The 4th Symposium on Management of Future Motorway and Urban Traffic Systems (MFTS) was held in Dresden, Germany, from November 30th to December 2nd, 2022. Organized by the Chair of Traffic Process Automation (VPA) at the “Friedrich List” Faculty of Transport and Traffic Sciences of the TU Dresden, the proceedings of this conference are published as volume 9 in the Chair’s publication series “Verkehrstelematik” and contain a large part of the presented conference extended abstracts. The focus of the MFTS conference 2022 was cooperative management of multimodal transport and reflected the vision of the professorship to be an internationally recognized group in ITS research and education with the goal of optimizing the operation of multimodal transport systems. In 14 MFTS sessions, current topics in demand and traffic management, traffic control in conventional, connected and automated transport, connected and autonomous vehicles, traffic flow modeling and simulation, new and shared mobility systems, digitization, and user behavior and safety were discussed. In addition, special sessions were organized, for example on “Human aspects in traffic modeling and simulation” and “Lesson learned from Covid19 pandemic”, whose descriptions and analyses are also included in these proceedings.:1 Connected and Automated Vehicles 1.1 Traffic-based Control of Truck Platoons on Freeways 1.2 A Lateral Positioning Strategy for Connected and Automated Vehicles in Lane-free Traffic 1.3 Simulation Methods for Mixed Legacy-Autonomous Mainline Train Operations 1.4 Can Dedicated Lanes for Automated Vehicles on Urban Roads Improve Traffic Efficiency? 1.5 GLOSA System with Uncertain Green and Red Signal Phases 2 New Mobility Systems 2.1 A New Model for Electric Vehicle Mobility and Energy Consumption in Urban Traffic Networks 2.2 Shared Autonomous Vehicles Implementation for a Disrupted Public Transport Network 3 Traffic Flow and Simulation 3.1 Multi-vehicle Stochastic Fundamental Diagram Consistent with Transportations Systems Theory 3.2 A RoundD-like Roundabout Scenario in CARLA Simulator 3.3 Multimodal Performance Evaluation of Urban Traffic Control: A Microscopic Simulation Study 3.4 A MILP Framework to Solve the Sustainable System Optimum with Link MFD Functions 3.5 On How Traffic Signals Impact the Fundamental Diagrams of Urban Roads 4 Traffic Control in Conventional Traffic 4.1 Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics 4.2 AI-based Multi-class Traffic Model Oriented to Freeway Traffic Control 4.3 Exploiting Deep Learning and Traffic Models for Freeway Traffic Estimation 4.4 Automatic Design of Optimal Actuated Traffic Signal Control with Transit Signal Priority 4.5 A Deep Reinforcement Learning Approach for Dynamic Traffic Light Control with Transit Signal Priority 4.6 Towards Efficient Incident Detection in Real-time Traffic Management 4.7 Dynamic Cycle Time in Traffic Signal of Cyclic Max-Pressure Control 5 Traffic Control with Autonomous Vehicles 5.1 Distributed Ordering and Optimization for Intersection Management with Connected and Automated Vehicles 5.2 Prioritization of an Automated Shuttle for V2X Public Transport at a Signalized Intersection – a Real-life Demonstration 6 User Behaviour and Safety 6.1 Local Traffic Safety Analyzer (LTSA) - Improved Road Safety and Optimized Signal Control for Future Urban Intersections 7 Demand and Traffic Management 7.1 A Stochastic Programming Method for OD Estimation Using LBSN Check-in Data 7.2 Delineation of Traffic Analysis Zone for Public Transportation OD Matrix Estimation Based on Socio-spatial Practices 8 Workshops 8.1 How to Integrate Human Aspects Into Engineering Science of Transport and Traffic? - a Workshop Report about Discussions on Social Contextualization of Mobility 8.2 Learning from Covid: How Can we Predict Mobility Behaviour in the Face of Disruptive Events? – How to Investigate the Mobility of the Future / Das 4. Symposium zum Management zukünftiger Autobahn- und Stadtverkehrssysteme (MFTS) fand vom 30. November bis 2. Dezember 2022 in Dresden statt und wurde vom Lehrstuhl für Verkehrsprozessautomatisierung (VPA) an der Fakultät Verkehrswissenschaften„Friedrich List“ der TU Dresden organisiert. Der Tagungsband erscheint als Band 9 in der Schriftenreihe „Verkehrstelematik“ des Lehrstuhls und enthält einen Großteil der vorgestellten Extended-Abstracts des Symposiums. Der Schwerpunkt des MFTS-Symposiums 2022 lag auf dem kooperativen Management multimodalen Verkehrs und spiegelte die Vision der Professur wider, eine international anerkannte Gruppe in der ITS-Forschung und -Ausbildung mit dem Ziel der Optimierung des Betriebs multimodaler Transportsysteme zu sein. In 14 MFTS-Sitzungen wurden aktuelle Themen aus den Bereichen Nachfrage- und Verkehrsmanagement, Verkehrssteuerung im konventionellen, vernetzten und automatisierten Verkehr, vernetzte und autonome Fahrzeuge, Verkehrsflussmodellierung und -simulation, neue und geteilte Mobilitätssysteme, Digitalisierung sowie Nutzerverhalten und Sicherheit diskutiert. Darüber hinaus wurden Sondersitzungen organisiert, beispielsweise zu „Menschlichen Aspekten bei der Verkehrsmodellierung und -simulation“ und „Lektionen aus der Covid-19-Pandemie“, deren Beschreibungen und Analysen ebenfalls in diesen Tagungsband einfließen.:1 Connected and Automated Vehicles 1.1 Traffic-based Control of Truck Platoons on Freeways 1.2 A Lateral Positioning Strategy for Connected and Automated Vehicles in Lane-free Traffic 1.3 Simulation Methods for Mixed Legacy-Autonomous Mainline Train Operations 1.4 Can Dedicated Lanes for Automated Vehicles on Urban Roads Improve Traffic Efficiency? 1.5 GLOSA System with Uncertain Green and Red Signal Phases 2 New Mobility Systems 2.1 A New Model for Electric Vehicle Mobility and Energy Consumption in Urban Traffic Networks 2.2 Shared Autonomous Vehicles Implementation for a Disrupted Public Transport Network 3 Traffic Flow and Simulation 3.1 Multi-vehicle Stochastic Fundamental Diagram Consistent with Transportations Systems Theory 3.2 A RoundD-like Roundabout Scenario in CARLA Simulator 3.3 Multimodal Performance Evaluation of Urban Traffic Control: A Microscopic Simulation Study 3.4 A MILP Framework to Solve the Sustainable System Optimum with Link MFD Functions 3.5 On How Traffic Signals Impact the Fundamental Diagrams of Urban Roads 4 Traffic Control in Conventional Traffic 4.1 Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics 4.2 AI-based Multi-class Traffic Model Oriented to Freeway Traffic Control 4.3 Exploiting Deep Learning and Traffic Models for Freeway Traffic Estimation 4.4 Automatic Design of Optimal Actuated Traffic Signal Control with Transit Signal Priority 4.5 A Deep Reinforcement Learning Approach for Dynamic Traffic Light Control with Transit Signal Priority 4.6 Towards Efficient Incident Detection in Real-time Traffic Management 4.7 Dynamic Cycle Time in Traffic Signal of Cyclic Max-Pressure Control 5 Traffic Control with Autonomous Vehicles 5.1 Distributed Ordering and Optimization for Intersection Management with Connected and Automated Vehicles 5.2 Prioritization of an Automated Shuttle for V2X Public Transport at a Signalized Intersection – a Real-life Demonstration 6 User Behaviour and Safety 6.1 Local Traffic Safety Analyzer (LTSA) - Improved Road Safety and Optimized Signal Control for Future Urban Intersections 7 Demand and Traffic Management 7.1 A Stochastic Programming Method for OD Estimation Using LBSN Check-in Data 7.2 Delineation of Traffic Analysis Zone for Public Transportation OD Matrix Estimation Based on Socio-spatial Practices 8 Workshops 8.1 How to Integrate Human Aspects Into Engineering Science of Transport and Traffic? - a Workshop Report about Discussions on Social Contextualization of Mobility 8.2 Learning from Covid: How Can we Predict Mobility Behaviour in the Face of Disruptive Events? – How to Investigate the Mobility of the Future

Der vierte Band der Schriftenreihe Verkehrstelematik stellt die intermodalen Forschungsthemen und ihre praktischen Anwendungen der Professur für Verkehrsleitsysteme und -prozessautomatisierung an der Fakultät Verkehrswissenschaften „Friedrich List“ der Technischen Universität Dresden mit ausgewählten Veröffentlichungen des Jahres 2013 vor. Die Schwerpunkte der Forschungsarbeit liegen einerseits im Bereich der energieoptimalen Steuerung im Schienenverkehr und zugehörigen Fahrerassistenzsystemen, andererseits auf dem Verkehrsmanagement des Straßenverkehrs. Die energieoptimale Steuerung im Schienenverkehr wird mittels Fahrerassistenzsystemen nicht nur im Eisenbahnverkehr, beispielsweise auf den Zügen des Harz-Elbe-Express, sondern auch auf Straßenbahn- und U-Bahn-Fahrzeugen umgesetzt. Darüber hinaus werden die Methoden und Verfahren der energieoptimalen Steuerung im Rahmen von Ansätzen für ein modularisiertes Verkehrsmanagement bei Eisenbahnen verwendet. Das Verkehrsmanagementsystem der Stadt Dresden VAMOS bildet die Basis für den zweiten Forschungsschwerpunkt. Hierbei werden im vorliegenden Band einerseits Möglichkeiten untersucht, den Straßenverkehr mikroskopisch zu simulieren und dabei die Verkehrszustandsdaten des VAMOS einfließen zu lassen. Andererseits wird der Frage nachgegangen, in wie weit man anhand der im System vorhandenen Floating-Car-Daten Straßensperrungen sicher identifizieren kann. Zu beiden Forschungsschwerpunkten sind fünf weitere Artikel im Rahmen der vom Lehrstuhl organisierten internationalen Konferenz „3rd Models and Technologies for Intelligent Transportation Systems (3. MT-ITS)“ entstanden. Diese Beiträge sind im Konferenzband als dritter Band der vorliegenden Schriftenreihe Verkehrstelematik bereits erschienen.

Mit dem sechsten Band der Schriftenreihe Verkehrstelematik wird ein Überblick über die intermodalen Forschungsthemen des Jahres 2015 der Professur für Verkehrsleitsysteme und ‑prozessautomatisierung der Fakultät Verkehrswissenschaften „Friedrich List“ der Technischen Universität Dresden anhand ausgewählter Veröffentlichungen gegeben. Sieben ausgewählte Artikel der Mitarbeiter, hauptsächlich veröffentlicht im Rahmen nationaler und internationaler Konferenzen, wurden dafür zusammengestellt. Die ersten Schwerpunkte bilden dabei die energieoptimale Steuerung und das Verkehrsmanagement im Schienenverkehr. Hier wird der Frage nachgegangen, wie Störungen des Bahnbetriebs im Echtzeit-Betriebsmanagement mit mathematischen Methoden begegnet werden kann. Als ein Ansatzpunkt wird das Erzeugen von robusten, stabilen und dabei auch energieeffizienten Fahrplänen diskutiert. Weiterhin wird versucht, im Rahmen des Betriebsmanagements mittels Konfliktlösungsalgorithmen operativ aktualisierte Fahrpläne so aufzubereiten, dass eine Umsetzung mit fahrzeugseitigen Fahrerassistenzsystemen ermöglicht und ein energieeffizienter Betrieb sichergestellt ist. Im zweiten Teil des Bandes wird gezeigt, wie die Methoden und Algorithmen der energieoptimalen Fahrweise und eines entsprechenden Fahrerassistenzsystems auf die Straßenbahn und auch den Bus übertragen werden können. Anschließend wird gänzlich auf den Individualverkehr fokussiert und der Frage der Reichweitenoptimierung elektrischer Fahrzeuge durch energieeffiziente Routing-Algorithmen unter Berücksichtigung von Echtzeit-Verkehrslagedaten nachgegangen. Wie im Schienenverkehr wird das Finden der optimalen Fahrstrategie auch hier durch Fahrerassistenzsysteme unterstützt.

Der nunmehr achte Band der Schriftenreihe Verkehrstelematik widmet sich den Forschungsthemen und ausgewählten Veröffentlichungen des Jahres 2016 der Professur für Verkehrsleitsysteme und -prozessautomatisierung der Fakultät Verkehrswissenschaften „Friedrich List“ der Technischen Universität Dresden. Elf Beiträge von nationalen und internationalen Konferenzen und aus Zeitschriftenveröffentlichungen der Mitarbeiter mit den Themengebieten Bahnverkehr, ÖPNV, Straßenverkehr und Radverkehr zeigen das breite Spektrum verkehrstelematischer Anwendungen und Forschungen der Professur. Im Bereich der energieoptimalen Steuerung im Schienenverkehr werden Ergebnisse des EU-geförderten Projektes ON-TIME mit Fragestellungen zum echtzeitfähigen Verkehrsmanagement, zur Fahrerassistenz sowie Fahrplanbewertung und -optimierung in Bahnsystemen vorgestellt. Beiträge zum EU-Projekt CAPACITY4RAIL ergänzen die Betrachtungen um Fragestellungen zu Datenaustauschformaten und formalen Beschreibungen von Störungsmanagementprozessen im Eisenbahnbetrieb. Neue Einsatzfelder für Fahrerinformations- und -assistenzsysteme werden mit Beiträgen zu BikeNow – einer mobilen Applikation für Fahrradfahrer – sowie COSEL – einem System für Straßenbahnen gezeigt. Zwei Artikel stellen die vielversprechenden Ergebnisse der ersten Praxisanwendung der Systeme vor. Und auch im Bereich des Verkehrsmanagements des Straßenverkehrs sind automatisierte Fahrfunktionen und Fahrerassistenzsysteme ein Forschungsthema. Für das Projekt REMAS wird hier beispielhaft auf Simulationsuntersuchungen mit SUMO eingegangen. Ferner wurde im Rahmen des Qualitätsmanagements für das Dresdner Verkehrsmanagementsystem VAMOS analysiert, welche Güte bei der Verkehrslageeinschätzung erreicht wird und wie diese durch Nutzung eines aktuellen Verkehrslagebilds und Verkehrslageinformationen aufgewertet werden kann.:Inhaltsverzeichnis / Gliederung - Störungsmanagement bei Extremwetter-Ereignissen 1 - Datenformate, -modelle und -konzepte für den Eisenbahnbetrieb – Ausgewählte Ergebnisse des Arbeitspakets 3.4 im EU-Projekt CAPACITY4RAIL 11 - The ON-TIME real-time railway traffic management framework: a proof-ofconcept using a scalable standardised data communication architecture 31 - Zur Kopplung von Konfliktlösung und Fahrerassistenz – Herausforderungen, Lösungsansätze, Ergebnisse 77 - Erschließung neuer Anwendungsfelder mittels RailTopoModel für Planung, Simulation und Betrieb bei Eisenbahnen 89 - A three-level framework for performance-based railway timetabling 101 - Einsatz von Informationssystemen zum energieeffizienten Fahren im städtischen Personennahverkehr 141 - Simulation of highly automated vehicles using SUMO 153 - Einsatzmöglichkeiten von Biofunktionsmesswerten in verkehrstelematischen Anwendungen 159 - Verkehrslageprognose unter Berücksichtigung der dynamischen Kapazitäten an LSA-abhängigen Knotenpunkten 173 - BikeNow: A Pervasive Application for Crowdsourcing Bicycle Traffic Data 189 - Mitarbeiter der Professur für Verkehrsleitsysteme und -prozessautomatisierung 201

Multimodality is a main requirement for future Urban Traffic Control (UTC). For cities and traffic engineers to implement multimodal UTC, a holistic, multimodal assessment of UTC measures is needed. This paper proposes a Multimodal Performance Index (MPI), which considers the delays and number of stops of different transport modes that are weighted to each other. To determine suitable mode-specific weights, a case study for the German city Ingolstadt is conducted using the microscopic simulation tool SUMO. In the case study, different UTC measures (bus priority, coordination for cyclists, coordination for private vehicle traffic) are implemented to a varying extent and evaluated according to different weight settings. The MPI calculation is done both network-wide and intersection-specific. The results indicate that a weighting according to the occupancy level of modes, as mainly proposed in the literature so far, is not sufficient. This applies particularly to cycling, which should be weighted according to its positive environmental impact instead of its occupancy. Besides, the modespecific weights have to correspond to the traffic-related impact of the mode-specific UTC measures. For Ingolstadt, the results are promising for a weighting according to the current modal split and a weighting with incentives for sustainable modes.

High speed facilities are considered the backbone of any successful transportation system; Interstates, freeways, and expressways carry the majority of daily trips on the transportation network. Although these types of roads are relatively considered the safest among other types of roads, they still experience many crashes, many of which are severe, which not only affect human lives but also can have tremendous economical and social impacts. These facts signify the necessity of enhancing the safety of these high speed facilities to ensure better and efficient operation. Safety problems could be assessed through several approaches that can help in mitigating the crash risk on long and short term basis. Therefore, the main focus of the research in this dissertation is to provide a framework of risk assessment to promote safety and enhance mobility on freeways and expressways. Multi-level Safety Performance Functions (SPFs) were developed at the aggregate level using historical crash data and the corresponding exposure and risk factors to identify and rank sites with promise (hot-spots). Additionally, SPFs were developed at the disaggregate level utilizing real-time weather data collected from meteorological stations located at the freeway section as well as traffic flow parameters collected from different detection systems such as Automatic Vehicle Identification (AVI) and Remote Traffic Microwave Sensors (RTMS). These disaggregate SPFs can identify real-time risks due to turbulent traffic conditions and their interactions with other risk factors. In this study, two main datasets were obtained from two different regions. Those datasets comprise historical crash data, roadway geometrical characteristics, aggregate weather and traffic parameters as well as real-time weather and traffic data. iii At the aggregate level, Bayesian hierarchical models with spatial and random effects were compared to Poisson models to examine the safety effects of roadway geometrics on crash occurrence along freeway sections that feature mountainous terrain and adverse weather. At the disaggregate level; a main framework of a proactive safety management system using traffic data collected from AVI and RTMS, real-time weather and geometrical characteristics was provided. Different statistical techniques were implemented. These techniques ranged from classical frequentist classification approaches to explain the relationship between an event (crash) occurring at a given time and a set of risk factors in real time to other more advanced models. Bayesian statistics with updating approach to update beliefs about the behavior of the parameter with prior knowledge in order to achieve more reliable estimation was implemented. Also a relatively recent and promising Machine Learning technique (Stochastic Gradient Boosting) was utilized to calibrate several models utilizing different datasets collected from mixed detection systems as well as real-time meteorological stations. The results from this study suggest that both levels of analyses are important, the aggregate level helps in providing good understanding of different safety problems, and developing policies and countermeasures to reduce the number of crashes in total. At the disaggregate level, real-time safety functions help toward more proactive traffic management system that will not only enhance the performance of the high speed facilities and the whole traffic network but also provide safer mobility for people and goods. In general, the proposed multi-level analyses are useful in providing roadway authorities with detailed information on where countermeasures must be implemented and when resources should be devoted. The study also proves that traffic data collected from different detection systems could be a useful asset that should be utilized iv appropriately not only to alleviate traffic congestion but also to mitigate increased safety risks. The overall proposed framework can maximize the benefit of the existing archived data for freeway authorities as well as for road users.

No description available.

<p dir="ltr">The absence or limited availability of operational statistics at general aviation airports restricts airport managers and operators from assessing comprehensive operational data. The traditional manual compilation of operational statistics is labor-intensive and lacks the depth and accuracy to depict a holistic picture of a general aviation airport’s operations. This research developed a reliable and efficient approach to address the problem by providing a comprehensive and versatile flight status identification framework. </p><p dir="ltr">Leveraging the BlueSky flight simulation module, the research can generate a synthetic flight database to emulate real-world general aviation aircraft’s flight scenarios. Two neural network architectures, namely, an RNN-GAN network and a refined Seq2Seq network, were explored to examine their capability to reconstruct flight trajectories. The Seq2Seq network, which demonstrated better performance, was further employed to estimate the simulated aircraft’s different metrics, such as internal mechanical metrics and flight phase. Additionally, this research undertook an array of diverse tailored evaluation techniques to assess the efficacy of flight status predictions and conducted comparative analyses between various configurations. </p><p dir="ltr">Furthermore, the research concluded by discussing the future development of the framework, emphasizing its potential for generalization across various flight data applications and scenarios. The enhanced methodology for collecting operational statistics and the analysis tool will enable airport managers and regulators to better receive a comprehensive view of the airport’s operations, facilitating airport planning and development.</p>

The Civil Air Navigation Services Organisation, the International Federation of Air Traffic Control Associations, the International Air Transport Association and the Civil Air Navigation Services Organisation agree that professionals in the Air Navigation Services Provider Sector require successful organisational leadership to facilitate and manage transformation within the highly regulated Air Navigation Services Provider Sector. Detailed organisational leadership requirements and associated leadership training and development needs are, however, not specified by the Civil Air Navigation Services Organisation. An opportunity therefore existed to investigate leadership traits and behaviours within a specific context. This research project is contextualised within a safety-conscious, highly regulated and technology-driven industry (the South African Aviation Industry), a safety-critical sector (Air Navigation Services) and specifically the Air Traffic and Navigation Services Company. It was found that little academic research has been done to address the role of followers in the leadership process and to determine what followers expect and require from their leaders. The research problem statement, in response to this research necessity, is: “How can follower experiences and expectations of leadership behaviours in a safety-critical commercial environment be collected, analysed, understood, structured and utilised to aid leadership development?” An ethnographic research case study approach allowed the researcher to investigate the multifarious phenomena that constitute the current views (experiences and expectations) held by followers with regard to leadership behaviour qualities. A mixed methods approach was followed. Data collection was facilitated by means of individual interviews, focus group interviews, field notes and a structured questionnaire. Qualitative data were inductively analysed to identify the recurring patterns and common themes and quantitative data were deductively analysed to assess the nature of existing conditions and relevance. Data and method triangulation was implemented to determine whether multiple sources of data agreed, and to obtain better, cross-checked insights. Findings from this research study provided academic, industry, process and methodology insights into views held by followers regarding leadership and followership constructs. Definitions and perspectives held and reported by followers regarding leaders and leadership, characteristics of preferred and undesired leadership styles, relational and emotional bonds between followers and their leaders acknowledged the presence, value and influence of follower mental models. In this case followers contextualised leadership roles and responsibilities and suggested a transformational leadership style as a desired state. Findings also emphasised a need to appreciate the importance of the social exchange and social contingency theories of leadership in order to create a better understanding of leadership by emphasising the importance of context when studying leaders and leadership from a follower perspective. Obtained follower insights resulted in a structured leadership training and development needs analysis process framed within the specific context. Future research efforts in this regard may be aimed at determining the necessity to educate followers to critically appreciate and evaluate leadership performance and creating a better understanding of how followers’ mental models internally represent complex, dynamic systems and how these representations change over time. / Business Management / DBL

Cette thèse traite de la gestion du trafic aérien et plus précisément, de l’optimisation globale des plans de vol déposés par les compagnies aériennes sous contrainte du respect de la capacité de l’espace aérien. Une composante importante de ce travail concerne la gestion de l’incertitude entourant les trajectoires des aéronefs. Dans la première partie du travail, nous identifions les principales causes d’incertitude au niveau de la prédiction de trajectoires. Celle-ci est la composante essentielle à l’automatisation des systèmes de gestion du trafic aérien. Nous étudions donc le problème du réglage automatique et en-ligne des paramètres de la prédiction de trajectoires au cours de la phase de montée avec l’algorithme d’optimisation CMA-ES. La principale conclusion, corroborée par d’autres travaux de la littérature, implique que la prédiction de trajectoires des centres de contrôle n’est pas suffisamment précise aujourd’hui pour supporter l’automatisation complète des tâches critiques. Ainsi, un système d’optimisation centralisé de la gestion du traficaérien doit prendre en compte le facteur humain et l’incertitude de façon générale.Par conséquent, la seconde partie traite du développement des modèles et des algorithmes dans une perspective globale. De plus, nous décrivons un modèle stochastique qui capture les incertitudes sur les temps de passage sur des balises de survol pour chaque trajectoire. Ceci nous permet d’inférer l’incertitude engendrée sur l’occupation des secteurs de contrôle par les aéronefs à tout moment.Dans la troisième partie, nous formulons une variante du problème classique du Air Traffic Flow and Capacity Management au cours de la phase tactique. L’intérêt est de renforcer les échanges d’information entre le gestionnaire du réseau et les contrôleurs aériens. Nous définissons donc un problème d’optimisation dont l’objectif est de minimiser conjointement les coûts de retard et de congestion tout en respectant les contraintes de séquencement au cours des phases de décollage et d’attérissage. Pour combattre le nombre de dimensions élevé de ce problème, nous choisissons un algorithme évolutionnaire multiobjectif avec une représentation indirecte du problème en se basant sur des ordonnanceurs gloutons. Enfin, nous étudions les performances et la robustesse de cette approche en utilisant le modèle stochastique défini précédemment. Ce travail est validé à l’aide de problèmes réels obtenus du Central Flow Management Unit en Europe, que l’on a aussi densifiés artificiellement. / In this thesis, we investigate the issue of optimizing the aircraft operators' demand with the airspace capacity by taking into account uncertainty in air traffic management. In the first part of the work, we identify the main causes of uncertainty of the trajectory prediction (TP), the core component underlying automation in ATM systems. We study the problem of online parameter-tuning of the TP during the climbing phase with the optimization algorithm CMA-ES. The main conclusion, corroborated by other works in the literature, is that ground TP is not sufficiently accurate nowadays to support fully automated safety-critical applications. Hence, with the current data sharing limitations, any centralized optimization system in Air Traffic Control should consider the human-in-the-loop factor, as well as other uncertainties. Consequently, in the second part of the thesis, we develop models and algorithms from a network global perspective and we describe a generic uncertainty model that captures flight trajectories uncertainties and infer their impact on the occupancy count of the Air Traffic Control sectors. This usual indicator quantifies coarsely the complexity managed by air traffic controllers in terms of number of flights. In the third part of the thesis, we formulate a variant of the Air Traffic Flow and Capacity Management problem in the tactical phase for bridging the gap between the network manager and air traffic controllers. The optimization problem consists in minimizing jointly the cost of delays and the cost of congestion while meeting sequencing constraints. In order to cope with the high dimensionality of the problem, evolutionary multi-objective optimization algorithms are used with an indirect representation and some greedy schedulers to optimize flight plans. An additional uncertainty model is added on top of the network model, allowing us to study the performances and the robustness of the proposed optimization algorithm when facing noisy context. We validate our approach on real-world and artificially densified instances obtained from the Central Flow Management Unit in Europe.