Skip to main content
SpringerLink
Log in

Evolutionary Deep Learning for Car Park Occupancy Prediction in Smart Cities

  • Conference paper
  • First Online:
Learning and Intelligent Optimization (LION 12 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11353))

Included in the following conference series:

Abstract

This study presents a new technique based on Deep Learning with Recurrent Neural Networks to address the prediction of car park occupancy rate. This is an interesting problem in smart mobility and we here approach it in an innovative way, consisting in automatically design a deep network that encapsulates the behavior of the car occupancy and then is able to make an informed guess on the number of free parking spaces near to the medium time horizon. We analyze a real world case study consisting of the occupancy values of 29 car parks in Birmingham, UK, during eleven weeks and compare our results to other predictors in the state-of-the-art. The results show that our approach is accurate to the point of being useful for being used by citizens in their daily lives, as well as it outperforms the existing competitors.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://github.com/acamero/dlopt.

  2. 2.

    https://github.com/tensorflow/models.

References

  1. Alba, E., Martí, R.: Metaheuristic Procedures for Training Neural Networks, vol, p. 35. Springer Science & Business Media, Berlin (2006)

    Google Scholar 

  2. Back, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, Oxford (1996)

    Google Scholar 

  3. Bakici, T., Almirall, E., Wareham, J.: A smart city initiative: the case of Barcelona. J. Knowl. Econ. 4(2), 135–148 (2013). https://doi.org/10.1007/s13132-012-0084-9

    Article  Google Scholar 

  4. Benevolo, C., Dameri, R.P., D’Auria, B.: Smart mobility in smart city. In: Torre, T., Braccini, A.M., Spinelli, R. (eds.) Empowering Organizations, pp. 13–28. Springer International Publishing (2016)

    Google Scholar 

  5. Bergstra, J., Yamins, D., Cox, D.: Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In: International Conference on Machine Learning, pp. 115–123 (2013)

    Google Scholar 

  6. Camero, A., Toutouh, J., Alba, E.: DLOPT: deep learning optimization library (2018). arXiv:1807.03523

  7. Cintrano, C., Stolfi, D.H., Toutouh, J., Chicano, F., Alba, E.: Ctpath: A real world system to enable green transportation by optimizing environmentaly friendly routing paths. In: Alba, E., Chicano, F., Luque, G. (eds.) Smart Cities, pp. 63–75. Springer International Publishing (2016)

    Google Scholar 

  8. Doerr, C.: Non-static parameter choices in evolutionary computation. In: Genetic and Evolutionary Computation Conference, GECCO 2017, Berlin, Germany, July 15–19, 2017, Companion Material Proceedings. ACM (2017). https://doi.org/10.1145/3067695.3067707

  9. Fortin, F.A., De Rainville, F.M., Gardner, M.A., Parizeau, M., Gagné, C.: DEAP: Evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)

    Google Scholar 

  10. Giuffré, T., Siniscalchi, S.M., Tesoriere, G.: A novel architecture of parking management for smart cities. Procedia - Soc. Behav. Sci. 53, 16–28 (2012) (sIIV-5th Intl. Congress - Sustainability of Road Infrastructures 2012)

    Google Scholar 

  11. Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)

    Article  Google Scholar 

  12. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. The MIT Press (2016)

    Google Scholar 

  13. Haykin, S.: Neural Networks and Learning Machines, vol. 3. Pearson (2009)

    Google Scholar 

  14. Holland John, H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan, USA (1975)

    Google Scholar 

  15. Jaeger, H.: Tutorial on Training Recurrent Neural Networks, Covering BPPT, RTRL, EKF and the Echo State Network Approach, vol. 5. GMD (2002)

    Google Scholar 

  16. Klappenecker, A., Lee, H., Welch, J.L.: Finding available parking spaces made easy. Ad Hoc Netw. 12, 243–249 (2014)

    Article  Google Scholar 

  17. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)

    Article  Google Scholar 

  18. Lin, T.: Smart parking: network, infrastructure and urban service. Ph.D. thesis, Lyon, INSA (2015)

    Google Scholar 

  19. Lin, T., Rivano, H., Mouël, F.L.: A survey of smart parking solutions. IEEE Trans. Intell. Transp. Syst. 18(12), 3229–3253 (2017). https://doi.org/10.1109/TITS.2017.2685143. Dec

    Article  Google Scholar 

  20. Massobrio, R., Toutouh, J., Nesmachnow, S., Alba, E.: Infrastructure deployment in vehicular communication networks using a parallel multiobjective evolutionary algorithm. Int. J. Intell. Syst. 32(8), 801–829 (2017). https://doi.org/10.1002/int.21890

    Article  Google Scholar 

  21. Morse, G., Stanley, K.O.: Simple evolutionary optimization can rival stochastic gradient descent in neural networks. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, pp. 477–484. GECCO ’16, ACM (2016)

    Google Scholar 

  22. Nesmachnow, S., Rossit, D., Toutouth, J.: Comparison of multiobjective evolutionary algorithms for prioritized urban waste collection in montevideo, uruguay. Electron. Notes Discret. Math. (2018) (in press)

    Google Scholar 

  23. Office for National Statistics: Population Estimates for UK. http://www.nomisweb.co.uk/articles/747.aspx (2016). Accessed 16 Dec 2017

  24. Ojha, V.K., Abraham, A., Snášel, V.: Metaheuristic design of feedforward neural networks: a review of two decades of research. Eng. Appl. Artif. Intell. 60, 97–116 (2017)

    Article  Google Scholar 

  25. Pullola, S., Atrey, P.K., Saddik, A.E.: Towards an intelligent GPS-based vehicle navigation system for finding street parking lots. In: 2007 IEEE International Conference on Signal Processing and Communications, pp. 1251–1254 (2007). https://doi.org/10.1109/ICSPC.2007.4728553

  26. Rajabioun, T., Foster, B., Ioannou, P.A.: Intelligent parking assist. In: Control & Automation (MED), 2013 21st Mediterranean Conference, pp. 1156–1161. IEEE (2013)

    Google Scholar 

  27. Rajabioun, T., Ioannou, P.A.: On-street and off-street parking availability prediction using multivariate spatiotemporal models. IEEE Trans. Intell. Transp. Syst. 16(5), 2913–2924 (2015). Oct

    Article  Google Scholar 

  28. Reed, R., Marks, R., Oh, S.: Similarities of error regularization, sigmoid gain scaling, target smoothing, and training with jitter. IEEE Trans. Neural Netw. 6(3), 529–538 (1995)

    Article  Google Scholar 

  29. Richter, F., Martino, S.D., Mattfeld, D.C.: Temporal and spatial clustering for a parking prediction service. In: 2014 IEEE 26th International Conference on Tools with Artificial Intelligence, pp. 278–282 (2014)

    Google Scholar 

  30. Rumelhart, D., Hinton, G.E., Williams, R.j.: Learning internal representations by error propagation. Technical Report No. ICS-8506, California University San Diego La Jolla Inst for Cognitive Science (1985)

    Google Scholar 

  31. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  32. Stolfi, D.H., Alba, E., Yao, : X.: Predicting car park occupancy rates in smart cities, pp. 107–117. Springer (2017)

    Google Scholar 

  33. Stolfi, D.H., Armas, R., Alba, E., Aguirre, H., Tanaka, K.: Fine tuning of traffic in our cities with smart panels: the Quito city case study. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, pp. 1013–1019. GECCO ’16, ACM (2016)

    Google Scholar 

  34. Vlahogianni, E.I., Kepaptsoglou, K., Tsetsos, V., Karlaftis, M.G.: A real-time parking prediction system for smart cities. J. Intell. Transp. Syst. 20(2), 192–204 (2016). https://doi.org/10.1080/15472450.2015.1037955

    Article  Google Scholar 

  35. Vlahogianni, E., Kepaptsoglou, K., Tsetsos, V., Karlaftis, M.G.: Exploiting new sensor technologies for real-time parking prediction in urban areas. In: Transportation Research Board 93rd Annual Meeting Compendium of Papers, pp. 14–1673 (2014)

    Google Scholar 

  36. Yao, X.: Evolving artificial neural networks. Proc. IEEE 87(9), 1423–1447 (1999). https://doi.org/10.1109/5.784219

    Article  Google Scholar 

  37. Zheng, Y., Rajasegarar, S., Leckie, C.: Parking availability prediction for sensor-enabled car parks in smart cities. In: 2015 IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 1–6 (2015)

    Google Scholar 

Download references

Acknowledgements

This research was partially funded by Ministerio de Economía, Industria y Competitividad, Gobierno de España, and European Regional Development Fund grant numbers TIN2014-57341-R (http://moveon.lcc.uma.es), TIN2016-81766-REDT (http://cirti.es), and TIN2017-88213-R (http://6city.lcc.uma.es). Daniel H. Stolfi is supported by a FPU grant (FPU13/00954) from the Spanish Ministry of Education, Culture and Sports. Universidad de Málaga. Campus Internacional de Excelencia, Andalucía TECH.

Author information

Authors and Affiliations

  1. Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, 29071, Málaga, Spain

    Andrés Camero, Jamal Toutouh, Daniel H. Stolfi & Enrique Alba

Authors
  1. Andrés Camero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jamal Toutouh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel H. Stolfi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Enrique Alba
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrés Camero .

Editor information

Editors and Affiliations

  1. University of Trento, Trento, Italy

    Roberto Battiti

  2. University of Trento, Trento, Italy

    Mauro Brunato

  3. Wilfrid Laurier University, Waterloo, ON, Canada

    Ilias Kotsireas

  4. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Camero, A., Toutouh, J., Stolfi, D.H., Alba, E. (2019). Evolutionary Deep Learning for Car Park Occupancy Prediction in Smart Cities. In: Battiti, R., Brunato, M., Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 12 2018. Lecture Notes in Computer Science(), vol 11353. Springer, Cham. https://doi.org/10.1007/978-3-030-05348-2_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-05348-2_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05347-5

  • Online ISBN: 978-3-030-05348-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation