Improving Air Traffic Control Through Advanced Machine Learning Algorithms: A Focus on Safety and Efficiency
Keywords:
Air Traffic data; stepwise linear regression; Coarse Gaussian SVM; Exponential GPR; Ensemble Boosted Trees; Optimizable Neural NetworkAbstract
Due to constrained airspace and airport capacity, excessive air traffic demand overwhelms air traffic control and results in traffic delays. The current air traffic management system is limited by the workload of the air traffic controllers (ATC). ATC is crucial for maintaining human health because airspace is a frequent site of fatal flight accidents. The parameters of the airline system can be predicted in order to control or avoid air traffic.In this paper, Hybrid algorithms such asstepwise linear regression, Coarse Gaussian SVM, Exponential GPR, Ensemble Boosted Trees and Optimizable Neural Networkproposed to predict and control the air traffic. The proposed hybrid algorithms are predicting the air traffic from air traffic dataset. Stepwise linear regression, Coarse Gaussian SVM, Exponential GPR and Ensemble Boosted Treesalgorithms 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 algorithm Optimizable Neural Networkgives high accuracy prediction compared to other statistical algorithms. Optimizable Neural network algorithm gives high accuracy of about 89% compared to other algorithms.According to the analytical findings, the suggested method can increase both the compliance rate and the mean expected delay.
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