AIR TRAFFIC CONTROL USING MACHINE LEARNING AND ARTIFICIAL NEURAL NETWORK

Abstract

Air Traffic Control (ATC) is important for human health because flight accidents occur in air space often and it leads to death. Air traffic can control or avoid through predict the parameters of airline system.  In this paper, Machine Learning (ML) and Artificial Intelligence (AI) methods proposed to predict and control the air traffic. The proposed methods machine learning and artificial intelligence are predicting the air traffic from air traffic dataset. Air traffic can predict through different statistical methods such as logistic regression (LR), decision tree (DT) and naïve bayes. These algorithms are performing less due to prediction of air traffic based on accuracy and time. These algorithms give huge difference in prediction such as accuracy level and speed. To solve the above problem, air traffic data fed to the pre-trained for prediction of air traffic. The proposed method machine learning and artificial intelligence gives high accuracy prediction compared to other statistical algorithms. Machine learning and artificial intelligence methods gives high accuracy of about 96% compared to conventional methods.