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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MDPI AG
2018-05-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/6/1696 |
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doaj-8f7985b413c34f5a8e3ced6aa78867572020-11-25T00:21:38ZengMDPI AGSensors1424-82202018-05-01186169610.3390/s18061696s18061696Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric ClassifierHesham El-Sayed0Sharmi Sankar1Yousef-Awwad Daraghmi2Prayag Tiwari3Ekarat Rattagan4Manoranjan Mohanty5Deepak Puthal6Mukesh Prasad7College of Information Technology, UAE University, Al Ain 15551, United Arab EmiratesDepartment of Information Technology, Ibri College of Applied Sciences (MoHE), Ibri 516, Sultanate of OmanDepartment of Computer Systems Engineering, Palestine Technical University, Tulkarem 007, PalestineDepartment of Information Engineering, University of Padova, 35131 Padova, ItalyFaculty of Information Science and Technology, Mahanakorn University of Technology, Bangkok 10530, ThailandDepartment of Computer Science, University of Auckland, Auckland 1010, New ZealandSchool of Electrical and Data Engineering, FEIT, University of Technology Sydney, Sydney 2007, AustraliaCentre for Artificial Intelligence, School of Software, FEIT, University of Technology Sydney, Sydney 2007, AustraliaHeterogeneous 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.http://www.mdpi.com/1424-8220/18/6/1696HETVNETQoSSVMRBFinternet of vehicles |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Hesham El-Sayed Sharmi Sankar Yousef-Awwad Daraghmi Prayag Tiwari Ekarat Rattagan Manoranjan Mohanty Deepak Puthal Mukesh Prasad |
| spellingShingle |
Hesham El-Sayed Sharmi Sankar Yousef-Awwad Daraghmi Prayag Tiwari Ekarat Rattagan Manoranjan Mohanty Deepak Puthal Mukesh Prasad Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier Sensors HETVNET QoS SVM RBF internet of vehicles |
| author_facet |
Hesham El-Sayed Sharmi Sankar Yousef-Awwad Daraghmi Prayag Tiwari Ekarat Rattagan Manoranjan Mohanty Deepak Puthal Mukesh Prasad |
| author_sort |
Hesham El-Sayed |
| title |
Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier |
| title_short |
Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier |
| title_full |
Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier |
| title_fullStr |
Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier |
| title_full_unstemmed |
Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier |
| title_sort |
accurate traffic flow prediction in heterogeneous vehicular networks in an intelligent transport system using a supervised non-parametric classifier |
| publisher |
MDPI AG |
| series |
Sensors |
| issn |
1424-8220 |
| publishDate |
2018-05-01 |
| description |
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. |
| topic |
HETVNET QoS SVM RBF internet of vehicles |
| url |
http://www.mdpi.com/1424-8220/18/6/1696 |
| work_keys_str_mv |
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