RT Journal Article
T1 Predicting Car Park Occupancy Rates in Smart Cities
A1 Stolfi, Daniel H.
A1 Alba-Torres, Enrique
A1 Yao, Xin
K1 Análisis de series temporales
K1 Aparcamientos
AB 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.
PB Springer
YR 2017
FD 2017
LK http://hdl.handle.net/10630/13975
UL http://hdl.handle.net/10630/13975
LA eng
NO 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
NO DOI: 10.1007/978-3-319-59513-9_11
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Spanish MINECO project TIN2014-57341-R (http://moveon.lcc.uma.es). FPU grant (FPU13/00954) from the Spanish Ministry of Education, Culture and Sports.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026