WSEAS Transactions on Computers

Print ISSN: 1109-2750
E-ISSN: 2224-2872

Volume 17, 2018

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

Decision Tree Classification for Traffic Congestion Detection using Data Mining

AUTHORS: R. Sujatha, S. Srinithibharathi, S. Subhapradha

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ABSTRACT: Traffic congestion could be a challenge in Gauteng province of Republic of South Africa and it's a negative impact on the economy of this province therein services and merchandise aren't being rendered on time. Holdup affects the standard of lives of Gauteng residents and guests alike. This information was used for constructing the traffic flow prediction models. To propose a way to spot road holdup levels from rate of mobile sensors with high accuracy .According to Human perceptions holdup levels divided into 3 levels: lightweight, heavy, and jam. The ratings and rate were fed into a choice tree learning model (J48).The slippery windows technique to capture the consecutive moving average velocities, that was referred to as a moving pattern .Then road users’ judgments and connected info were learned utilizing a decision tree model (J48) classification algorithmic program. The evaluations disclosed that the decision tree model. The achieved Associate in Nursing overall accuracy as high as 91% with a precision as high as 94%.

KEYWORDS: - Classification , Decision tree, Holdup


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WSEAS Transactions on Computers, ISSN / E-ISSN: 1109-2750 / 2224-2872, Volume 17, 2018, Art. #27, pp. 220-226

Copyright © 2018 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

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