Abstract
ABSTRACT Finding an available parking space can be difficult in parts of most cities, especially in city centers. In this article, we address the study of parking occupancy data in Birmingham, Glasgow, Norfolk, and Nottingham in the United Kingdom. We test several prediction strategies, such as polynomial fitting, Fourier series, K-means clustering, and time series, and analyze their results. We use cross-validation to train the predictors and then test them with unseen occupancy data. Additionally, we develop a web service to visualize the current and historical parking data in a map, allowing users to consult the occupancy rate forecast up to one week in advance to satisfy their parking needs. We believe that the use of these accurate, intelligent techniques creates user services for citizens living in real smart cities as a way of improving their quality of life, decreasing wait times, and reducing fuel consumption.
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