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

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 delay...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2018-05, Vol.18 (6), p.1696
Main Authors: El-Sayed, Hesham, Sankar, Sharmi, Daraghmi, Yousef-Awwad, Tiwari, Prayag, Rattagan, Ekarat, Mohanty, Manoranjan, Puthal, Deepak, Prasad, Mukesh
Format: Article
Language:English
Subjects:
Basis functions
HETVNET
Intelligent transportation systems
internet of vehicles
Mobile ad hoc networks
Nonparametric statistics
QoS
Radial basis function
RBF
Researchers
Support vector machines
SVM
Traffic flow
Traffic models
Vehicles
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s18061696