期刊
SMART CITIES
卷 10268, 期 -, 页码 107-117出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-319-59513-9_11
关键词
Smart city; Smart mobility; Parking; K-means; Time series; Machine learning
资金
- Spanish MINECO project [TIN2014-57341-R]
- Spanish Ministry of Education, Culture and Sports [FPU13/00954]
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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