Analysis of Different Cost Functions in the Geosect Airspace Partitioning Tool

A new cost function representing air traffic controller workload is implemented in the Geosect airspace partitioning tool. Geosect currently uses a combination of aircraft count and dwell time to select optimal airspace partitions that balance controller workload. This is referred to as the aircraft count/dwell time hybrid cost function. The new cost function is based on Simplified Dynamic Density, a measure of different aspects of air traffic controller workload. Three sectorizations are compared. These are the current sectorization, Geosect's sectorization based on the aircraft count/dwell time hybrid cost function, and Geosect s sectorization based on the Simplified Dynamic Density cost function. Each sectorization is evaluated for maximum and average workload along with workload balance using the Simplified Dynamic Density as the workload measure. In addition, the Airspace Concept Evaluation System, a nationwide air traffic simulator, is used to determine the capacity and delay incurred by each sectorization. The sectorization resulting from the Simplified Dynamic Density cost function had a lower maximum workload measure than the other sectorizations, and the sectorization based on the combination of aircraft count and dwell time did a better job of balancing workload and balancing capacity. However, the current sectorization had the lowest average workload, highest sector capacity, and the least system delay

Full Automation of Air Traffic Management in High Complexity Airspace

The thesis is that automation of en-route Air Traffic Management in high complexity airspace can be achieved with a combination of automated tactic planning in a look-ahead time horizon of up to two hours complemented with automated tactic conflict resolution functions. The literature review reveals that no significant results have yet been obtained and that full automation could be approached with a complementary integration of automated tactic resolutions AND planning. The focus shifts to ‘planning for capacity’ and ‘planning for resolution’ and also – but not only – for ‘resolution’. The work encompasses a theoretical part on planning, and several small scale studies of empirical, mathematical or simulated nature. The theoretical part of the thesis on planning under uncertainties attempts to conceive a theoretical model which abstracts specificities of planning in Air Traffic Management into a generic planning model. The resulting abstract model treats entities like the planner, the strategy, the plan and the actions, always considering the impact of uncertainties. The work innovates in specifying many links from the theory to the application in planning of air traffic management, and especially the new fields of tactical capacity management. The second main part of the thesis comprises smaller self-containing works on different aspects of the concept grouped into a section on complexity, another on tactic planning actions, and the last on planners. The produced studies are about empirical measures of conflicts and conflict densities to get a better understanding of the complexity of air traffic; studies on traffic organisation using tactical manoeuvres like speed control, lateral offset and tactical direct using fast time simulation; and studies on airspace design like sector optimisation, dynamic sectorisation and its optimisation using optimisation techniques. In conclusion it is believed that this work will contribute to further automation attempts especially by its innovative focus which is on planning, base on a theory of planning, and its findings already influence newer developments

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

Dynamically Evolving Sectors for Convective Weather Impact

A new strategy for altering existing sector boundaries in response to blocking convective weather is presented. This method seeks to improve the reduced capacity of sectors directly affected by weather by moving boundaries in a direction that offers the greatest capacity improvement. The boundary deformations are shared by neighboring sectors within the region in a manner that preserves their shapes and sizes as much as possible. This reduces the controller workload involved with learning new sector designs. The algorithm that produces the altered sectors is based on a force-deflection mesh model that needs only nominal traffic patterns and the shape of the blocking weather for input. It does not require weather-affected traffic patterns that would have to be predicted by simulation. When compared to an existing optimal sector design method, the sectors produced by the new algorithm are more similar to the original sector shapes, resulting in sectors that may be more suitable for operational use because the change is not as drastic. Also, preliminary results show that this method produces sectors that can equitably distribute the workload of rerouted weather-affected traffic throughout the region where inclement weather is present. This is demonstrated by sector aircraft count distributions of simulated traffic in weather-affected regions

Comparing Methods for Dynamic Airspace Configuration

This paper compares airspace design solutions for dynamically reconfiguring airspace in response to nominal daily traffic volume fluctuation. Airspace designs from seven algorithmic methods and a representation of current day operations in Kansas City Center were simulated with two times today's demand traffic. A three-configuration scenario was used to represent current day operations. Algorithms used projected unimpeded flight tracks to design initial 24-hour plans to switch between three configurations at predetermined reconfiguration times. At each reconfiguration time, algorithms used updated projected flight tracks to update the subsequent planned configurations. Compared to the baseline, most airspace design methods reduced delay and increased reconfiguration complexity, with similar traffic pattern complexity results. Design updates enabled several methods to as much as half the delay from their original designs. Freeform design methods reduced delay and increased reconfiguration complexity the most

Enhanced air traffic flow and capacity management under trajectory based operations considering traffic complexity

Tesi amb menció internacional.(English) The Air Traffic Flow and Capacity Management (ATFCM) aims at maintaining the forecast traffic demand below the estimated capacity in airports and airspace sectors. The purpose is to maintain the workload of the air traffic controllers under safe limits and avoid overloaded situations. At present, the demand and the capacity management initiatives are deployed separately. Given a forecast traffic demand, the different air navigation service providers allocate their air traffic control resources providing the airspace sectorisations. Then, the network manager addresses the remaining overloads by allocating delay using the CASA algorithm based on a ration-by-schedule principle. It should be noted that some ad-hoc flights might be re-rerouted or limited in cruise altitude in order to avoid congested airspace by submitting a new flight plan. Hence, the previously chosen sectorisations may be not optimum once the demand management initiatives are deployed. Moreover, the flexibility of the airspace users is limited since they cannot express their preferences. Furthermore, the demand and the capacity are currently measured using entry counts as proxy of the air traffic control workload, which is rather easy to measure or estimate. Yet, this metric cannot evaluate the difficulty to handle different traffic patterns inside the sectors leading to the use of capacity buffers. This PhD focuses on overcoming the limitations of the current ATFCM system outlined before by the introduction of complexity metrics (instead of entry counts) in order to measure the traffic load, the better consideration of the airspace users preferences allowing the possibility of submitting alternative trajectories to avoid congested airspace, and the holistic integration of the demand and capacity management into the same optimisation problem. First, the integration of two capacity management initiatives, i.e. Dynamic Airspace Configuration (DAC) and Flight Centric ATC (FCA), is studied proving some benefits when such integration is dynamic. Next, a new concept of operation is proposed where the airspace users have the option of submitting alternative trajectories and the network manager is the responsible for the demand management (delay allocation and choice of the used trajectory) and the capacity management (selection of the airspace sectorisation), considering a network-wide optimisation. This concept of operations is mathematically modelled with two Demand and Capacity Balancing (DCB) models addressing only demand management and three holistic DCB models where the demand and the capacity management measures are considered together in the same optimisation problem. A first model aims at choosing the best trajectory and delay allocation per flight while analysing the traffic load with entry counts at traffic volume level. It is solved in a realistic case study using the historical regulations providing a 76.84% of reduction in the arrival delay if compared to the current system.(Català) La gestió dels fluxos de trànsit i de la capacitat (ATFCM) té com a objectiu mantenir la demanda de trànsit prevista per sota de la capacitat estimada dels aeroports i els sectors de l’espai aeri. Actualment, les iniciatives de gestió de la demanda i de gestió de la capacitat es duen a terme separadament. Donada una previsió de trànsit, els diferents proveïdors de serveis de navegació aèria assignen els seus recursos proporcionant les sectoritzacions de l’espai aeri. Després l’administrador de la xarxa tracta les sobrecàrregues restants mitjançant l’assignació de retards utilitzant l'algoritme CASA, basat en l'ordenació per ordre d’arribada. A alguns vols també se’ls pot canviar la ruta o se’ls pot restringit l’altitud del creuer per tal d’evitar zones congestionades requerint la presentació d’un nou pla de vol. Així doncs, les sectoritzacions prèviament escollides poden ser no òptimes una vegada s’implementin les iniciatives de gestió de la demanda. A més, la flexibilitat dels usuaris de l’espai aeri és limitada ja que no poden expressar les seves preferències. Altrament, la demanda i la capacitat es mesuren actualment comptant el nombre d’arribades com a proxy de la càrrega de treball del control del trànsit aeri. No obstant això, aquesta mètrica no pot evaluar la dificultat de gestionar diferents patrons de trànsit dins els sectors, la qual cosa condueix a la utilització de marges de capacitat. Aquest PhD es centra en superar les limitacions de l’actual sistema d’ATFCM indicades anteriorment mitjançant la introducció de mètrics de complexitat (en lloc del número d’arribades) per a mesurar el trànsit, la millor consideració de les preferències dels usuaris de l’espai aeri permetent la possibilitat d’utilitzar trajectories alternatives per a evitar la congestió de l’espai aeri, i la integració holística de la gestió de la demanda i de la capacitat en el mateix problema d’optimització. Primer, s’estudia la integració de dues iniciatives de gestió de la capacitat: DAC i FCA. S’obtenen beneficis quan la integració és dinàmica. Després, es proposa un nou concepte operacional on els usuaris de l’espai aeri tenen l'opció de proposar trajectories alternatives i l’administrador de la xarxa és el responsable de la gestió de la demanda (assignació de retards i elecció de la trajectòria utilitzada) i de la capacitat (selecció de la sectorització de l’espai aeri) considerant l’optimització de tota la xarxa. Aquest concepte operacional es formula amb dos models de DCB que aborden només la gestió de la demanda i tres models holístics on la gestió de la demanda i de la capacitat es consideren conjuntament en el mateix problema d’optimització. Un primer model es centra en escollir la millor trajectòria i assignació de retard per vol, mentre que el trànsit s'avalua mitjançant el número d’arribades als volums de trànsit. Es resol un cas d’estudi realista on s’utilitzen les regulacions històriques aconseguint un 76.84% menys de retard a l'arribada si es compara amb els sistema actual. Un dels tres models holístics de s’estudia en detall, en concret el que utilitza mètriques de complexitat i optimitza les sectoritzacions de l’espai aeri escollint entre un seguit de configuracions disponibles. Aquest model es tracta amb un nou mètode híbrid presentat en aquest PhD i que combina la simulació del recuit i la programació dinàmica. En un primer cas d'estudi, aquest nou mètode es compara amb el mètode exacte resolt amb Gurobi proporcionant un millor rendiment principalment quan la dificultat del problema augmenta. En un segon cas d’estudi es realitza un estudi de sensibilitat del paràmetre que modela una penalització per a diferents configuracions consecutives. Finalment, es resol un escenari a gran escala amb el mètode híbrid proporcionant un 74.01% menys de retard a l'arribada i un 28.47% menys en el cost de la sectorització resultant en comparació amb un escenari de referència que representa les millors condicions del sistema actual.(Español) La gestión de los flujos de tráfico y de la capacidad (ATFCM) pretende mantener la demanda de tráfico prevista por debajo de la capacidad estimada de los aeropuertos y los sectores del espacio aéreo. Actualmente, las iniciativas de gestión de la demanda y de la capacidad se implementan por separado. Ante una previsión de tráfico, los diferentes proveedores de servicios de navegación aérea asignan sus recursos proporcionando las sectorizaciones del espacio aéreo. Después, el administrador de la red trata las sobrecargas restantes mediante la asignación de retrasos utilizando el algoritmo CASA basado en un principio de ordenación por orden de llegada. A algunos vuelos también se les puede cambiar de ruta o limitar la altitud de crucero para evitar la congestión del espacio aéreo requiriendo de un nuevo plan de vuelo. Así pues, las sectorizaciones elegidas anteriormente pueden no ser óptimas una vez que se implementen las iniciativas de gestión de la demanda. Adicionalmente, la flexibilidad de los usuarios del espacio aéreo es limitada ya que no pueden expresar sus preferencias. Además, la demanda y la capacidad se miden actualmente contando el número de llegadas como proxy de la carga de trabajo del control del tráfico aéreo. Sin embargo, esta métrica no puede evaluar la dificultad de controlar diferentes patrones de tráfico dentro de los sectores lo que conduce al uso de márgenes de capacidad. Este PhD se centra en superar las limitaciones del sistema de ATFCM actual descritas anteriormente mediante la introducción de métricas de complejidad (en lugar del número de llegadas) para medir la carga de tráfico, la mejor consideración de las preferencias de los usuarios del espacio aéreo permitiendo la posibilidad de la presentación de trayectorias alternativas para evitar la congestión, y la integración holística de la gestión de la demanda y de la capacidad en un mismo problema de optimización. Primero, se estudia la integración de dos iniciativas de gestión de la capacidad, DAC y FCA, demostrando beneficios cuando dicha integración es dinámica. A continuación, se propone un nuevo concepto operacional donde los usuarios del espacio aéreo tienen la opción de presentar trayectorias alternativas y el administrador de la red es el responsable de la gestión de la demanda (asignación de retrasos y elección de la trayectoria utilizada) y la gestión de la capacidad (selección de la sectorización), considerando una optimización de toda la red. Este concepto operacional se modela con dos modelos de DCB que abordan sólo la gestión de la demanda y tres modelos holísticos donde las medidas de gestión de la demanda y de la capacidad se consideran conjuntamente en el mismo problema de optimización. Un primer modelo pretende elegir la mejor asignación de trayectoria y retraso por vuelo mientras se analiza la carga de tráfico con el número de llegadas a nivel de volumen de tráfico. Se resuelve un caso de estudio utilizando las regulaciones históricas proporcionando un 76.84% de reducción en el retraso en la llegada si se compara con el sistema actual. El model holístico que utiliza métricas de complejidad y optimiza las sectorizaciones del espacio aéreo escogiendo entre un conjunto de configuraciones disponibles se estudia en detalle. Este modelo se trata con un nuevo método híbrido basado en el recocido simulado y la programación dinámica. En un primer caso de estudio, se compara este nuevo método con el método exacto resuelto con Gurobi proporcionando un mejor rendimiento cuando aumenta la dificultad del problema. En un segundo caso de estudio se realiza un estudio de sensibilidad del parámetro que modela una penalización para diferentes configuraciones consecutivas. Finalmente, se resuelve un escenario a gran escala con el método Híbrido proporcionando menores valores de retraso en llegada y menores costes en la sectorización resultante en comparación con un escenario de referencia que representa las mejores condiciones del sistema actual.Postprint (published version

Dynamic optimization of airspace sector grouping

The current airspace configuration is highly structured, fixed and is less responsive to changes causing the overall system to lack the flexibility, adaptability, and responsibility needed to handle the increasing air traffic demands in the near future. The work presented in this thesis aims at improving the flexibility and adaptability of today's airspace management in Europe in a pretactical context. We focus on the development of a method to support a process of automatic generation of a sequence of sector configurations composed of predefined sectors. Airspace configurations should be dynamically adjusted to provide maximum efficiency and flexibility in response to demand fluctuations. We dynamically build configurations by combining existing elementary sectors. In this step, any sector combination which forms controllable airspace blocks is eligible and may be used during the day of operation. In this work, we developed efficient methods to solve DAC problem. We formulated and study the sectorization problem from an algorithmic point of view. We proposed methods based on a mathematical modeling and heuristic optimization techniques. We also introduced here an approach to evaluate the workload inside sectors

Forecasting workload and airspace configuration with neural networks and tree search methods

International audienceThe aim of the research presented in this paper is to forecast air traffic controller workload and required airspace configuration changes with enough lead time and with a good degree of realism. For this purpose, tree search methods were combined with a neural network. The neural network takes relevant air traffic complexity metrics as input and provides a workload indication (high, normal, or low) for any given air traffic control (ATC) sector. It was trained on historical data, i.e. archived sector operations, considering that ATC sectors made up of several airspace modules are usually split into several smaller sectors when the workload is excessive, or merged with other sectors when the workload is low. The input metrics are computed from the sector geometry and from simulated or real aircraft trajectories. The tree search methods explore all possible combinations of elementary airspace modules in order to build an optimal airspace partition where the workload is balanced as well as possible across the ATC sectors. The results are compared both to the real airspace configurations and to the forecast made by flow management operators in a French "en-route" air traffic control centre

Trajectory-Oriented Approach to Managing Traffic Complexity: Operational Concept and Preliminary Metrics Definition

This document describes preliminary research on a distributed, trajectory-oriented approach for traffic complexity management. The approach is to manage traffic complexity in a distributed control environment, based on preserving trajectory flexibility and minimizing constraints. In particular, the document presents an analytical framework to study trajectory flexibility and the impact of trajectory constraints on it. The document proposes preliminary flexibility metrics that can be interpreted and measured within the framework