Exploiting Recurring Patterns to Improve Scalability of Parking Availability Prediction Systems
1
Department of Electrical Engineering and Information Technologies, University of Naples Federico II, 80125 Naples, Italy
2
UrbanECO, University of Naples Federico II, 80125 Naples, Italy
*
Author to whom correspondence should be addressed.
†
The authors contributed equally to this work.
Electronics 2020, 9(5), 838; https://doi.org/10.3390/electronics9050838
Received: 21 March 2020 / Revised: 4 May 2020 / Accepted: 8 May 2020 / Published: 19 May 2020
(This article belongs to the Special Issue Recent Machine Learning Applications to Internet of Things (IoT))
Parking Guidance and Information (PGI) systems aim at supporting drivers in finding suitable parking spaces, also by predicting the availability at driver’s Estimated Time of Arrival (ETA), leveraging information about the general parking availability situation. To do these predictions, most of the proposals in the literature dealing with on-street parking need to train a model for each road segment, with significant scalability issues when deploying a city-wide PGI. By investigating a real dataset we found that on-street parking dynamics show a high temporal auto-correlation. In this paper we present a new processing pipeline that exploits these recurring trends to improve the scalability. The proposal includes two steps to reduce both the number of required models and training examples. The effectiveness of the proposed pipeline has been empirically assessed on a real dataset of on-street parking availability from San Francisco (USA). Results show that the proposal is able to provide parking predictions whose accuracy is comparable to state-of-the-art solutions based on one model per road segment, while requiring only a fraction of training costs, thus being more likely scalable to city-wide scenarios.
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Keywords:
internet of vehicles; parking availability predictions; smart mobility; dataset reduction; clustering; scalability
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MDPI and ACS Style
Di Martino, S.; Origlia, A. Exploiting Recurring Patterns to Improve Scalability of Parking Availability Prediction Systems. Electronics 2020, 9, 838.
AMA Style
Di Martino S, Origlia A. Exploiting Recurring Patterns to Improve Scalability of Parking Availability Prediction Systems. Electronics. 2020; 9(5):838.
Chicago/Turabian StyleDi Martino, Sergio; Origlia, Antonio. 2020. "Exploiting Recurring Patterns to Improve Scalability of Parking Availability Prediction Systems" Electronics 9, no. 5: 838.
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