| Title |
Predicting Car Park Occupancy Rates in Smart Cities |
| ID_Doc |
44822 |
| Authors |
Stolfi, DH; Alba, E; Yao, X |
| Title |
Predicting Car Park Occupancy Rates in Smart Cities |
| Year |
2017 |
| Published |
|
| DOI |
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 |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) |
| EID |
WOS:000432192700011 |
| WoS Category |
Computer Science, Theory & Methods; Urban Studies |
| Research Area |
Computer Science; Urban Studies |
| PDF |
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