Title
Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier.
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.
Year
DOI
Venue
2018
10.3390/s18061696
SENSORS
Keywords
Field
DocType
HETVNET,QoS,SVM,RBF,internet of vehicles
Traffic flow,Support vector machine,Quality of service,Supervised learning,Electronic engineering,Artificial intelligence,Engineering,Intelligent transportation system,Wireless ad hoc network,Machine learning,Vehicular ad hoc network,Computational complexity theory
Journal
Volume
Issue
Citations 
18
6.0
2
PageRank 
References 
Authors
0.37
11
8
Name
Order
Citations
PageRank
Hesham El-Sayed19919.59
Sharmi Sankar241.09
Yousef-Awwad Daraghmi3113.32
Prayag Tiwari44315.01
Ekarat Rattagan520.37
Manoranjan Mohanty65311.12
Deepak Puthal725133.94
Mukesh Prasad816626.33