2026-05-30T03:19:35Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/139752026-02-03T11:32:27Zcom_10630_2254col_10630_37953

Predicting Car Park Occupancy Rates in Smart Cities Stolfi, Daniel H. Alba-Torres, Enrique Yao, Xin Análisis de series temporales Aparcamientos 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. 2017-06-22T06:54:47Z 2017-06-22T06:54:47Z 2017-06-22T06:54:47Z 2017 journal article 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 http://hdl.handle.net/10630/13975 eng Smart-CT 2017 Málaga Junio de 2017 open access by-nc-nd Springer