Air Traffic Flow Management Data Mining and Analysis for In-flight Cost Optimization

As the air traffic volume has increased significantly over the world, the great mass of traffic management data, named as Big Data, have also accumulated day by day. This factor presents more opportunities and also challenges as well in the study and development of Air Traffic Management (ATM). Usually, Decision Support Systems (DSS) are developed to improve the efficiency of ATM. The main problem for these systems is the data analysis to acquisition sufficient knowledge for the decision. This paper introduces the application of the methods of Data Mining to get the knowledge from air traffic Big Data in management processes. The proposed approach uses a Bayesian network for the data analysis to reduce the costs of flight delay. The process makes possible to adjust the flight plan such as the schedule of arrival at or departure from an airport and also checks the airspace control measurements considering weather conditions. An experimental study is conducted based on the flight scenarios between Los Angeles International Airport (LAX) and Miami International Airport (MIA).