Aircraft 4D contract based operation: The 4DCo-GC project

2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC)

2018

Global air-traffic demand is continuously increasing. To handle such a tremendous traffic volume while maintaining at least the same level of safety, a more efficient strategic trajectory planning is necessary. Static 4D trajectory planning with constant 4D segments, where aircraft have to stay all along their flights, ensures a strong predictability of traffic and may reduce congestion in airspace. The main limitation of this approach is linked to the 4D constraint associated to aircraft. As a matter of fact, each aircraft has to comply to this 4D segment to maintain separation from other aircraft, but this induces a real control of the engine in order to stay all the time in this 4D segment. This could result in extra fuel consumption and shorter engine life. In this work, we present an adaptive 4D strategic trajectory planning methodology which aims to minimize interaction between aircraft at the European-continent scale. The main purpose of this work is to associate to each aircraft a 4D bubble which is adapted to the current traffic situation. When aircraft are located in low density areas, the size of such bubbles can extend (with a maximum range of 20 minutes) and when aircraft enter high congestion areas, such bubbles can shrink until a minimum size of 2 minutes. The size of bubbles is then optimized according to the local density of aircraft. This adaptive process, avoid to constrain aircraft in 4D all along their trajectories. The proposed methodology separates aircraft by modifying their trajectories and departure times. This route/departure-time assignment problem is modeled as a mixedinteger optimization problem. Due to the very high combinatorics involved in the continent-scale context (involving more than 30,000 flights), we develop and implement a hybrid-metaheuristic optimization algorithm. This first optimization is done with a minimum bubble size of 2 minutes. A second optimization loop uses the solution produced by the first algorithm in order to optimally extend the size of the 4D bubbles along trajectories in order to minimize the time constraint of aircraft.

Section: Previous Related Workmentioning

confidence: 99%

Large Scale Adaptive 4D Trajectory Planning

Dieumegard

Chaimatanan²,

2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC)

2018

“…The concept of 4D strategic deconfliction that aims to generate conflict-free trajectories for aircraft from origin to destination airports is introduced in the Innovative Future Air Transport System (IFATS) project [17] and the 4 Dimension Contract-Guidance and Control (4D CO-GC) project [18]. In [19,20], preliminary studies on the optimization of individual 4D trajectories are presented.…”

Section: Previous Related Workmentioning

confidence: 99%

Aircraft 4D Trajectories Planning Under Uncertainties

Chaimatanan

2015 IEEE Symposium Series on Computational Intelligence

Mongeau

2015

To sustain the rapidly increasing air traffic demand, the future air traffic management system will rely on a concept, called Trajectory-Based Operations (TBO), that will require aircraft to follow an assigned 4D trajectory (time-constrained trajectory) with high precision. TBO involves separating aircraft via strategic (long-term) trajectory deconfliction rather than the currently-practicing tactical (short-term) conflict resolution. In this context, this paper presents a strategic 4D aircraft trajectory planning approach aiming at minimizing interaction between aircraft trajectories for a given day. The proposed methodology allocates an alternative departure time, a horizontal flight path, and a flight level to each flight at a country and a continent scale. Uncertainties of aircraft position and arrival time on its curvilinear abscissa are taken into account in the 1 trajectory planning process. The proposed approach optimizes the 4D trajectory of each aircraft so as to minimize the interaction between trajectories. A hybrid-metaheuristic optimization algorithm has been developed to solve this large-scale mixed-variable optimization problem. The algorithm is implemented and tested with real air traffic data taking into account uncertainty over the French and the European airspace for which a conflict-free and robust 4D trajectory plan is produced. 1 A terminal control area (also known as a terminal maneuvering area) is a controlled airspace surrounding major airports, generally designed as a cylindrical or upside down wedding-cake shape airspace of 30 to 50 mile radius and high of 10,000 feet. 2 Flight level (FL) is a pressure altitude, expressed in hundreds of feet, e.g. and altitude of 32,000 feet is referred to as FL 320.

“…The uncertainty increases the difficulty of the problems, and it reduces the solution space. 4 According to the famous NP = P conjecture, a Non-deterministic Polynomial-time hard (NP hard) problem cannot be solved in polynomial time (with respect to the size of the instance). The reader interested in complexity of decision problems and combinatorial optimization problems is referred for example to [10].…”

Section: Previous Related Work and Proposal Outlinementioning

confidence: 99%

“…altitude of 41,000 feet is referred to as FL 410. System (IFATS) project [3] and the 4 Dimension Contract-Guidance and Control (4D CO-GC) project [4]. This 4D trajectory concept will reduce recourse to controllers' intervention during the tactical phase.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Metaheuristic Optimization Algorithm for Strategic Planning of 4D Aircraft Trajectories at the Continental Scale

Chaimatanan

IEEE Comput. Intell. Mag.

Mongeau

2014

Global air-traffic demand is continuously increasing. To handle such a tremendous traffic volume while maintaining at least the same level of safety, a more efficient strategic trajectory planning is necessary. In this work, we present a strategic trajectory planning methodology which aims to minimize interaction between aircraft at the Europeancontinent scale. In addition, we propose a preliminary study that takes into account uncertainties of aircraft positions in the horizontal plane. The proposed methodology separates aircraft by modifying their trajectories and departure times. This route/departure-time assignment problem is modeled as a mixed-integer optimization problem. Due to the very high combinatorics involved in the continent-scale context (involving more than 30,000 flights), we develop and implement a hybrid-metaheuristic optimization algorithm. In addition, we present a computationally-efficient interaction detection method for large trajectory sets. The proposed methodology is successfully implemented and tested on a full-day simulated air traffic over the European airspace, yielding to an interaction-free trajectory plan.