Abstract

International audience; The constant increase in air traffic, since the beginning of the commercial aviation, has led to problems of saturation on airports, approaching areas,or higher airspace.Whereas the aircraft are largely optimized and automated, the air traffic control is still essentially relying on human experience.The present case study details two problems of air traffic management (ATM) for which a genetic algorithm based solution has been proposed. Thefirst application deals with the en route conflict resolution problem. The second application deals with the traffic management problem in an airport platform.

Abstract

International audience; 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 chapter presents a strategic trajectory planning approach aiming at minimizing the number of conflicts 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 aircraft at a nation-wide scale.In real-life situations, aircraft may arrive at a given position with some uncertainties on its curvilinear abscissa due to external events. To ensure robustness of the strategic trajectory plan, the aircraft arrival time to any given position will be represented here by a probabilistic distribution over its nominal assigned arrival time.The proposed approach optimizes the 4D trajectory of each aircraft so as to minimize the probability of potential conflicts 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 airspace for which a conflict-free and robust 4D trajectory plan is produced.

Abstract

Study of interactions between two consecutive layers of ATM under increased traffic.Results highlight an exponential growth in delay costs if constant network capacity.Results highlight an unmanageable controller workload if network capacity is high.We find a compromise by controlling delay costs growth with a sector capacity growth.We quantify research objectives for future work on air traffic control optimization. Air traffic in Europe is predicted to increase considerably over the next decades. In this context, we present a study of the interactions between the costs due to ground-holding regulations and the costs due to en-route air traffic control. We dulator that considers the regulation delays, aircraft trajectories, and air conflict resolution. Through intensive simulations based on traffic forecasts extrapolated from French traffic data for 2012, we compute the regulation delays and avoidance maneuvers according to two scenarios: the current regulations and no regulations. The resulting cost analysis highlights the exponential growth in regulation costs that can be expected if the procedures and the airspace capacity do not change. Compared to the delay costs, the costs of the air traffic control are negligible with or without regulation. The analysis reveals the heavy controller workloads when there are no regulations, suggesting the need for regulations that are appropriate for large traffic volumes and an improved ATC system. These observations motivate the design of a third scenario that computes the sector capacities to find a compromise between the increase in the delay costs due to ground-holding regulations and the increase in the controller workload. The results reveal the sensitivity of the delay costs to the sector capacity; this information will be useful for further research into ATM sector capacity and ATC automated tool design. Finally, because of the growing interest in the free flight paradigm, we also perform a traffic and cost analysis for aircraft following direct routes. The results obtained highlight the fuel and time savings and the spatial restrictions that companies use to avoid congested areas. Document type: Article

Abstract

This report provides an insight into the mathematical methods developed to model the speed regulation problem in Air Traffic Flow Management. The speed regulation problem aims at optimizing aircraft speeds along their flights in order to smooth the air traffic flow. We chose to develop a linear framework for the speed regulation problem therefore most of the techniques presented aim at providing a linear formulation of the model.

Abstract

n Terminal Manoeuvring Area (TMA) at hub airports is the main problem in Chinese air transportation system. At most of the hub airports, the capacity is near saturated or even overloaded. Civil Aviation Administration of China (CAAC) reported that Air Traffic Management (ATM) is the main cause of delays. Despite the already overloaded ATM system, the Chinese airplanes fleet is continuing to expand. China will become the largest traffic flow in the world before the end of 2035. There is an urgent need to develop a new more efficient method for sequencing and merging arrival flows in TMA, so that airports can maximise the benefits from the emerging Communication, Navigation and Surveillance (CNS) techniques, and consequently increasing capacity. Automation can be highly efficient in ATM, however, due to safety considerations, full automation in ATM is still a challenge. Facing extremely dense operations in complex TMA, we can consider reducing traffic complexity by solving all potential conflicts in advance with a feasible trajectory control for controllers, or automating a large proportion of routine operations, such as sequencing, merging and spacing. As parallel runways are a common structure of Chinese hub airports, in this thesis, we propose a novel system to integrated sequencing and merging aircraft to parallel runways. Our methodology integrates a Area Navigation (RNAV)-based 3D Multi-Level and Multi-Point Merge System (MLMPMS), a hybrid heuristic optimization algorithm and a simulation module to find good, systematic, operationally-acceptable solutions. First, a Receding Horizon Control (RHC) technique is applied to divide 24-hour traffic optimization problem into several sub-problems. Then, in each sub-problem, a tailored Simulated Annealing (SA) algorithm and a trajectory generation module worn together to find a near-optimal solution. Our primary objective is to rapidly generate conflict-free and economical trajectories with easy, flexible and feasible control methods. Based on an initial solution, we continuously explore possible good solutions with less delay and shorter landing interval on runway. Taking Beijing Capital International Airport (BCIA) as a case to study, numerical results show that our optimization system performs well. First, it has very stable de-conflict performance to handle continuously dense traffic flows. Compared with Hill Climbing (HC), the tailored SA algorithm can always guarantee a conflict-free solution not only for the mixed iii or segregated parallel approach (arrivals only) pattern, but also for the independent parallel operation (integrated departures and arrivals) pattern. Second, with its unique Multi-Level Point Merge (ML-PM) route network, it can provide a good trajectory control solution to efficiently and economically handle different kinds of arrival flows. It can realize a shorter flying time and a near-Continuous Descent Approach (CDA) descent for arrival aircraft, compared with baseline. For normal traffic, with near-equal traffic demand for two runways, with landing balancing function ON, the average flying time on different Standard Terminal ARrival (STAR) routes can be reduced up to 8 minutes compared with the baseline. It also realizes an easier re-sequencing of aircraft with more relaxed position shifting as well, compared with conventional sequencing method. Theoretically, the Maximum Position Shifting (MPS) can be up to 6 positions, overcoming the hard constraint of 3 position shifts (MPS "= 3). For asymmetric traffic, with big difference on traffic demand for two runways, with runway balancing function ON, it is more likely to find a conflict-free solution compared with the runway balancing function OFF, and again reduces the average flying time. Third, it is efficient for the segregated parallel approach patterns. Compared with hard constrained position shifting, which is often used in current Arrival Manager (AMAN) system and controller’s manual-control First Come First Served (FCFS) method, it can reduce the average delay, average additional transit time in super dense arrival situations. The average time flown level per flight is less than 12% of total transit time in TMA. Fourth, in independent parallel patterns, it can provide a range of useful information concerning the associated objective value, the average flying time, crossing trajectories in hot spots between arrivals and departures, the efficiency of using different designed sequencing legs in ML-PM route network. Thus, it helps the Air Navigation Service Provider (ANSP) to find the best configuration of ML-PM route network to efficiently satisfy the traffic demand. Last but not least, the computation time of our system is reasonable. It generally needs around 290s-350s for 2 hours of heavy traffic demand with the mixed parallel approach. In conclusion, theoretically, our system realizes good trajectory planning of dense flows at busy airports. It can guarantee a conflict-free solution, increase runway throughput, and minimize delay. At the same time, it can simplify merging, re-sequencing, and improve the economical descent profile with advanced ML-PM route design. Although the methodology defined here is illustrated using the BCIA airport, it could be easily applied to airports worldwide.; La congestion dans les Terminal Manoeuvring Area (TMA) des aéroports en hub est le principal problème dans le transport aérien chinois. Face au trafic extrêmement dense dans les TMAs, nous pouvons envisager d'automatiser une grande partie des opérations de routine, comprenant la planification, le séquencement et la séparation. Nous proposons dans cette thèse un nouveau système automatisé de séquencement des avions et de fusion des flux vers des pistes parallèles, qui sont utilisées dans la plupart des aéroports chinois. Notre méthodologie intègre un réseau de route 3D nommé Multi-Level and Multi-Point Merge System (MLMPMS) basé sur le concept de l'Area Navigation (RNAV) et un algorithme d'optimisation heuristique hybride pour trouver une solution correcte, opérationnellement acceptable. Un algorithme de Simulated Annealing (SA) spécifique et un module de génération de trajectoire collaborent pour rechercher la solution quasi optimale. Notre objectif est de générer en temps réel des trajectoires sans conflit, minimisant la consommation de carburant et permettant des méthodes de contrôle faciles et flexibles. Dans ce but, nous explorons en permanence les solutions possibles avec le moins de retard et assuront l'atterrissage le plus rapide. Nous déterminons quatre variables de décision pour contrôler chaque vol : l'heure et la vitesse d'entrée dans la TMA, le temps de vol sur l'arc de séquencement et le choix de la piste utilisée. La simulation de trajectoire dans les différentes phases de vol est basée sur le modèle de performances BADA. Dans le cas de l'aéroport de Beijing Capital International Airport (BCIA), les résultats numériques montrent que notre système d'optimisation de bonnes performances sur le séquencement et la fusion des trajectoires. Tout d'abord, il permet d'assurer des performances de résolution conflit très stables pour gérer les flux de trafic continuellement denses. Par rapport à l'algorithme Hill Climbing (HC), le SA peut toujours trouver une solution sans conflit, non seulement pour l'approche parallèle mixte ou séparée~(pour les arrivées), mais aussi pour les configurations parallèles indépendantes~(départs et arrivées intégrés). Ensuite, avec un réseau d'itinéraires Multi-Level Point Merge (ML-PM) unique, il peut fournir une bonne solution de contrôle de la trajectoire pour traiter efficacement et économiquement différents types de flux d'arrivée. Il peut réaliser un temps de vol plus court et une descente vers le bas en Continuous Descent Approach (CDA) pour l'avion d'arrivée. Il peut réaliser un re-séquencement plus facile des avions avec un déplacement de position plus relâché. Théoriquement, les Maximum Position Shifting (MPS) peuvent atteindre 6 positions, surpassant la contrainte difficile de 3 positions. Troisièmement, l'algorithme montre son efficacité dans un modèle d'approche parallèle séparé avec une capacité de séquencement plus relâché. Par rapport au décalage de position forcé dur, qui est souvent utilisé dans le système actuel Arrival Manager (AMAN) et la méthode First Come First Served (FCFS) utilisé par les contrôleurs, il peut réduire le délai et le temps de transit moyens dans une situation d'arrivée très dense. Le palier par vol est inférieur à 12\\% du temps de transit total dans la TMA. Quatrièmement, en configuration parallèle indépendant, il peut fournir des informations différentes concernant la valeur objectif associée, le temps de vol moyen, les trajectoires de croisement en point chaud entre les arrivées et les départs, l'efficacité avec différents arcs de séquencement conçus dans le réseau de route ML-PM etc.. Enfin, le temps de calcul de notre système est raisonnable. Il nécessite généralement environ 290s-350s pour 2 heures de forte demande de trafic avec une approche parallèle mixte. En conclusion, théoriquement, notre système réalise une bonne planification de trajectoire en flux denses. Il peut assurer une solution sans conflit, augmenter le débit, minimiser le délai. Dans le même temps, il peut simplifier la fusion, le re-séquencement, l'amélioration du profil de descente économique associé avec la conception de l'itinéraire avancé ML-PM. Bien que la méthodologie définie ici soit appliquée à l'aéroport BCIA, elle pourrait également être appliquée à d'autres aéroports dans le monde.

Abstract

To sustain the continuously increasing air traffic demand, the future air traffic management system will rely on a so-called Trajectory Based Operations (TBO) concept that will increase air traffic capacity by reducing the controller's workload. This will be achieved by transferring tactical conflict detection and resolution tasks to the strategic planning phase. In this future air traffic management paradigm context, this thesis presents a methodology to address such strategic trajectory planning at nation-wide and continent scale. The proposed methodology aims at minimizing the global interaction between aircraft trajectories by allocating alternative departure times, alternative horizontal flight paths, and alternative flight levels to the trajectories involved in the interaction. To improve robustness of the strategic trajectory planning, uncertainty of aircraft position and aircraft arrival time to any given position on the trajectory are considered. This thesis proposes a mathematical formulation of this strategic trajectory planning problem leading to a discrete-optimization and a mixed-integer optimization problem whose objective function relies on the new concept of interaction between trajectories. A computationally efficient algorithm to compute interaction between trajectories for large-scale applications is introduced and implemented. Resolution methods based on metaheuristic and hybrid-metaheuristic algorithms have been developed to solve the above large-scale optimization problems. Finally, the overall methodology is implemented and tested with air traffic data taking into account uncertainty over the French and the European airspaces, involving more than 30,000 trajectories. Conflict-free and robust 4D trajectory planning are produced within computational time acceptable for the operation context, which shows the viability of the approach.; Afin de pouvoir satisfaire la demande sans cesse croissante du trafic aérien, le futur système de gestion du trafic aérien utilisera le concept d'opérations basées sur les trajectoires (Trajectory Based Operations), qui augmentera la capacité du trafic aérien, en réduisant la charge de travail du contrôleur. Pour ce faire, les tâches de détection et de résolution de conflits seront transférées depuis la phase tactique vers la phase stratégique de la planification. Dans le cadre de ce nouveau paradigme pour le système de gestion du trafic aérien, nous introduisons dans cette thèse une méthodologie qui permet d'aborder ce problème de planification stratégique de trajectoires d'avion à l'échelle d'un pays ou d'un continent. Le but de la méthodologie proposée est de minimiser l'interaction globale entre les trajectoires d'avion, en affectant de nouveaux créneaux de décollage, de nouvelles routes et de nouveaux niveaux de vols aux trajectoires impliquées dans l'interaction. De plus, afin d'améliorer la robustesse du plan stratégique de vols obtenu, nous prenons en compte l'incertitude de la position de l'avion et de son heure d'arrivée à un point donné de la trajectoire de l'avion. Nous proposons une formulation mathématique de ce problème de planification stratégique conduisant à un problème d'optimisation discrète et un problème d'optimisation en variables mixtes, dont la fonction objectif est basée sur le nouveau concept d'interaction. Un algorithme efficace en termes de temps de calcul pour évaluer l'interaction entre des trajectoires d'avion pour des applications de grande taille est introduit et mis en œuvre. Des méthodes de résolution basées sur des algorithmes de type métaheuristique et métaheuristique hybride ont été développées pour résoudre ces problèmes d'optimisation de grande taille. Enfin, la méthodologie globale de planification stratégique de trajectoires d'avion est mise en œuvre et testée sur des données de trafic, prenant en compte des incertitudes, pour l'espace aérien français et l'espace aérien européen, impliquant plus de 30000 vols. Des plans de vols 4D sans conflits et robustes ont pu être produits avec des temps de calcul acceptables dans un contexte opérationnel, ce qui démontre la viabilité de l'approche proposée.

Abstract

_ Best of Session (UTM-6: sUAS Traffic Management and Deconfliction) Award, 38 DASC 38th IEEE/AIAA Digital Avionics Systems Conference This paper studies strategic conflict resolution for air traffic based on sum coloring. We consider two application scenarios: manned and unmanned air traffic, with similar targets: to improve efficiency of operations and to reduce the costs. For the Unmanned Air Vehicles Traffic Management (UTM) we consider also a payment mechanism which incentivizes the operators to share information necessary to find a socially optimal solution. We quantify the potential savings via a series of experiments, showing that our methods drastically outperform the widely used First-Come-First-Serve (FCFS) strategy. Peer Reviewed Award-winning

Abstract

International audience; Trajectory prediction estimates the future position of aircraft along their planned trajectories in order to detect potential conflicts and to optimize air space occupancy. This prediction is a critical task in the Air Traffic Control (ATC) process and has been studied for many years. For the future automation processes developed in the SESAR [1], NextGen [2] and CARATS [3] projects, such trajectory prediction will be even more critical. In these projects the trajectory predictors generate aircraft forecast trajectories, typically for client applications. As there is always a deviation between the predicted wind (from the weather forecasts) and the encountered wind, the main longitudinal (along-track) error source between the predicted and the actual trajectory is linked to wind estimation. Based on the current performances of Air Traffic Control systems, controllers are able to efficiently detect conflict 20 minutes in advance; for a larger time horizon (look-ahead time), the induced trajectory prediction uncertainty strongly reduces the reliability of the conflict detection. The goal of this work is to measure the potential benefit produced by sharing wind measures between aircraft (this concept will be called wind networking (WN)). To reach this goal, aircraft measure their local atmospheric data (wind, temperature, density and pressure) and broadcast them to the other aircraft. Having such distributed weather information, each aircraft is able to compute an enhanced local wind map as a function of location (3D) and time. These updated wind fields could be shared with other aircraft and/or with ground systems. Using this enhanced weather information, each aircraft is able to improve drastically its own trajectory prediction. This concept has been simulated in the French airspace with 8 000 flights. Comparisons have been investigated on trajectory prediction performances with and without wind networking. Statistics have been conducted in order to measure the bene- it of such concept in both time and space dimensions showing higher improvement in high traffic areas, as expected.

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

International audience; The main ideas behind the design of CATS were to have a general purpose simulator able to be a stable basis for the development of other modules, representing different levels of Air Traffic Control and Air Traffic Management. The basic framework and hypothesis of the CATS simulator are introduced. Two approaches of automatic conflict resolution are detailed as examples. The first one deals with a centralized control system for high density areas and medium term (10 to 15 minutes ahead) control. The second one deals with an on board reactive short term (3 to 10 minutes ahead) automatic conflict solver. As both solvers use a conflict detection that is robust to uncertainties, a convex modeling of uncertainties is first introduced.