Framework for Traffic Congestion Prediction

Leiden Repository

Framework for Traffic Congestion Prediction

Type: Article / Letter to editor
Title: Framework for Traffic Congestion Prediction
Author: Zaki, J.F.W.Ali-Eldin, A.M.T.Hussein, S.E.Saraya, S.F.Areed, F.F.
Journal Title: International Journal of Scientific & Engineering Research
Issue: 5
Volume: 7
Start Page: 1205
End Page: 1210
Pages: 6
Issue Date: 2016
Abstract: Traffic Congestion is a complex dilemma facing most major cities. It has undergone a lot of research since the early 80s in an attempt to predict traffic in the short-term. Recently, Intelligent Transportation Systems (ITS) became an integral part of traffic research which helped in modeling and forecasting traffic conditions. In this paper, two frameworks for traffic congestion prediction are proposed. The first framework is based on NeuroFuzzy model which is well surveyed in traffic literature. The second framework is based on Hidden Markov Models (HMM) which is rarely used in traffic prediction. The methods are used to define traffic congestion during morning rush hours. The results of the two methods are compared.
Uri: http://www.ijser.org/onlineResearchPaperViewer.aspx?Framework-for-Traffic-Congestion-Prediction.pdf
Handle: http://hdl.handle.net/1887/46907
 

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