<p class="normal">The objective of this paper is to introduce an effective and efficient way of traffic signal light control to optimize the traffic signal duration across each lanes and thereby, to minimize or completely eliminate traffic congestion. This paper introduces a new approach to resolve the traffic congestion problem at junctions by making use of decision trees. The vehicle count in the real time traffic video is determined by Image Processing technique. This information is fed to the decision tree based on which the decision is made regarding the status of traffic signal lights of each lane at the junction at any given instant of time.</p>
Abstract-Classification is one of the most predominant tasks for wide range of applications such as Sentiment analysis in text, voice recognition, image recognition, genetic engineering, data classification etc. Though many efficient classification algorithms have been introduced in the past few decades, due to the drastic increase in the amount of data generated across industry and academia there is a demand for classification algorithms with very high accuracy and robustness. This paper presents a new approach to enhance the accuracy of the classifier by combining Support Vector Machine (Classification algorithm) with K-Means Clustering algorithm and, finally using K Nearest Neighbours to make optimal choice on the classification problem .Experiments have shown that this new methodology has increased the accuracy of the classification problem and thus serves the intended purpose.
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