Feedback Control of the National Airspace System

This paper proposes a general modeling framework adapted to the feedback control of traffic flows in Eulerian models of the National Airspace System. It is shown that the problems of scheduling and routing aircraft flows in the National Airspace System can be posed as the control of a network of queues with load-dependent service rates. Focus can then shift to developing techniques to ensure that the aircraft queues in each airspace sector, which are an indicator of the air traffic controller workloads, are kept small. This paper uses the proposed framework to develop control laws that help prepare the National Airspace System for fast recovery from a weather event, given a probabilistic forecast of capacities. In particular, the model includes the management of airport arrivals and departures subject to runway capacity constraints, which are highly sensitive to weather disruptions.National Science Foundation (U.S.) (Contract ECCS-0745237)United States. National Aeronautics and Space Administration (Contract NNA06CN24A

Modelling for sustainable cities: Conceptual approach and an audit of existing sectoral models for transport, air pollution, land use, and population modelling.

Urban modelling theories and operational models date back to the sixties and seventies, and have been constantly improved since then. It seems therefore that there should be no problem with addressing city sustainability issues. After all, these models claimed to be tools to help planners in choosing the best policies, exactly the same objectives that we need sustainability models to fulfil. The problem is that urban models have ignored many problems considered today as most pressing. They have not only ignored environmental issues, but also most quality of life issues. If we look at diagrams by Wilson (1981, p.265; 1977, p.3) or Wegener (1994), it is clear that these models focus on land use (understood as location and intensity of activities) and transport problems. The name “urban model” might be then misleading. This does not mean that environmental problems were not modelled at all: they were, but this research area was outside the interests of urban researchers and planners. One possibility for the way forward is to use old models, integrate them and extend them to include missing components. In order to do this, one should first specify the components to be included in the integrated model, taking the sustainability concept and the new modelling objectives as a point of reference

Cyber-Threat Assessment for the Air Traffic Management System: A Network Controls Approach

Air transportation networks are being disrupted with increasing frequency by failures in their cyber- (computing, communication, control) systems. Whether these cyber- failures arise due to deliberate attacks or incidental errors, they can have far-reaching impact on the performance of the air traffic control and management systems. For instance, a computer failure in the Washington DC Air Route Traffic Control Center (ZDC) on August 15, 2015, caused nearly complete closure of the Centers airspace for several hours. This closure had a propagative impact across the United States National Airspace System, causing changed congestion patterns and requiring placement of a suite of traffic management initiatives to address the capacity reduction and congestion. A snapshot of traffic on that day clearly shows the closure of the ZDC airspace and the resulting congestion at its boundary, which required augmented traffic management at multiple locations. Cyber- events also have important ramifications for private stakeholders, particularly the airlines. During the last few months, computer-system issues have caused several airlines fleets to be grounded for significant periods of time: these include United Airlines (twice), LOT Polish Airlines, and American Airlines. Delays and regional stoppages due to cyber- events are even more common, and may have myriad causes (e.g., failure of the Department of Homeland Security systems needed for security check of passengers, see [3]). The growing frequency of cyber- disruptions in the air transportation system reflects a much broader trend in the modern society: cyber- failures and threats are becoming increasingly pervasive, varied, and impactful. In consequence, an intense effort is underway to develop secure and resilient cyber- systems that can protect against, detect, and remove threats, see e.g. and its many citations. The outcomes of this wide effort on cyber- security are applicable to the air transportation infrastructure, and indeed security solutions are being implemented in the current system. While these security solutions are important, they only provide a piecemeal solution. Particular computers or communication channels are protected from particular attacks, without a holistic view of the air transportation infrastructure. On the other hand, the above-listed incidents highlight that a holistic approach is needed, for several reasons. First, the air transportation infrastructure is a large scale cyber-physical system with multiple stakeholders and diverse legacy assets. It is impractical to protect every cyber- asset from known and unknown disruptions, and instead a strategic view of security is needed. Second, disruptions to the cyber- system can incur complex propagative impacts across the air transportation network, including its physical and human assets. Also, these implications of cyber- events are exacerbated or modulated by other disruptions and operational specifics, e.g. severe weather, operator fatigue or error, etc. These characteristics motivate a holistic and strategic perspective on protecting the air transportation infrastructure from cyber- events. The analysis of cyber- threats to the air traffic system is also inextricably tied to the integration of new autonomy into the airspace. The replacement of human operators with cyber functions leaves the network open to new cyber threats, which must be modeled and managed. Paradoxically, the mitigation of cyber events in the airspace will also likely require additional autonomy, given the fast time scale and myriad pathways of cyber-attacks which must be managed. The assessment of new vulnerabilities upon integration of new autonomy is also a key motivation for a holistic perspective on cyber threats

Control and optimization algorithms for air transportation systems

Modern air transportation systems are complex cyber-physical networks that are critical to global travel and commerce. As the demand for air transport has grown, so have congestion, flight delays, and the resultant environmental impacts. With further growth in demand expected, we need new control techniques, and perhaps even redesign of some parts of the system, in order to prevent cascading delays and excessive pollution. In this survey, we consider examples of how we can develop control and optimization algorithms for air transportation systems that are grounded in real-world data, implement them, and test them in both simulations and in field trials. These algorithms help us address several challenges, including resource allocation with multiple stakeholders, robustness in the presence of operational uncertainties, and developing decision-support tools that account for human operators and their behavior. Keywords: Air transportation; Congestion control; Large-scale optimization; Data-driven modeling; Human decision processe

Computational optimization of networks of dynamical systems under uncertainties: application to the air transportation system

To efficiently balance traffic demand and capacity, optimization of air traffic management relies on accurate predictions of future capacities, which are inherently uncertain due to weather forecast. This dissertation presents a novel computational efficient approach to address the uncertainties in air traffic system by using chance constrained optimization model. First, a chance constrained model for a single airport ground holding problem is proposed with the concept of service level, which provides a event-oriented performance criterion for uncertainty. With the validated advantage on robust optimal planning under uncertainty, the chance constrained model is developed for joint planning for multiple related airports. The probabilistic capacity constraints of airspace resources provide a quantized way to balance the solution’s robustness and potential cost, which is well validated against the classic stochastic scenario tree-based method. Following the similar idea, the chance constrained model is extended to formulate a traffic flow management problem under probabilistic sector capacities, which is derived from a previous deterministic linear model. The nonlinearity from the chance constraint makes this problem difficult to solve, especially for a large scale case. To address the computational efficiency problem, a novel convex approximation based approach is proposed based on the numerical properties of the Bernstein polynomial. By effectively controlling the approximation error for both the function value and gradient, a first-order algorithm can be adopted to obtain a satisfactory solution which is expected to be optimal. The convex approximation approach is evaluated to be reliable by comparing with a brute-force method.Finally, the specially designed architecture of the convex approximation provides massive independent internal approximation processes, which makes parallel computing to be suitable. A distributed computing framework is designed based on Spark, a big data cluster computing system, to further improve the computational efficiency. By taking the advantage of Spark, the distributed framework enables concurrent executions for the convex approximation processes. Evolved from a basic cloud computing package, Hadoop MapReduce, Spark provides advanced features on in-memory computing and dynamical task allocation. Performed on a small cluster of six workstations, these features are well demonstrated by comparing with MapReduce in solving the chance constrained model

Identification of Robust Routes using Convective Weather Forcasts

Convective weather is responsible for large delays and widespread disruptions in the U.S. National Airspace System (NAS), especially during summer months when travel demand is high. This has been the motivation for Air Traffic Flow Management (ATFM) algorithms that optimize flight routes in the presence of reduced airspace and airport capacities. These models assume either the availability of reliable probabilistic weather forecasts or accurate predictions of robust routes; unfortunately, such forecasts do not currently exist. This paper adopts a data-driven approach that identifies robust routes and derives stochastic capacity forecasts from deterministic convective weather forecasts. Using techniques from machine learning and extensive data sets of forecast and observed convective weather, the proposed approach classifies routes that are likely to be viable in reality. The resultant model for route robustness can also be mapped into probabilistic airspace capacity forecasts.National Science Foundation (ECCS- 0745237)National Aeronautics and Space Administration NGATSATM Airspace Program (NNA06CN24A

Cost-based linear holding practice and collaborative air traffic flow management under trajectory based operations

The current air transportation system is reaching the capacity limit in many countries/regions across the world. It tends to be less efficient or even incapable sometimes to deal with the enormous air traffic demand that continues growing year by year. This has been evidenced by the record-breaking flight delays reported in various places in recent years, which, have resulted in notable economical loses. To mitigate this imbalance between demand and capacity, air traffic flow management (ATFM) is usually one of the most useful options. It regulates traffic flows according to air traffic control capacity while preserving safety and efficiency of flights. ATFM initiatives can be considered well in advance of the flight execution - more than one year earlier - based on air traffic forecasts and capacity plans, and continue in effect, with information updated, to eventually the day of operation. This long effective period will inevitably allow substantial collaboration among different stakeholders, including the ATFM authority, airspace users (AUs), air navigation service providers (ANSPs), airports, etc. Under the forthcoming paradigm of trajectory based operations (TBO), the flight 4-Dimensional trajectory has been anticipated to further enhance the connection between flight planning and execution phases, thus fostering such collaboration in ATFM. Moreover, under nowadays operations, ground holding is a typical measure undertaken in many widely-used ATFM programs. Even though holding on the ground, at the origin airport, has the advantage of fuel efficiency over the air holding, it turns out that its feature of low flexibility would, in some circumstances, affect the ATFM performance. Yet, with proper flight trajectory management, it is also possible to have delay airborne at no extra fuel cost than performing ground holding. This PhD thesis firstly focuses on this trajectory management, specifically on a cost-based linear holding practice. The linear holding is realized progressively along the planned trajectory through precise speed control which can be enabled by aircraft trajectory optimization techniques. Some typical short/mid haul flights are simulated for achieving the maximum airborne delay that can be yielded using same fuel consumption as initially scheduled. Based on this, its potential applicability is demonstrated. A network ATFM model is adapted from the well-studied Bertsimas Stock-Patterson (BSP) model, incorporating different types of delay (including the linear holding) to flexibly handle the traffic flow with a set of given (yet changeable) capacities. In order that the benefits of the model can be fully realized, AUs are required to participate in the decision-making process, submitting for instance the maximum linear holding bound per flight along the planned trajectory. Next, increased AUs' participation is expected for a proposed Collaborative ATFM framework, in which not only various delay initiatives are considered, but also alternative trajectories which allow flights to route out of the identified hotspot areas. A centralized linear programming optimization model then computes for the best trajectory selections and the optimal delay distributions across all concerned flights. Finally, ANSPs' involvement is additionally considered for the framework, through dynamic airspace reconfiguration, further enhancing the collaboration between ATFM stakeholders. As such, the traffic flow regulation and sector opening scheduling are bounded into an integrated optimization model, and thus are conducted in a synchronized way. Results indicate that the performance of demand and capacity balancing can be even improved if compared with the previous ATFM models presented in this PhD thesis.El sistema de transport aeri actual està arribant al seu límit de capacitat en molts països i regions del món. Una gestió del flux de trànsit aeri (ATFM) més adequada podria mitigar aquest desequilibri entre la demanda i la capacitat. La funció de l'ATFM és regular els fluxos de trànsit aeri segons la capacitat de control del trànsit aeri, i alhora assegurar que els vols siguin segurs i eficients. Les regulacions del sistema d'ATFM es poden aplicar molt abans de l'execució del vol més d'un any abans. Un cop aplicades, aquestes regulacions continuaran evolucionant, amb informació actualitzada, fins el dia de la seva execució. El llarg període entre la planificació del vol i la seva execució permetrà una important col·laboració entre els diferents membres implicats, inclosa l'autoritat de l'ATFM, els usuaris de l'espai aeri (AUs), els proveïdors de serveis de navegació aèria (ANSP), els aeroports, etc. En les operacions d'avui en dia l'espera a terra és una de les regulacions que més aplica el sistema d'ATFM per tal d'evitar congestions als aeroports o sectors de l'espai aeri. Tot i que esperar a terra, a l'aeroport d'origen, té l'avantatge de consumir menys combustible que esperar a l'aire a l'aeroport de destí, la seva poca flexibilitat podria afectar negativament al rendiment de l'ATFM en algunes circumstàncies. Tanmateix, amb una gestió adequada de la trajectòria de vol, també és possible efectuar cert retard a l'aire sense cap cost addicional de combustible respecte al que resultaria esperant a terra. Aquesta tesi doctoral s'enfoca en primer lloc en aquesta gestió de trajectòria de vol, específicament en una pràctica d'espera tenint en compte els costos per l'aerolínia. L'espera lineal s'efectua progressivament al llarg de la trajectòria planificada mitjançant un control precís de la velocitat. Les velocitats que generen l'espera desitjada durant el vol és calculen mitjançant tècniques d'optimització. Alguns vols típics de curt i mig abast es simulen per quantificar el màxim retard a l'aire que es podria generar utilitzant el mateix consum de combustible que el previst inicialment. Basant-se en els resultats obtinguts, s'explora la seva aplicabilitat potencial. Es desenvolupa un model de la xarxa d'ATFM basat en el model de Bertsima Stock-Patterson. Com a novetat, el model desenvolupat en aquesta tesi incorpora diferents tipus de retard (incloent-hi l'espera lineal) per gestionar de forma més flexible el flux de trànsit donat un conjunt de capacitats pre-definides. Per tal d'explotar al màxim els beneficis del model proposat en aquesta tesi, les autoritats regionals estan obligades a participar en el procés de presa de decisions, declarant, per exemple, la màxima espera lineal associada a cada vol al llarg de la trajectòria planejada. Tot seguit, s'inclou la participació dels AUs en un sistema d'ATFM col·laboratiu, en el qual no només es consideren diverses tipus de retard per balancejar la capacitat i la demanda, sinó també trajectòries alternatives que permeten que els vols evitin de forma òptima els sectors de l?espai aeri congestionats. Un model d'optimització centralitzat basat en programació lineal calcula les millors seleccions de trajectòria i les distribucions òptimes de retard en tots els vols afectats per la regulació. Es demostra que incloure trajectòries alternatives pot reduir notablement la quantitat de retards. Finalment, es considera també la participació de l'ANSP en el sistema d'ATFM, a través de la configuració dinàmica de l'espai aeri, millorant encara més la col·laboració entre els membres implicats en el sistema. Com a tal, la regulació del flux de trànsit i la programació d'obertura dels diferents sectors de l'espai aeri s'inclouen en un model integrat d'optimització i, per tant, es programen de forma sincronitzada. Els resultats suggereixen que el rendiment del balanc¸ de la demanda i la capacitat es pot millorar encara m´es amb aquest sistema ATFM col·laboratiu complert. El nou model de balanc¸ de demanda i capacitat millora encara ées els resultats, si es compara amb els altres models d’ATFM presentats també en aquesta tesi doctoral.El sistema de transporte aéreo actual está llegando a su límite de capacidad en muchos países y regiones del mundo. Como consecuencia, éste tiende a ser menos eficiente e incluso en ocasiones incapaz de afrontar la enorme demanda de tráfico aéreo que incluso hoy en día crece rápidamente. Este hecho se ha visto evidenciado por los enormes retrasos registrados en diferentes lugares los últimos años, lo cual ha comportado enormes pérdidas económicas para la sociedad. Una gestión del flujo del tráfico aéreo (ATFM) más adecuada podría mitigar este desequilibrio entre la demanda y la capacidad. La función del ATFM es regular los flujos de tráfico aéreo según la capacidad de control del tráfico aéreo, siempre asegurando que los vuelos sean seguros y eficientes. Las regulaciones del sistema de ATFM se pueden aplicar mucho antes de la ejecución del vuelo –más de un año antes– en función de las previsiones de tráfico aéreo y de la capacidad esperada. Una vez aplicadas, estas regulaciones continuarán evolucionando, con información actualizada, hasta el día de su ejecución. El largo periodo entre la planificación del vuelo y su ejecución permitirá una importante colaboración entre los diferentes miembros implicados, incluida la autoridad del ATFM, los usuarios del espacio aéreo (AUs), los proveedores de servicios de navegación aérea (ANSP), los aeropuertos, etc. En el marco del futuro paradigma de las operaciones basadas en trayectorias, la introducción de vuelos con control sobre la trayectoria en las 4 dimensiones espera mejorar aún más la conexión entre las fases de planificación del vuelo y su ejecución, fomentando así la colaboración en el proceso de toma de decisiones del sistema ATFM. En las operaciones de hoy en día la espera en tierra es una de las regulaciones que más se aplica en el sistema de ATFM con el fin de evitar congestiones en los aeropuertos o en los sectores del espacio aéreo. Aun teniendo en cuenta que esperar en tierra, en el aeropuerto de origen, tiene la ventaja de consumir menos combustible que esperar en el aire en el aeropuerto de destino, su poca flexibilidad podría afectar negativamente al rendimiento del ATFM en algunas circunstancias. Aun así, con una gestión adecuada de la trayectoria de vuelo, también es posible efectuar cierto retraso en el aire sin ningún coste adicional de combustible respecto a lo que resultaría esperando en tierra. Esta tesis doctoral se centra en primer lugar en esta gestión de la trayectoria de vuelo, específicamente en una práctica de espera lineal considerando los costes para la aerolínea. La espera lineal se efectúa progresivamente a lo largo de la trayectoria planificada mediante un control preciso de la velocidad. Las velocidades que generan la espera deseada durante el vuelo se calculan mediante técnicas de optimización. Algunos vuelos típicos de corto y medio alcance se simulan para cuantificar el máximo retraso en el aire que se podría generar utilizando el mismo consumo de combustible que el previsto inicialmente. Basándose en los resultados obtenidos, se investiga su potencial aplicabilidad, como por ejemplo mejorar la planificación de programas de flujo del espacio aéreo, y ayudar a neutralizar los retrasos no deseados adicionales debidos a la incertidumbre del sistema. Se desarrolla un modelo de la red de ATFM basado en el conocido modelo Bertsimas Stock-Patterson (BSP). Como novedad, el modelo desarrollado en esta tesis incorpora diferentes tipos de retraso (incluyendo la espera lineal) para gestionar de manera más flexible el flujo de tráfico dado un conjunto de capacidades predefinidas. Con el fin de explotar al máximo los beneficios del modelo propuesto en esta tesis, se asume que las aerolíneas participaran en el proceso de toma de decisiones, declarando, por ejemplo, la máxima espera lineal asociada a cada vuelo a lo largo de la trayectoria planeada. Este concepto se ilustra con un caso de estudio, donde se demuestra una reducción significativa de los retrasos, comparado con el modelo BSP. Seguidamente, se incluye la participación de las aerolíneas en un sistema de ATFM colaborativo, en el cual no tan sólo se consideran diferentes tipos de retrasos para balancear la capacidad y la demanda, sino también trayectorias alternativas que permiten que los vuelos eviten de forma óptima los sectores del espacio aéreo congestionados. Un modelo de optimización centralizado basado en programación lineal calcula las mejores selecciones de la trayectoria y las distribuciones óptimas de retraso en todos los vuelos afectado por la regulación. Se demuestra que incluir trayectorias alternativas puede reducir notablemente la cantidad de retrasos. Finalmente, se considera también la participación de los ANSP en el sistema de ATFM, a través de la configuración dinámica del espacio aéreo, mejorando aún más la colaboración entre los miembros implicados en el sistema. Como tales, la regulación del flujo de tráfico aéreo y la programación de apertura de los diferentes sectores del espacio aéreo se incluyen en un modelo integrado de optimización y, por lo tanto, se programan de manera sincronizada. El nuevo modelo de balance de demanda y capacidad mejora aún más los resultados, si se compara con los otros modelos ATFM presentados también en esta tesis doctoralPostprint (published version

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