文章基本信息

标题：EMBEDDING MACHINE LEARNING IN AIR TRAFFIC CONTROL SYSTEMS TO GENERATE EFFECTIVE ROUTE PLANS FOR AIRCRAFTS IN ORDER TO AVOID COLLISIONS
本地全文：下载
作者：MUKESH MADANAN ; NORLAILA HUSSAIN ; NITHA C VELAYUDHAN 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2019
卷号：97
期号：2
页码：605-616
出版社：Journal of Theoretical and Applied
摘要：Air Traffic Controllers play a vital role in managing and directing flights both in and off the air. The most challenging task assigned to the controllers is to avoid collisions and to plan routes for the flights and make sure that these flights take off and reach the destination airports in time. Most of the route planning in such cases is done in accordance with humans and the decision making is solely dependent on human intelligence which is sometimes time consuming and error-prone. Artificial intelligence capabilities could be embedded in these controllers to make quick decisions and be free of human interventions. The paper focuses on the route planning activity of the controllers and has an in depth consideration towards the pros and cons of designing and implementing an artificial intelligence system to the air traffic controllers. The paper also focuses on the issues faced by air traffic controllers in order to maintain airspace suitable for safe flying.
关键词：Artificial Intelligence; Air Traffic Control; Machine Learning; Plan generator; Route Plan