An Incentive Compatible, Efficient Market for Air Traffic Flow Management

We present a market-based approach to the Air Traffic Flow Management (ATFM) problem. The goods in our market are delays and buyers are airline companies; the latter pay money to the FAA to buy away the desired amount of delay on a per flight basis. We give a notion of equilibrium for this market and an LP whose solution gives an equilibrium allocation of flights to landing slots as well as equilibrium prices for the landing slots. Via a reduction to matching, we show that this equilibrium can be computed combinatorially in strongly polynomial time. Moreover, there is a special set of equilibrium prices, which can be computed easily, that is identical to the VCG solution, and therefore the market is incentive compatible in dominant strategy.Comment: arXiv admin note: substantial text overlap with arXiv:1109.521

A Market for Air Traffic Flow Management

The two somewhat conflicting requirements of efficiency and fairness make ATFM an unsatisfactorily solved problem, despite its overwhelming importance. In this paper, we present an economics motivated solution that is based on the notion of a free market. Our contention is that in fact the airlines themselves are the best judge of how to achieve efficiency and our market-based solution gives them the ability to pay, at the going rate, to buy away the desired amount of delay on a per flight basis. The issue of fairness is simply finessed away by our solution -- whoever pays gets smaller delays. We show how our solution has the potential of enabling travelers from a large spectrum of affordability and punctuality requirements to achieve an end that is most desirable to them. Our market model is particularly simple, requiring only one parameter per flight from the airline company. Furthermore, we show that it admits a combinatorial, strongly polynomial algorithm for computing an equilibrium landing schedule and prices

Fairness in Slot Allocation

The recent interpretations of fairness in slot allocation of flights are considered as the word equity and upon these interpretations for fairness, aviation agencies as airspace administrators along with stakeholders have been applying ground delay problem procedure with ration by schedule and compression algorithms as fair distribution of slots among them in reduced capacity airports. The drawback of these approaches is that the slots to be allocated to flights are all of the equal size or duration since the flights to be assigned to slots can not be differentiated. In fact, the absence of a scientific framework of fairness in air traffic management has led to the different contradictory interpretations for it. As proposed in this study, fairness is the minimum deviation from the planned outcome in terms of time, quantity and quality under the optimum share management rule for each stakeholder. To achieve fairness in slot allocation of the airport under reduced and normal capacity, a new allocation rule of ration by fairness is proposed in which the elements of time, quantity and quality are proposed to be the original time of departure or arrival, slot size or duration, and airspace safety and preflight checklist, respectively

Evaluating air traffic flow management in a collaborative decision-making environment

The collaborative decision-making (CDM) framework introduced into ground delay programs in the late 1990s is an integral component of FAA's traffic flow management (TFM) procedures. CDM allows FAA to act as a mediator when managing TFM programs, transferring as much decision making as possible to the individual airlines. Although this approach has been highly successful in practice, it creates a new question for the research community: How should proposed enhancements to TFM be evaluated in a CDM environment? A sequential evaluation procedure, developed in this paper, addresses this question. The procedure includes airline disruption responses and a quasi-compression operation, attempting to mimic the three-stage CDM process. To model airline disruption responses, an integer optimization model was developed to balance operational and passenger considerations in determining which flights to cancel, swap, or delay. The value of this procedure is demonstrated by analyzing an optimization-based TFM approach in the CDM environment

Fair task allocation in transportation

Task allocation problems have traditionally focused on cost optimization. However, more and more attention is being given to cases in which cost should not always be the sole or major consideration. In this paper we study a fair task allocation problem in transportation where an optimal allocation not only has low cost but more importantly, it distributes tasks as even as possible among heterogeneous participants who have different capacities and costs to execute tasks. To tackle this fair minimum cost allocation problem we analyze and solve it in two parts using two novel polynomial-time algorithms. We show that despite the new fairness criterion, the proposed algorithms can solve the fair minimum cost allocation problem optimally in polynomial time. In addition, we conduct an extensive set of experiments to investigate the trade-off between cost minimization and fairness. Our experimental results demonstrate the benefit of factoring fairness into task allocation. Among the majority of test instances, fairness comes with a very small price in terms of cost

RNN-CNN hybrid model to predict C-ATC CAPACITY regulations for en-route traffic

Meeting the demand with the available airspace capacity is one of the most challenging problems faced by Air Traffic Management. Nowadays, this collaborative Demand–Capacity Balancing process often ends up enforcing Air Traffic Flow Management regulations when capacity cannot be adjusted. This process to decide if a regulation is needed is time consuming and relies heavily on human knowledge. This article studies three different Air Traffic Management frameworks aiming to improve the cost-efficiency for Flow Manager Positions and Network Manager operators when facing the detection of regulations. For this purpose, two already tested Deep Learning models are combined, creating different hybrid models. A Recurrent Neural Network is used to process scalar variables to extract the overall airspace characteristics, and a Convolutional Neural Network is used to process artificial images exhibiting the specific airspace configuration. The models are validated using historical data from two of the most regulated European regions, resulting in a novel framework that could be used across Air Traffic Control centers. For the best hybrid model, using a cascade architecture, an average accuracy of 88.45% is obtained, with an average recall of 92.16%, and an average precision of 86.85%, across different traffic volumes. Moreover, two different techniques for model explainability are used to provide a theoretical understanding of its behavior and understand the reasons behind the predictionsThis work was funded EUROCONTROL under Ph.D. Research Contract No. 18-220569-C2 and by the Ministry of Economy, Industry, and Competitiveness of Spain under GrantNumber PID2020-116377RB-C21. This project has also received funding from the SESAR Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 783287.Peer ReviewedPostprint (published version

Reducing ATFM delays through strategic flight planning

This paper presents an integer programming model for strategic redistribution of flights so as to respect nominal sector capacities, in short computation times for large-scale instances. The main contribution lies in the combination of tackling large-scale strategic flight planning using hard capacity constraints, while considering the whole network (i.e., both airports and sectors). Real historic data for network and traffic description are used for our test instance. Strategic and tactical impact assessments show that early flight planning can lead to the reduction of delays and their costs, showing potential for actual implementation

Noise-Aware and Equitable Urban Air Traffic Management: An Optimization Approach

Urban air mobility (UAM), a transformative concept for the transport of passengers and cargo, faces several integration challenges in complex urban environments. Community acceptance of aircraft noise is among the most noticeable of these challenges when launching or scaling up a UAM system. Properly managing community noise is fundamental to establishing a UAM system that is environmentally and socially sustainable. In this work, we develop a holistic and equitable approach to manage UAM air traffic and its community noise impact in urban environments. The proposed approach is a hybrid approach that considers a mix of different noise mitigation strategies, including limiting the number of operations, cruising at higher altitudes, and ambient noise masking. We tackle the problem through the lens of network system control and formulate a multi-objective optimization model for managing traffic flow in a multi-layer UAM network while concurrently pursuing demand fulfillment, noise control, and energy saving. Further, we use a social welfare function in the optimization model as the basis for the efficiency-fairness trade-off in both demand fulfillment and noise control. We apply the proposed approach to a comprehensive case study in the city of Austin and perform design trade-offs through both visual and quantitative analyses.Comment: 30 pages, 15 figure

A strategic prioritization approach to airline scheduling during disruptions

textAir disruption scenarios due to inclement weather or air traffic congestion can result in significant imbalances in the demands and capacities of the affected airports. The Federal Aviation Administration (FAA) resolves these imbalances by implementing the Ground Delay Programs (GDP). In a GDP, the FAA first assigns new arrival slots to the airlines using the Ration By Schedule (RBS) approach, which is an allocation procedure that assigns slots to incoming flights based on a First-Scheduled, First-Served (FSFS) criterion. The FAA uses these new arrival slots to determine the expected delays and accommodates them as ground delays at departure airports. The notion of FSFS that forms the basis of RBS, is considered to be an industry standard of fairness. One of the major shortcomings of the RBS approach is that it does not distinguish flights based on factors like aircraft size, number of passengers, future aircraft schedules, etc. This results in an inefficient utilization of the airport capacities. To address this concern, Fearing and Kash proposed a two-stage, non-monetary strategic prioritization game in which airlines could participate and bid for priorities at different airports by taking into account their internal costs. This approach has several advantages over different market-based mechanisms like slot auctions, congestion pricing and slot exchanges. In this thesis, therefore, we develop their approach further both mathematically as well as empirically. Specifically, we prove that a pure strategy Nash equilibrium exists in the second stage of the game for the general multiple airlines and multiple airports case. In addition, by imposing the diagonal strict concavity conditions on the airlines' payoffs, we show that this pure strategy Nash equilibrium is unique for the two-airlines case. Our experimental simulations on historical data further show that this approach can achieve significant congestion cost benefits in comparison to the current RBS procedure.Operations Research and Industrial Engineerin

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