VEHICLE COUNT PREDICTION APPROACH BY USING MACHINE LEARNING METHODOLOGY (International Journal of Food and Nutritional Sciences, 2022)
Rapidly growing cities with rising population mobility have results an exponential growth in the number of vehicles on the road. Historical data is used to count the vehicles for vehicle count prediction, previous information is gathered from a variety of sources, including daily newspapers, Internet sources, and other repositories. Artificial intelligence techniques are best for prediction giving accurate results. One of the subsets is Machine learning for artificial intelligence. This includes the random forest regression, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression, and Naive Bayes. These predictions are compared and assessed in order to find the best-performing optimized solutions.The aim of this paper is to utilize different Machine Learning Algorithms to predict the number of vehicles that will pass through a particular junction. By counting the number of vehicles which are moving in particular area, one can able to get information and may reduce the traffic.