133

    Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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. 239–244

    Google 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. 10–17

    Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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. 110–113

    Google 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. 146–160

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to R. Massobrio.

Additional information

The article is published in the original.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Massobrio, R., Nesmachnow, S., Tchernykh, A. et al. Towards a Cloud Computing Paradigm for Big Data Analysis in Smart Cities. Program Comput Soft 44, 181–189 (2018).

Download citation


  • cloud computing
  • big data
  • smart cities
  • intelligent transportation systems