UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 6 Issue 6
June-2019
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1908B06


Registration ID:
215986

Page Number

28-36

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Title

Estimation And Prediction Of Traffic Parameters Under Mixed Traffic Conditions.

Abstract

Approaches for mining this wealth of information to benefit the drivers and traffic authorities. The main reason is most likely related to fundamental challenges in dealing with big data of various types and uncertain frequency coming from diverse sources. Mainly, the issues of types of data and uncertainty analysis in the predictions are indicated as the most challenging areas of study that have not been tackled yet. Important issues such as the nature of the data, i.e., stationary or non-stationary, and the prediction tasks, i.e., short-term or long-term, should also be taken into consideration. Based on this observation, a data-driven traffic flow prediction framework within the context of big data environment is proposed in this thesis. The main goal of this framework is to enhance the quality of traffic flow predictions, which can be used to assist travelers and traffic authorities in the decision-making process (whether for travel or management purposes). Static models and simulations are commonly used in urban traffic management but none feature a dynamic element for near real-time traffic control. This work presents an artificial neural network forecaster methodology applied to traffic flow condition prediction. The spatially distributed architecture uses life-long learning with a novel adaptive Artificial Neural Network based filter to detect and remove outliers from training data. The system has been designed to support traffic engineers in their decision making to react to traffic conditions before they get out of control. We performed experiments using feed-forward back propagation, cascade-forward back-propagation, radial basis, and generalized regression Artificial Neural Networks for this purpose. Test results on actual data collected from the city of Leicester, UK, confirm our approach to deliver suitable forecasts

Key Words

Traffic, signal, optimization, cost.

Cite This Article

"Estimation And Prediction Of Traffic Parameters Under Mixed Traffic Conditions.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.28-36, June 2019, Available :http://www.jetir.org/papers/JETIR1908B06.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Estimation And Prediction Of Traffic Parameters Under Mixed Traffic Conditions.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp28-36, June 2019, Available at : http://www.jetir.org/papers/JETIR1908B06.pdf

Publication Details

Published Paper ID: JETIR1908B06
Registration ID: 215986
Published In: Volume 6 | Issue 6 | Year June-2019
DOI (Digital Object Identifier):
Page No: 28-36
Country: Anantnag, jammu and kashmir, India .
Area: Engineering
ISSN Number: 2349-5162


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