Next Article in Journal
An Efficient Delay Tolerant Networks Routing Protocol for Information-Centric Networking
Next Article in Special Issue
Intellino: Processor for Embedded Artificial Intelligence
Previous Article in Journal
Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai
Previous Article in Special Issue
Towards Near-Real-Time Intrusion Detection for IoT Devices using Supervised Learning and Apache Spark
Open AccessArticle

Exploiting Recurring Patterns to Improve Scalability of Parking Availability Prediction Systems

by 1,*,† and 2,†
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. View Full-Text
Keywords: internet of vehicles; parking availability predictions; smart mobility; dataset reduction; clustering; scalability internet of vehicles; parking availability predictions; smart mobility; dataset reduction; clustering; scalability
Show Figures

Figure 1

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 Style

Di Martino, Sergio; Origlia, Antonio. 2020. "Exploiting Recurring Patterns to Improve Scalability of Parking Availability Prediction Systems" Electronics 9, no. 5: 838.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop