Author:
Kökhan Ahmet,Kökhan Serhan,Gökdalay Meriç
Abstract
Purpose
The purpose of this study is to develop an operational level decision support system model for air traffic controllers (ATCos) within the framework of the Flexible Use of Airspace (FUA) concept to enable more efficient use of airspace capacity. This study produces a systematic solution to the route selection process so that the ATCo can determine the most efficient route with an operational decision support system model using Dijkstra’s Shortest Path Algorithm.
Design/methodology/approach
In this study, a new decision support system model for ATCos in decision-making positions was recommended and used. ATCos use this model as a main model for determining the shortest and safest route for aircraft as an operational-level decision support system. Dijkstra Algorithm, used in the model, is defined step by step and then explained with the pseudocode.
Findings
It has been determined that when the FUA concept and DSS are used while the ATCo chooses a route, significant fuel, time and capacity savings are achieved in flight operations. Emissions resulting from the negative environmental effects of air transportation are reduced, and significant capacity increase can be achieved. The operational level decision support system developed in the study was tested with 55 scenarios on the Ankara–Izmir flight route compared to the existing fixed route. The results for the proposed most efficient route were achieved at 11.22% distance (nm), 9.36%-time (min) savings and 837.71 kg CO2 emission savings.
Originality/value
As far as the literature is reviewed, most studies aimed at increasing airspace efficiency produce solutions that try to improve rather than replace the normal process. Considering the literature positioning of this study compared to other studies, the proposed model provides a new systematic solution to the problems that cause human-induced route inefficiency within the framework of the FUA concept.
Reference27 articles.
1. The design and analysis of optimal descent profiles using real flight data;Transportation Research Part D: Transport and Environment,2021
2. Usage of machine learning algorithms in flexible use of airspace concept,2019
3. Simultaneous optimization of airspace congestion and flight delay in air traffic network flow management;IEEE Transactions on Intelligent Transportation Systems,2017
4. A network based dynamic air traffic flow model for en route airspace system traffic flow optimization;Transportation Research Part E: Logistics and Transportation Review,2017
5. European airline delay cost reference values;EUROCONTROL Performance Review Unit,2015
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献