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Smart city article details

Title	Predicting Car Park Occupancy Rates In Smart Cities
ID_Doc	42679
Authors	Stolfi, DH; Alba, E; Yao, X
Year	2017
Published	SMART CITIES, 10268
DOI	http://dx.doi.org/10.1007/978-3-319-59513-9_11
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.
Author Keywords	Smart city; Smart mobility; Parking; K-means; Time series; Machine learning

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