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Sensors 2018, 18(6), 1696; https://doi.org/10.3390/s18061696

Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier

1
College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates
2
Department of Information Technology, Ibri College of Applied Sciences (MoHE), Ibri 516, Sultanate of Oman
3
Department of Computer Systems Engineering, Palestine Technical University, Tulkarem 007, Palestine
4
Department of Information Engineering, University of Padova, 35131 Padova, Italy
5
Faculty of Information Science and Technology, Mahanakorn University of Technology, Bangkok 10530, Thailand
6
Department of Computer Science, University of Auckland, Auckland 1010, New Zealand
7
School of Electrical and Data Engineering, FEIT, University of Technology Sydney, Sydney 2007, Australia
8
Centre for Artificial Intelligence, School of Software, FEIT, University of Technology Sydney, Sydney 2007, Australia
*
Authors to whom correspondence should be addressed.
Received: 27 March 2018 / Revised: 9 May 2018 / Accepted: 9 May 2018 / Published: 24 May 2018
(This article belongs to the Special Issue Sensors for Green Computing)
Full-Text   |   PDF [1294 KB, uploaded 24 May 2018]   |  

Abstract

Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy. View Full-Text
Keywords: HETVNET; QoS; SVM; RBF; internet of vehicles HETVNET; QoS; SVM; RBF; internet of vehicles
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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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El-Sayed, H.; Sankar, S.; Daraghmi, Y.-A.; Tiwari, P.; Rattagan, E.; Mohanty, M.; Puthal, D.; Prasad, M. Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier. Sensors 2018, 18, 1696.

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