Toward an Intelligent Traffic Management Based on Big Data for Smart City

  • Yassine Karouani
  • Ziyati Elhoussaine
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 37)


It is anticipated that the Smart City research initiative will create new breakthroughs to revolutionize transportation system operations, infrastructure design, construction and management, as Big Data progresses. This latter will focus on the modeling, analysis and optimization of data-intensive intelligent transport systems, which will allow for more efficient system-wide operations. The focus is on the use of non-traditional data generated by smart city initiatives and emerging mobile applications, including data from social media, smart phones and more generally all connected objects. Research on this subject allows us to have a global view on the studies carried out in this field not on the infrastructure side but control and management of road traffic, based on the main objectives according to the users of the road. These objectives are the elaboration of a shortest path between a source and a destination, as well as the time required to traverse this path. We study different existing solutions such as solution employed by: Google, Japan (VICS, PCS) trying to find the advantage, the weak points and the common points to better bring out a new model which gathers the maximum advantages of these methods.


Smart city Big Data Mongo DB Traffic road Traffic congestion Vehicle routing 


  1. 1.
    Elgarej, M., Mansouri, K., Youssfi, M.: An improved swarm optimization algorithm for vehicle path planning problem. In: 4th IEEE International Colloquium on Information Science and Technology (CiSt) (2016)Google Scholar
  2. 2.
    Al Nuaimi, E., Al Neyadi, H., Mohamed, N., Al-Jaroodi, J.: Applications of big data to smart cities. J. Internet Serv. Appl. 6, 25 (2015)CrossRefGoogle Scholar
  3. 3.
    Wang, S., Djahel, S., Zhang, Z., McManis, J.: Next road rerouting: a multiagent system for mitigating unexpected urban traffic congestion. IEEE Trans. Intell. Transp. Syst. 17(10), 2888–2899 (2016)CrossRefGoogle Scholar
  4. 4.
    Koyama, A., Inoue, D., Shoji, S.: An implementation of visualization system for vehicles and pedestrians. In: The 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA) (2016)Google Scholar
  5. 5.
    Schmied, R., Moser, D., Waschl, H., del Re, L.: Scenario model predictive control for robust adaptive cruise control in multi-vehicle traffic situations. In: Intelligent Vehicles Symposium (IV). IEEE (2016)Google Scholar
  6. 6.
    Ma, D., Luo, X., Li, W., Jin, S., Guo, W., Wang, D.: Traffic demand estimation for lane groups at signal-controlled intersections using travel times from video-imaging detectors. IET Intell. Transp. Syst. 11(4), 222–229 (2017)CrossRefGoogle Scholar
  7. 7.
    El Hatri, C., Boumhidi, J.: Q-learning based intelligent multi-objective particle swarm optimization of light control for traffic urban congestion management. In: 4th IEEE International Colloquium on Information Science and Technology (CiSt) (2016)Google Scholar
  8. 8.
    Sánchez-Medina, J., Gálan-Moreno, M.J., Rubio-Royo, E.: Traffic signal optimization in “La Almozara” district in Saragossa under congestion conditions, using genetic algorithms, traffic microsimulation, and cluster computing. IEEE Trans. Intell. Transp. Syst. 11(1), 132–141 (2010)CrossRefGoogle Scholar
  9. 9.
    Ram, S., Wang, Y., Currim, F., Dong, F., Dantas, E., Sabóia, L.A.: SMARTBUS: a web application for smart urban mobility and transportation. In: 25th International Conference on World Wide Web Companion (2016)Google Scholar
  10. 10.
    Zhou, K., Fu, C., Yang, S.: Big Data driven smart energy management: from big data to big insights. Renew. Sustain. Energy Rev. 56, 215–225 (2016)CrossRefGoogle Scholar
  11. 11.
    Mohamed, N., Al-Jaroodi, J.: Real-time big data analytics: applications and challenges. In: 2014 International Conference on High Performance Computing and Simulation (HPCS), pp. 305–310 (2014)Google Scholar
  12. 12.
    Sharma, S.: Expanded cloud plumes hiding Big Data ecosystem. Future Gener. Comput. Syst. 59, 63–92 (2016)CrossRefGoogle Scholar
  13. 13.
    Xu, Z., Frankwick, G.L., Ramirez, E.: Effects of big data analytics and traditional marketing analytics on new product success: a knowledge fusion perspective. J. Bus. Res. 69(5), 1562–1566 (2015). Glova, J., Sabol, T., Vajda, V.: Business models for the Internet of Things environment. Procedia Econ. Financ.CrossRefGoogle Scholar
  14. 14.
    Kyriazis, D., Varvarigou, T.: Smart, autonomous and reliable Internet of Things. Procedia Comput. Sci. 21, 442–448 (2013)CrossRefGoogle Scholar
  15. 15.
    Henze, M., Hermerschmidt, L., Kerpen, D., Häußling, R., Rumpe, B., Wehrle, K.: A comprehensive approach to privacy in the cloud-based Internet of Things. Future Gener. Comput. Syst. 56, 701–718 (2015)CrossRefGoogle Scholar
  16. 16.
    Yan, Z., Zhang, P., Vasilakos, A.V.: A survey on trust management for Internet of Things. J. Netw. Comput. Appl. 42, 120–134 (2014)CrossRefGoogle Scholar
  17. 17.
    Botta, A., de Donato, W., Persico, V., Pescapé, A.: Integration of cloud computing and Internet of Things: a survey. Future Gener. Comput. Syst. 56, 684–700 (2015)CrossRefGoogle Scholar
  18. 18.
    Weber, R.H.: Internet of Things: privacy issues revisited. Comput. Law Secur. Rev. 31(5), 618–627 (2015)CrossRefGoogle Scholar
  19. 19.
    Lee, I., Lee, K.: The Internet of Things (IoT): applications, investments, and challenges for enterprises. Bus. Horiz. 58(4), 431–440 (2015)CrossRefGoogle Scholar
  20. 20.
    Liu, B., Li, L., Liu, K.: Study on the evaluation method of probe car system. In: IEEE Intelligent Vehicles Symposium (2010)Google Scholar
  21. 21.
    Rathore, M.M., Ahmad, A., Paul, A., Thikshaja, U.K.: Exploiting real-time big data to empower smart transportation using big graphs. In: 2016 IEEE Region 10 Symposium (TENSYMP), pp. 135–139 (2016)Google Scholar
  22. 22.
  23. 23.

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.RITM Laboratory, Computer Science and Networks TeamENSEM-ESTC-UH2CCasablancaMorocco

Personalised recommendations