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Accurate traffic flow prediction in heterogeneous vehicular networks in an intelligent transport system using a supervised non-parametric classifier
Authors
YA Daraghmi
H El-Sayed
+6 more
M Mohanty
M Prasad
D Puthal
E Rattagan
S Sankar
P Tiwari
Publication date
1 May 2018
Publisher
'MDPI AG'
Doi
Cite
Abstract
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. 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
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OPUS - University of Technology Sydney
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oai:opus.lib.uts.edu.au:10453/...
Last time updated on 18/10/2019
Directory of Open Access Journals
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Last time updated on 16/06/2018
Multidisciplinary Digital Publishing Institute
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Last time updated on 20/10/2022