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

Developments of commercial self-driving vehicle (SDV) technology has a potential for a paradigm shift in traffic control technology. Contrary to some previous research approaches, this research argues that, as any other technology, traffic control technology for SDVs should be developed having in mind improved quality of life through a sustainable developmental approach. Consequently, this research emphasizes upon the social perspective of sustainability, considering its neglect in the conventional control principles, and the importance of behavioral considerations for accurately predicting impacts upon economic or environmental factors. The premise is that traffic control technology can affect the distribution of advantages and disadvantages in a society, and thus it requires a framework of social justice. The framework of social justice is inspired by John Rawls' Theory of Justice as fairness, and tries to protect the inviolability of each user in a system. Consequently, the control objective is the distribution of delay per individual, considering for example that the effect of delay is not the same if a person is traveling to a grocery store as opposed to traveling to a hospital. The notion of social justice is developed as a priority system, with end-user responsibility, where user is able to assign a specific Priority Level for each individual trip with SDV. Selected Priority Level is used to determine the right-of-way for each self-driving vehicle at an intersection. As a supporting mechanism to the priority system, there is a structure of non-monetary Priority Credits. Rules for using Priority Credits are determined using knowledge from social science research and through empirical evaluation using surveys, interviews, and web-based experiment. In the physical space, the intersection control principle is developed as hierarchical self-organization, utilizing communication, sensing, and in-vehicle technological capabilities. This distributed control approach should enable robustness against failure, and scalability for future expansion. The control mechanism has been modeled as an agent-based system, allowing evaluation of effects upon safety and user delay. In conclusion, by reaching across multiple disciplines, this development provides the promise and the challenge for evolving SDV control technology. Future efforts for SDV technology development should continue to rely upon transparent public involvement and understanding of human decision-making. / Ph. D.

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.

Ce document synthétise mes travaux de thèse de doctorat en Automatique Productiqueà Grenoble INP (Institut National Polytechnique), thèse préparée au sein dudépartement automatique du laboratoire GIPSA-lab (Grenoble Image Parole Signal etAutomatique). Ce travail s’inscrit dans le cadre du contrôle local et de la supervisiondes systèmes de trafic routier. Les principales contributions portent sur la modélisation,la supervision et la commande locale des systèmes de trafic routier.La contribution apportée à la modélisation du trafic est l’ajout d’un modèle d’incertitudesur le modèle CTM (Cell Transmission Model [Daganzo, 1994]). Ce nouveaumodèle permet de prendre en compte les incertitudes sur différents paramètres dumodèle pour in-fine proposer de nouvelles stratégies de commandes commutées robustes.Outre cette approche de modélisation, nous proposons un niveau de supervisionpermettant d’une part d’estimer en temps réel le mode de fonctionnement et d’autrepart de détecter, localiser et estimer certaines fautes sur le système. L’estimation dynamiquede mode de fonctionnement nous permet de connaître l’état de congestion (ou denon-congestion) de l’aménagement routier considéré. Nous sommes en mesure de détecterdes fautes telles que des chutes de vitesse ou des chutes de capacité survenant sur la route.Enfin, nous proposons deux lois de commandes locales basées sur la théorie dessystèmes à commutations. Ainsi, le schéma de contrôle s’adaptera dynamiquementaux changements de propriétés du système. Ces lois de commande ont pour objet des’insérer dans un schéma de régulation hiérarchique. / This document synthesizes my Phd thesis work in Automatic Control in Grenoble-INP. This thesis has been prepared in the automatic control department of thelaboratory GIPSA-lab. This work is situated in the area of traffic systems control andsupervision. Our contributions are about modeling, supervision and local traffic control.The CTM traffic model has been extended with a model of uncertainties. Thisnews model allows us to take into account the uncertain parameters of the model, topropose new robust switched control law.In addition to this modeling approach, we propose some developments on supervisionof trafic systems. On one hand, we can estimate the operating mode of thesystem in real time and on the other hand to estimate some faults on the system. Thedynamical estimation of the operating mode allows us to know the state of congestion(or non congestion) of the road. We are able to estimate faults such as speed fall andcapacities drop that may appear.Finally, we propose two control laws based on switching systems control. The developedcontrollers adapt their geometry to the properties of the system. The purposeof these controllers is to be inserted in a hierarchic control scheme.

Afin de répondre à la problématique de la régulation multi-objectif et multi-mode des carrefours à feux, nous proposons trois modèles de programmation linéaire mixte en nombres entiers constituant les moteurs d'un système de régulation pleinement adaptatif, ainsi que deux procédures interactives d'optimisation multi-objectif permettant d'adapter itérativement une “politique de régulation” à la situation de trafic. Les critères pris en compte, tous à minimiser, sont le temps d'attente et le nombre d'arrêts des véhicules particuliers, et un critère dédié aux transports en commun permettant de fixer un temps d'attente souhaité pour chaque bus. Des expérimentations ont montré qu'un des trois modèles, dit hybride, se démarque positivement des deux autres. Ce modèle a alors été mis en œuvre avec une des deux procédures interactives, permettant de contrôler un trafic simulé sur une période d'une heure dans différents scénarios types, et comparé à un système de régulation semi-adaptatif. / In order to answer the multi-objective and multi-mode adaptive traffic control problem, we propose three models of mixed integer linear programming, usable with two multi-objective optimization interactive methods, allowing to adapt a “traffic control policy” iteratively to the current traffic situation. The considered criteria, all of them to be minimized, are the total waiting time and the number of stops for private vehicles and a criterion dedicated to public transports allowing to set a target waiting time for every bus. Experiments showed that one of the three models, called hybrid model, distinguishes itself positively from the others. This model was implemented with one of the two interactive methods, allowing to control a traffic simulated over one hour in different scenarios, and was compared to a semi-adaptive traffic control system.

The Multiple Remote Tower Center concept (mRTC) is a cutting edge project which allows one air traffic control officer (ATCO) to be in charge of multiple remotely situated airports simultaneously. When implemented, it will revolutionise how air traffic is managed at smaller airports and allow for increased efficiency and decreased operational costs. Consequently, at the time of writing a lot of effort is going into evaluating this new way of air traffic management from a safety perspective. Air traffic management has been defined as an issue maintaining situational awareness and exercising control. This thesis aims to investigate how the phenomenon target fascination affects the ATCOs ability to exercise control over its controlled airspace and maintain its situation awareness. It does so by creating a baseline scenario of work in a mRTC, and then comparing the ATCOs performance in the baseline scenario with its performance in the same corresponding scenario, but with elements of target fascination introduced. The differences in the scenarios are analysed using the Contextual Control Model, the Extended Control Model and a holistic framework for studying situation awareness. The analysis shows that target fascination does affect the ATCOs ability to maintain control, but not radically so, and only for a short period of time. The target fascination forces the ATCO to rely on information in the immediate environment to a higher degree than during regular work, as opposed to making decisions based on a holistic understanding of the situation and high level goals. However, once the understanding of the situation have been re-established, the level of control quickly returns to normal levels. Situation awareness is thus a key concept in maintaining control. The situation awareness analysis show that target fascination affects situation awareness by causing the ATCOs understanding of the situation to become outdated without the ATCOs knowledge. Because of this, there may be developments in the situation that the ATCO is not aware of, which hinders it from acting as it normally would. In some cases an intervention from an external actor or element may be necessary to break the fascination and re-establish the ATCOs understanding for the situation. As soon as the fascination is broken, the ATCO quickly takes steps to re-establish its situation awareness and return to normal operations.

La régulation adaptative des feux de signalisation est un problème très important. Beaucoup de chercheurs travaillent continuellement afin de résoudre les problémes liés à l’embouteillage dans les intersections urbaines. Il devient par conséquent très utile d’employer des algorithmes intelligents afin d’améliorer les performances de régulation et la qualité du service. Dans cette thèse, nous essayons d'étudier ce problème d’une part à travers une modèlisation microscopique et dynamique en temps discret, et d’autre part en explorant plusieurs approches de résoltion pour une intersection isolée ainsi que pour un réseau distribué d'intersections.La première partie se concentre sur la modélisation dynamique des problèmes des feux de signalisation ainsi que de la charge du réseau d’intersections. Le mode de la “séquence de phase adaptative” (APS) dans un plan de feux est d'abord considéré. Quant à la modélisation du contrôle des feux aux intersections, elle est formulée grâce à un processus décisionnel de markov (MDP). En particulier, la notion de “l'état du système accordable” est alors proposée pour la coordination du réseau de trafic. En outre, un nouveau modèle de “véhicule-suiveur” est proposé pour l'environnement de trafic. En se basant sur la modélisation proposée, les méthodes de contrôle des feux dans cette thèse comportent des algorithmes optimaux et quasi-optimaux. Deux algorithmes exacts de résolution basées sur la programmation dynamique (DP) sont alors étudiés et les résultats montrent certaines limites de cette solution DP surtout dans quelques cas complexes où l'espace d'états est assez important. En raison de l’importance du temps d’execution de l'algorithme DP et du manque d'information du modèle (notamment l’information exacte relative à l’arrivée des véhicules à l’intersection), nous avons opté pour un algorithme de programmation dynamique approximative (ADP). Enfin, un algorithme quasi-optimal utilisant l'ADP combinée à la méthode d’amélioration RLS-TD (λ) est choisi. Dans les simulations, en particulier avec l'intégration du mode de phase APS, l'algorithme proposé montre de bons résultats notamment en terme de performance et d'efficacité de calcul. / Adaptive traffic signal control is a decision making optimization problem. People address this crucial problem constantly in order to solve the traffic congestion at urban intersections. It is very popular to use intelligent algorithms to improve control performances, such as traffic delay. In the thesis, we try to study this problem comprehensively with a microscopic and dynamic model in discrete-time, and investigate the related algorithms both for isolated intersection and distributed network control. At first, we focus on dynamic modeling for adaptive traffic signal control and network loading problems. The proposed adaptive phase sequence (APS) mode is highlighted as one of the signal phase control mechanisms. As for the modeling of signal control at intersections, problems are fundamentally formulated by Markov decision process (MDP), especially the concept of tunable system state is proposed for the traffic network coordination. Moreover, a new vehicle-following model supports for the network loading environment.Based on the model, signal control methods in the thesis are studied by optimal and near-optimal algorithms in turn. Two exact DP algorithms are investigated and results show some limitations of DP solution when large state space appears in complex cases. Because of the computational burden and unknown model information in dynamic programming (DP), it is suggested to use an approximate dynamic programming (ADP). Finally, the online near-optimal algorithm using ADP with RLS-TD(λ) is confirmed. In simulation experiments, especially with the integration of APS, the proposed algorithm indicates a great advantage in performance measures and computation efficiency.