International Journal of Advanced Research in Engineering and Technology (IJARET)

Source ID:00000006
Volume 11, Issue 3,March 2020, Pages 437-441, Page Count - 5


Nazirkar Reshma Ramchandra (1) C. Rajabhushanam (2)

(1) Research Scholar, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, India.
(2) Professor, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, India.

Manuscript ID:- 00000-05106
Access Type : Open Access
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Cite this article:Nazirkar Reshma Ramchandra,C. Rajabhushanam,  Architecture Of Traffic Flow Prediction Based On Ccf-deep Lstm Method, International Journal of Advanced Research in Engineering and Technology(IJARET), 2020, 11(3), PP.437-441

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The foremost reason for traffic congestion is the more number of vehicles that is because of the increase in population rate and also because of the development of the economy. Due to many reason the developed cities don`t have chance to eliminate traffic, but the modern and developed technology helps to manage traffic. Over last some years, traffic data have been exploding. The traffic in the area can be predicted is done using Deep Learning concept. Deep learning is a subdivision of Machine learning algorithms. The deep leaning algorithm is applied for the detection of traffic. This method is commonly known as traffic flow model prediction. In this research article a new architecture has been proposed to predict the traffic control in concern area. Here various indicators are used to analyze the traffic data. The important indicators are CCI, ADX and DEMA.
Author Keywords
Framework Prediction Traffic System Technical Indicators.

ISSN Print: 0976-6480 ISSN Online: 0976-6499
Source Type: Journals Document Type: Journal Article
Publication Language: English DOI: 10.34218/IJARET.11.3.2020.039
Abbreviated Journal Title: IJARET Access Type: Open Access
Publisher Name: IAEME Publication Resource Licence: CC BY-NC
Major Subject:Physical Sciences Subject Area classification: Computer Science
Subject area: General Computer Science Source: SCOPEDATABASE

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