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Taxis are an important transportation mode in many cities due to their convenience and accessibility. In the taxi-dispatching problem, sometimes it is more beneficial for the supplier if taxis cruise in the network after serving the first request to pick up the next passenger, while sometimes it is better that they wait in stations for new trip requests. In this article, we propose a rolling horizon scheme that dynamically optimizes taxi dispatching considering the actual traffic conditions. To optimize passenger satisfaction, we define a limitation for passenger waiting time. To be able to apply the method to large-scale networks, we introduce a clustering-based technique that can significantly improve the computation time without harming the solution quality. Finally, we test our method on a real test case considering taxi requests with personal car trips to reproduce actual network loading and unloading congestion during peak hours.

Recurrent road traffic congestion in large cities is a blight that wastes a great deal of time and increases greenhouse gas emissions, fuel consumption, noise and stress. One way of combating such congestion is traffic control by means of traffic signals. The role of traffic signals is to temporally separate the different traffic flows at each intersection and alternately distribute the right of way with appropriate time durations. The real-time traffic control systems that have been developed during the three last decades include CRONOS-2 which has several advantages under congested situations, in particular, its capacity to control several intersections in a centralized way. A real-site trial has been performed on a zone consisting of five adjacent intersections. The results show significant benefits for total delay in the zone during congestion compared to the usual control strategy for this zone. These results vary for each intersection considered on its own. This trial has demonstrated the effectiveness of this system in complex and constrained traffic situations.

TRB 2021, Transportation Research Board 100th Annual Meeting - A Virtual Event, Washington DC, ETATS-UNIS, 05-/01/2021 - 29/01/2021; Departure time choice models have a crucial role in transportation network planning in order to provide planners with predictions about the optimal traffic load. This paper introduces a new dynamic departure time choice framework based on the so-called Mean-field Games (MFGs) theory proposed by Lasry and Lions. MFGs models aim to analyze large population differential games. This feature gives us a unique opportunity to design a departure time choice equilibrium model in a scale-free transportation system. The proposed framework is the combination of two components: (i) the reaction of travelers to the traffic congestion by choosing their departure time to optimize the travel cost; (ii) the aggregation of the actions of the travelers, which determines the congestion level of the system. The first component corresponds to a classical game theory model, while the second one captures the interactions of the travelers at the macroscopic level. We use the generalized bathtub model recently proposed by Jin to represent the traffic dynamics at the network level. First, we provide a continuous model to investigate the equilibria and then give a discrete approximation of the system from deterministic differential games models. Finally, we show on a real test case that the model can represent the departure time choice equilibrium for multiple preferred travel time with heterogeneous trip lengths wile the framework is capable of considering a large number of players.

TRB 2021, Transportation Research Board 100th Annual Meeting - A Virtual Event, Washington DC, ETATS-UNIS, 05-/01/2021 - 29/01/2021; The work proposes a multi-modal regional mean speed regression analysis for the city network of Athens, Greece. The dataset from pNUEMA experiment is used in the present context. Accumulations and mean speeds of different modes are estimated and compared to each other. Besides, the mean running speeds of all modes are also computed and compared. The mean speed of each mode is assumed to be a linear combination of accumulations of all modes and regression analysis is performed. Ordinary Least Squares (OLS) and Partial Least Squares (PLS) methods are used in the current work. It is noticed that multicollinearity between different modes is weak in the current dataset. The quality of multi-modal regression fits is compared to the uni-modal ones, where the mean speed of a given mode is assumed to be a function of the accumulation of that mode only. It is concluded that multi-modal regression fits outperform their uni-modal counterparts in terms of R2 and Root Mean Squared Relative Error (RMSRE).

Can dynamic ride-sharing reduce traffic congestion? In this paper we show that the answer is yes if the trip density is high, which is usually the case in large-scale networks but not in medium-scale networks where opportunities for sharing in time and space become rather limited. When the demand density is high, the dynamic ride-sharing system can significantly improve traffic conditions, especially during peak hours. Sharing can compensate extra travel distances related to operating a mobility service. The situation is entirely different in small and medium-scale cities when trip shareability is small, even if the ride-sharing system is fully optimized based on the perfect demand prediction in the near future. The reason is simple, mobility services significantly increase the total travel distance, and sharing is simply a means of combating this trend without eliminating it when the trip density is not high enough. This paper proposes a complete framework to represent the functioning of the ride-sharing system and multiple steps to tackle the curse of dimensionality when solving the problem. We address the problem for two city scales in order to compare different trip densities. A city scale of 25 km 2 with a total market of 11,235 shareable trips for the medium-scale network and a city scale of 80 km2 with 205,308 demand for service vehicles for the large-scale network over a 4-hour period with a rolling horizon of 20 minutes. The solutions are assessed using a dynamic trip-based macroscopic simulation to account for the congestion effect and dynamic travel times that may influence the optimal solution obtained with predicted travel times. This outperforms most previous studies on optimal fleet management that usually consider constant and fully deterministic travel time functions.

CETRA 2020, 6th International Conference on Road and Rail Infrastructure - Online, Pula, CROATIE, 20-/05/2021 - 21/05/2021; In a context of climate change, lowering road vehicles consumption is a key point to meet CO2 reduction requirements. In addition to car technological advances, eco-driving is part of the solution but the road infrastructure should ensure its development. In a previous study, a gain of 5% in the spent energy was estimated on specific route by slightly moving some speed signs, but under the assumptions that drivers practice eco-driving and the traffic is free-flow. This paper deepens and widens these first results. The base of this research is to provide a simulation model to study the impact of traffic and speed-sectioning on the environment. Inside this model, the impact of different approach speeds to a speed-sectioning is assessed. The simulation is conducted within the Trafficware Synchro environment where parameters according to road infrastructure, vehicle and driver are based on real traffic data. Moving a speed limitation sign can contribute to a reduction of fuel consumption up to 8% depending on driver structure. This new methodology improves the accuracy of our first results and detects adverse effects as the possible emergence of congestion due to the modification of speed sectioning. In perspective this methodology represents a significant argument in road managers strategy. In addition it also represents an orienting point to investigate different action scenarios and a first step to a global optimization policy in managing road infrastructure.

TRB 2021, Transportation Research Board 100th Annual Meeting - A Virtual Event, Washington DC, ETATS-UNIS, 05-/01/2021 - 29/01/2021; An excessive demand of vehicles to a motorway bottleneck leads to traffic jams. Motorbikes are narrow and can drive next to each other in a lane, or in-between lanes in low speeds. This paper analyses the resulting traffic characteristics and presents numerical scheme for a macroscopic traffic flow model for these two classes. The behavior included is as follows. If there are two motorbikes behind each other, they can travel next to each other in one lane, occupying the space of one car. Also, at low speeds of car traffic, they can go in between the main lanes, creating a so-called filtering lane. The paper numerically derives functions of class-specific speeds as function of the density of both classes, incorporating flexible lane usage dependent on the speed. The roadway capacity as function of the motorbike fraction is derived, which interesting can be in different types of phases (with motorbikes at higher speeds or not). We also present a numerical scheme to analyse the dynamics of this multi-class system. We apply the model to an example case, revealing the properties of the traffic stream , queue dynamics and class specific travel times. The model can help in showing the relative advantage in travel time of switching to a motorbike.

The economic analysis of urban transport congestion today suffers from at least two 'blind spots': a) a good measure of the contribution of freight transport to the loss of time on the roads; b) study of congestion in public transport and the resulting collective losses. The first part of this manuscript synthesizes the contributions of several articles that have addressed these two phenomena. We first present the 'Pigouvian' theoretical framework which was originally transposed to the estimation of the welfare losses linked to the underpricing of the externality of discomfort in the subways. This model is then extended to the analysis of road congestion in a multi-class setting, when trucks and LCVs share the road with cars. We consider both the loss of time suffered by road users, linked to excessive traffic compared to the economic optimum, as well as the 'excess emissions' of pollutants caused by lower speeds. We then describe the empirical strategies developed to bring out the congestion functions necessary for the resolution of theoretical models. Regarding road congestion, we propose econometric estimates using individual data and we combine the results of various modeling tools (of traffic and pollutant emissions). The costs - private and external - of a given type of vehicle are increasing functions of the flow of similar vehicles, but also of the volume of other vehicles on the roads. Regarding congestion in public transport, we study the 'declared preferences' of Paris metro users who have been confronted with hypothetical trade-offs between more or less fast and more or less comfortable journeys. Econometric analysis confirms that the subjective value of time increases with the density of passengers in the trains. Our main results are as follows. While they represent 8% of the distances traveled in Ile-de-France, freight vehicles account for 40% of collective well-being and 30% of the environmental cost attributable to road traffic. We offer pricing schemes for cars, LCVs and / or trucks, aimed at limiting collective losses linked to the overuse of infrastructure. In the case of Parisian metros, we show that there are about 9% of travelers 'in excess' compared to the optimum. If the social losses linked to the underpricing of this congestion are currently limited, they could quickly increase and the external cost is high, compared to the environmental costs of cars in particular. In the end, the methods and parameters proposed in our articles 'equip' researchers and practitioners interested in the socioeconomic evaluation of urban transport projects.; L'analyse économique de la congestion des transports urbains souffre aujourd'hui d'au moins deux « angles morts » : a) une bonne mesure de la contribution du transport de fret aux pertes de temps sur les routes ; b) l'étude de la saturation dans les transports en commun et des pertes collectives qui en découlent. La première partie de ce manuscrit synthétise les apports de plusieurs articles ayant abordé ces deux phénomènes. Nous présentons tout d'abord le cadre théorique « pigouvien » qui a été originalement transposé à l'estimation des pertes de bien-être liées à la sous-tarification de l'externalité d'inconfort dans les métros. Ce modèle est ensuite étendu à l'analyse de la congestion routière dans un cadre multi-classes, lorsque les camions et les VUL partagent la voirie avec les voitures. Nous considérons aussi bien les pertes de temps subies par les usagers des routes, liées à une fréquentation excessive par rapport à l'optimum économique, que les « surémissions » de polluants induites par la baisse des vitesses. Nous décrivons ensuite les stratégies empiriques développées pour faire émerger les fonctions de congestion nécessaires à la résolution des modèles théoriques. Concernant la congestion routière, nous proposons des estimations économétriques utilisant des données individuelles et nous couplons les résultats de divers outils de modélisation (du trafic et des émissions de polluants). Les coûts - privés et externes - d'un type donné de véhicules sont des fonctions croissantes du flux de véhicules similaires, mais aussi du volume des autres véhicules présents sur les routes. Concernant la congestion dans les transports publics, nous étudions les « préférences déclarées » d'usagers du métro parisien ayant été confrontés à des arbitrages hypothétiques entre des voyages plus ou moins rapides et plus ou moins confortables. L'analyse économétrique confirme que la valeur subjective du temps augmente avec la densité de passagers dans les rames. Nos principaux résultats sont les suivants. S'ils représentent 8% des distances parcourues en Ile-de-France, les véhicules de fret comptent pour 40% du bien-être collectif et 30% du coût environnemental imputable au trafic routier. Nous proposons des schémas de tarification, pour les voitures, les VUL et/ou les camions, visant à limiter les pertes collectives liées à la sur-utilisation des infrastructures. Dans le cas des métros parisiens, nous montrons qu'il y a environ 9% de voyageurs « en trop » par rapport à l'optimum. Si les pertes sociales liées à la sous-tarification de cette congestion sont actuellement limitées, elles pourraient vite augmenter et le coût externe est élevé, comparativement aux coûts environnementaux des voitures notamment. Au final, les méthodes et paramètres proposés dans nos articles « équipent » les chercheurs et praticiens intéressés par l'évaluation socioéconomique des projets de transports urbains.

The economic analysis of urban transport congestion today suffers from at least two 'blind spots': a) a good measure of the contribution of freight transport to the loss of time on the roads; b) study of congestion in public transport and the resulting collective losses. The first part of this manuscript synthesizes the contributions of several articles that have addressed these two phenomena. We first present the 'Pigouvian' theoretical framework which was originally transposed to the estimation of the welfare losses linked to the underpricing of the externality of discomfort in the subways. This model is then extended to the analysis of road congestion in a multi-class setting, when trucks and LCVs share the road with cars. We consider both the loss of time suffered by road users, linked to excessive traffic compared to the economic optimum, as well as the 'excess emissions' of pollutants caused by lower speeds. We then describe the empirical strategies developed to bring out the congestion functions necessary for the resolution of theoretical models. Regarding road congestion, we propose econometric estimates using individual data and we combine the results of various modeling tools (of traffic and pollutant emissions). The costs - private and external - of a given type of vehicle are increasing functions of the flow of similar vehicles, but also of the volume of other vehicles on the roads. Regarding congestion in public transport, we study the 'declared preferences' of Paris metro users who have been confronted with hypothetical trade-offs between more or less fast and more or less comfortable journeys. Econometric analysis confirms that the subjective value of time increases with the density of passengers in the trains. Our main results are as follows. While they represent 8% of the distances traveled in Ile-de-France, freight vehicles account for 40% of collective well-being and 30% of the environmental cost attributable to road traffic. We offer pricing schemes for cars, LCVs and / or trucks, aimed at limiting collective losses linked to the overuse of infrastructure. In the case of Parisian metros, we show that there are about 9% of travelers 'in excess' compared to the optimum. If the social losses linked to the underpricing of this congestion are currently limited, they could quickly increase and the external cost is high, compared to the environmental costs of cars in particular. In the end, the methods and parameters proposed in our articles 'equip' researchers and practitioners interested in the socioeconomic evaluation of urban transport projects.; L'analyse économique de la congestion des transports urbains souffre aujourd'hui d'au moins deux « angles morts » : a) une bonne mesure de la contribution du transport de fret aux pertes de temps sur les routes ; b) l'étude de la saturation dans les transports en commun et des pertes collectives qui en découlent. La première partie de ce manuscrit synthétise les apports de plusieurs articles ayant abordé ces deux phénomènes. Nous présentons tout d'abord le cadre théorique « pigouvien » qui a été originalement transposé à l'estimation des pertes de bien-être liées à la sous-tarification de l'externalité d'inconfort dans les métros. Ce modèle est ensuite étendu à l'analyse de la congestion routière dans un cadre multi-classes, lorsque les camions et les VUL partagent la voirie avec les voitures. Nous considérons aussi bien les pertes de temps subies par les usagers des routes, liées à une fréquentation excessive par rapport à l'optimum économique, que les « surémissions » de polluants induites par la baisse des vitesses. Nous décrivons ensuite les stratégies empiriques développées pour faire émerger les fonctions de congestion nécessaires à la résolution des modèles théoriques. Concernant la congestion routière, nous proposons des estimations économétriques utilisant des données individuelles et nous couplons les résultats de divers outils de modélisation (du trafic et des émissions de polluants). Les coûts - privés et externes - d'un type donné de véhicules sont des fonctions croissantes du flux de véhicules similaires, mais aussi du volume des autres véhicules présents sur les routes. Concernant la congestion dans les transports publics, nous étudions les « préférences déclarées » d'usagers du métro parisien ayant été confrontés à des arbitrages hypothétiques entre des voyages plus ou moins rapides et plus ou moins confortables. L'analyse économétrique confirme que la valeur subjective du temps augmente avec la densité de passagers dans les rames. Nos principaux résultats sont les suivants. S'ils représentent 8% des distances parcourues en Ile-de-France, les véhicules de fret comptent pour 40% du bien-être collectif et 30% du coût environnemental imputable au trafic routier. Nous proposons des schémas de tarification, pour les voitures, les VUL et/ou les camions, visant à limiter les pertes collectives liées à la sur-utilisation des infrastructures. Dans le cas des métros parisiens, nous montrons qu'il y a environ 9% de voyageurs « en trop » par rapport à l'optimum. Si les pertes sociales liées à la sous-tarification de cette congestion sont actuellement limitées, elles pourraient vite augmenter et le coût externe est élevé, comparativement aux coûts environnementaux des voitures notamment. Au final, les méthodes et paramètres proposés dans nos articles « équipent » les chercheurs et praticiens intéressés par l'évaluation socioéconomique des projets de transports urbains.

The main goal of this dissertation is to answer one of the critical questions about dynamic ride-sharing services: Can dynamic ride-sharing reduce congestion? In this thesis, we propose a simulation-based optimization framework for dynamic ride-sharing. Then using this framework, we assess the dynamic ride-sharing impact on two different network scales to find the answer to this question. When assessing the dynamic ride-sharing problem, two important points should be considered. First, how the ride-sharing system serves the network demand and second, how the ride-sharing system is impacted by the network and in particular by congestion. Then we can assess the impact of such a service on the network. Most of the existing approaches focus on the first point, i.e., designing the demand matching while using basic assumptions for the second point, mainly constant travel times. The proposed method in this thesis can outperform the existing methods in the literature. The optimization algorithm can provide high-quality solutions in a short time. Our solution approach is designed to be exact for small samples. Then, to be able to handle the large-scale problems, it is extended with several heuristics that keep the general design for the solution method but significantly reduce its computation time. In the simulation component, a "Plant Model" is applied based on the "Trip-based Macroscopic Fundamental Diagram (MFD)" to represent the traffic dynamics reality and a "Prediction Model" is applied based on the mean-speed to be used during the assignment process. We perform an extensive simulation study (based on real-world traffic patterns) to assess the influence of dynamic ride-sharing systems on traffic congestion. In the medium-scale (Lyon 6 + Villeurbanne), the results showed that ride-sharing could not significantly improve the traffic situation. High levels of market-share add additional travel distance and travel time to the trips and lead to more traffic in the network. In large cities, the results are entirely different from those in small and medium-sized cities. In large-scale (Lyon city in France) simulations, the proposed dynamic ride-sharing system can significantly improve traffic conditions, especially during peak hours. Increasing the market-share and the number of sharing can enhance this improvement. Therefore, the proposed dynamic ride-sharing system is a viable option, alleviating stress on existing public transport, to reduce the network traffic in populated and large-scale cities., L'objectif principal de cette thèse est de répondre à l'une des questions essentielles sur les services de covoiturage dynamique : le covoiturage dynamique peut-il réduire la congestion ? Dans cette thèse, nous proposons un framework d'optimisation basé sur la simulation pour le covoiturage dynamique. Ensuite, à l'aide de ce framework, nous évaluons l'impact du covoiturage dynamique sur deux échelles de réseau différentes pour trouver la réponse à cette question. Lors de l'évaluation du problème du covoiturage dynamique, deux points importants doivent être pris en compte. Premièrement, comment le système de covoiturage répond à la demande du réseau et deuxièmement, comment le système de covoiturage est influencé par le réseau et en particulier par la congestion. Ensuite, nous pouvons évaluer l'impact d'un tel service sur le réseau. La plupart des approches existantes se concentrent sur le premier point, c'est-à-dire sur la conception de la correspondance de la demande, tout en utilisant des hypothèses de base pour le second point, principalement des temps de trajet constants. La méthode proposée dans cette thèse peut être plus performante que les méthodes existantes dans la littérature. L'algorithme d'optimisation peut fournir des solutions de haute qualité en peu de temps. Notre approche de solution est conçue pour être exacte pour les petits échantillons. Ensuite, pour pouvoir traiter les problèmes à grande échelle, elle est complétée par plusieurs heuristiques qui conservent la conception générale de la méthode de solution mais réduisent considérablement son temps de calcul. Dans la composante de simulation, un "Modèle d'usine" est appliqué sur la base du "Diagramme macroscopique fondamental (MFD) basé sur les déplacements" pour représenter la réalité de la dynamique du trafic et un "Modèle de prédiction" est appliqué sur la base de la vitesse moyenne à utiliser pendant le processus d'affectation. Nous réalisons une étude de simulation approfondie (basée sur des modèles de trafic réels) pour évaluer l'influence des systèmes de covoiturage dynamiques sur la congestion du trafic. À l'échelle moyenne (Lyon 6 + Villeurbanne), les résultats ont montré que le covoiturage ne pouvait pas améliorer de manière significative la situation du trafic. Des parts de marché élevées augmentent la distance et la durée des trajets et entraînent une augmentation du trafic sur le réseau. Dans les grandes villes, les résultats sont totalement différents de ceux des villes petites et moyennes. Dans les simulations à grande échelle (ville de Lyon en France), le système de covoiturage dynamique proposé peut améliorer considérablement les conditions de circulation, en particulier aux heures de pointe. L'augmentation de la part de marché et du nombre de covoiturages peut renforcer cette amélioration. Par conséquent, le système de covoiturage dynamique proposé est une option viable pour réduire la pression sur les transports publics existants et diminuer le trafic du réseau dans les villes peuplées et de grande taille.

The main goal of this dissertation is to answer one of the critical questions about dynamic ride-sharing services: Can dynamic ride-sharing reduce congestion? In this thesis, we propose a simulation-based optimization framework for dynamic ridesharing. Then using this framework, we assess the dynamic ride-sharing impact on two different network scales to find the answer to this question. When assessing the dynamic ride-sharing problem, two important points should be considered. First, how the ridesharing system serves the network demand and second, how the ride-sharing system is impacted by the network and in particular by congestion. Then we can assess the impact of such a service on the network. Most of the existing approaches focus on the first point, i.e., designing the demand matching while using basic assumptions for the second point, mainly constant travel times. The proposed method in this thesis can outperform the existing methods in the literature. The optimization algorithm can provide high-quality solutions in a short time. Our solution approach is designed to be exact for small samples. Then, to be able to handle the large-scale problems, it is extended with several heuristics that keep the general design for the solution method but significantly reduce its computation time. In the simulation component, a "Plant Model" is applied based on the "Trip-based Macroscopic Fundamental Diagram (MFD)" to represent the traffic dynamics reality and a "Prediction Model" is applied based on the mean-speed to be used during the assignment process. We perform an extensive simulation study (based on real-world traffic patterns) to assess the influence of dynamic ride-sharing systems on traffic congestion. In the medium-scale (Lyon 6 + Villeurbanne), the results showed that ride-sharing could not significantly improve the traffic situation. High levels of market-share add additional travel distance and travel time to the trips and lead to more traffic in the network. In large cities, the results are entirely different from those in small and medium-sized cities. In large-scale (Lyon city in France) simulations, the proposed dynamic ride-sharing system can significantly improve traffic conditions, especially during peak hours. Increasing the market-share and the number of sharing can enhance this improvement. Therefore, the proposed dynamic ride-sharing system is a viable option, alleviating stress on existing public transport, to reduce the network traffic in populated and large-scale cities.; The main goal of this dissertation is to answer one of the critical questions about dynamic ride-sharing services: Can dynamic ride-sharing reduce congestion? In this thesis, we propose a simulation-based optimization framework for dynamic ridesharing. Then using this framework, we assess the dynamic ride-sharing impact on two different network scales to find the answer to this question. When assessing the dynamic ride-sharing problem, two important points should be considered. First, how the ridesharing system serves the network demand and second, how the ride-sharing system is impacted by the network and in particular by congestion. Then we can assess the impact of such a service on the network. Most of the existing approaches focus on the first point, i.e., designing the demand matching while using basic assumptions for the second point, mainly constant travel times. The proposed method in this thesis can outperform the existing methods in the literature. The optimization algorithm can provide high-quality solutions in a short time. Our solution approach is designed to be exact for small samples. Then, to be able to handle the large-scale problems, it is extended with several heuristics that keep the general design for the solution method but significantly reduce its computation time. In the simulation component, a "Plant Model" is applied based on the "Trip-based Macroscopic Fundamental Diagram (MFD)" to represent the traffic dynamics reality and a "Prediction Model" is applied based on the mean-speed to be used during the assignment process. We perform an extensive simulation study (based on real-world traffic patterns) to assess the influence of dynamic ride-sharing systems on traffic congestion. In the medium-scale (Lyon 6 + Villeurbanne), the results showed that ride-sharing could not significantly improve the traffic situation. High levels of market-share add additional travel distance and travel time to the trips and lead to more traffic in the network. In large cities, the results are entirely different from those in small and medium-sized cities. In large-scale (Lyon city in France) simulations, the proposed dynamic ride-sharing system can significantly improve traffic conditions, especially during peak hours. Increasing the market-share and the number of sharing can enhance this improvement. Therefore, the proposed dynamic ride-sharing system is a viable option, alleviating stress on existing public transport, to reduce the network traffic in populated and large-scale cities.

KES AMSTA 2020, 14th International Conference on Agents and Multi-Agent Systems: Technology and Applications - Virtual conference, Split, CROATIE, 17-/06/2020 - 19/06/2020; Today policy makers face an ever more complex traffic system. While they need to ensure smooth traffic flows in the cities, at the same time an acceptable level of service must be provided in remote areas. Autonomous taxis (AT) offer the opportunity to manage car traffic at low operational cost and they can be appropriate alternatives for the driven vehicles. In this paper, we propose a multi-agent system to find the best dispatching strategy for a fleet of AT. In the dispatching process, we aim to satisfy both passengers and providers objectives and priorities. To be able to apply the method on large-scale networks, we introduce a clustering method to cluster the requests every minute and then solve the assignment problem for the requests inside each cluster. As the network congestion can have significant impacts on the vehicles speed and travel time especially considering the private cars that are driving in the system besides the taxis, an agent-based simulation platform is used to simulate the function of the AT fleet. We use the trip-based Macroscopic Fundamental Diagram (MFD) to simulate the time evolution of traffic flows on the road network and update the traffic situation in the system every second to represent the real traffic dynamics. We address the problem for a large city scale of 80 km2 (Lyon city in France) with more than 480,000 trips over 4 hours period containing the morning peak. The experimental results with real data show that the proposed multi-agent system is efficient in terms of serving all the requests in a short time satisfying both passengers and providers objectives.

Over the past century, cities around the world have experienced rapid growth in their populations. As a consequence, the capacity of their transport networks has been exceeded, causing traffic congestion problems that are associated to economic losses, high levels of air pollution and the emission of greenhouse gases. It is in this context that it is important to implement traffic regulation strategies to mitigate congestion. Traffic simulators are essential tools for developing and testing such strategies. The traffic conditions in the transport network are consequence of the travellers' choices at any given time, but at the same time, the traffic conditions on the network influence the decisions of the travellers. This relationship between travellers' decisions and network traffic is the main idea in traffic assignment, which traffic simulators use to produce network traffic patterns. It is in this sense that understanding and approximating the behaviour of travellers is indispensable to forecast the states of a transportation network, which could help design regulations to alleviate congestion. The objective of this thesis is to find route choice models that scale-up at network level, i.e., models that predict the choices of travellers over the diversity of situations found in a transport network. The approach in this thesis to investigate travellers' behaviour in transportation networks is through computer-based experiments at large scale, for which a platform named the Mobility Decision Game (MDG), has been developed. The MDG permits to observe the choices of the participants on a diverse set of scenarios (OD pairs and routes) with varying traffic conditions and travel time information. In this thesis, the experiments focus on the route choices of uni modal car trips that are based on the map of the city of Lyon, France. To attain the objective of this thesis, firstly a methodology to find OD pairs that are representative of the whole network is proposed. The representative OD experiments to obtain data to estimate choice models that generalize to the various OD pair configurations in the network. Secondly, the choices of participants in the experiments are analyzed from the rational and boundedly rational behaviour perspectives in order to establish the principle that best describe their choices. Finally, the choice models are assessed in terms of their predictive accuracy. This thesis is part of a European ERC project entitled MAGnUM: Multiscale and Multimodal Traffic Modeling Approach for Sustainable Management of Urban Mobility.; Au cours du siècle dernier, les villes du monde entier ont connu une croissance rapide de leur population. En conséquence, la capacité de leurs réseaux de transport a été dépassée, entrainant des problèmes de congestion du trafic qui sont à leur tour associés à des pertes économiques, à des niveaux élèves de pollution atmosphérique et à l'émission de gaz à effet de serre. C'est dans ce contexte qu'il est important de mettre en oeuvre des stratégies de régulation du trafic pour atténuer les encombrements. Les simulateurs de trafic sont essentiels au développement et au test de telles stratégies. Les conditions de circulation dans le réseau de transport sont la conséquence des choix des voyageurs `a un moment donné, mais en même temps, les conditions de circulation sur le réseau influencent les décisions des voyageurs. Cette relation entre les décisions des voyageurs et le trafic réseau est l'idée principale de l'attribution du trafic, que les simulateurs de trafic utilisent pour produire des modèles de trafic réseau. C'est en ce sens que la compréhension et l'approximation du comportement des voyageurs sont indispensables pour prévoir les états d'un réseau de transport, ce qui pourrait aider à concevoir des réglementations pour réduire la congestion. Lorsqu'ils voyagent dans une ville, les voyageurs sont confrontés à plusieurs décisions concernant (i) l'activité à entreprendre, (ii) la destination du voyage, (iii) le mode de transport, (iv) l'heure de départ et (v) l'itinéraire. Ces décisions individuelles façonnent les états du trafic dans le réseau de transport `a un moment donné. Cependant, en même temps que les états du réseau sont la conséquence des choix des voyageurs, les caractéristiques et les états de trafic du réseau influencent les décisions des voyageurs. On pourrait penser, par exemple, aux voyageurs qui adaptent leur heure de départ et leurs choix d'itinéraire en anticipant des embouteillages sur la route habituelle. Cette relation bidirectionnelle entre le comportement des voyageurs et la dynamique du trafic est l'idée centrale des problèmes d'affectation du trafic et elle est étudiée sous deux angles différents. Le premier est lié au problème d'affectation du trafic, dans lequel les modèles de trafic au niveau du réseau sont obtenus à la suite des interactions entre les choix de tous les voyageurs dans le réseau de transport, sur la base d'hypothèses générales sur l'équilibre du réseau. Le second est lié aux choix des voyageurs, avec un accent particulier sur l'identification et la classification des facteurs qui influencent leurs choix. La motivation de cette thèse est d'obtenir des modèles de choix à la fois cohérents avec le comportement observé des voyageurs et généralisant bien au niveau du réseau à grande échelle. En d'autres termes, des modèles qui font des prédictions précises dans toutes les paires origine-destination (OD) dans un réseau de transport urbain, mais qui sont estimés avec des observations sur un nombre limité de paires OD. L'idée générale est de mettre en oeuvre, à l'avenir, ces modèles de choix dans des algorithmes d'affectation dynamique du trafic, et ainsi d'obtenir des schémas de trafic simulés dans une perspective comportementale. Les modèles de choix sont déduits des données collectées par des expériences informatiques réalisées sur une plateforme informatique Mobility Decision Game (MDG). Cependant, comme les choix des voyageurs ne peuvent pas être observés dans un si grand nombre de paires de OD et d'itinéraires que l'on trouve dans un réseau urbain, une m?méthodologie pour collecter les observations des choix des voyageurs par des expériences informatiques est d'abord nécessaire. Les modèles estimés avec des données provenant de cette méthodologie devraient pouvoir évoluer, d'un petit ensemble de paires de OD observables, jusqu'au niveau du réseau complet

Over the past century, cities around the world have experienced rapid growth in their populations. As a consequence, the capacity of their transport networks has been exceeded, causing traffic congestion problems that are associated to economic losses, high levels of air pollution and the emission of greenhouse gases. It is in this context that it is important to implement traffic regulation strategies to mitigate congestion. Traffic simulators are essential tools for developing and testing such strategies. The traffic conditions in the transport network are consequence of the travellers' choices at any given time, but at the same time, the traffic conditions on the network influence the decisions of the travellers. This relationship between travellers' decisions and network traffic is the main idea in traffic assignment, which traffic simulators use to produce network traffic patterns. It is in this sense that understanding and approximating the behaviour of travellers is indispensable to forecast the states of a transportation network, which could help design regulations to alleviate congestion. The objective of this thesis is to find route choice models that scale-up at network level, i.e., models that predict the choices of travellers over the diversity of situations found in a transport network. The approach in this thesis to investigate travellers' behaviour in transportation networks is through computer-based experiments at large scale, for which a platform named the Mobility Decision Game (MDG), has been developed. The MDG permits to observe the choices of the participants on a diverse set of scenarios (OD pairs and routes) with varying traffic conditions and travel time information. In this thesis, the experiments focus on the route choices of uni modal car trips that are based on the map of the city of Lyon, France. To attain the objective of this thesis, firstly a methodology to find OD pairs that are representative of the whole network is proposed. The representative OD experiments to obtain data to estimate choice models that generalize to the various OD pair configurations in the network. Secondly, the choices of participants in the experiments are analyzed from the rational and boundedly rational behaviour perspectives in order to establish the principle that best describe their choices. Finally, the choice models are assessed in terms of their predictive accuracy. This thesis is part of a European ERC project entitled MAGnUM: Multiscale and Multimodal Traffic Modeling Approach for Sustainable Management of Urban Mobility., Au cours du siècle dernier, les villes du monde entier ont connu une croissance rapide de leur population. En conséquence, la capacité de leurs réseaux de transport a été dépassée, entrainant des problèmes de congestion du trafic qui sont à leur tour associés à des pertes économiques, à des niveaux élèves de pollution atmosphérique et à l'émission de gaz à effet de serre. C'est dans ce contexte qu'il est important de mettre en oeuvre des stratégies de régulation du trafic pour atténuer les encombrements. Les simulateurs de trafic sont essentiels au développement et au test de telles stratégies. Les conditions de circulation dans le réseau de transport sont la conséquence des choix des voyageurs `a un moment donné, mais en même temps, les conditions de circulation sur le réseau influencent les décisions des voyageurs. Cette relation entre les décisions des voyageurs et le trafic réseau est l'idée principale de l'attribution du trafic, que les simulateurs de trafic utilisent pour produire des modèles de trafic réseau. C'est en ce sens que la compréhension et l'approximation du comportement des voyageurs sont indispensables pour prévoir les états d'un réseau de transport, ce qui pourrait aider à concevoir des réglementations pour réduire la congestion. Lorsqu'ils voyagent dans une ville, les voyageurs sont confrontés à plusieurs décisions concernant (i) l'activité à entreprendre, (ii) la destination du voyage, (iii) le mode de transport, (iv) l'heure de départ et (v) l'itinéraire. Ces décisions individuelles façonnent les états du trafic dans le réseau de transport `a un moment donné. Cependant, en même temps que les états du réseau sont la conséquence des choix des voyageurs, les caractéristiques et les états de trafic du réseau influencent les décisions des voyageurs. On pourrait penser, par exemple, aux voyageurs qui adaptent leur heure de départ et leurs choix d'itinéraire en anticipant des embouteillages sur la route habituelle. Cette relation bidirectionnelle entre le comportement des voyageurs et la dynamique du trafic est l'idée centrale des problèmes d'affectation du trafic et elle est étudiée sous deux angles différents. Le premier est lié au problème d'affectation du trafic, dans lequel les modèles de trafic au niveau du réseau sont obtenus à la suite des interactions entre les choix de tous les voyageurs dans le réseau de transport, sur la base d'hypothèses générales sur l'équilibre du réseau. Le second est lié aux choix des voyageurs, avec un accent particulier sur l'identification et la classification des facteurs qui influencent leurs choix. La motivation de cette thèse est d'obtenir des modèles de choix à la fois cohérents avec le comportement observé des voyageurs et généralisant bien au niveau du réseau à grande échelle. En d'autres termes, des modèles qui font des prédictions précises dans toutes les paires origine-destination (OD) dans un réseau de transport urbain, mais qui sont estimés avec des observations sur un nombre limité de paires OD. L'idée générale est de mettre en oeuvre, à l'avenir, ces modèles de choix dans des algorithmes d'affectation dynamique du trafic, et ainsi d'obtenir des schémas de trafic simulés dans une perspective comportementale. Les modèles de choix sont déduits des données collectées par des expériences informatiques réalisées sur une plateforme informatique Mobility Decision Game (MDG). Cependant, comme les choix des voyageurs ne peuvent pas être observés dans un si grand nombre de paires de OD et d'itinéraires que l'on trouve dans un réseau urbain, une m?méthodologie pour collecter les observations des choix des voyageurs par des expériences informatiques est d'abord nécessaire. Les modèles estimés avec des données provenant de cette méthodologie devraient pouvoir évoluer, d'un petit ensemble de paires de OD observables, jusqu'au niveau du réseau complet

TRB 2020, 99th Annual Meeting Transportation Research Board, Washington, ETATS-UNIS, 12-/01/2020 - 16/01/2020; Traffic congestion and emission are the crucial problems due to its significant impact on the environment, economy, and health of the inhabitants. In this paper, we propose an approach to reduce network-wide emissions in an urban traffic network. The approach is comprised of two layers. The first layer (optimal green routing) which predicts the optimal splitting coefficient for all origin-destination (OD) pairs (only two alternatives are possible for each OD) crossing the city center or bypass it. As implementing optimal routing strategies on the field is almost impossible as we don't have direct control on the users decisions, we choose to balance travel time through gating at the city perimeter in order the usual dynamic user equilibrium (DUE) discipline matches the optimal splitting coefficient derived from the first layer. The second layer then adjusts inflows at the city gates to DUE based on the predictive travel time corresponds to the optimal routing strategy for each OD pair. The accumulation-based macroscopic fundamental diagram (MFD) model of a single reservoir city with seven atrial routes and six bypass alternatives is developed. Linear programming (LP) problem is formulated to determine the optimal splitting coefficients (first layer) and the nonlinear model predictive control (NMPC)-based gating control strategy is designed to track the optimal splitting coefficients (second layer). Combination of two layers indirectly track the network-wide emission levels by manipulating the traffic flows into the reservoir. The network wide emission control approach is compared with another usual gating strategy (perimeter control) aiming to optimize traffic conditions in the inner-city only. The comprehensive analysis conducted on proposed approach is presented. We compare the results of controlled case with respect to the uncontrolled case and with respect to each other. The comparison results show: (i) the proposed network-wide emission control approach outperforms congestion control approach in reducing the total emission, (ii) significant improvements in total time spent, mean speed, and emission are obtained when considering both the inner-city and the bypass traffic with the network-wide strategy and (iii) improvements come at the cost of increased complexity (need to solve two optimization problems).

The main goal of this dissertation is to answer one of the critical questions about dynamic ride-sharing services: Can dynamic ride-sharing reduce congestion? In this thesis, we propose a simulation-based optimization framework for dynamic ridesharing. Then using this framework, we assess the dynamic ride-sharing impact on two different network scales to find the answer to this question. When assessing the dynamic ride-sharing problem, two important points should be considered. First, how the ridesharing system serves the network demand and second, how the ride-sharing system is impacted by the network and in particular by congestion. Then we can assess the impact of such a service on the network. Most of the existing approaches focus on the first point, i.e., designing the demand matching while using basic assumptions for the second point, mainly constant travel times. The proposed method in this thesis can outperform the existing methods in the literature. The optimization algorithm can provide high-quality solutions in a short time. Our solution approach is designed to be exact for small samples. Then, to be able to handle the large-scale problems, it is extended with several heuristics that keep the general design for the solution method but significantly reduce its computation time. In the simulation component, a "Plant Model" is applied based on the "Trip-based Macroscopic Fundamental Diagram (MFD)" to represent the traffic dynamics reality and a "Prediction Model" is applied based on the mean-speed to be used during the assignment process. We perform an extensive simulation study (based on real-world traffic patterns) to assess the influence of dynamic ride-sharing systems on traffic congestion. In the medium-scale (Lyon 6 + Villeurbanne), the results showed that ride-sharing could not significantly improve the traffic situation. High levels of market-share add additional travel distance and travel time to the trips and lead to more traffic in the network. In large cities, the results are entirely different from those in small and medium-sized cities. In large-scale (Lyon city in France) simulations, the proposed dynamic ride-sharing system can significantly improve traffic conditions, especially during peak hours. Increasing the market-share and the number of sharing can enhance this improvement. Therefore, the proposed dynamic ride-sharing system is a viable option, alleviating stress on existing public transport, to reduce the network traffic in populated and large-scale cities. The main goal of this dissertation is to answer one of the critical questions about dynamic ride-sharing services: Can dynamic ride-sharing reduce congestion? In this thesis, we propose a simulation-based optimization framework for dynamic ridesharing. Then using this framework, we assess the dynamic ride-sharing impact on two different network scales to find the answer to this question. When assessing the dynamic ride-sharing problem, two important points should be considered. First, how the ridesharing system serves the network demand and second, how the ride-sharing system is impacted by the network and in particular by congestion. Then we can assess the impact of such a service on the network. Most of the existing approaches focus on the first point, i.e., designing the demand matching while using basic assumptions for the second point, mainly constant travel times. The proposed method in this thesis can outperform the existing methods in the literature. The optimization algorithm can provide high-quality solutions in a short time. Our solution approach is designed to be exact for small samples. Then, to be able to handle the large-scale problems, it is extended with several heuristics that keep the general design for the solution method but significantly reduce its computation time. In the simulation component, a "Plant Model" is applied based on the "Trip-based Macroscopic Fundamental Diagram (MFD)" to represent the traffic dynamics reality and a "Prediction Model" is applied based on the mean-speed to be used during the assignment process. We perform an extensive simulation study (based on real-world traffic patterns) to assess the influence of dynamic ride-sharing systems on traffic congestion. In the medium-scale (Lyon 6 + Villeurbanne), the results showed that ride-sharing could not significantly improve the traffic situation. High levels of market-share add additional travel distance and travel time to the trips and lead to more traffic in the network. In large cities, the results are entirely different from those in small and medium-sized cities. In large-scale (Lyon city in France) simulations, the proposed dynamic ride-sharing system can significantly improve traffic conditions, especially during peak hours. Increasing the market-share and the number of sharing can enhance this improvement. Therefore, the proposed dynamic ride-sharing system is a viable option, alleviating stress on existing public transport, to reduce the network traffic in populated and large-scale cities.

MFTS 2020, The 3rd Symposium on Management of Future Motorway and Urban Traffic Systems - Virtual conference, Luxembourg, LUXEMBOURG, 06-/07/2020 - 08/07/2020; The development of Advanced Traveler Information Systems (ATIS) with enroute navigation systems has opened the door to new traffic regulation policies such as collective route guidance based on the minimization of total travel time. However, a critical element for their efficiency is the compliance of travelers with route recommendations. In this paper, we use a stated preference survey based on route choice situations from a real network to investigate traveler compliance for different levels of travel time sacrifice vs several social benefits. Two kinds of social benefits are considered: congestion alleviation and emission reduction. The data collected allows us to precisely quantify the travel time sacrifice that a given proportion of travelers would be ready to accept in order to take a different alternative that has a given level of social benefit. In line with the literature, our analysis confirms the decrease of compliance with the increase of sacrifice. Moreover, it suggests that the way the recommendation is intended could play a significant role in the level of compliance: the display of an advice message for the social path (in the congestion alleviation case) is shown to be more efficient than only showing additional information (in the emission reduction case).

The objective of this thesis is to find route choice models that scale-up at network level, i.e., models that predict the choices of travellers over the diversity of situations found in a transport network. The approach in this thesis to investigate travellers' behaviour in transportation networks is through computer-based experiments at large scale, for which a platform named the Mobility Decision Game (MDG), has been developed. The MDG permits to observe the choices of the participants on a diverse set of scenarios (OD pairs and routes) with varying traffic conditions and travel time information. In this thesis, the experiments focus on the route choices of uni-modal car trips that are based on the map of the city of Lyon, France. To attain the objective of this thesis, firstly a methodology to find OD pairs that are representative of the network is proposed. The representative OD pairs are used in route choice experiments to obtain choice models that generalise to the various OD configurations in the network. Secondly, the choices of participants in the experiments are analysed from the rational and boundedly rational behaviour perspectives, in order to establish the principle that best describe their choices. Finally, the choice models are assessed in terms of their predictive accuracy. This thesis is part of a European ERC project entitled MAGnUM: Multiscale and Multimodal Traffic Modeling Approach for Sustainable Management of Urban Mobility., L'objectif de cette thèse est de trouver des modèles de choix d'itinéraire qui évoluent au niveau du réseau, c'est-à-dire des modèles qui rapprochent les choix des voyageurs sur la diversité des situations rencontrées dans un réseau de transport. L'approche de cette thèse pour étudier le comportement des voyageurs dans les réseaux de transport passe par des expériences informatiques à grande échelle, pour lesquelles une plateforme nommée Mobility Decision Game (MDG) a été développée. Le MDG permet d'observer les choix des participants sur un ensemble diversifié de scénarios (paires OD et itinéraires) avec des conditions de circulation et des informations de temps de trajet variables. Dans cette thèse, les expériences se concentrent sur les choix d'itinéraire des trajets en voiture qui sont basés sur la carte de la ville de Lyon, France. Pour atteindre l'objectif de cette thèse, une méthodologie de recherche de couples OD représentatifs du réseau est tout d'abord proposée. Les paires OD représentatives sont utilisées dans les expériences de choix de route pour obtenir des modèles de choix qui se généralisent aux différentes configurations OD dans le réseau. Deuxièmement, les choix des participants aux expériences sont analysés du point de vue du comportement rationnel et borné, afin d'établir le principe qui décrit le mieux leurs choix. Enfin, les modèles de choix sont évalués en fonction de leur précision prédictive. Cette thèse fait partie d'un projet européen ERC intitulé MAGnUM: Approche de modélisation du trafic multi-échelle et multimodale pour la gestion durable de la mobilité urbaine.

Flow limitation due to spillbacks between reservoirs in Macroscopic Fundamental Diagram (MFD) based approaches is still not fully understood. In a previous paper, we analyzed flow exchange properties and formulated new merging and diverging models, different from those previously proposed in the literature. However, both the latter and our approach received very little support from aggregated link-scale data (real or simulated). The contribution of this study is to validate different MFD-based modeling approaches vs. microscopic simulation using an artificial Manhattan network and then compare the microsimulation results with the MFD-based ones. These comparisons first allow us to investigate and calibrate the network entry capacity, known as the entry supply function. Besides demand pro-rata and endogenous merging scheme, entry flow based on First-In-First-Out rule is also analyzed. Finally, we also show that the outflow diverging scheme is critical to reproduce microsimulation results well regarding the network exit and demonstrate the1 limitations of widely used approach of a decreasing outflow demand with independent partial outflow treatment.

L'analyse économique de la congestion des transports urbains souffre aujourd'hui d'au moins deux « angles morts » : a) une bonne mesure de la contribution du transport de fret aux pertes de temps sur les routes ; b) l'étude de la saturation dans les transports en commun et des pertes collectives qui en découlent. La première partie de ce manuscrit synthétise les apports de plusieurs articles ayant abordé ces deux phénomènes. Nous présentons tout d'abord le cadre théorique « pigouvien » qui a été originalement transposé à l'estimation des pertes de bien-être liées à la sous-tarification de l'externalité d'inconfort dans les métros. Ce modèle est ensuite étendu à l'analyse de la congestion routière dans un cadre multi-classes, lorsque les camions et les VUL partagent la voirie avec les voitures. Nous considérons aussi bien les pertes de temps subies par les usagers des routes, liées à une fréquentation excessive par rapport à l'optimum économique, que les « surémissions » de polluants induites par la baisse des vitesses. Nous décrivons ensuite les stratégies empiriques développées pour faire émerger les fonctions de congestion nécessaires à la résolution des modèles théoriques. Concernant la congestion routière, nous proposons des estimations économétriques utilisant des données individuelles et nous couplons les résultats de divers outils de modélisation (du trafic et des émissions de polluants). Les coûts - privés et externes - d'un type donné de véhicules sont des fonctions croissantes du flux de véhicules similaires, mais aussi du volume des autres véhicules présents sur les routes. Concernant la congestion dans les transports publics, nous étudions les « préférences déclarées » d'usagers du métro parisien ayant été confrontés à des arbitrages hypothétiques entre des voyages plus ou moins rapides et plus ou moins confortables. L'analyse économétrique confirme que la valeur subjective du temps augmente avec la densité de passagers dans les rames. Nos principaux résultats sont les suivants. S'ils représentent 8% des distances parcourues en Ile-de-France, les véhicules de fret comptent pour 40% du bien-être collectif et 30% du coût environnemental imputable au trafic routier. Nous proposons des schémas de tarification, pour les voitures, les VUL et/ou les camions, visant à limiter les pertes collectives liées à la sur-utilisation des infrastructures. Dans le cas des métros parisiens, nous montrons qu'il y a environ 9% de voyageurs « en trop » par rapport à l'optimum. Si les pertes sociales liées à la sous-tarification de cette congestion sont actuellement limitées, elles pourraient vite augmenter et le coût externe est élevé, comparativement aux coûts environnementaux des voitures notamment. Au final, les méthodes et paramètres proposés dans nos articles « équipent » les chercheurs et praticiens intéressés par l'évaluation socioéconomique des projets de transports urbains. The economic analysis of urban transport congestion today suffers from at least two 'blind spots': a) a good measure of the contribution of freight transport to the loss of time on the roads; b) study of congestion in public transport and the resulting collective losses. The first part of this manuscript synthesizes the contributions of several articles that have addressed these two phenomena. We first present the 'Pigouvian' theoretical framework which was originally transposed to the estimation of the welfare losses linked to the underpricing of the externality of discomfort in the subways. This model is then extended to the analysis of road congestion in a multi-class setting, when trucks and LCVs share the road with cars. We consider both the loss of time suffered by road users, linked to excessive traffic compared to the economic optimum, as well as the 'excess emissions' of pollutants caused by lower speeds. We then describe the empirical strategies developed to bring out the congestion functions necessary for the resolution of theoretical models. Regarding road congestion, we propose econometric estimates using individual data and we combine the results of various modeling tools (of traffic and pollutant emissions). The costs - private and external - of a given type of vehicle are increasing functions of the flow of similar vehicles, but also of the volume of other vehicles on the roads. Regarding congestion in public transport, we study the 'declared preferences' of Paris metro users who have been confronted with hypothetical trade-offs between more or less fast and more or less comfortable journeys. Econometric analysis confirms that the subjective value of time increases with the density of passengers in the trains. Our main results are as follows. While they represent 8% of the distances traveled in Ile-de-France, freight vehicles account for 40% of collective well-being and 30% of the environmental cost attributable to road traffic. We offer pricing schemes for cars, LCVs and / or trucks, aimed at limiting collective losses linked to the overuse of infrastructure. In the case of Parisian metros, we show that there are about 9% of travelers 'in excess' compared to the optimum. If the social losses linked to the underpricing of this congestion are currently limited, they could quickly increase and the external cost is high, compared to the environmental costs of cars in particular. In the end, the methods and parameters proposed in our articles 'equip' researchers and practitioners interested in the socioeconomic evaluation of urban transport projects.

Multi-modal interactions at the network-level remain unexplored due to the lack of high-resolution data for all transportation modes involved. The current work investigates the effect of multi-modal interactions at space-mean network speed for each mode using the dataset from pNEUMA experiment that was carried out in a congested city centre network of Athens, Greece. Explanatory variables considered are the accumulation and the stopped fraction of vehicles of each mode. Firstly, a multi-modal mean speed MFD is considered by assuming that the mean speed of each mode can be expressed in terms of accumulations of all modes. The quality of multi-modal MFD fits is compared to the uni-modal ones, where the mean speed of a given mode is assumed to be a function of the accumulation of that mode only. Secondly, the classical two-fluid model is extended to multi-modal networks. An analysis on the ergodicity assumption in the context of stopped fraction is also presented. This work also introduces a network-level dynamic model that uses the stopped fraction of vehicles. This so-called extended trip-based model simulates the stop-and-go pattern of the vehicles thereby reproducing the evolution of network congestion. This work is the first to explore in this direction. The results from the classical trip-based and the extended trip-based models are compared and validated with the empirical data.

hEART 2019, 8th Symposium of the European Association for Research in Transportation, Budapest, HONGRIE, 04-/09/2019 - 06/09/2019; In recent years, intelligent transportation systems made it possible for operators to adapt in real-time the transportation supply to travel demand via new mobility services. Among these services, ride-sharing is becoming popular. The dynamic ride-sharing problem involves two sub-problems: (1) How to serve the upcoming trips (Optimal fleet management) and (2) How to accurately predict the travel times to determine vehicles availability and pick up/drop off times. In this paper, we express the optimal fleet management problem as a constrained multi objective integer linear programming. Our aim is to find the global optimal solution for the ride-sharing problem without uncertainty in demand to have a vision of these services performance in optimal situation. Then we compare the results and experiments with the exact optimal condition. We have designed an algorithm to find the exact solution for matching problem based on the branch and bound algorithm. To solve the second sub-problem, we define two different models to assess the impact of traffic conditions on the dynamic ride-sharing system performance for large-scale problems.

KES-AMSTA 2019, 13th KES International Conference Agents and Multi-agent Systems: Technologies and Applications, Saint Julian's, MALTE, 17-/06/2019 - 19/06/2019; Sharing rides can be an effective solution for traffic management in populated urban areas. Real-time ride-sharing is a dynamic and complex optimization problem. Indeed, the problem data are not known a priori in a dynamic context. However, most of the approaches in the literature consider that the missing data concerns the travelers, which are revealed online. Very few consider traffic changes during optimization or execution. More precisely, they assume that the predicted travel times used during optimization remain the same when executing the vehicle schedule, which is usually not the case in practice. In this paper, we propose a multi-agent system to solve the real-time ride-sharing problem. In this system, two models are defined to deal with dynamic traffic conditions. On the one side, the currently observed average speed in the network is used to predict travel times when calculating the optimal schedule for the ride-sharing fleet. On the other side, the traffic situation is updated every 10 seconds using a simulator as the plant model to represent the real traffic dynamics. The experimental results with real data on the city of Lyon show that the proposed multi-agent system is efficient in terms of congestion reduction, especially during peak hours and if sufficient rides are shared. The system can also reduce the providers cost with a small increase in passengers waiting time and travel time.

MT-ITS 2019, 6th International Conference on Models and Technologies for Intelligent Transportation Systems, Cracovie, POLOGNE, 05-/06/2019 - 07/06/2019; Graph-based analysis has proven to be a good approach to study topological vulnerabilities of road networks through specific metrics, such as betweenness centrality (BC). Even though BC of unweighted, undirected graphs has been widely adopted to identify critical road segments and intersections, given the very high number of potentially highly-traversed paths flowing through them, congestion and vulnerability are strongly influenced also by static and dynamic context factors, such as road capacity, speed limits, travellers' behaviors, accidents, social gatherings and maintenance operations. In this paper, we focus on the analysis of BC on dynamically weighted graphs, used as a model of a road network and associated dynamic information (e.g. travel time). The aim is to discover correlations between the centrality metric and vehicle flows, both in space and in time. The analysis proves the existence of relevant spatio-temporal correlations that provide useful information about the characteristics of road networks and the behavior of drivers. In particular, we identify the existence of anti-correlations that point out forecasting properties of BC when computed on dynamic graphs.These properties justify the usage of the metric for the implementation of next-generation proactive, data-driven urban monitoring systems. These systems are expected to empower urban planners and traffic operators with novel intelligent solutions to reduce traffic congestion and vulnerability risks, therefore contributing to implement the vision of a more resilient and sustainable city.

TAP 2019, 23rd International Transport and Air Pollution Conference, Thessalonique, GRECE, 15-/05/2019 - 17/05/2019; Effects of road traffic on climate change and health are now well established. As mobility needs are constantly increasing, policy decision-makers are looking for solutions to reduce greenhouse gases and pollutant emissions in large cities. Various traffic management measures can be considered. One of the possible strategies is based on changing users' departure time. Indeed, spreading users over time can potentially calm traffic, reduce peak congestion and related emissions. This initial analysis showed that changing the departure time can lead to improved network performance and reduced modestly related emissions. The proposed approach is based on MFD simulation, which allows a large number of simulations to be performed in a reasonable time.

TRB 2019, Transportation Research Board 98th Annual Meeting, Washington DC, ETATS-UNIS, 13-/01/2019 - 17/01/2019; When assessing the dynamic ride-sharing problem, two important points should be considered. First, how the ride-sharing system serves the network demand and second, how the ride-sharing system is impacted by the network and in particular by congestion. Most of the existing approaches focus on the first point, i.e. designing the demand matching while using basic assumptions for the second point, mainly constant travel times. Furthermore, most assume that predicted travel times used for the demand-matching are observed when executing the vehicle schedule, which is usually not the case in practice. In this paper two models are defined to deal with dynamic traffic conditions: current mean speed in the network is used over the next 10 minutes to predict travel times when calculating the optimal schedule for the ride-sharing fleet. This fleet is assumed composed of autonomous cars to avoid considering constraints about the drivers. Then, cars travels are simulated and the traffic situation is updated every 10 seconds using a trip-based MFD model as the plant model to represent the traffic dynamics. Some important details are discussed: improvements in the objective functions and also traffic conditions with different values for the number of sharing, the market-rate, and pickup/drop off time window. We find out that the proposed system is really efficient in terms of reducing congestion, especially in peak hours if sufficient sharing happens. Also it can reduce the providers cost while it has small increase in passengers waiting time and travel time.

TRB 2019, Transportation Research Board 98th Annual Meeting, Washington, ETATS-UNIS, 13-/01/2019 - 17/01/2019; Multi-reservoir systems based on theMacroscopic Fundamental Diagram (MFD) look appealing to simulate traffic states at large urban scales. Nevertheless, flow limitation due to spillbacks between reservoirs is still not fully understood yet. Recently, we analyzed flow exchange properties and formulated new merging and diverging models, different from the ones previously proposed in the literature. However, both the latter and our approach received very little support from aggregated link-scale data (real or simulated one). The contribution of this study is to validate different MFD-based modeling approaches by using microscopic simulation. To this end, we focus on an artificial Manhattan network crossed by two regional paths (West-East and North-South). We then analyze the aggregated accumulation, inflow and outflow of these paths for different congestion scenarios, and compare the results with the outputs predicted by the MFD-based models. It is notably found that the outflow diverging scheme is critical to fairly reproduce microsimulation results. During congestion on-set, the widely used approach of a decreasing outflow demand with independent partial outflow treatment provides good estimation of transient states, but fails to predict reliable network unloading during congestion off-set. The modified approach we developed in our previous study takes advantage of setting the reservoir outflow demand to maximum and applying inter-dependency relationships between outflows to overcome this issue. To a lesser extend, using a demand pro-rata merging scheme, as it is usually done in the literature, would be the best option to determine partial inflows properly.

TRB 2019, Transportation Research Board 98th Annual Meeting, Washington, ETATS-UNIS, 13-/01/2019 - 17/01/2019; Generally, in perimeter control, congestion from inside the reservoir ismoved to the outside by manipulating the inflows which create queue. As in our settings, one of the routes have an alternative route (freeway), the total input demand of the network is distributed along the transfer route and a freeway. To solve the problem of demand flow distribution, in this work we applied a routing discipline corresponding to user equilibrium (UE). Therefore, drivers can choose a route with lower travel time that is no longer crossing the reservoir. Doing so, we are not only considering the side-effects of perimeter gating control in terms of congestion but we are also taking into account the external user adaptation to it. The main contribution of this paper compared to previous works is that the external UE discipline makes the input demand elastic from the reservoir point of view. The evaluation of the global system performance is then much more realistic. Note that the way we implement that external UE discipline with the MFD framework is also a contribution by itself. Further, we analyze the impact of routing on perimeter gating control and sheds some light on the side-effects (in terms of the queue, emissions, and total time spent) of perimeter gating control.

KES-AMSTA-2019, 13th KES International Conference Agents and Multi-agent Systems: Technologies and Applications 2019, Saint Julian's, MALTE, 17-/06/2019 - 19/06/2019; Sharing rides can be an effective solution for traffic management in populated urban areas. Real-time ride sharing is a dynamic and complex optimization problem. Indeed, the problem data are not known a priori in a dynamic context. However, most of the approaches in the literature consider that the missing data concerns the travelers, which are revealed online. Very few consider traffic changes during optimization or execution. More precisely, they assume that the predicted travel times used during optimization remain the same when executing the vehicle schedule, which is usually not the case in practice. In this paper, we propose a multi-agent system to solve the real-time ride-sharing problem. In this system, two models are defined to deal with dynamic traffic conditions. On the one side, the currently observed average speed in the network is used to predict travel times when calculating the optimal schedule for the ride-sharing fleet. On the other side, the traffic situation is updated every 10 s using a simulator as the plant model to represent the real traffic dynamics. The experimental results with real data on the city of Lyon show that the proposed multi-agent system is efficient in terms of congestion reduction, especially during peak hours and if sufficient rides are shared. The system can also reduce the provider's cost with a small increase in passengers waiting time and travel time.

This paper investigates how network and traffic heterogeneities influence the accuracy of a simulation based on the Macroscopic Fundamental Diagram (MFD). To this end, the MFD modeling of a simple grid network is compared to the outputs of a mesoscopic kinematic wave model simulating traffic in the same network. Heterogeneous distributions of demand and supply at the boundaries are set to the local entries and exits of the mesoscopic model to generate heterogeneous network loadings. These boundary conditions challenge the MFD simulation, as significant discrepancies are observed between both modeling approaches in steady state. While the accurate calibration of the MFD and the average trip length can reduce the discrepancies for heterogeneous demand settings, no simple solution exists for heterogeneous supply settings, because they may drive very different internal congestion patterns in the network. We propose a correction method to adjust the MFD model outputs in such a case.

What we did: This report examines strategies and technologies applied to better utilise existing road networks. It has long been recognised that capacity expansion through the construction of new roads alone is not an efficient approach to meeting peak demand, especially in cities with a well-developed road network. This approach to congestion is often unsustainable because pent up demand quickly fills up new road space and congestion will return. The report summarises the findings of a workshop on alternative responses to congestion organised by the ITF's Working Group on the Smart Use of Roads. The Workshop brought together 39 experts and practitioners from 11 countries in Tokyo in October 2018. Participants discussed the use of real-time data to identify congestion bottlenecks and to provide dynamic route guidance to travellers and reviewed localised infrastructure measures to relieve congestion in the short to medium term. The Workshop also examined the way in which road tolls have been modified to relieve congestion on the expressway networks of Tokyo and Chinese Taipei and the evolution of Singapore's road pricing system to maintain its effectiveness in managing demand. What we found: Making smarter use of roads is increasingly the focus of investment in trunk road networks and their management. This involves investment in information and communications technology, and the use of a mixture of localised and network measures, to optimise use of the network and maximise flow on existing roads rather than simply adding capacity. The strategy is a response to the unsustainable costs of unlimited expansion and the difficulties of expansion in densely built up areas. More fundamentally it responds to the failure of strategies based on expansion of capacity to manage congestion in cities with a well-developed network, with expansion generating additional demand and driving mobility to be more and more car-dependent. There are three broad categories of real-time traffic information. Those collected from Intelligent Transport Systems (ITS) such as systems with roadside sensors, Electronic Toll Collection (ETC) systems and mobile communications networks. Data from ETC systems combined with traffic counts can be a useful substitute for ITS data, particularly when the on-board units (OBUs) record probe data on driving behaviour (such as braking and acceleration) and trip patterns (origin, destination, parked time). Combined, this can provide information on trip purpose. Mobile phone network data can provide similar if less complete data to that obtained from ITS systems, but with a wider geographical coverage and can contribute to assessment of traffic volumes to make projections of network performance. Past experience shows that investment in ITS smart motorway technologies can achieve better value for money than network expansion. But such investment can be costly and therefore ITS infrastructure is likely to be installed mainly on selected strategic corridors. Mobile network data can be a valuable supplement to ITS and ETC data, but its use is still at an early stage and business-to-government data sharing is not common. A number of investments that cost less than adding lanes to motorways can relieve congestion, with much higher benefit-cost ratios (BCRs) than network expansion. These include adapting hard-shoulders (safety lanes) to take peak hour traffic, controlling access to express ways through ramp metering and variable speed limits and speed calming devices to smooth and increase traffic flow. The latter include pulsing banks of LED strips providing a visual signal that acts to synchronise and adjust vehicle speeds. The safety implications of hard-shoulder running are addressed by lowering variable speed limits when in use and Variable Message Sign (VMS) posting to evacuate lanes in case of emergency. In practice hardshoulder running is found to reduce both safety risk and travel time and improve journey time reliability. Tolls on tolled roads can be adjusted to steer traffic away from congested sections of the network. This has been done very successfully in Tokyo, following completion of its trunk ring roads. What were largely distance-based tolls were modified in 2016 to equalise charges for crossing the city through the centre or via the ring roads. This resulted in a large shift of traffic to the ring roads, cutting congestion sharply. Tolls on the national expressway network in Chinese Taipei have been rationalised and modified to begin to steer demand away from congested locations and periods. The biggest reduction in congestion with the modification of Chinese Taipei's motorway tolls has been achieved by removing toll plazas. Manual fee collection has been replaced entirely with drive-through ETC technology after a phase-in period to allow for reassignment of labour. Other tolled motorway systems should replicate this experience. Congestion is most successfully addressed by a complete set of tools for managing traffic demand and integrating transport and land use planning. Singapore has developed the most comprehensive policies. These include joint planning of transport - infrastructure investment and public transport services - with land-use change and property development, together with a full suite of measures to manage car ownership and use. The tools include auctioning of permits to buy cars that limits car ownership as well charges on the use of cars - fuel taxes, parking charges and road pricing. Tokyo uses a similarly broad set of demand management instruments to cut congestion to current low levels. For example, car ownership is limited by the requirement to own or lease off-street parking space before purchase and urban expressways are tolled, with congestion-based tolling being considered for the future. At the same time, the expressway network is being expanded, including development of ring routes around urban area, to complete Tokyo's basic road network. What we recommend : Make use of smart technologies part of the response to congestion Intelligent Transport Systems (ITS) use information and communication technologies to enable users to be better informed and make more coordinated and smarter use of transport networks. Investment in ITS provides better value for money than network capacity expansion in some circumstances and should be considered as part of strategies to address congestion. For cities with existing tolling systems, upgrading tolling technologies can reduce congestion at charging points by eliminating the need for motorists to stop for payment. Modifying existing tolling systems to manage congestion at specific locations and time of day is a particularly cost-effective option. Invest in improving junctions where these create bottlenecks. In general, one of the major causes of congestions on motorways is reduced traffic flow around interchanges and ramps where lanes merge and diverge. Experience in Tokyo demonstrates that localised investment to improve road geometry and expand capacity at these points can be very successful in eliminating congestion on the network, at least where overall traffic demand is constrained by tolls. Use hard-shoulder running and ramp metering to get the most out of trunk road capacity. A range of effective, active traffic demand management measures, such as hard-shoulder running, ramp metering, and speed harmonisation, have been used in many place with proven results. These measures provide better value for money than network expansion, at least in the medium-term. Use congestion pricing for active traffic management as part of integrated urban policies. Charging for the use of roads with prices differentiated by route, location, time of day and day of the week has proven to be effective in changing behaviour and limiting congestion, notably in Singapore. Demand-responsive road pricing is the most efficient way of balancing supply and demand for road use in the long-term. In cities it works best as part of a comprehensive set of measures to manage car ownership and use and integrate land-use and transport planning. Without road pricing and/or instruments that price the impact of consuming road and parking space into the cost of owning a car, it is congestion itself that regulates demand. Adopt barrier-free electronic tolling to remove bottlenecks. Even simple forms of congestion pricing, without time and distance-based differentiated pricing, can be effective in managing demand, as London's Congestion Charge demonstrates. A range of technologies can be used for barrier-free toll collection and enforcement and experience in Chinese Taipei and Singapore shows how simple systems can evolve over time to incorporate technological innovation for more effective traffic management. Replacing manual fee collection with barrier-free systems should be a priority for removing congestion bottlenecks. Whatever system is selected, the focus should first and foremost be on ensuring the basic toll collection and enforcement function is operational before incorporating other functions.

TRB 2019, Transportation Research Board 98th Annual Meeting, Washington, ETATS-UNIS, 13-/01/2019 - 17/01/2019; This work focuses on the application of so-called accumulation-based and trip-based MFD approaches to real transportation networks and discusses the calibration of the MFD shape and trip lengths estimation using a thorough validation of the network dynamics with micro-simulation data. As network state observations are often scattered close to the saturation, this work not only investigates a classical unimodal approach to fit the production MFD, but also a bimodal MFD curve. A method to integrate the production hysteresis into the MFD-based approach is briefed. Different methods of calibrating trip lengths in the reservoir are introduced to study the influence of trip lengths estimation on the accuracy of MFD models. MFD models are validated against micro-simulations that are carried out using the real OD matrix and demand that are estimated from the data of Lyon city in France. Two different scenarios namely, a free flow scenario and a network saturation scenario are considered to study the influence of calibration of production hysteresis and trip length estimation. The proposed bimodal production MFD curve captures the hysteresis in the production MFD to a good extent. Following, it is shown that the refined description of trip lengths gives more accurate estimates of accumulation evolution for the trip-based approach, whereas a single trip with mean trip length produces good evolution of state variables in accumulation-based model. Finally, a test case is presented with a modified OD matrix to study the effect of OD matrix changes on accuracy of MFD simulations.

TRISTAN X, The Tenth Triennial Symposium on Transportation Analysis, Hamilton island, AUSTRALIE, 17-/06/2019 - 21/06/2019; Ride-sharing is becoming one of the most important transportation modes in many cities due to its convenience and accessibility. But it is important to assess the impact of network on the ride-sharing performance and vice versa. This paper proposes a complete framework to represent the ride-sharing system functioning. We address the problem for a city scale of 25 km2, a total market of 11,235 shareable trips over a 4 hours period with a rolling horizon of 20 minutes. Furthermore, the solutions are assessed using a dynamic trip-based macroscopic simulation to account for congestion effect and dynamic travel times that may influence the optimal solution obtained with predicted travel times. This outperforms most previous studies about optimal fleet management that usually consider constant and fully deterministic travel time functions. Then we assess the network performance under different levels of ride-sharing.

Large-scale traffic flow models based on the Network Macroscopic Fundamental Diagram (MFD) are usually grounded on the bathtub analogy and a conservation equation for vehicle accumulation inside a given urban area. Recent studies have proposed a different approach where the MFD defines the spatial mean speed that is shared by all vehicles in a region while their traveling distance is tracked individually. The former approach is also referred to as accumulation-based while the latter is usually named trip-based. While extensive studies of both model properties have been carried out for the single reservoir case (a unique region), the multi-reservoir setting still requires some research effort in particular to clearly understand how inflow merge at a reservoir entry and outflow diverge at exit should be managed. These two components play a significant role in the evolution of the whole system, when flows are exchanged between multiple reservoirs. One of the crucial questions is to ensure that congestion properly propagates backwards through a succession of reservoirs when oversaturated situations are observed. In this paper, we propose a thorough analysis of how to handle congestion propagation in the accumulation-based framework with several trip lengths or categories, e.g. internal and external trips. This allows to derive a congestion propagation model for the trip-based approach in a multi-reservoir setting. Based on theoretical considerations and simulation studies, we develop a consistent framework to restrict the inflow and adapt to oversaturated traffic conditions in a reservoir including several trip lengths. Two inflow merging schemes are investigated. The first one is inspired from the existing literature and shares the available supply based on the demand flow ratio at the entry boundary. It is called exogeneous in contrast to the second endogenous scheme, which shares the supply with respect to the internal accumulation ratio on the different routes. At the reservoir exit, a new outflow diverging scheme is also introduced to better reproduce the effect of queuing vehicles that are prevented from exiting the reservoir when congestion spills back from neighboring reservoirs. Compared to the conventional outflow model, our new approach proves to avoid unrealistic gridlocks when the reservoir becomes oversaturated. Both entry and exit flow models are investigated in details considering the accumulation-based and trip-based frameworks. Finally, the most consistent approach is compared with two other existing MFD models for multiple reservoirs. This demonstrates the importance of properly handling entry and exit flows at boundaries.

Aggregated dynamic traffic models based on the network macroscopic fundamental diagram (MFD) have been founded on stationary or steady-state approximations. Theoretically, they are only qualified with slow-varying demand profiles as inputs. In practice, MFD models are often fed with fast-varying demand patterns classically observed during peak hours. This paper investigates the accuracy of existing MFD model outputs in such a case. It also provides a fresh look at hysteresis loops that may arise on MFD shapes and shows how they are connected to the demand profile. First derived are the exact solutions of the Lighthill, Whitham, and Richards (LWR) model for a simple but meaningful test case corresponding to the loading and the recovery of an arterial with a single bottleneck. The spatial integration of these solutions permits to derive the average flow- and outflow-MFD patterns. The average flow-MFD exhibits a clockwise hysteresis loop with higher flows during the network loading than during the recovery. The outflow MFD shows an opposite trend with a counterclockwise hysteresis loop. The detailed analysis of the LWR solution highlights that these patterns are fully driven by the congestion dynamics and different wave propagation during both periods. This complements existing studies that tend to explain hysteresis by heterogeneous network loading due to unbalanced flows at intersections. It should be noted that the size of the production-MFD hysteresis loop is influenced by the demand loading profile, which means that the MFD is more sensitive to the demand than initially expected. The second part of the paper is about the implications of these observations on the MFD simulation. The main conclusion is that the accumulation-based formulation captures properly the aggregate dynamics of the LWR model during saturation but not in free-flow. It should be calibrated with an average flow-MFD that does not account for the hysteresis pattern to properly estimate the outflow. On the contrary, the original formulation of the trip-based model is only valid in free-flow. During saturation, it should be patched to modify the outflow values. The best approach is a hybrid model that combines the trip-based model in free-flow and the accumulation-based model during saturation. Note that in free-flow the trip-based model is better fed by the average flow-MFD with hysteresis as it gives better estimation for the mean speed. Finally, some evidences are presented and tend to confirm that all the conclusions remain valid for more complex urban settings.

Congestion in urban areas has become a major issue in terms of economic, social or environmental impact. For short or mid term, using dynamic road traffic simulation can help analyzing and providing guidelines to optimization policies of existing infrastructures. Today, because of the complexity of transport systems, classical modeling tools are limited to small geographical areas (of a district size). Computational time, together with simulation calibration, are notably very constraining at large scales. However, a new generation of models designed for metropolitan areas has arisen over the past decades. These models are based on a phenomenological relationship between travel production and the number of vehicles in a given spatial area of a road network, known as the Macroscopic Fundamental Diagram (MFD). This relationship, supported by empirical evidences from several cities around the world, has allowed the study of different traffic control schemes at a whole city scale, but was rarely used for traffic state forecasting. The aim of this PhD is to propose an efficient modeling tool, based upon the concept of MFD, to simulate and analyze traffic states in large metropolitan areas. The theoretical framework of this tool must be consistent and applicable for traffic state forecasting, development of new control policies, traffic emission estimation, etc. There are two major contributions in this PhD. The first one is analyzing the mathematical and physical properties of existing models, and formalizing the dynamics of several trip lengths inside the same urban zone. In particular, this formalization distinguishes between internal trips and trips crossing the zone. Flow merging and diverging issues are also addressed when congestion propagates from one zone to another. The second contribution is proposing a new trip-based model based on individual traveled distance. This approach allows to treat users independently (previously represented with continuous flows), and thus to define their characteristics more precisely to couple their trips with assignment models on different paths. Finally, examples of application from various collaborations are given in the last part of this thesis. It includes a simulation study of the Grand Lyon urban area (France), as well as new modules to simulate search-for-parking or perimeter control. This PhD is part of a European ERC project entitled MAGnUM: Multiscale and Multimodal Traffic Modeling Approach for Sustainable Management of Urban Mobility., La congestion en milieu urbain est un enjeu majeur que ce soit d’un point vue économique, social ou environnemental. À court et moyen terme, l’utilisation de la simulation dynamique du trafic routier peut permettre d’analyser et de guider des politiques d’optimisation des infrastructures existantes. Aujourd’hui, du fait de la complexité des systèmes de transport, les outils de modélisation classiques sont limités à des échelles géographiques peu étendues (de l’ordre du quartier). À grande échelle, le temps de calcul devient rapidement un facteur limitant tout comme le calibrage et la scénarisation. Néanmoins les dernières décennies ont vu l’apparition d’une nouvelle génération de modèles bien adaptés aux métropoles urbaines. Ceux-ci sont basés sur une relation phénoménologique entre la production de déplacements et le nombre de véhicules dans une zone spatiale d’un réseau routier, appelée Diagramme Fondamental de Zone (Macroscopic Fundamental Diagram, MFD). Cette relation, validée empiriquement sur de nombreuses villes, a permis d’étudier différentes méthodes de contrôle du trafic pour une ville entière, mais a été peu utilisée à des fins de prévision de la congestion. L’objectif de cette thèse est de proposer un premier outil opérationnel de simulation et d’analyse des grands réseaux de métropoles, en utilisant et développant les modèles de trafic basés sur la relation MFD. Cet outil doit posséder un cadre théorique cohérent qui puisse convenir à des applications telles que la prévision d’états de trafic, le développement de nouvelles politiques de contrôle, l’estimation de pollutions liées au trafic, etc. Les contributions de la thèse portent sur deux aspects. Le premier est l’analyse des propriétés mathématiques et physiques des modèles existants, en incluant une formalisation complète de la gestion de plusieurs longueurs de parcours au sein d’une même zone urbaine. En particulier, cette formalisation traite de la distinction des trajets internes à la zone et des problèmes de flux convergents et divergents pour les trajets traversant la zone lorsque la congestion se propage d’une zone à l’autre. Le deuxième aspect est la proposition d’un nouveau modèle basé sur la distance individuelle parcourue à l’intérieur d’une zone urbaine (trip-based). Cette approche permet d’individualiser les usagers (auparavant représentés sous forme de flux continus) et donc de définir plus finement leurs caractéristiques, en vue de coupler leurs déplacements à des modèles d’affectations sur différentes routes. Enfin, des exemples d’application illustrant diverses collaborations sont donnés en dernière partie de la thèse. La simulation du trafic sur l’aire urbaine du Grand Lyon (France) y est présentée, ainsi que de nouveaux modules de modélisation de la recherche de parking ou de contrôle périphérique. Cette thèse est partie intégrante d’un projet européen ERC intitulé MAGnUM : Approche multi-échelle et multimodale de la modélisation du trafic pour une gestion durable de la mobilité urbaine.

RelStat 2018, International Conference on Reliability and Statistics in Transportation and Communication, Riga, LETTONIE, 17-/10/2018 - 20/10/2018; Nowadays, the tram knows a new golden age since that modern cities prefer a sustainable mobility. It can give a decent image to the urban area and contributes to meet the increasing mobility demand. Without an optimal operation and a better service, the tram can't be an efficient means of transport that may help in decongesting road traffic. Passengers would prefer to use vehicles than trams, therefore road traffic will increase and this, will complicate more the state of traffic in cities. This study uses the opinion of tram users. The main objective is to show the strong characteristics that attract the population to use tram and also its weaknesses. This last, may be improved to reach the maximum satisfaction of users. In addition, this article attempts to show the preferred means of transport for passengers who travel on the same line as the tram. The results show that despite the different advantages of the tram, the population is not all satisfied about its exploitation and about its service quality. Almost 40% of passengers, declare that they prefer to use their cars or taxis than a tram; as a result, the road traffic situation may get complicated in the future. Finally, solutions to ameliorate the performance of the tram and its services' quality are presented, in order to attract more tram users and to reduce traffic congestion in urban areas.

hEART 2018, 7th Symposium of the European Association for Research in Transportation, Athènes, GRECE, 05-/09/2018 - 07/09/2018; We investigated the ideas of an aggregated traffic modeling , as initially introduced by Godfrey (1969) and later revisited by Daganzo (2007) and Geroliminis & Daganzo (2008). For this type of modeling, the city is divided into reservoirs (Fig. 1) where the traffic conditions are approximately homogeneous and are given by a well-defined low scatter Macroscopic Fundamental Diagram (MFD). The MFD is a relationship between the aggregated flow q and aggregated density k. The traffic dynamic inside a reservoir is governed by the evolution of the vehicles accumulation n at a given time instant t Daganzo (2007).

DTA 2018, 7th International Symposium on Dynamic Traffic Assignment, Hong Kong, CHINE, 06-/06/2018 - 08/06/2018; In this work, we propose to revisit some route choice frameworks and investigate how different kinds of user's behavior influences the network performance in terms of level of congestion and travel times. We also analyze the differences between the individual route flows. For this, we consider time-dependent route costs that account for congestion and spillback effects. Traffic dynamics is reproduced by a LWR mesoscopic simulator (Leclercq and Becarie, 2012) and the case study is a Manhattan network.

Disruptive events occur on road networks on a daily basis and affect traffic flow. Resilience analysis aims to assess the consequences of such disruptions by quantifying the ability of a network to absorb and react to adverse events. In this paper, we advance a methodological approach based on resilience stress testing and a dynamic mesoscopic simulator. We aim to identify and rank the links most critical to the overall performance of the road network, taking into account the dynamic properties of road traffic and focusing on day-to-day disruptions. As a metric to quantify road network performance in the presence of such disruptions, we use the increase in overall travel cost. We then compare our approach with purely topological approaches. We discuss the advantages and drawbacks of the different analyzed metrics. We prove that link ranking varies greatly depending on the metric. Specifically, the proposed stress testing methodology can produce very accurate results by taking into account demand and congestion, but requires computationally intensive simulations, being therefore prohibitive even on medium-sized networks. Conversely, purely static topological metrics can be inaccurate if they do not take into account traffic demand and network dynamics. Our study highlights the need for joining traditional traffic-agnostic topological resilience analysis with demand-aware dynamic stress testing techniques.

TRB'18, 97th Annual Meeting of the Transportation Research Board, Washington, Etats-Unis, 06-/01/2018 - 11/01/2018; In this paper, we propose a thorough analysis of the way congestion is usually handled in the accumulation-based framework. This serves as a basis to implement a proper congestion propagation model in the trip-based approach. Theoretical and simulation studies show that in case of several trip lengths in a zone, there exists only one form of inflow limitation at the reservoir entry that complies with the global constraints on flow and production.

In this paper, we revisit the principle of bounded rationality applied to dynamic traffic assignment to evaluate its influences on network performance. We investigate the influence of different types of bounded rational user behavior on (i) route flows at equilibrium and (ii) network performance in terms of its internal, inflow, and outflow capacities. We consider the implementation of a bounded rational framework based on Monte Carlo simulation. A Lighthill-Whitham-Richards (LWR) mesoscopic traffic simulator is considered to calculate time-dependent route costs that account for congestion, spillback, and shock-wave effects. Network equilibrium is calculated using the Method of Successive Averages. As a benchmark, the results are compared against both Deterministic and Stochastic User Equilibrium. To model different types of bounded rational user behavior we consider two definitions of user search order (indifferent and strict preferences) and two settings of the indifference band. We also test the framework on a toy Braess network to gain insight into changes in the route flows at equilibrium for both search orders and increasing values of aspiration levels.

Dedicated bus lanes (DBLs) are a common traffic management strategy in cities as they improve the efficiency of the transit system by preventing buses from getting trapped in traffic jams. Nevertheless, DBLs also have certain disadvantages: they consume space, reduce available capacity for general traffic, and can thus lead to even more congested car traffic situations. It is appealing to find more efficient alternatives that maintain a sufficient network supply for general traffic while guaranteeing high commercial speeds for the bus system. This paper investigates whether perimeter control (gating) could be such an alternative to DBL strategies. This solution aims at controlling the traffic conditions of a given area by monitoring vehicle accumulations and adapting traffic signal parameters to reach the targeted conditions. If free-flow states can be maintained within the zone, then DBLs become superfluous. This hypothesis is examined through a simulation case study with an urban arterial acting as the targeted area. A dual-objective control approach was applied to allow for not only the vehicle accumulation inside the area but the queue lengths at its perimeter, thereby addressing one of the main issues associated with gating schemes. Due to the gating strategy, traffic performance in the arterial, measured through vehicle accumulation plus mean speed and density, improved significantly. Moreover, results showed that bus operations reach almost the same efficiency level when DBLs are replaced by perimeter control. Furthermore, the availability of an additional lane for general traffic in the control case significantly increased the arterial capacity for cars.

TRB 2018, Transportation research board annual meeting, Washington, ETATS-UNIS, 06-/01/2018 - 11/01/2018; In this paper, we discuss three methods to calculate the macroscopic trip lengths based on the aggregation of microscopic trips, within each reservoir considering: (i) no further information; (ii) the next reservoir to be traveled; and (iii) the related macro-path (i.e., the list of reservoirs that are crossed from the Origin to the Destination).

According to COP21, COP22 a COP23 recommendations, and European commission policy in terms of mobility for growth and sustainable development, many experiences are tested in Europe for reducing road transportation, congestions, CO2 emission, and increasing people quality of life, environment protection, and company performance by proposing alternative solutions (rail, waterborne, bike…) in urban logistics and mobility. This paper presents collaborative concepts developed for reducing road transportation and congestion and improving urban logistics and mobility by implementing alternative solutions (combination of road with waterborne and rail) in a region. A simulation tool is used for describing actual situation of a city. Then this model is analyzed and scenarios containing alternative solutions are proposed. The specifications of a software tool being developed for implementing co-creative and co-innovative solutions on a city or region is presented. This tool will be deployed in website, mobile and public terminal (Human/Machine Interface) for capitalizing citizens opinions and propositions for improving their city. An illustration is proposed by explaining in detail how they are being used in the area of GPS (Grand Paris Sud), the south-east of Paris region. The “Grand Paris Sud” region, is composed of 24 small cities with 342 000 peoples and 19 000 companies. This conglomeration is attractive for logistic companies, due to its proximity with Paris, Orly Airport (second airport of Paris) and highways.

In this paper, we investigate the day-to-day regularity of urban congestion patterns. We first partition link speed data every 10 min into 3D clusters that propose a parsimonious sketch of the congestion pulse. We then gather days with similar patterns and use consensus clustering methods to produce a unique global pattern that fits multiple days, uncovering the day-to-day regularity. We show that the network of Amsterdam over 35 days can be synthesized into only 4 consensual 3D speed maps with 9 clusters. This paves the way for a cutting-edge systematic method for travel time predictions in cities. By matching the current observation to historical consensual 3D speed maps, we design an efficient real-time method that successfully predicts 84% trips travel times with an error margin below 25%. The new concept of consensual 3D speed maps allows us to extract the essence out of large amounts of link speed observations and as a result reveals a global and previously mostly hidden picture of traffic dynamics at the whole city scale, which may be more regular and predictable than expected.

ITEA Annual Conference, BARCELONE, ESPAGNE, 21-/06/2017 - 23/06/2017; This paper shows that when analysing the optimal provision of road transport in an urban area, passenger transport and freight transport should be distinguished. Their cost structure, demand structure, and externalities are different, and this should be accounted for.

Le transport de marchandises en ville (TMV) est essentiel à la vitalité économique des espaces urbains, mais également source de nombreuses externalités négatives, notamment la congestion routière. Si la quantification des pertes de temps est aujourd'hui une pratique banalisée, la contribution du TMV est moins fréquemment étudiée. Par ailleurs, l'impact réciproque de la congestion sur les opérations de TMV est relativement peu abordé, surtout dans une perspective urbanistique et géographique. L'occupation de la voirie par les véhicules de fret et leurs impacts sur l'organisation du TMV peuvent pourtant être considérés comme étant le résultat de divers phénomènes socio-spatiaux. Cette thèse se propose d'analyser l'occupation de la voirie par les véhicules de fret, l'importance des coûts de congestion routière associés ainsi que les incidences sur l'organisation du TMV. D'un point de vue théorique, le TMV est pensé comme un ensemble de processus industriels (à envisager dans un cadre plus large que la simple opération de transport) aux caractéristiques variées, mis en place par des entreprises pour répondre aux besoins d'approvisionnement des entreprises d'une aire urbaine. Nous analysons l'articulation de ces processus industriels avec les formes urbaines de la ville, et en particulier la relation difficile qu'entretiennent le TMV et la densité spatiale. D'un point de vue empirique, nous mobilisons plusieurs sources de données décrivant les flux de marchandises en Ile-de-France (dont l'enquête Transport de Marchandises en Ville (ETMV) réalisée en 2010). Outre son ancrage fort dans un référentiel spatial, nos analyses empiriques de la contribution du TMV à la congestion routière s'inspirent de plusieurs cadres disciplinaires, l'ingénierie des trafics et l'économie des transports notamment. Urban goods movements (UGM) are essential for economic activities in urban areas, but they are also generators of negative externalities, including road congestion. However, while the measure of time losses is a common research practice, the contribution of UGM is less frequently studied. The reciprocal impact of congestion on UGM operations is also rarely addressed, especially from the perspective of urban planning and geographic analysis. Yet, road occupancy by freight vehicles, and their impacts on UGM operations and organization can be considered as the results of socio-spatial phenomena. This thesis proposes an analysis of road occupancy by freight vehicles, of the induced congestion costs, and of the impacts on UGM operations. From a theoretical perspective, we analyze UGM as various industrial processes (which should be considered in a wider perspective than mere transport operations), with diverse characteristics, implemented by companies to meet the supply needs of companies of an urban area. We also emphasize the need to analyze the links between those industrial processes and urban forms, and we focus on the difficult relationship between UGM and urban density. From an empirical perspective, we rely on several data sources (including the Urban Goods Movements Survey (UGMS) performed in Ile-de-France in 2010). While our framework is grounded in spatial analysis, our empirical analyses of road congestion induced by urban freight also borrow from several disciplinary fields, including traffic engineering and transport economics.

Road congestion is not only an issue for major European urban agglomerations, but also for smaller ones. It is also the case of the Lens urban area, where car use is much higher than the average for medium-sized urban agglomerations in France. Local authorities put forward tramway projects to deal with the strongly negative externalities of congestion: travel time losses and pollution. To analyze its medium-term impact, we have developed a commuting with congestion model, inspired by the four-step traditional model, but with data made available from an origin- destination matrix. The results are encouraging but insufficient, and it is necessary to adopt supplementary measures in order to retrieve and justify the sizeable investments needed. Some measures prove to be very efficient, such as parking fees and urban tolls. Other measures, such as the subsidization of public transport, are partially efficient since they have an impact mainly on intra-urban commutes.; La congestion routière n'est pas seulement un problème pour les grandes agglomérations urbaines européennes, mais aussi pour les plus petites. C'est également le cas de la zone urbaine de Lens, où l'utilisation des voitures est nettement supérieure à la moyenne des agglomérations urbaines de taille moyenne en France. Les autorités locales ont présenté des projets de tramway pour faire face aux externalités fortement négatives de la congestion, soit d'une part, les pertes de temps de déplacement et d'autre part, les problèmes liés à la pollution. Pour analyser l'impact à moyen terme de ce projet de tramway sur l'agglomération lensoise, nous avons développé un modèle de déplacements domicile-travail qui tient compte de la congestion, inspiré du modèle traditionnel en quatre étapes, utilisant des données disponibles à partir d'une matrice d'origine-destination. Les résultats sont encourageants mais insuffisants et il est nécessaire d'adopter des mesures complémentaires pour rentabiliser et justifier les investissements importants nécessaires d'un tel projet. Certaines mesures se réÏlent très efficaces, comme la tarification du stationnement et les péages urbains. D'autres mesures, telles que le subventionnement des transports publics, sont partiellement efficaces car elles ont un impact principalement sur les déplacements intra-urbains.

This paper investigates the dynamic user equilibrium (DUE) on a single origin-destination pair with two alternative routes, a freeway with a fixed capacity and the surrounding city-streets network, modeled with a network macroscopic fundamental diagram (NMFD). We find using suitable transformations that only a single network parameter is required to characterize the DUE solution, the freeway to NMFD capacity ratio. We also show that the stability and convergence properties of this system are captured by the constant demand case, which corresponds to an autonomous dynamical system that admits analytical solutions. This solution is characterized by two critical accumulation values that determine if the steady state is in free-flow or gridlock, depending on the initial accumulation. Additionally, we also propose a continuum approximation to account for the spatial evolution of congestion, by including variable trip length and variable NMFD coverage area in the model. It is found that gridlock cannot happen and that the steady-state solution is independent of surface network parameters. These parameters do affect the rate of convergence to the steady-state solution, but convergence rates appear virtually identical when time is expressed in units of the NMFD free-flow travel time.