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La centralisations du travail, la croissance économique et celle de la population autant que l’urbanisation continue sont les causes principales de la congestion. Lors que les villes s’efforcent pour mettre à jour leurs infrastructures du trafic, l’utilisation de nouvelles techniques pour la modélisation, l’analyse de ces systèmes ainsi que l’intégration des mega données aux algorithmes aident à mieux comprendre et combattre les congestions, un aspect crucial pour le bon développement de nos villes intelligentes du XXIe siècle. Les outilsd’assistance de trafic spécialement conçus pour détecter, prévoir et alerter des conditions particulières sont très demandés dans nos jours.Cette recherche est consacrée au développement des algorithmes pour l’estimation et la prédiction sur des réseaux de trafic routier. Tout d’abord, nous considérons le problème de prévision à court terme du temps de trajet dynamique basé sur des méthodes pilotées par les données. Nous proposons deux techniques de fusion pour calculer les prévisions à court terme. Dans un première temps, nous considérons la matrice de covariance d’erreur et nous utilisons ses informations pour fusionner les prévisions individuelles créées á partir de clusters. Dans un deuxième temps, nous exploitons les mesures de similarité parmi le signal á prédire et des clusters dans l’histoire et on propose une fusion en tant que moyenne pondérée des sorties des prédicteurs de chaque cluster. Les résultats des deux méthodes on été validés dans le Grenoble Traffic Lab, un outil en temps réel qui permet la récupération de données d’une autoroute d’environ (10.5Km) qui couvre le sud de Grenoble.Postérieurement nous considérons le problème de reconstruction de la densité / et le débit de façon simultanée à partir de sources d’information hétérogènes. Le réseau de trafic est modélisé dans le cadre de modèles de trafic macroscopique, où nous adoptons l’équation de conservation Lighthill-Whitham-Richards avec un diagramme fondamental linaire par morceaux. Le problème d’estimation repose sur deux principes clés. Dans un premier temps, nous considérons la minimisation des erreurs entre les débits et les densités mesurés et reconstruits. Finalement, nous considérons l’état d’équilibre du réseau qui établit la loi de propagation des flux entrants et sortants dans le réseau. Tous les principes sont intégrés et le problème est présenté comme une optimisation quadratique avec des contraintes d’égalité a fin de réduire l’espace de solution des variables à estimer. Des scénarios de simulation basés sur des données synthétiques pour un réseau de manhattan sont fournis avec l’objectif de valider les performances de l’algorithme proposé. / Centralization of work, population and economic growth alongside continued urbanization are the main causes of congestion. As cities strive to update or expand aging infrastructure, the application of big data, new models and analytics to better understand and help to combat traffic congestion is crucial to the health and development of our smart cities of XXI century. Traffic support tools specifically designed to detect, forecast and alert these conditions are highly requested nowadays.This dissertation is dedicated to study techniques that may help to estimate and forecast conditions about a traffic network. First, we consider the problem Dynamic Travel Time (DTT) short-term forecast based on data driven methods. We propose two fusion techniques to compute short-term forecasts from clustered time series. The first technique considers the error covariance matrix and uses its information to fuse individual forecasts based on best linear unbiased estimation principles. The second technique exploits similarity measurements between the signal to be predicted and clusters detected in historical data and it performs afusion as a weighted average of individual forecasts. Tests over real data were implemented in the study case of the Grenoble South Ring, it comprises a highway of 10.5Km monitored through the Grenoble Traffic Lab (GTL) a real time application was implemented and open to the public.Based on the previous study we consider then the problem of simultaneous density/flow reconstruction in urban networks based on heterogeneous sources of information. The traffic network is modeled within the framework of macroscopic traffic models, where we adopt Lighthill-Whitham-Richards (LWR) conservation equation and a piecewise linear fundamental diagram. The estimation problem considers two key principles. First, the error minimization between the measured and reconstructed flows and densities, and second the equilibrium state of the network which establishes flow propagation within the network. Both principles are integrated together with the traffic model constraints established by the supply/demand paradigm. Finally the problem is casted as a constrained quadratic optimization with equality constraints in order to shrink the feasible region of estimated variables. Some simulation scenarios based on synthetic data for a manhattan grid network are provided in order to validate the performance of the proposed algorithm.

<p dir="ltr">The Air Traffic Management (ATM) system encompasses complex and safety-critical operations which are mainly managed by Air Traffic Controllers (ATCs) and pilots to ensure safety and efficiency. This air traffic operation becomes more complex and challenging as demands continue to increase. Indeed, the demand for air transport is expected to increase by an average of 4.3% annually over the next 20 years, and the projected number of flights is expected to reach around 90 million by 2040 [1]. This continuous growth of demands can lead to an excessive workload for both ATCs and pilots, thereby resulting in the degradation of the ATM system. To effectively respond to this problem, a lot of effort has been put into developing decision support tools. This dissertation explores and focuses on the development of algorithms for decision support tools in air traffic control, emphasizing specific desirable properties essential for tasks such as tracking the position of aircraft and monitoring air traffic. The primary focus of this dissertation is to combine a data-driven model and a physics-based model systematically, thereby addressing the limitations of previous works in trajectory prediction and anomaly detection. Through a literature review, important properties, including real-time applicability, interpretability, and feasibility, are identified and pursued for practical applications. These properties are integrated into the proposed algorithms which combine data-driven and physics-based models to address dynamic air traffic scenarios effectively. To meet the requirement of real-time applicability, the algorithms are designed to be computationally efficient and adaptable to continuously changing conditions, ensuring timely provision of immediate information and near-instantaneous responses to assist ATCs. Subsequently, interpretability allows controllers to understand the reasoning behind the algorithm’s predictions. This is facilitated by the use of attention mechanisms and explicit physics-based guidance, making the predictions more intuitive and understandable. In addition, anomaly detection algorithms provide human-readable decision boundaries for flight states for a clear understanding. Lastly, feasibility ensures that the algorithms generate realistic aircraft trajectory predictions based on current flight states and air traffic conditions. This is achieved by physics-guided machine learning which leverages both data-driven and physics-based approaches, accounting for the aircraft dynamics and uncertainties. Moreover, practical and operational considerations are integrated into algorithms for real-world applications. This includes developing anomaly detection models that are adaptable to dynamic trajectory patterns to address the complexities of flexible area navigation airspace. Additionally, to reduce the workload of ATCs, providing immediate advisories for anomaly resolution and arrival sequencing is targeted by learning from historical data. By considering these properties with practical considerations, the dissertation presents a suite of algorithms that can effectively support human operators for air traffic control. </p>

<p>When traffic in large cities increases, it becomes more vulnerable to disturbances such as accidents, stalled vehicles or construction; therefore, traffic jams are more likely to occur. For better control of the traffic at a disturbance the Swedish National Road Administration (SNRA) has traffic equipment which can be controlled from a command centre. This traffic equipment consists of gates and signs with variable messages. This report will discuss the system used for traffic control in Stockholm. It will present proposals designed to improve it. </p><p>These proposals were prepared based on interviews with people from SNRA and their contractors as well as on comparisons with existing systems. </p><p>The focus of this report is the handling of system alarms and graphical user interface. By implementing the proposals in this report, the following will be achieved:</p><p>· More efficient alarmcontrol - the errors are discovered immediately or soon after they occur </p><p>· Clearer information regarding alarms - the traffic operator receives better information about the error </p><p>· Better control of the traffic equipment - the control will be easier and more flexible.</p>

When traffic in large cities increases, it becomes more vulnerable to disturbances such as accidents, stalled vehicles or construction; therefore, traffic jams are more likely to occur. For better control of the traffic at a disturbance the Swedish National Road Administration (SNRA) has traffic equipment which can be controlled from a command centre. This traffic equipment consists of gates and signs with variable messages. This report will discuss the system used for traffic control in Stockholm. It will present proposals designed to improve it. These proposals were prepared based on interviews with people from SNRA and their contractors as well as on comparisons with existing systems. The focus of this report is the handling of system alarms and graphical user interface. By implementing the proposals in this report, the following will be achieved: · More efficient alarmcontrol - the errors are discovered immediately or soon after they occur · Clearer information regarding alarms - the traffic operator receives better information about the error · Better control of the traffic equipment - the control will be easier and more flexible.

This paper presents results from a workshop focusing on human demands of mobility that was conducted during the MFTS conference 2022. It shows, how the international participants, most of them male researchers with a background in engineering, dealt with concepts and findings from mobility research conducted by scholars of social sciences, humanities and cultural studies that focus on human mobility demands including gender and diversity aspects.

Introduction: With the beginning of the COVID-19 outbreak and the restrictions put in place to prevent an uncontrolled spread of the virus, the circumstances for daily activities changed. A remarkable shift in the modal split distribution was observed [Ank21]. Moreover, the changes in mobility during the COVID-19 pandemic had multiple impacts on road traffic [Yas21]. By now, several researchers have looked at the impact of COVID-19 as a disruptive event on mobility behaviour. This workshop within the 4th Symposium on Management of Future Motorway and Urban Traffic Systems aimed to discuss insights from these research projects and how they enable experts to transfer this newfound knowledge to future disruptive events such as climate change, rising energy costs and events related to a possible energy transition. Thus, the research question this workshop investigated reads as follows: What can we learn from the pandemic to be able to predict how different future disruptive events can shape the mobility of tomorrow?

Air traffic controllers (ATCos) monitor flight operations and resolve predicted aircraft conflicts to ensure safe flights, making them one of the essential human operators in air traffic control systems. Researchers have been studying ATCos with human subjective approaches to understand their tasks and air traffic managing processes. As a result, models were developed to predict ATCo actions. However, there is a gap between our knowledge and the real-world. The developed models have never been validated against the real-world, which creates uncertainties in our understanding of how ATCos detect and resolve predicted aircraft conflicts. Moreover, we do not know how information from air traffic control systems affects their actions. This Ph.D. dissertation work introduces methods to evaluate existing ATCo action prediction models. It develops a prediction model based on flight contextual information (information describing flight operations) to explain the relationship between ATCo actions and information. Unlike conventional approaches, this work takes data-driven approaches that collect large-scale flight tracking data. From the collected real-world data, ATCo actions and corresponding predicted aircraft conflicts were identified by developed algorithms. Comparison methods were developed to measure both qualitative and quantitative differences between solutions from the existing prediction models and ATCo actions on the same aircraft conflicts. The collected data is further utilized to develop an ATCo action prediction model. A hierarchical structure found from analyzing the collected ATCo actions was applied to build a structure for the model. The flight contextual information generated from the collected data was used to predict the actions. Results from this work found that the collected ATCo actions do not show any preferences on the methods to resolve aircraft conflicts. Results found that the evaluated existing prediction model does not reflect the real-world. Also, a large portion of the real conflicts was to be solved by the model both physically and operationally. Lastly, the developed prediction model showed a clear relationship between ATCo actions and applied flight contextual information. These results suggest the following takeaways. First, human actions can be identified from closed-loop data. It could be an alternative approach to collect human subjective data. Second, the importance of evaluating models before implications. Third, potentials to utilize the flight contextual information to conduct high-end prediction models.

T-intersections are one of the most common places where collisions happen. An intelligent traffic mirror is one the possible solutions to reduce the accident rate. The mirror detects the situation around the intersection, process the data and provides the driver with an information, whether the situation is safe and the driver can enter the junction safely. The aim of the thesis is a feasibility study of reliable detection of non-stationary objects based on cameras. The core of the intended product – the detection algorithm – detected the object on short distance from the camera reliably but as the distance was growing, the detection quality degraded. One of the possible solutions to achieve better detection results on longer distances may be achieved by using a camera with greater zoom. Based on the example improvement proposal, the feasibility of the solution based on optical methods was finally confirmed.

Challenges arising from an increasing traffic demand, limited resource availability and growing quality expectations of the customers can only be met successfully, if each transport mode is regarded as an intelligent transportation system itself, but also as part of one intelligent transportation system with “intelligent” intramodal and intermodal interfaces. This topic is well reflected in the Third International Conference on “Models and Technologies for Intelligent Transportation Systems” which took place in Dresden 2013 (previous editions: Rome 2009, Leuven 2011). With its variety of traffic management problems that can be solved using similar methods and technologies, but with application specific models, objective functions and constraints the conference stands for an intensive exchange between theory and practice and the presentation of case studies for all transport modes and gives a discussion forum for control engineers, computer scientists, mathematicians and other researchers and practitioners. The present book comprises fifty short papers accepted for presentation at the Third Edition of the conference. All submissions have undergone intensive reviews by the organisers of the special sessions, the members of the scientific and technical advisory committees and further external experts in the field. Like the conference itself the proceedings are structured in twelve streams: the more model-oriented streams of Road-Bound Public Transport Management, Modelling and Control of Urban Traffic Flow, Railway Traffic Management in four different sessions, Air Traffic Management, Water Traffic and Traffic and Transit Assignment, as well as the technology-oriented streams of Floating Car Data, Localisation Technologies for Intelligent Transportation Systems and Image Processing in Transportation. With this broad range of topics this book will be of interest to a number of groups: ITS experts in research and industry, students of transport and control engineering, operations research and computer science. The case studies will also be of interest for transport operators and members of traffic administration.