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

    15 Citations (Scopus)

    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 languageEnglish
    Article number1696
    JournalSensors (Switzerland)
    Volume18
    Issue number6
    DOIs
    Publication statusPublished - Jun 2018

    Keywords

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

    ASJC Scopus subject areas

    • Analytical Chemistry
    • Biochemistry
    • Atomic and Molecular Physics, and Optics
    • Instrumentation
    • Electrical and Electronic Engineering

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