Abstract

International audience; 4D Trajectory optimization in dense terminal control area is one of the most challenging problems in air traffic management research. In order to efficiently and robustly land more aircraft at Beijing Capital International Airport (BCIA), one of the busiest airport in the world, a novel trajectory operation model is proposed, i.e. Multi-layer Point Merge (ML-PM) based Autonomous Arrival Management System. This paper aims at the evaluation of its potential operational benefits in terms of flight efficiency and runway throughput. Horizontal and Vertical profiles of ML-PM route network are introduced, the objective and constraints of this optimizing mathematical model are analyzed, especially the speed change profile and the conflict detection mode for merging zone. Then a case study is made by simulating arrival flows under three different operational modes: baseline, traditional point merge, and the ML-PM. Finally, the results show that rational arrival sequence and conflict-free trajectories are generated in ML-PM system, the benefits gained are very positive. Comparing with baseline and the traditional point merge system, ML-PM system shows good performance on flight time, fuel consumption, CO2 emission. The saving of fuel with ML-PM system is expected around 26838 Yuan per hour at BCIA compared with baseline scenario by numerical simulation. Furthermore, more flexible sequence position shift and continuous descent are possible in ML-PM system, and it is capable to handle the high-density operation environment.

Abstract

Although the application of new, reduced aircraft separation minima can directly increase runway throughput, the impact thereof on the traffic flow of aircraft arriving at the destination airport has not been discussed yet. This paper proposes a data-driven and queue-based modeling approach and presents an analysis of the impact on the delay time of arriving aircraft in the airspace within a radius of 100 nautical miles around an airport. The parameters of our queuing model were estimated by analysing the data contained in the radar tracks and flight plans for flights that arrived at Tokyo International Airport during the 2 years of 2016 and 2017. The results clarified the best arrival strategy according to the distance from the arrival airport: The combination of airspace capacity control and reduction of the flight time and separation variance is the most powerful solution to mitigate delays experienced by arriving traffic while also allowing an increase in the amount of arrival traffic. The application of new wake vortex categories would enable us to increase the arrival traffic to 120%. In addition, the arrival delay time could be minimised by implementing the proposed arrival traffic strategies together with automation support for air traffic controllers. Document type: Article

Abstract

International audience; Air transportation traffic is progressively increasing over the years and dealing with it is an essential task to guarantee fluid flights in the future. Several works already indexed multiple aspects of aviation, among them, the E-MAN system. It introduced the sequencing of arriving traffic, starting from early stages of the En-route phase. This change facilitated the work for the approach controllers but also increased the workload of the En-route controllers. To handle that workload, controllers are now assisted by tools that consider the new constraints introduced by the arrival management system and propose advisories. From that same perspective, our project focuses on an algorithm for a helper tool that will combine both aspects of traffic sequencing in the En-route phase and conflict resolution. With this novel approach, we automatically generate near-to-optimal flight decisions, given that we can modify the speed and the flight level to respect the sequencing constraints and cut down potential conflicts. We categorize the problem as a mathematical optimization case. Thus, we describe a detailed mathematical model which covers all the aspects of the problem. This model gives a basis for the implementation of the flight optimizer. Later, we propose a solution based on a sliding window simulated annealing algorithm which reduces the complexity and takes into account uncertainties. Finally, we successfully test an implementation of the solution with real-life traffic data. It corresponds to flights within France going towards Paris CDG airport over a period of 24 hours. The results demonstrate a total conflicts resolution with satisfying compliance with sequencing constraints.

Abstract

International audience; Air transportation traffic is progressively increasing over the years and dealing with it is an essential task to guarantee fluid flights in the future. Several works already indexed multiple aspects of aviation, among them, the E-MAN system. It introduced the sequencing of arriving traffic, starting from early stages of the En-route phase. This change facilitated the work for the approach controllers but also increased the workload of the En-route controllers. To handle that workload, controllers are now assisted by tools that consider the new constraints introduced by the arrival management system and propose advisories. From that same perspective, our project focuses on an algorithm for a helper tool that will combine both aspects of traffic sequencing in the En-route phase and conflict resolution. With this novel approach, we automatically generate near-to-optimal flight decisions, given that we can modify the speed and the flight level to respect the sequencing constraints and cut down potential conflicts. We categorize the problem as a mathematical optimization case. Thus, we describe a detailed mathematical model which covers all the aspects of the problem. This model gives a basis for the implementation of the flight optimizer. Later, we propose a solution based on a sliding window simulated annealing algorithm which reduces the complexity and takes into account uncertainties. Finally, we successfully test an implementation of the solution with real-life traffic data. It corresponds to flights within France going towards Paris CDG airport over a period of 24 hours. The results demonstrate a total conflicts resolution with satisfying compliance with sequencing constraints.