Aeronautical Study for Flight Time Estimation Applying Artificial Neural Network and Genetic Algorithm

Abstract:

Since the adoption of open-air policy, people make more frequent use of air travel to do various business or tourism activities. The volume of air traffic has greatly increased, along with the occurrences of traffic jam in the air. Delays of landings or take-offs and the congestions in the approach air space have become commonplace, exacerbating the already heavy workload of air-traffic controllers and the inadequacies of ATC system. Therefore, a study of flight time in ATC operation to help alleviate airspace congestions has become more and more urgent and important. Taking international airway A1 as an example, this study makes use of the known entry time, flight altitude, speed, penetrating and descending as the input of artificial neural networks; the time between departure and transfer point as the output of Artificial Neural Networks, to establish artificial neural network. Applying artificial neural networks and genetic algorithm to the study to simulate the result of actual flight, one can precisely estimate the flight time, thereby making it an efficient air-traffic-control instrument. It can help controllers handle different time segments of air traffic, thus upgrading the quality of air traffic control service.

• Periodical: Advanced Materials Research (Volumes 919-921)
• Pages: 1063-1074
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.919-921.1063
• Online since: April 2014
• Authors: Yung Ching Lin*, Lee Kuo Lin, Shao Hong Tsai
• Keywords: Air Traffic Control (ATC), Artificial Neural Network (ANN), Genetic Algorithm (GA)

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