Predicting Car Park Occupancy Rates in Smart Cities
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.
KeywordsSmart city Smart mobility Parking K-means Time series Machine learning
This research is partially funded by the Spanish MINECO project TIN2014-57341-R (http://moveon.lcc.uma.es). Daniel H. Stolfi is supported by a FPU grant (FPU13/00954) from the Spanish Ministry of Education, Culture and Sports. University of Malaga. International Campus of Excellence Andalucia TECH.
- 2.Hertel, O., Jensen, S.S., Hvidberg, M., Ketzel, M., Berkowicz, R., Palmgren, F., Wåhlin, P., Glasius, M., Loft, S., Vinzents, P., Raaschou-Nielsen, O., Sørensen, M., Bak, H.: Assessing the impacts of traffic air pollution on human exposure and health. In: Jensen-Butler, C., Sloth, B., Larsen, M.M., Madsen, B., Nielsen, O.A. (eds.) Road Pricing, the Economy and the Environment. Advances in Spatial Science, pp. 277–299. Springer, Heidelberg (2008)Google Scholar
- 5.Zheng, Y., Rajasegarar, S., Leckie, C.: Parking availability prediction for sensor-enabled car parks in smart cities. In: 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 1–6 (2015)Google Scholar
- 7.Fan, J., Gijbels, I.: Local Polynomial Modelling and its Applications: Monographs on Statistics and Applied Probability, vol. 66. CRC Press, Boca Raton (1996)Google Scholar
- 10.Fuller, W.A.: Introduction to Statistical Time Series, vol. 428. Wiley, New York (2009)Google Scholar
- 11.Sugar, C.A.: Techniques for clustering and classification with applications to medical problems. Ph.D. thesis, Stanford University (1998)Google Scholar
- 12.Draper, N.R., Smith, H., Pownell, E.: Applied Regression Analysis, vol. 3. Wiley, New York (1966)Google Scholar