PREDICTING AIR TRAFFIC DENSITY IN AN AIR TRAFFIC CONTROL SECTOR

Abstract

Aviation Industry plays a very important role in the economic development of a nation. An efficient Air Transport system results in economic and social benefits. In order to ensure benefit for both the industry and the economic sectors it interacts with, a proper assessment of air transport needs to be made, taking into consideration the associated resources that are to be provided. Air Traffic over the Indian skies and at Airports has seen a rapid increase and it is also expected to grow in the future too. The resultant increase in demand requires a corresponding effort in effectively balancing the demand with capacity. The concept of Air Traffic Flow Management (ATFM) enables improved management of demand and capacity and helps the stakeholders to deal with the increased complexity of Indian air routes. In the Civil Aviation Industry, forecasts are vital to the Planning process of States, Airports, Airlines, Air Navigation Service Providers (ANSP) and other allied organizations. It helps States in the orderly development of Civil Aviation and also in the planning of Airspace and Airport Infrastructure. It assists Airlines in air route planning and flight scheduling. Being able to predict the traffic in Air Traffic Control (ATC) sectors is thus vital for effectively managing the flow of Air Traffic. It gives an indication of an impending breach of a sector capacity, so that flow restrictions or delay procedures or bifurcation of the ATC sector, is implemented, to avoid potential overloading of a sector, which in turn will lead to safety breach. Determining the traffic in an ATC sector involves analysis of various hidden factors that require careful evaluation. Given a sector and an hourly analysis of real time Air Traffic data of the sector, the prediction task is implemented using two machine learning algorithms- Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), that analyze the data and detect a pattern, which is then used to predict the traffic in the sector at any given time in the near future. Given the real time data and the experimental results obtained from this study, it is evident that Long Short Term Memory provides a better indication of the purpose of this study.