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Sensors 2016, 16(10), 1575; doi:10.3390/s16101575

Data Analytics for Smart Parking Applications

1
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Parc Mediterrani de la Tecnologia, Av. Carl Friedrich Gauss, 7, Castelldefels, 08860 Barcelona, Spain
2
Department of Information Engineering (DEI), University of Padova, Via Gradenigo 6/B, 35131 Padova, Italy
3
Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya (UOC), Parc Mediterrani de la Tecnologia, Av. Carl Friedrich Gauss 5, Castelldefels, 08860 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Andrea Zanella and Toktam Mahmoodi
Received: 9 August 2016 / Revised: 15 September 2016 / Accepted: 20 September 2016 / Published: 23 September 2016
(This article belongs to the Special Issue Smart City: Vision and Reality)
View Full-Text   |   Download PDF [538 KB, uploaded 23 September 2016]   |  

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. View Full-Text
Keywords: data analytics; smart parking data; wireless sensing; Self-Organizing Maps (SOM); data clustering; Internet of Things data analytics; smart parking data; wireless sensing; Self-Organizing Maps (SOM); data clustering; Internet of Things
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Piovesan, N.; Turi, L.; Toigo, E.; Martinez, B.; Rossi, M. Data Analytics for Smart Parking Applications. Sensors 2016, 16, 1575.

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