Estimating Fuel-Efficient Air Plane Trajectories Using Machine Lear  

作　　者：Jaiteg Singh Gaurav Goyal Farman Ali Babar Shah Sangheon Pack 

机构地区：[1]Chitkara University Institute of Engineering&Technology,Chitkara University,Punjab,140401,India [2]Department of Software,Sejong University,Seoul,05006,Korea [3]College of Technological Innovation,Zayed University,UAE [4]School of Electrical Engineering,Korea University,Seoul,Korea

出　　处：《Computers, Materials & Continua》2022年第3期6189-6204,共16页计算机、材料和连续体（英文）

基　　金：This work was supported by the MSIT(Ministry of Science&ICT),Korea,under the ITRC support program(IITP-2021-2017-0-01633).This research work was also supported by the Research Incentive Grant R20129 of Zayed University,UAE。

摘　　要：Airline industry has witnessed a tremendous growth in the recent past.Percentage of people choosing air travel as first choice to commute is continuously increasing.Highly demanding and congested air routes are resulting in inadvertent delays,additional fuel consumption and high emission of greenhouse gases.Trajectory planning involves creation identification of cost-effective flight plans for optimal utilizationof fuel and time.This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required.In this paper,an algorithm for dynamic planning of optimized flight trajectories has been proposed.The proposed algorithm divides the airspace into four dimensional cubes and calculate a dynamic score for each cube to cumulatively represent estimated weather,aerodynamic drag and air traffic within that virtual cube.There are several constraints like simultaneous flight separation rules,weather conditions like air temperature,pressure,humidity,wind speed and direction that pose a real challenge for calculating optimal flight trajectories.To validate the proposed methodology,a case analysis was undertaken within Indian airspace.The flight routes were simulated for four different air routes within Indian airspace.The experiment results observed a seven percent reduction in drag values on the predicted path,hence indicates reduction in carbon footprint and better fuel economy.

关 键 词：Airplane trajectory coefficient of drag four-dimensional trajectory prediction machine learning route planning stochastic processes 

分 类 号：F562[经济管理—产业经济] TP181[自动化与计算机技术—控制理论与控制工程]