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Sensors (Basel). 2016 Sep 23;16(10). pii: E1575. doi: 10.3390/s16101575.

Data Analytics for Smart Parking Applications.

Author information

1
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Parc Mediterrani de la Tecnologia, Av. Carl Friedrich Gauss, 7, Castelldefels, 08860 Barcelona, Spain. nicola.piovesan@cttc.es.
2
Department of Information Engineering (DEI), University of Padova, Via Gradenigo 6/B, 35131 Padova, Italy. turileo.1218@gmail.com.
3
Department of Information Engineering (DEI), University of Padova, Via Gradenigo 6/B, 35131 Padova, Italy. toigoenrico.91@gmail.com.
4
Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya (UOC), Parc Mediterrani de la Tecnologia, Av. Carl Friedrich Gauss 5, Castelldefels, 08860 Barcelona, Spain. bmartinezh@uoc.edu.
5
Department of Information Engineering (DEI), University of Padova, Via Gradenigo 6/B, 35131 Padova, Italy. rossi@dei.unipd.it.

Abstract

We consider real-life smart parking systems where parking lot occupancy data are collected from field sensor devices and sent to backend servers for further processing and usage for applications. Our objective is to make these data useful to end users, such as parking managers, and, ultimately, to citizens. To this end, we concoct and validate an automated classification algorithm having two objectives: (1) outlier detection: to detect sensors with anomalous behavioral patterns, i.e., outliers; and (2) clustering: to group the parking sensors exhibiting similar patterns into distinct clusters. We first analyze the statistics of real parking data, obtaining suitable simulation models for parking traces. We then consider a simple classification algorithm based on the empirical complementary distribution function of occupancy times and show its limitations. Hence, we design a more sophisticated algorithm exploiting unsupervised learning techniques (self-organizing maps). These are tuned following a supervised approach using our trace generator and are compared against other clustering schemes, namely expectation maximization, k-means clustering and DBSCAN, considering six months of data from a real sensor deployment. Our approach is found to be superior in terms of classification accuracy, while also being capable of identifying all of the outliers in the dataset.

KEYWORDS:

Internet of Things; Self-Organizing Maps (SOM); data analytics; data clustering; smart parking data; wireless sensing

Conflict of interest statement

The authors declare no conflict of interest. The founding sponsors had collected and, upon anonymizing it, provided the dataset that was used for the results in this paper. The sponsors has likewise agreed to publish the techniques and the results herein.

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