In recent years, unmanned aerial vehicles (UAVs) have become a dominant force in the aerospace industry. Recent technological developments have moved these aircraft from remote operation roles to more active response missions. Of particular interest is the possibility of applying UAVs toward solving complex problems in long-endurance missions. Under that belief, the feasibility of utilizing UAVs for wildfire detection and response was investigated in a partnership that included NASA’s Aeronautics Research Mission Directorate and Science Mission Directorate, and the United States Forest Service. Under NASA’s Intelligent Mission Management (IMM) project, research was conducted to develop a mission architecture that would enable use of a high altitude UAV to search for reported wildfires with a separate low altitude UAV supporting ground assets. This research proposes a “straw man” concept incorporating both a High Altitude Long Endurance (HALE) UAV and a Low Altitude Short Endurance (LASE) UAV in a loosely coupled, low cost solution tailored towards wildfire response. This report identifies the communications architecture, algorithms, and required system configuration that meets the outlined goals of the IMM project by mitigating wildfires and addressing the United States Forest Service immediate needs. The end product is a defined parent-child framework capable of meeting all wildfire mission goals. The concept has been implemented in simulation, the results of which are presented in this report.

The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control.

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Ever wondered how trains arrive and depart on time? Traffic Control systems are there to help control the traffic flow, with many operators monitoring and taking actions when necessary. Operators of the traffic control systems use a keyboard and a mouse to interact. Current user interface and work setup consist of many usability issues that can be improved in order to increase operator’s efficiency and productivity in controlling the traffic. Interviewing users of the system and researching on related topics led to a newly proposed design in interaction, user interface as well as some suggestions for increasing productivity. Gesture-based interaction is introduced and simulated for traffic control systems that tend to improve the operation. Various gestures are designed such as panning, zooming and hovering the map using Leap Motion controller which enables intuitive interaction. These gestures aim to solve identified usability issues discovered during the interview with the user. The project aims to answer the following question: Can gesture-based interaction solve usability issues and establish the intuitive use of the CTC system? Performing exploratory research on this topic involved designing, implementing and testing hand gestures with users. From an ergonomic perspective, body posture and hand position of the operator is examined and suggested to use sit-to-stand workstations in order to reduce pain and discomfort while working. Gesture-based interaction eliminates finding mouse cursor on large screens, it enables fast request of detailed information and also it provides a better overview of the map surroundings. Laboratory tests confirm that gesture-based interaction brings more natural and intuitive use of traffic control systems. There is a big potential for gesture-based interaction to increase usability and bring efficient controlling for operators. It would reduce delays of the train and maintain safe traffic flow. / Har du någonsin undrat hur tåg anländer och avgaå i tid? Trafikledningssystem (CTC-system) hjälper till att kontrollera trafikflöet där operatörer övervakar och vidtar åtgärder vid behov. Operatörer av ett trafikledningssystem använder idag ett tangentbord och en mus för att interagera. Det nuvarande användargränssnittet och arbetsinstallationen består av många användbarhetsproblem som kan förbättras för att öka operatörens effektivitet och produktivitet för att kontrollera trafiken. Intervjuer med användare av systemet samt forskning om ämnet ledde till ett nytt föreslag av interaktion, utformning av användargränssnitt samt några förslag för att öka produktiviteten. Den gestbaserade interaktionen som infördes och simulerades för trafikkontrollsystemet tenderar att förbättra funktionen. Olika gester utformades som möjliggör för användaren att panorera, zooma och sväva över kartan. Gesterna implementerades med hjälp av Leap Motion Controller som möjliggör intuitiv interaktion. Dessa gester syftar till att lösa identifierade användbarhetsproblem som upptäcks under intervjuerna med användarna. Syftet med detta arbete var att svara på följande forskningsfråga: Kan gestbaserad interaktion lösa användbarhetsproblem och etablera intuitiv användning av CTC-systemet? Den explorativa forskning som utfördes i detta arbete inkluderade att utforma, genomföra och testa gester med användare. Kroppshållning och handposition för operatorerna undersöktes ur ett ergonomiskt perspektiv och studien föreslår att använda sitt-till-stå arbetsstationer för att minska smärta och obehag under arbetet. Gestbaserad interaktion eliminerar problemet att hitta muspekaren på stora skärmar, vilket gör det enkelt att snabbt hitta detaljerad information och ger även en bättre överblick över kartans omgivning. Laboratorietester bekräftar att gestbaserad interaktion ger mer naturlig och intuitiv användning av trafikledningssystemet. Det finns en stor potential för gestbaserad interaktion för att öka användbarheten och ge en effektiv kontroll för operatörerna. Det skulle minska förseningarna av tåget och upprätthålla ett säkert trafikflöde.

We develop and evaluate two significant modeling concepts within the context of a large-scale Airspace Planning and Collaborative Decision-Making Model (APCDM) and, thereby, enhance its current functionality in support of both strategic and tactical level flight assessments. The first major concept is a new severe weather-modeling paradigm that can be used to assess existing tactical en route flight plan strategies such as the Flight Management System (FMS) as well as to provide rerouting strategies. The second major concept concerns modeling the mediated bartering of slot exchanges involving airline trade offers for arrival/departure slots at an arrival airport that is affected by the Ground Delay Program (GDP), while simultaneously considering issues related to sector workloads, airspace conflicts, as well as overall equity concerns among the airlines. This research effort is part of an $11.5B, 10-year, Federal Aviation Administration (FAA)-sponsored program to increase the U.S. National Airspace (NAS) capacity by 30 percent by the year 2010. Our innovative contributions of this research with respect to the severe weather rerouting include (a) the concept of "Probability-Nets" and the development of discretized representations of various weather phenomena that affect aviation operations; (b) the integration of readily accessible severe weather probabilities from existing weather forecast data provided by the National Weather Service (NWS); (c) the generation of flight plans that circumvent severe weather phenomena with specified probability levels, and (d) a probabilistic delay assessment methodology for evaluating planned flight routes that might encounter potentially disruptive weather along its trajectory. Given a fixed set of reporting stations from the CONUS Model Output Statistics (MOS), we begin by constructing weather-specific probability-nets that are dynamic with respect to time and space. Essential to the construction of the probability-nets are the point-by-point forecast probabilities associated with MOS reporting sites throughout the United States. Connections between the MOS reporting sites form the strands within the probability-nets, and are constructed based upon a user-defined adjacency threshold, which is defined as the maximum allowable great circle distance between any such pair of sites. When a flight plan traverses through a probability-net, we extract probability data corresponding to the points where the flight plan and the probability-net strand(s) intersect. The ability to quickly extract this trajectory-related probability data is critical to our weather-based rerouting concepts and the derived expected delay and related cost computations in support of the decision-making process. Next, we consider the superimposition of a flight-trajectory-grid network upon the probability-nets. Using the U.S. Navigational Aids (Navaids) as the network nodes, we develop an approach to generate flight plans that can circumvent severe weather phenomena with specified probability levels based on determining restricted, time-dependent shortest paths between the origin and destination airports. By generating alternative flight plans pertaining to specified threshold strand probabilities, we prescribe a methodology for computing appropriate expected weather delays and related disruption factors for inclusion within the APCDM model. We conclude our severe weather-modeling research by conducting an economic benefit analysis using a k-means clustering mechanism in concert with our delay assessment methodology in order to evaluate delay costs and system disruptions associated with variations in probability-net refinement-based information. As a flight passes through the probability-net(s), we can generate a probability-footprint that acts as a record of the strand intersections and the associated probabilities from origin to destination. A flight plan's probability-footprint will differ for each level of data refinement, from whence we construct route-dependent scenarios and, subsequently, compute expected weather delay costs for each scenario for comparative purposes. Our second major contribution is the development of a novel slot-exchange modeling concept within the APCDM model that incorporates various practical issues pertaining to the Ground Delay Program (GDP), a principal feature in the FAA's adoption of the Collaborative Decision-Making (CDM) paradigm. The key ideas introduced here include innovative model formulations and several new equity concepts that examine the impact of "at-least, at-most" trade offers on the entire mix of resulting flight plans from respective origins to destinations, while focusing on achieving defined measures of "fairness" with respect to the selected slot exchanges. The idea is to permit airlines to barter assigned slots at airports affected by the Ground Delay Program to their mutual advantage, with the FAA acting as a mediator, while being cognizant of the overall effect of the resulting mix of flight plans on air traffic control sector workloads, collision risk and safety, and equity considerations. We start by developing two separate slot-exchange approaches. The first consists of an external approach in which we formulate a model for generating a set of package-deals, where each package-deal represents a potential slot-exchange solution. These package-deals are then embedded within the APCDM model. We further tighten the model representation using maximal clique cover-based cuts that relate to the joint compatibility among the individual package-deals. The second approach significantly improves the overall model efficiency by automatically generating package-deals as required within the APCDM model itself. The model output prescribes a set of equitable flight plans based on admissible trades and exchanges of assigned slots, which are in addition conformant with sector workload capabilities and conflict risk restrictions. The net reduction in passenger-minutes of delay for each airline is the primary metric used to assess and compare model solutions. Appropriate constraints are included in the model to ensure that the generated slot exchanges induce nonnegative values of this realized net reduction for each airline. In keeping with the spirit of the FAA's CDM initiative, we next propose four alternative equity methods that are predicated on different specified performance ratios and related efficiency functions. These four methods respectively address equity with respect to slot-exchange-related measures such as total average delay, net delay savings, proportion of acceptable moves, and suitable value function realizations. For our computational experiments, we constructed several scenarios using real data obtained from the FAA based on the Enhanced Traffic Management System (ETMS) flight information pertaining to the Miami and Jacksonville Air Route Traffic Control Centers (ARTCC). Through our experimentation, we provide insights into the effect of the different proposed modeling concepts and study the sensitivity with respect to certain key parameters. In particular, we compare the alternative proposed equity formulations by evaluating their corresponding slot-exchange solutions with respect to the net reduction in passenger-minutes of delay for each airline. Additionally, we evaluate and compare the computational-effort performance, under both time limits and optimality thresholds, for each equity method in order to assess the efficiency of the model. The four slot-exchange-based equity formulations, in conjunction with the internal slot-exchange mechanisms, demonstrate significant net savings in computational effort ranging from 25% to 86% over the original APCDM model equity formulation. The model has been implemented using Microsoft Visual C++ and evaluated using a C++ interface with CPLEX 9.0. The overall results indicate that the proposed modeling concepts offer viable tools that can be used by the FAA in a timely fashion for both tactical purposes, as well as for exploring various strategic issues such as air traffic control policy evaluations; dynamic airspace resectorization strategies as a function of severe weather probabilities; and flight plan generation in response to various disruption scenarios. / Ph. D.

We develop a detailed, large-scale, airspace planning and collaborative decision-making model (APCDM), that is part of an $11.5B, 10-year, Federal Aviation Administration (FAA)-sponsored effort to increase U.S. National Airspace (NAS) capacity by 30 percent. Given a set of flights that must be scheduled during some planning horizon, we use a mixed-integer programming formulation to select a set of flight plans from among alternatives subject to flight safety, air traffic control workload, and airline equity constraints. Novel contributions of this research include three-dimensional probabilistic conflict analyses, the derivation of valid inequalities to tighten the conflict safety representation constraints, the development of workload metrics based on average (and its variance from) peak load measures, and the consideration of equity among airline carriers in absorbing the costs related to re-routing, delays, and cancellations. We also propose an improved set of flight plan cost factors for representing system costs and investigating fairness issues by addressing flight dependencies occurring in hubbed operations, as well as market factors such as schedule convenience, reliability, and the timeliness of connections. The APCDM model has potential use for both tactical and strategic applications, such as air traffic control in response to severe weather phenomenon or spacecraft launches, FAA policy evaluation, Homeland Defense contingency planning, and military air campaign planning. The model is tested to consider various airspace restriction scenarios imposed by dynamic severe weather systems and space launch Special Use Airspace (SUA) impositions. The results from this model can also serve to augment the FAA's National Playbook of standardized flight profiles in different disruption-prone regions of the National Airspace. / Ph. D.

Develop new strategies to design and operate the multilateration systems, used for air traffic control operations, in a more efficient way. The design strategies are based on the utilization of metaheuristic optimization techniques and they are intended to found the optimal spatial distribution of the system ground stations, taking into account the most relevant system operation parameters. The strategies to operate the systems are based on the development of new positioning methods which allow solving the problems of uncertainty position and poor accuracy that the current systems can present. The new strategies can be applied to design, deploy and operate the multilateration systems for airport surface surveillance as well as takeoff-landing, approach and enroute control. An important advance in the current knowledge of air traffic control is expected from the development of these strategies, because they solve several deficiencies that have been made clear, by the international scientific community, in the last years. / Mantilla Gaviria, IA. (2013). New Strategies to Improve Multilateration Systems in the Air Traffic Control [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/29688

A typical problem in traffic control is the steering over a network of vehicles with different origins and destinations. In this report this scenario is formulated as a multi-commodity network flow problem, a linear programming problem whose objective is to transport, with minimum cost, different commodities from their respective sources to their sinks through a network, while respecting the capacity constraints of the roads. The dynamic network flow formulation of the problem is also presented, extending the network over time to incorporate the temporal dimension. Different algorithms for solving the multi-commodity network flow problem are examined. First, the simplex method, more precisely its revised version, is considered, and then the Dantzig-Wolfe decomposition is illustrated, an optimization algorithm which exploits specific block structures in the constraints. These methods are applied using state-of-the-art linear programming solvers and evaluated with a simulation based on the road network in central Stockholm. The results show that both methods allow for solving the traffic flow problem, with limitations given by the specifics of the solvers and by the space and time discretization of the problem. In particular, the revised simplex algorithm results the faster method.

[ES] Desde hace aproximadamente dos décadas, las ayudas tecnológicas a la conducciónn han ido creciendo a un ritmo vertiginoso con la intención de hacer estos sistemas más eficientes y seguros. Estas ayudas a la conducción han ido cubriendo fallos que los investigadores denominan "conducción errática" ó "comportamientos inseguros al volante" y que son decisiones arbitrarias tomadas por un conductor humano, que ponen en peligro a todos los usuarios de la carretera. Estas malas decisiones, sumadas al creciente número de viajes en coche en una ciudad hoy en día (post pandemia), muestran la necesidad de seguir haciendo propuestas tecnológicas, enfocadas a donde se producen interacciones más complejas entre vehículos; por ejemplo, una intersección en hora punta. Los desarrollos en ayudas a la conducción se han orientado en dos temas: el primero sobre la automatización de la conducción (Sistemas Avanzados de Asistencia al Conductor - ADAS y Vehículos Automatizados - AV) y el segundo sobre la gestión del tráfico vial (algoritmos centralizados o distribuidos para el control del tráfico). Aunque en la actualidad hay varias empresas automotrices y centros de investigación trabajando en los dos temas, y en especial en algunos casos eliminando de la ecuación el comportamiento humano, todavía hay carencias en las configuraciones, para que un vehículo autónomo sea capaz de tomar decisiones óptimas, frente a todas las posibles condiciones disponibles en un tráfico vial. Ahora bien, y teniendo en cuenta los dos temas antes mencionados sobre los desarrollos en ayudas a la conducción, los investigadores prevén a grandes rasgos, que para alcanzar mayores niveles de conducción autónoma en la próxima década, es necesario estudiar cómo hacer más eficientes las interacciones autónomas entre vehículos. Por ello, las intersecciones viales son un ejemplo clave, donde es posible analizar casos de interacciones de alta complejidad entre vehículos, ya que se trata de una parte de la infraestructura vial, donde los vehículos comparten carriles, vías, cruces o cambios de carril a voluntad, y que podría generar colisiones en puntos de conflicto y retrasos en los desplazamientos si no existe una cooperación adecuada. De esta forma, en esta tesis se propone una serie de algoritmos distribuidos para el control del tráfico en intersecciones, basados en el intercambio de comunicaciones entre vehículos autónomos (interacciones locales) cercano a las intersecciones y donde se muestran comportamientos emergentes en el tráfico, resultando en cruces de forma cooperativa, segura y eficiente, desde bajas a altas densidades de tráfico vehicular en las intersecciones. Esta investigación se desarrolla utilizando simuladores de tráfico vial, con calles estilo Manhattan; primero implementando escenarios menos complejos con calles urbanas de un carril, y luego incrementando la complejidad con múltiples carriles. / [CA] Des de fa aproximadament dues dècades, les ajudes tecnològiques a la conducció han anat creixent a un ritme vertiginós amb la intenció de fer aquests sistemes més eficients i segurs. Aquestes ajudes a la conducció han anat cobrint fallades que els investigadors denominen "conducció erràtica" o "comportaments insegurs al volant" i que són decisions arbitràries preses per un conductor humà, que posen en perill a tots els usuaris de la carretera. Aquestes males decisions, sumades al creixent nombre de viatges en cotxe en una ciutat avui dia (post pandèmia), mostren la necessitat de seguir fent propostes tecnològiques, enfocades a on es produeixen interaccions més complexes entre vehicles; per exemple, una intersecció en hora punta. Els desenvolupaments en ajudes a la conducció s'han orientat en dos temes: el primer sobre l'automatització de la conducció (Sistemes Avançats d'Assistència al Conductor - ADAS i Vehicles Automatitzats - AV) i el segon sobre la gestió del trànsit vial (algoritmes centralitzats o distribuïts per al control del trànsit). Encara que actualment hi ha diverses empreses automobilístiques i centres de recerca treballant en els dos temes, i en especial en alguns casos eliminant de l'equació el comportament humà, encara hi ha mancances en les configuracions, perquè un vehicle autònom siga capaç de prendre decisions òptimes, davant totes les possibles condicions disponibles en un trànsit vial. Ara bé, i tenint en compte els dos temes abans esmentats sobre els desenvolupaments en ajudes a la conducció, els investigadors preveuen a grans trets, que per assolir majors nivells de conducció autònoma en la propera dècada, és necessari estudiar com fer més eficients les interaccions autònomes entre vehicles. Per això, les interseccions vials són un exemple clau, on és possible analitzar casos d'interaccions d'alta complexitat entre vehicles, ja que es tracta d'una part de la infraestructura vial, on els vehicles comparteixen carrils, vies, creus o canvis de carril a voluntat, i que podria generar col·lisions en punts de conflicte i retards en els desplaçaments si no existeix una cooperació adequada. D'aquesta manera, en aquesta tesi es proposa una sèrie d'algoritmes distribuïts per al control del trànsit en interseccions, basats en l'intercanvi de comunicacions entre vehicles autònoms (interaccions locals) properes a les interseccions i on es mostren comportaments emergents en el trànsit, resultant en creus de forma cooperativa, segura i eficient, des de baixes a altes densitats de trànsit vehicular en les interseccions. Aquesta investigació es desenvolupa utilitzant simuladors de trànsit vial, amb carrers estil Manhattan; primer implementant escenaris menys complexos amb carrers urbans d'un carril, i després incrementant la complexitat amb múltiples carrils. / [EN] Since a couple of decades, the technological driving aids have gone growing at a dizzying pace with the intention of making these systems more efficient and safe. These driving aids have been covering failures that the researchers name "erratic driving" or "unsafe driving behaviors" and are arbitrary decisions taken by a human driver which endanger all road users. These bad decisions in addition to the increasing number of driving commutes in a city nowadays (post pandemic), show the need to continue doing technological proposals focused on where there are more complex interactions between vehicles when density increases; for instance, an intersection in rush hour. The developments in driving aids have been orientated in two topics: the first driving automation (Advanced Driver Assistance Systems - ADAS and Automated Vehicles - AV) and the second road traffic management (centralized or distributed algorithms to traffic control). Although there are currently several automotive companies and research centers working in the two topics, and in special in some cases removing the equation the human behavior, there are still lacks in the configurations for an vehicle autonomous be able to make optimal decisions front to all possible conditions available in a road traffic. Now, and take into account the two topics aforementioned about driving aids developments, researchers broadly envisage that in order to reach autonomous driving levels higher (first topic) in the next decade, is necessary to study how to do autonomous vehicle interactions (second topic) more efficient. Therefore, road intersections are an instance where it is possible to analyse cases of highly complexity interactions between vehicles, because it is a part of road infrastructure where the vehicles sharing lanes, paths, crossings or lane changes at will and it could generate collisions on conflict points and time delay in the commutes if there is not an appropriate cooperation. Hence, this thesis proposes a series of distributed algorithms to traffic control on intersections, based on interchange of communications between autonomous vehicles (local interactions) near to the intersections that show emergent behaviors to crossing cooperative, safe way and efficiency with high densities the traffic system on intersections. This research is developed using simulators with Manhattan-style streets; first implementing scenarios less complex with one-lane city streets and then increase the complexity with multiple-lanes. / González Pinzón, CL. (2024). Distributed Intersection Management Algorithms for Autonomous Vehicles [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/204406

The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control.