This study employs Bayesian Hidden Markov Models as method to explore vehicle traffic flow conditions in an urban area in Stockholm, based on sensor data from separate road positions. Inter-arrival times are used as the observed sequences. These sequences of inter-arrival times are assumed to be generated from the distributions of four different (and hidden) traffic flow states; nightly free flow, free flow, mixture and congestion. The filtered and smoothed probability distributions of the hidden states and the most probable state sequences are obtained by using the forward, forward-backward and Viterbi algorithms. The No-U-Turn sampler is used to sample from the posterior distributions of all unknown parameters. The obtained results show in a satisfactory way that the Hidden Markov Models can detect different traffic flow conditions. Some of the models have problems with divergence, but the obtained results from those models still show satisfactory results. In fact, two of the models that converged seemed to overestimate the presence of congested traffic and all the models that not converged seem to do adequate estimations of the probability of being in a congested state. Since the interest of this study lies in estimating the current traffic flow condition, and not in doing parameter inference, the model choice of Bayesian Hidden Markov Models is satisfactory. Due to the unsupervised nature of the problematization of this study, it is difficult to evaluate the accuracy of the results. However, a model with simulated data and known states was also implemented, which resulted in a high classification accuracy. This indicates that the choice of Hidden Markov Models is a good model choice for estimating traffic flow conditions.

Nous considérons deux modèles EDP-EDO couplés: un pour modéliser des goulots d’étranglementmobiles et l’autre pour décrire la distribution du trafic sur une bretelle d’accès. Le premier modèle a étéintroduit pour décrire le mouvement d’un bus, qui roule à une vitesse inférieure à celle des autresvoitures, en réduisant la capacité de la route et générant ainsi un goulot d’étranglement. Une loi deconservation scalaire avec une contrainte mobile sur le flux décrit le trafic et une EDO décrit latrajectoire du bus. Nous présentons un résultat d’existence des solutions du modèle et nous proposonsune méthode numérique “front/capturing" et une méthode basée sur une technique de reconstructiondes ondes de chocs. Dans la deuxième partie, nous introduisons un nouveau modèle macroscopique dejonction pour les bretelles d’autoroute. Nous considérons le modèle de trafic de Lighthill-Whitham-Richards sur une jonction composée d’une voie principale, une bretelle d’accès et une bretelle de sortie,toutes reliées par un nœud. Une loi de conservation scalaire décrit l’évolution de la densité des véhiculessur la voie principale et une EDO décrit l’évolution de la longueur de la file d’attente sur la bretelled’accès. La définition de la solution du problème de Riemann à la jonction est basée sur la résolutiond’un problème d’optimisation linéaire et sur l’utilisation d’un paramètre de priorité. Ensuite, ce modèleest étendu aux réseaux et discrétisé en utilisant un schéma de Godunov qui prend en compte les effetsde la bretelle d’accès. Enfin, nous présentons un modèle d’optimisation de la circulation sur les ronds points. / In this thesis we consider two coupled PDE-ODE models. One to model moving bottlenecks and theother one to describe traffic flow at junctions. First, we consider a strongly coupled PDE-ODE systemthat describes the influence of a slow and large vehicle on road traffic. The model consists of a scalarconservation law accounting for the main traffic evolution, while the trajectory of the slower vehicle isgiven by an ODE depending on the downstream traffic density. The moving constraint is expressed byan inequality on the flux, which models the bottleneck created in the road by the presence of the slowerDépôt de thèse – Donnéescomplémentairesvehicle. We prove the existence of solutions to the Cauchy problem for initial data of bounded variation.Moreover, two numerical schemes are proposed. The first one is a finite volume algorithm that uses alocally nonuniform moving mesh. The second one uses a reconstruction technique to display thebehavior of the vehicle. Next, we consider the Lighthill-Whitham-Richards traffic flow model on ajunction composed by one mainline, an onramp and an offramp, which are connected by a node. Theonramp dynamics is modeled using an ordinary differential equation describing the evolution of thequeue length. The definition of the solution of the Riemann problem at the junction is based on anoptimization problem and the use of a right of way parameter. The numerical approximation is carriedout using a Godunov scheme, modified to take into account the effects of the onramp buffer. Aftersuitable modification, the model is used to solve an optimal control problem on roundabouts. Two costfunctionals are numerically optimized with respect to the right of way parameter.

L’objectif de ce travail consiste en la modélisation des flux de véhicules d’un grand et dense réseau de transport multimodal. Le travail s’organise en deux parties: un aspect théorique et un aspect développement. L’étude théorique met l’accent sur la façon dont un réseau multimodal peut être modélisé et comment sa performance en termes d’offre peut être optimisée. Pour ce faire, trois études principales sont réalisées: la prévision et la régulation des flux de trafic sur les grands réseaux de surface, la multimodalité véhiculaire dans les grands réseaux de surface prenant en compte les nouvelles formes de mobilité, et enfin l’impact de l’information sur le coût des itinéraires. La partie développement consiste en la conception d’un simulateur de flux de trafic pour réguler le trafic multimodal véhiculaire. Le simulateur développé devrait aider les opérateurs de transport et les collectivités territoriales dans leurs stratégies de gestion des flux de trafic / The objective of this work consists on the modeling of traffic flow of a large multimodaltransportation network. The work is organized in two parts: a theoretical study part anda development part. The theoretical study emphasizes on how a multimodal network canbe model and how its performance in terms of supply can be optimized. To do so, threemain studies are discussed: the traffic flow prediction and regulation on large surface net-works, the vehicular multimodality in big surface networks taking into account new forms ofmobility, and finally the impact of the information on the cost of the itineraries. The devel-opment part consists on the conception of a traffic flow simulator to regulate the vehicularmultimodal traffic. The developed simulator should assist transport operators and territorialcommunities in their traffic flow management strategies

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