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Volume & Issue no: Volume 11, Issue 4, July - August 2022

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Title:
Traffic Prediction using Random Forest Machine Learning Algorithms
Author Name:
Ajay C N, Dr. H V Kumaraswamy
Abstract:
Abstract: Numerous traffic prediction algorithms have been used in recent years to increase the effectiveness of the transportation system. Intelligent Transport System (ITS) is an organization that aids in traffic management and develops applications for the future. The ITS analyses vehicle speed and counts the number of vehicles passing it on the road using a variety of sensors. Recent years had the breakthrough in vehicle safety which reduced the traffic accidents. The optimum route during heavy traffic can be determined with the use of traffic prediction, which can save time and gasoline. The different machine learning techniques are employed in this paper that predict traffic based on the data. In comparison to the previous Machine Learning (ML) algorithms using a particular models based on ML the traffic forecast is more effectively displayed and predicted with the dataset collected. The best efficiency of the traffic data prediction is accomplished by the Random Forest ML algorithms is 97.82 percent. Keywords: ITS, SVM, SVR, Random Forest Regression
Cite this article:
Ajay C N, Dr. H V Kumaraswamy , " Traffic Prediction using Random Forest Machine Learning Algorithms" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 11, Issue 4, July - August 2022 , pp. 055-059 , ISSN 2278-6856.
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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
ISSN 2278-6856
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