International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Science , Computer Science and Engineering

Volume5,April 2018,

Topic : A Novel Framework for Traffic Flow Prediction with Deep Learning Algorithms

Authors:S.Narmadha || Dr.V.Vijayakumar

Abstract:Traffic flow prediction is the process of predicting future traffic conditions in short-term or long term basis, based on real time and the past traffic data. It is an important component of an Intelligent Transportation System (ITS) in smart city development. Apart from traffic data from sensors, Incidents detection, Lane closure and Weather data also some of the main reasons to cause the congestion. Combine factors and provide an efficient prediction of congestion. It provides innovative and smarter services to the transport users. Deep learning is a promising technology used to predict the traffic congestion with high accuracy. Deep belief network, Recurrent neural network, Convolutional neural network are some of the well known existing algorithms used in transportation analysis. In this paper a novel framework is proposed for intelligent transport systems which can solve so many issues such as pollution, accidents, and unwanted delays. .

Keywords: Imputation Methods, Traffic flow analytics, Congestion, Deep learning algorithms

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DOI: 01.1617/vol5/iss4/pid32465


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Dr. Allon Guez
Professor, Drexel University,
USA


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IMPACT FACTOR: 4.890

ISSN(Online):2394-2320

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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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ACCEPTANCE RATIO: 28.69%
ARTICLES PUBLISHED:0521
PAPER RECEIVED:01730
Journal Code : IJERCSE
Electronic ISSN : 2394-2320
Impact Factor : 4.890
Frequency : monthly
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