Advertisement

An Artificial Intelligence Based Forecasting in Smart Parking with IoT

  • Petr FedchenkovEmail author
  • Theodoros Anagnostopoulos
  • Arkady Zaslavsky
  • Klimis Ntalianis
  • Inna Sosunova
  • Oleg Sadov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11118)

Abstract

Internet of Things (IoT) enables Smart Cities (SC) with novel services for the citizens’ well-being. A Smart Parking (SP) system is an important part of the SC infrastructure, which enables the efficient handling of the demanding SC traffic congestion conditions. Such a system also protects the urban environment towards a green ecosystem. In this paper, we consider Artificial Intelligence (AI) algorithms towards processing of data produced by parking lots and disseminated by IoT technology in the SC of St. Petersburg in Russia. Such algorithms enhance the proposed SP system to predict the number of unoccupied parking lots within the SC parking places. In addition, the SP system uses vehicle navigation to decide the optimal parking place according the current vehicle location and the availability of parking lots in the SC.

Keywords

Internet of Things (IoT) Smart cities Genetic algorithms Artificial Intelligence (AI) Recurrent Neural Networks (RNN) 

Notes

Acknowledgments

Part of this work has been carried out in the scope of the project bIoTope which is co-funded by the European Commission under the Horizon-2020 program, contract number H2020-ICT-2015/688203 – bIoTope. The research has been carried out with the financial support of the Ministry of Education and Science of the Russian Federation under grant agreement RFMEFI58716X0031.

References

  1. 1.
    Yu, F., Guo, J., Zhu, X., Shi, G.: Real time prediction of unoccupied parking space using time series model. In: 2015 International Conference on Transportation Information and Safety (ICTIS), pp. 370–374. IEEE (2015)Google Scholar
  2. 2.
    Yong, S., Chunping, L., Yihuai, W.: A forecasting model for parking guidance system. Comput. Sci. Inf. Eng. 607–611 (2009)Google Scholar
  3. 3.
    Qian, Z.(S.), Rajagopal, R.: Optimal parking pricing in general networks with provision of occupancy information. Procedia – Social Behav. Sci. 80, 779–805 (2013)CrossRefGoogle Scholar
  4. 4.
    Kurogo, H., Takada, K., Akiyama, H.: Concept of a Parking Guidance System and Its Effects in the Shinjuku Area Configuration, Performance, and Future Improvement of System. IEEE (1995)Google Scholar
  5. 5.
    Ma, M.: Research and Implementation of Information Forecasting in Parking Guidance System. Suzhou University, Suzhou (2006)Google Scholar
  6. 6.
    Skszek, S.L.: “State-of-the-Art” Report on Non-Traditional Traffic Counting Methods. 505 N. Tanque Verde Loop Rd. Tucson, AZ 85748, 58 p (2001)Google Scholar
  7. 7.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)CrossRefGoogle Scholar
  8. 8.
    Zhao, Z., Chen, W., Wu, X., Chen, P.C., Liu, J.: LSTM network: A deep learning approach for short-term traffic forecast. IET Intell. Transp. Syst. 11, 68–75 (2017)CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Sargsyan, S., Brunton, S.L., Kutz, J.N.: Online interpolation point refinement for reduced-order models using a genetic algorithm. SIAM J. Sci. Comput. 40(1), B283–B304 (2016)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Tian, Y., Pan, L.: Predicting short-term traffic flow by long short-term memory recurrent neural network. In: Proceedings of the IEEE International Conference on Smart City Socialcom Sustaincom, Chengdu, China, 19–21 December 2015, pp. 153–158 (2015)Google Scholar
  12. 12.
  13. 13.
    Vidnerová, P., Neruda, R.: Evolving KERAS architectures for sensor data analysis. Ann. Comput. Sci. Inf. Syst. 11, 109–112 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Petr Fedchenkov
    • 1
    Email author
  • Theodoros Anagnostopoulos
    • 1
    • 3
  • Arkady Zaslavsky
    • 1
    • 2
  • Klimis Ntalianis
    • 3
  • Inna Sosunova
    • 1
  • Oleg Sadov
    • 1
  1. 1.Department of Infocommunication TechnologiesITMO UniversitySaint PetersburgRussia
  2. 2.CSIRO Computational Informatics, CSIROPerthAustralia
  3. 3.Department of Business and MarketingUniversity of West AtticaAthensGreece

Personalised recommendations