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Predicting Car Park Occupancy Rates in Smart Cities

  • Daniel H. Stolfi
  • Enrique Alba
  • Xin Yao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10268)

Abstract

In this article we address the study of parking occupancy data published by the Birmingham city council with the aim of testing several prediction strategies (polynomial fitting, Fourier series, k-means clustering, and time series) and analyzing their results. We have used cross validation to train the predictors and then tested them on unseen occupancy data. Additionally, we present a web page prototype to visualize the current and historical parking data on a map, allowing users to consult the occupancy rate forecast to satisfy their parking needs up to one day in advance. We think that the combination of accurate intelligent techniques plus final user services for citizens is the direction to follow for knowledge-based real smart cities.

Keywords

Smart city Smart mobility Parking K-means Time series Machine learning 

Notes

Acknowledgements

This research is partially funded by the Spanish MINECO project TIN2014-57341-R (http://moveon.lcc.uma.es). Daniel H. Stolfi is supported by a FPU grant (FPU13/00954) from the Spanish Ministry of Education, Culture and Sports. University of Malaga. International Campus of Excellence Andalucia TECH.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Departamento de Lenguajes y Ciencias de la ComputaciónUniversity of MalagaMalagaSpain
  2. 2.CERCIA, School of Computer ScienceUniversity of BirminghamBirminghamUK

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