Analysis of Air Traffic Management Models
Shankaramma1, Supreetha H V2, Nagaraj G S3
1Shankaramma*, M.Tech, Department of Computer Science and Engineering, Rashtreeya Vidyalaya College of Engineering, Bengaluru, (Karnataka), India.
2Supreetha H V, M.Tech, Department of Computer Science and Engineering, Rashtreeya Vidyalaya College of Engineering, Bengaluru, (Karnataka), India.
3Prof. Nagaraj G. S, Associate Dean, Department of Computer Science and Engineering, Rashtreeya Vidyalaya College of Engineering, Bengaluru, (Karnataka), India..
Manuscript received on January 25, 2022. | Revised Manuscript received on January 31, 2022. | Manuscript published on February 28, 2022. | PP: 47-50 | Volume-11, Issue-3, January 2022 | Retrieval Number: 100.1/ijitee.C97520211322 | DOI: 10.35940/ijitee.C9752.0111322
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Abstract: The high growth of air traffic flow has increased more bottleneck traffic issues in the air traffic management (ATM) system. The challenges between flight flow, air traffic control service and airspace are the major key parameters which support capability of domestic and international air transportation need to be looked by stakeholders. Many models are designed to incorporate to address the potential bottleneck issues of ATM. However, in these models’ analysis was not clearly presented. The proposed research review paper presents an analysis and insights of different models used in an air traffic management which includes, Big Data, Artificial Neural Network, Cloud Computing and Enterprise models.
Keywords: Analysis, Air Traffic Management, Artificial Neural Network, Big Data, Cloud Computing, Enterprise Model.
Scope of the Article: Big Data Networking