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.
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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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Stolfi, D.H., Alba, E., Yao, X. (2017). Predicting Car Park Occupancy Rates in Smart Cities. In: Alba, E., Chicano, F., Luque, G. (eds) Smart Cities. Smart-CT 2017. Lecture Notes in Computer Science(), vol 10268. Springer, Cham. https://doi.org/10.1007/978-3-319-59513-9_11
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DOI: https://doi.org/10.1007/978-3-319-59513-9_11
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