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Evolutionary Deep Learning for Car Park Occupancy Prediction in Smart Cities

  • Andrés CameroEmail author
  • Jamal Toutouh
  • Daniel H. Stolfi
  • Enrique Alba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11353)

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.

Keywords

Deep neuroevolution Deep learning Evolutionary algorithms Smart cities Car park occupancy 

Notes

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.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Andrés Camero
    • 1
    Email author
  • Jamal Toutouh
    • 1
  • Daniel H. Stolfi
    • 1
  • Enrique Alba
    • 1
  1. 1.Departamento de Lenguajes y Ciencias de la ComputaciónUniversidad de MálagaMálagaSpain

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