Advisory Algorithm for Scheduling Open Sectors, Operating Positions, and Workstations

Air traffic controller supervisors configure available sector, operating position, and work-station resources to safely and efficiently control air traffic in a region of airspace. In this paper, an algorithm for assisting supervisors with this task is described and demonstrated on two sample problem instances. The algorithm produces configuration schedule advisories that minimize a cost. The cost is a weighted sum of two competing costs: one penalizing mismatches between configurations and predicted air traffic demand and another penalizing the effort associated with changing configurations. The problem considered by the algorithm is a shortest path problem that is solved with a dynamic programming value iteration algorithm. The cost function contains numerous parameters. Default values for most of these are suggested based on descriptions of air traffic control procedures and subject-matter expert feedback. The parameter determining the relative importance of the two competing costs is tuned by comparing historical configurations with corresponding algorithm advisories. Two sample problem instances for which appropriate configuration advisories are obvious were designed to illustrate characteristics of the algorithm. Results demonstrate how the algorithm suggests advisories that appropriately utilize changes in airspace configurations and changes in the number of operating positions allocated to each open sector. The results also demonstrate how the advisories suggest appropriate times for configuration changes

DEMAND-RESPONSIVE AIRSPACE SECTORIZATION AND AIR TRAFFIC CONTROLLER STAFFING

This dissertation optimizes the problem of designing sector boundaries and assigning air traffic controllers to sectors while considering demand variation over time. For long-term planning purposes, an optimization problem of clean-sheet sectorization is defined to generate a set of sector boundaries that accommodates traffic variation across the planning horizon while minimizing staffing. The resulting boundaries should best accommodate traffic over space and time and be the most efficient in terms of controller shifts. Two integer program formulations are proposed to address the defined problem, and their equivalency is proven. The performance of both formulations is examined with randomly generated numerical examples. Then, a real-world application confirms that the proposed model can save 10%-16% controller-hours, depending on the degree of demand variation over time, in comparison with the sectorization model with a strategy that does not take demand variation into account. Due to the size of realistic sectorization problems, a heuristic based on mathematical programming is developed for a large-scale neighborhood search and implemented in a parallel computing framework in order to obtain quality solutions within time limits. The impact of neighborhood definition and initial solution on heuristic performance has been examined. Numerical results show that the heuristic and the proposed neighborhood selection schemes can find significant improvements beyond the best solutions that are found exclusively from the Mixed Integer Program solver's global search. For operational purposes, under given sector boundaries, an optimization model is proposed to create an operational plan for dynamically combining or splitting sectors and determining controller staffing. In particular, the relation between traffic condition and the staffing decisions is no longer treated as a deterministic, step-wise function but a probabilistic, nonlinear one. Ordinal regression analysis is applied to estimate a set of sector-specific models for predicting sector staffing decisions. The statistical results are then incorporated into the proposed sector combination model. With realistic traffic and staffing data, the proposed model demonstrates the potential saving in controller staffing achievable by optimizing the combination schemes, depending on how freely sectors can combine and split. To address concerns about workload increases resulting from frequent changes of sector combinations, the proposed model is then expanded to a time-dependent one by including a minimum duration of a sector combination scheme. Numerical examples suggest there is a strong tradeoff between combination stability and controller staffing

A method of ATFCM based on trajectory based operations.

This thesis describes a method towards a more proactive approach for Air Traffic Flow and Capacity Management (ATFCM) Demand and Capacity Balancing (DCB). This new ATFCM DCB method focuses on reducing the expected Air Traffic Control (ATC) Separation Management (SM) tactical interventions. It is based on the identification of “hotspots” and mitigating them at pre-flight phase by applying minor adjustments on aircraft’s Times of Arrivals (TOAs) at points of conflict located at en-route crossing and merging junctions (hotspots). The adjustments of TOAs are achieved through optimal speed changes in aircraft speed profiles, applied before and after each junction whilst maintaining each aircraft’s flight time and the entropy of the whole traffic network. The approach postulates that the TOA adjustments may be transformed into a pre-tactical ATFCM DCB measure. This can be achieved by translating TOA adjustments into time constraints at junctions, issued by the Network Manager (NM) in the Reference Business Trajectories (RBTs) to produce de-randomized and well-behaved (conflict free) traffic scenarios to reduce the probability of conflicts. Several real high-density scenarios of the current and forecasted traffic in European Civil Aviation Conference (ECAC) airspace network are simulated using specialized modelling tools to validate the method. A novel Linear Programming (LP) optimisation model is formulated and used to compute optimal speed changes that remove all conflicts in the scenarios with minimum cascading effect. This method should enable a reduction in ATC workload, leading to improvements in airspace capacity, flight and network efficiency as well as safety. This approach is fully aligned to Trajectory Based Operation (TBO) principles. As a holistic solution, this new ATFCM DCB method should change the conventional capacity-limiting factor, currently established by the number of aircraft simultaneously entering each sector (sector count) to another factor where the level of traffic complexity, flying towards junctions is identified and mitigated at pre-flight phase.PhD in Aerospac

Optimisation des flux de trafic aérien

Cette thèse s'inscrit dans le domaine de l'optimisation globale appliquée aux flux de trafic aérien. Le problème abordé consiste à optimiser les flux de trafic aérien sans imposer de retards au décollage. On considère tout d'abord le système existant tel quel, en cherchant à améliorer l'écoulement du trafic simplement en équilibrant les regroupements des secteurs élémentaires d'espace sur les positions de contrôle. Des méthodes déterministes (A*, Branch and bound) et un algorithme génétique sont utilisés pour répartir au mieux la charge de trafic entre les positions. Dans un deuxième temps on s'autorise à modifier la structure de l'espace aérien, en partant des flux directs origine-destination pour construire, par une méthode de partitionnement et une triangulation de Delaunay, un réseau de routes aériennes répondant à certains critères d'espacement des points de croisement. On évalue dans un troisième temps l'intérêt de séparer verticalement les flux aériens, dans leur phase de croisière. Cette évaluation porte sur le nombre et la nature des conflits détectés lors de simulations en temps accéléré, en allouant ou non des niveaux de croisières séparés. Dans un quatrième temps, on génère pour les principaux flux de trafic des trajectoires 3D complètes, séparées les unes des autres, en tenant compte de la disparité des performances des avions sur chaque flux. Deux types de stratégies sont explorées : une approche séquentielle où un algorithme A* est appliqué successivement à chaque flux dans un ordre choisi, et une approche globale où toutes les trajectoires sont considérées simultanément, en utilisant un algorithme génétique. Les algorithmes sont d'abord testés sur des cas simples avant d'être appliqués aux données réelles, en France et en Europe. Enfin, en dernier lieu, la dimension temporelle est prise en compte afin de planifier dynamiquement des trajectoires 4D non-conflictuelles pour des trains d'avions. ABSTRACT : This work belongs to the field of global optimization, applied to air traffic flows. The problem being addressed consists of optimizing air traffic flows without regulating the traffic demand. Firstly, the current system is enhanced only by considering the sector configurations of the controllers working positions. Deterministic methods (A*, Branch and bound) and a genetic algorithm are used to balance the workload between control positions, by splitting and merging airspace sectors. Secondly, we allow ourselves to modify the airspace structure. A routes network is computed from the direct origin-destination flows, with crossing points satisfying constraints of minimum distance, using a partitioning method and a Delaunay triangulation. Thirdly, the profit brought by the vertical separation of air traffic flows is assessed through fast-time simulations, by considering the nature of conflicts detected with or without a cruise level allocation. Fourthly, full 3D-trajectories are computed for the main flows, taking into account the variety of aircraft performances within each flow. Two strategies are proposed : the 1 vs. n strategy uses an A* algorithm to compute each trajectory in turn, separating the new trajectory from the previous ones, and the global strategy applies a genetic algorithm to the whole set of trajectories. Both algorithms are first tried on basic flow configurations, and then applied to real traffic data over France and Europe. Finally, the time dimension is taken into account in order to generate conflict-free 4D-trajectories for groups of aircraft following the same route