Open AccessAnalysis of Air Traffic Management Models
Author(s) -
Shankaramma,
AUTHOR_ID,
H V Supreetha,
Prof. Nagaraj G. S,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2022
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.c9752.0111322
Subject(s) - bottleneck , air traffic control , air traffic management , computer science , traffic flow (computer networking) , advanced traffic management system , transport engineering , cloud computing , key (lock) , traffic congestion , traffic generation model , service (business) , operations research , engineering , intelligent transportation system , computer network , computer security , business , embedded system , aerospace engineering , operating system , marketing
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.