Optimal Sequencing and Scheduling Algorithm for Traffic Flows Based on Extracted Control Actions Near the Airport

This dissertation seeks to design an optimization algorithm, based on naturalistic flight data, with emphasis on safety to perform a benefits\u27 analysis when sequencing and scheduling aircraft at the runway. The viability of creating a decision-support tool to aid air traffic controllers in sequencing and optimizing airport operations is evaluated through the benefits\u27 analysis. Air traffic control is a complex and critical system that ensures the safe and efficient movement of aircraft within the airspace. This is particularly true in the immediate vicinity of an airport. Unlike in en-route or terminal area airspace where aircraft usually traverse well established routes and procedures, near the airport after completing a standard arrival procedure, the routes to the final approach are only partially defined. With safety being the foremost priority, the local tower controllers monitor and maintain separation between aircraft to prevent collisions and ensure the overall safety of the airspace. This involves constant surveillance, coordination, and decision-making to manage the dynamic movement of aircraft, changing weather conditions, and potential hazards. All the while, the controllers make decisions regarding tromboning or vectoring based on various factors, including traffic volume, airspace restrictions, weather conditions, operational efficiency, and safety considerations to ensure a safe traffic sequencing of aircraft at the runway. A novel framework is presented for modeling, characterizing, and clustering aircraft trajectories by extracting traffic control decisions of air traffic controllers. A hidden Markov model was developed and applied to transform trajectories from a sequence of temporal spatial position reports to a series of control actions. The edit distance is utilized for quantifying the dissimilarity of two variable-length trajectory strings, followed by the application of k-medoids algorithm to cluster the arrival flows. Next, a repeatable process for detecting and labeling outlier trajectories within a cluster is introduced. Through application on a set of historical trajectories at Ronald Reagan Washington National Airport (DCA), it is demonstrated that the proposed clustering framework overcomes the deficiency of the classical approach and successfully captures the arrival flows of trajectories, that undergo similar control actions. Leveraging on the set of arrival flows, statistical and machine learning models of air traffic controllers are created and evaluated when ordering aircraft to land at the runway. The potential inefficiencies are identified at DCA when sequencing aircraft. As such, there is a potential performance gap, and it appears that there is room for additional sequence optimization. With the goal of overcoming the potential inefficiencies at DCA, a mixed-integer zero-one formulation is designed for a single runway that takes into consideration safety constraints by means of separation constraints between aircraft imposed at each metering point from the entry to the airspace until landing. With the objective of maximizing runway throughput and minimizing the traversed distance, the model sequences and schedules arrivals and departures and generates safe and conflict-free arrival trajectories to actualize that scheduling. The output of the optimization shows that the model successfully recovers approximately 52% of the performance gap between the actual distance traversed and idealized (cluster centroids) distance traversed by all arrival aircraft. Moreover, each arrival aircraft, on average, traverses 2.12 nautical miles shorter than its historical trajectory and thus saving approximately 10 US gallons of jet fuel. By showcasing the potential benefits of the optimization, this dissertation takes a step towards achieving the long-term vision of developing a decision-support tool to assist air traffic controllers in optimally sequencing and scheduling aircraft. To fully leverage the potential benefits of optimization, further development and refinement of the algorithm are necessary to align it with real-world operational demands. As future work, the research would be expanded to integrate uncertainties like weather conditions, wind directions, etc. into the optimization

Analysis of sequencing and scheduling methods for arrival traffic

The air traffic control subsystem that performs scheduling is discussed. The function of the scheduling algorithms is to plan automatically the most efficient landing order and to assign optimally spaced landing times to all arrivals. Several important scheduling algorithms are described and the statistical performance of the scheduling algorithms is examined. Scheduling brings order to an arrival sequence for aircraft. First-come-first-served scheduling (FCFS) establishes a fair order, based on estimated times of arrival, and determines proper separations. Because of the randomness of the traffic, gaps will remain in the scheduled sequence of aircraft. These gaps are filled, or partially filled, by time-advancing the leading aircraft after a gap while still preserving the FCFS order. Tightly scheduled groups of aircraft remain with a mix of heavy and large aircraft. Separation requirements differ for different types of aircraft trailing each other. Advantage is taken of this fact through mild reordering of the traffic, thus shortening the groups and reducing average delays. Actual delays for different samples with the same statistical parameters vary widely, especially for heavy traffic

The trade-off between taxi time and fuel consumption in airport ground movement

Environmental impact is a very important agenda item in many sectors nowadays, which the air transportation sector is also trying to reduce as much as possible. One area which has remained relatively unexplored in this context is the ground movement problem for aircraft on the airport’s surface. Aircraft have to be routed from a gate to a runway and vice versa and it is still unknown whether fuel burn and environmental impact reductions will best result from purely minimising the taxi times or whether it is also important to avoid multiple acceleration phases. This paper presents a newly developed multi-objective approach for analysing the trade-off between taxi time and fuel consumption during taxiing. The approach consists of a combination of a graph-based routing algorithm and a population adaptive immune algorithm to discover different speed profiles of aircraft. Analysis with data from a European hub airport has highlighted the impressive performance of the new approach. Furthermore, it is shown that the trade-off between taxi time and fuel consumption is very sensitive to the fuel-related objective function which is used

A comparative study for merging and sequencing flows in TMA

Se ha previsto diversos escenarios para explorar el futuro Sistema de Transporte Aéreo. De acuerdo con EUROCONTROL, el escenario más probable de los movimientos de vuelo IFR en Europa hasta 2035, prevé 14,4 millones de vuelos, lo cual es 50% más que en 2012. [10] El aumento en el tráfico aéreo se está traduciendo en diversos problemas tanto en el lado aire como en tierra. En el lado aire, se hace más evidente en el espacio aéreo circundante a los aeropuertos, donde las llegadas y salidas sirven a un gran número de aviones que están sometidos a diversos problemas logísticos que continuamente hay que resolver para asegurarse de que cada vuelo y pasajero viaje con seguridad y eficiencia hasta su destino final. La presente investigación propone una metodología basada en algoritmos evolutivos para resolver el problema de fusión y secuenciación de un conjunto de aeronaves. Para dicho fin, se realiza un análisis del diseño de la topología de las rutas de aterrizaje. Este enfoque propone para cada aeronave una nueva ruta y perfil de velocidad con el fin de evitar posibles conflictos en los puntos de fusión, mientras que se mantienen las normas de separación de la OACI. La función objetivo se basa en adquirir la desviación mínima de cada aeronave con respecto a su plan de vuelo original. El algoritmo se ha aplicado con éxito en el aeropuerto de Gran Canaria en España con muestras de la demanda de tráfico reales para lo que se ha encontrado una configuración óptima para la alimentación óptima pista.The imminent growing in the Air transport System has forecast diverse scenarios to explore the future of the aviation. According to EUROCONTROL forecast of IFR flight movements in Europe up to 2035, the most likely scenario predicts 14.4 million flights, which is 50% more than in 2012. [10] This increase in the air traffic is translating into diverse problems in the airside and landside. In the airside, it becomes more evident in the airspace surrounding airports, where the arrivals and departures serve a large number of aircraft which are subjected to many logistical problems that must continuously be solved to make sure each flight and passenger travels safely and efficiently. The present research proposes a methodology based on evolutionary algorithms to tackle the merging and sequencing problem of a set of aircraft by analyzing the topology design of the landing routes. It is proposed to merge the arrivals from different routes by changing the topology design of the STARs (Standard Terminal Arrival Route). The approach proposes to each aircraft a new route and speed profile in order to avoid potential conflicts at merging points while maintaining ICAO separation standards. The objective function is based on achieving the minimum deviation of each aircraft from it original flight plan. This algorithm has been successfully applied to Gran Canaria airport in Spain with real traffic demand samples for which conflict free flow merging is produced smoothly with optimal runway feeding.Grupo de Transporte Aéreo - Grupo de Ingeniería Aplicada a la Industri

Traffic synchronization with controlled time of arrival for cost-efficient trajectories in high-density terminal airspace

The growth in air traffic has led to a continuously growing environmental sensitivity in aviation, encouraging the research into methods for achieving a greener air transportation. In this context, continuous descent operations (CDOs) allow aircraft to follow an optimum flight path that delivers major environmental and economic benefits, giving as a result engine-idle descents from the cruise altitude to right before landing that reduce fuel consumption, pollutant emissions and noise nuisance. However, this type of operations suffers from a well-known drawback: the loss of predictability from the air traffic control (ATC) point of view in terms of overfly times at the different waypoints of the route. In consequence, ATC requires large separation buffers, thus reducing the capacity of the airport. Previous works investigating this issue showed that the ability to meet a controlled time of arrival (CTA) at a metering fix could enable CDOs while simultaneously maintaining airport throughput. In this context, more research is needed focusing on how modern arrival managers (AMANs)—and extended arrival managers (E-AMANs)—could provide support to select the appropriate CTA. ATC would be in charge to provide the CTA to the pilot, who would then use four-dimensional (4D) flight management system (FMS) trajectory management capabilities to satisfy it. A key transformation to achieve a more efficient aircraft scheduling is the use of new air traffic management (ATM) paradigms, such as the trajectory based operations (TBO) concept. This concept aims at completely removing open-loop vectoring and strategic constraints on the trajectories by efficiently implementing a 4D trajectory negotiation process to synchronize airborne and ground equipment with the aim of maximizing both flight efficiency and throughput. The main objective of this PhD thesis is to develop methods to efficiently schedule arrival aircraft in terminal airspace, together with concepts of operations compliant with the TBO concept. The simulated arrival trajectories generated for all the experiments conducted in this PhD thesis, to the maximum possible extent, are considered to be energy-neutral CDOs, seeking to reduce the overall environmental impact of aircraft operations in the ATM system. Ultimately, the objective of this PhD is to achieve a more efficient arrival management of traffic, in which higher levels of predictability and similar levels of capacity are achieved, while the safety of the operations is kept. The designed experiments consider a TBO environment, involving a high synchronization between all the involved actors of the ATM system. Higher levels of automation and information sharing are expected, together with a modernization of both current ATC ground-support tools and aircraft FMSs to comply with the new TBO paradigm.L’increment de tràfic aeri ha portat a una major sensibilitat mediambiental en l’aviació, motivant la recerca en mètodes per aconseguir un transport aeri més ecològic. En aquest context, les operacions de descens continu (CDOs) permeten a les aeronaus seguir una trajectòria que aporta grans beneficis econòmics i ambientals, donant com a resultat descensos amb els motors al ralentí des de l’altitud de creuer fins just abans d’aterrar. Aquestes trajectòries redueixen el consum de combustible, les emissions contaminants i el soroll generat per les aeronaus. No obstant això, aquest tipus d’operacions té un gran desavantatge: la pèrdua de predictibilitat des del punt de vista del controlador aeri (ATC) en termes de temps de pas als diferents punts de la ruta. Com a conseqüència, l’ATC necessita assignar una major separació entre les aeronaus, la qual cosa comporta una reducció en la capacitat de l’aeroport. Estudis previs investigant aquest problema han demostrat que la capacitat de complir amb un temps controlat d’arribada (CTA) a un punt de la ruta (utilitzat per seqüenciar les aeronaus) podria habilitar les CDOs tot mantenint la capacitat de l’aeroport. En aquest context, es necessita investigar més en com els gestors d’arribades (AMANs) i els gestors d’arribades ampliats (E-AMANs) podrien donar suport en la selecció de la CTA més adequada. L’ATC seria l’encarregat d’enviar la CTA al pilot, el qual, per tal de complir amb la CTA, faria servir la capacitat de gestió de trajectòries d’un sistema de gestió de vol (FMS) de quatre dimensions (4D). Una transformació clau per aconseguir una gestió més eficient del tràfic d’arribada és l’ús de nous paradigmes de gestió del tràfic aeri (ATM), com per exemple el concepte d’operacions basades en trajectòries (TBO). Aquest concepte té com a objectiu eliminar completament de les trajectòries la vectorització en “bucle obert” i les restriccions estratègiques. Per aconseguir-ho, es proposa implementar de manera eficient una negociació de la trajectòria 4D, amb l’objectiu de sincronitzar l’equipament de terra amb el de l’aeronau, maximitzant d’aquesta manera l’eficiència dels vols i la capacitat del sistema. El principal objectiu d’aquest doctorat és desenvolupar mètodes per gestionar aeronaus de manera eficient en espai aeri terminal, juntament amb conceptes d’operacions que compleixin amb el concepte de TBO. Les trajectòries d’arribada simulades per tots els experiments definits en aquesta tesi doctoral, en la mesura que s’ha pogut, són CDOs d’energia neutral. D’aquesta manera, la idea és reduir el màxim possible l’impacte mediambiental de les operacions aèries al sistema ATM. En definitiva, l’objectiu d’aquest doctorat és aconseguir una gestió del tràfic d’arribada més eficient, obtenint una major predictibilitat i capacitat, i assegurant que la seguretat de les operacions es manté. Els experiments dissenyats consideren una situació on el concepte de TBO és present, el que comporta una sincronització elevada entre tots els actors implicats en el sistema ATM. Així mateix, s’esperen nivells majors d’automatització i de compartició d’informació, juntament amb una modernització de les eines de suport en terra a l’ATC i dels FMSs de les aeronaus, tot amb l’objectiu de complir amb el nou paradigma de TBO.El incremento de tráfico aéreo ha llevado a una mayor sensibilidad medioambiental en la aviación, motivando la investigación de métodos para conseguir un transporte aéreo más ecológico. En este contexto, las operaciones de descenso continuo (CDOs) permiten a las aeronaves seguir una trayectoria que aporta grandes beneficios económicos y ambientales, dando como resultado descensos con los motores al ralentí desde la altitud de crucero hasta justo antes de aterrizar. Estas trayectorias reducen el consumo de combustible, las emisiones contaminantes y el ruido generado por las aeronaves. No obstante, este tipo de operaciones tiene una gran desventaja: la pérdida de predictibilidad desde el punto de vista del controlador aéreo (ATC) en términos de tiempos de paso en los diferentes puntos de la ruta. Como consecuencia, el ATC necesita asignar una mayor separación entre las aeronaves, lo cual comporta una reducción en la capacidad del aeropuerto. Estudios previos investigando este problema han demostrado que la capacidad de cumplir con un tiempo controlado de llegada (CTA) en un punto de la ruta (utilizado para secuenciar las aeronaves) podría habilitar las CDOs manteniendo al mismo tiempo la capacidad del aeropuerto. En este contexto, es necesario investigar más en cómo los gestores de llegadas (AMANs)—y los gestores de llegadas extendidos (E-AMANs)—podrían dar soporte en la selección de la CTA más adecuada. El ATC sería el encargado de enviar la CTA al piloto, el cual, para cumplir con la CTA, usaría la capacidad de gestión de trayectorias de un sistema de gestión de vuelo (FMS) de cuatro dimensiones (4D). Una transformación clave para conseguir una gestión más eficiente del tráfico de llegada es el uso de nuevos paradigmas de gestión del tráfico aéreo (ATM), como por ejemplo el concepto de operaciones basadas en trayectorias (TBO). Este concepto tiene como objetivo eliminar completamente de las trayectorias la vectorización en “bucle abierto” y las restricciones estratégicas. Para conseguirlo, se propone implementar de manera eficiente una negociación de la trayectoria 4D, con el objetivo de sincronizar el equipamiento de tierra con el de la aeronave, maximizando de esta manera la eficiencia de los vuelos y la capacidad del sistema. El principal objetivo de este doctorado es desarrollar métodos para gestionar aeronaves de manera eficiente en espacio aéreo terminal, junto con conceptos de operaciones que cumplan con el concepto de TBO. Las trayectorias de llegada simuladas para todos los experimentos definidos en esta tesis doctoral, en la medida de lo posible, son CDOs de energía neutra. De esta manera, la idea es reducir lo máximo posible el impacto medioambiental de las operaciones aéreas en el sistema ATM. En definitiva, el objetivo de este doctorado es conseguir una gestión del tráfico de llegada más eficiente, obteniendo una mayor predictibilidad y capacidad, y asegurando que la seguridad de las operaciones se mantiene. Los experimentos diseñados consideran una situación xxi donde el concepto de TBO está presente, lo que comporta una sincronización elevada entre todos los actores implicados en el sistema ATM. Asimismo, se esperan mayores niveles de automatización y de compartición de información, junto con una modernización de las herramientas de soporte en tierra al ATC y de los FMSs de las aeronaves, todo con el objetivo de cumplir con el nuevo paradigma de TBO. Primero de todo, se define un marco para la optimización de trayectorias utilizado para generar las trayectorias simuladas para los experimentos definidos en esta tesis doctoral. A continuación, se evalúan los beneficios de volar CDOs de energía neutra comparándolas con trayectorias reales obtenidas de datos de vuelo históricos. Se comparan dos fuentes de datos, concluyendo cuál es la más adecuada para estudios de eficiencia en espacio aéreo terminal. Las CDOs de energía neutra son el tipo preferido de trayectorias desde un punto de vista medioambiental pero, dependiendo de la cantidad de tráfico, podría ser imposible para el ATC asignar una CTA que pueda ser cumplida por las aeronaves mientras vuelan la ruta de llegada publicada. En esta tesis doctoral, se comparan dos estrategias con el objetivo de cumplir con la CTA asignada: volar CDOs de energía neutra por rutas más largas/cortas o volar descensos con el motor accionado por la ruta publicada. Para ambas estrategias, se analiza la sensibilidad del consumo de combustible a diferentes parámetros, como la altitud inicial de crucero o la velocidad del viento. Finalmente, en esta tesis doctoral se analizan dos estrategias para gestionar de manera eficiente el tráfico de llegada en espacio aéreo terminal. Primero, se utiliza una estrategia provisional a medio camino entre la negociación completa de trayectorias 4D y la vectorización en “bucle abierto”: se propone una metodología para gestionar de manera eficaz tráfico de llegada donde las aeronaves vuelan CDOs de energía neutra en un procedimiento de navegación de área (RNAV) conocido como trombón. A continuación, se propone una nueva metodología para generar rutas de llegada dinámicas que se adaptan automáticamente a la demanda actual de tráfico. De igual manera, se aplican CDOs de energía neutra a todo el tráfico de llegada. Hay diferentes factores a considerar que podrían limitar los beneficios de las soluciones propuestas. La cantidad y distribución del tráfico de llegada tiene un gran efecto sobre los resultados obtenidos, limitando en algunos casos una gestión eficiente de las aeronaves de llegada. Además, algunas de las soluciones propuestas comportan elevadas cargas computacionales que podrían limitar su aplicación operacional, motivando mayor investigación en el futuro con el fin de optimizar los modelos y metodologías utilizados. Finalmente, permitir a algunos aviones volar descensos con el motor accionado podría facilitar la gestión de las aeronaves de llegada en los experimentos que se centran en el procedimiento de trombón y en la generación de rutas de llegada dinámicas.Postprint (published version

Design of automated system for management of arrival traffic

The design of an automated air traffic control system based on a hierarchy of advisory tools for controllers is described. Compatibility of the tools with the human controller, a key objective of the design, is achieved by a judicious selection of tasks to be automated and careful attention to the design of the controller system interface. The design comprises three interconnected subsystems referred to as the Traffic Management Advisor, the Descent Advisor, and the Final Approach Spacing Tool. Each of these subsystems provides a collection of tools for specific controller positions and tasks. The design of two of these tools, the Descent Advisor, which provides automation tools for managing descent traffic, and the Traffic Management Advisor, which generates optimum landing schedules is focused on. The algorithms, automation modes, and graphical interfaces incorporated in the design are described

Potential Operational Benefits of Multi-layer Point Merge System on Dense TMA Operation Hybrid arrival trajectory optimization applied to Beijing Capital International Airport

International audience4D 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

Empirical exploration of air traffic and human dynamics in terminal airspaces

Air traffic is widely known as a complex, task-critical techno-social system, with numerous interactions between airspace, procedures, aircraft and air traffic controllers. In order to develop and deploy high-level operational concepts and automation systems scientifically and effectively, it is essential to conduct an in-depth investigation on the intrinsic traffic-human dynamics and characteristics, which is not widely seen in the literature. To fill this gap, we propose a multi-layer network to model and analyze air traffic systems. A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN) encapsulate critical physical and operational characteristics; an Integrated Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network (ICCN) are formulated to represent air traffic flow transmissions and intervention from air traffic controllers, respectively. Furthermore, a set of analytical metrics including network variables, complex network attributes, controllers' cognitive complexity, and chaotic metrics are introduced and applied in a case study of Guangzhou terminal airspace. Empirical results show the existence of fundamental diagram and macroscopic fundamental diagram at the route, sector and terminal levels. Moreover, the dynamics and underlying mechanisms of "ATCOs-flow" interactions are revealed and interpreted by adaptive meta-cognition strategies based on network analysis of the ICCN. Finally, at the system level, chaos is identified in conflict system and human behavioral system when traffic switch to the semi-stable or congested phase. This study offers analytical tools for understanding the complex human-flow interactions at potentially a broad range of air traffic systems, and underpins future developments and automation of intelligent air traffic management systems.Comment: 30 pages, 28 figures, currently under revie