Accurate traffic flow prediction in heterogeneous vehicular networks in an intelligent transport system using a supervised non-parametric classifier

Hesham El-Sayed, Sharmi Sankar, Yousef Awwad Daraghmi, Prayag Tiwari, Ekarat Rattagan, Manoranjan Mohanty, Deepak Puthal, Mukesh Prasad

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

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.

Original languageBritish English
Article number1696
JournalSensors (Switzerland)
Volume18
Issue number6
DOIs
StatePublished - Jun 2018

Keywords

  • HETVNET
  • Internet of vehicles
  • QoS
  • RBF
  • SVM

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