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International audience; Advances in Information and Communication Technologies (ICT) allow the transportation community to foresee dramatic improvements for the incoming years in terms of a more efficient, environmental friendly and safe traffic management. In that context, new ITS paradigms like Cooperative Systems (C-ITS) enable an efficient traffic state estimation and traffic control. C-ITS refers to three levels of cooperation between vehicles and infrastructure: (i) equipped vehicles with Advanced Driver Assistance Systems (ADAS) adjusting their motion to surrounding traffic conditions; (ii) information exchange with the infrastructure; (iii) vehicle-to-vehicle communication. Therefore, C-ITS makes it possible to go a step further in providing real time information and tailored control strategies to specific drivers. As a response to an expected increasing penetration rate of these systems, traffic managers and researchers have to come up with new methodologies that override the classic methods of traffic modeling and control. In this paper, we discuss some potentialities of C-ITS for traffic management with the methodological issues following the expansion of such systems. Cooperative traffic models are introduced into an open-source traffic simulator. The resulting simulation framework is robust and able to assess potential benefits of cooperative traffic control strategies in different traffic configurations.

In a near future, connected and automated vehicles will progressively replace current vehicles, leading to deep changes in transportation. The driver will be soon assisted and then replaced by an embedded system, able to act quicker, relying on a more robust and precise representation of its surrounding environment. However, some steps are still needed before coming up with such a level of automation since the vehicle environment is complex and unpredictable.This work intends to anticipate the introduction of these new kinds of vehicles by providing cooperative behaviors at both infrastructure and vehicle levels, at the same time allowing a decentralized control of these systems. We propose a distributed modeling framework, using multi-agent systems, relying on the coupling of the system dynamics\string: information, communication and reliability (modeled through the concept of trust).The next step was to develop a simulation framework enabling the implementation of our models for connected vehicles applications. We present a new microscopic traffic simulator, built as an extension of an existing platform, and able to model information exchanges using messages between vehicles and with the infrastructure. All data are provided by sensors and all entities, modeled as agents, are autonomous regarding their decision process. Thanks to the simulator, it is possible to imagine new control strategies relying on recommendations disseminated by the connected infrastructure. Consistency and interdependence of the simulator components are ensured by the dynamic coupling. As for the vehicles' dynamics, we propose a bilateral multi-anticipative model that integrates additional information from communications in the vehicle decision process. Results in simulation confirm that the model is able to reduce the propagation of perturbation through the flow, leading to a more homogeneous and stable traffic.One of the major issues regarding traffic control strategies will be to dynamically adapt the action policy to the several deployment stages of cooperative transportation systems. The similarities with Artificial Intelligence problems like cognition motivate a more abstract study\string: how to model an autonomous system able to control its environment. We choose the constructivist approaches, that propose to model the cognition process as an iterative building process. For cooperative traffic, the benefits lie in the ability of the system to generate its own strategies, relying or not on domain specific knowledge, and then make them evolve to be adapted to vehicles in the flow. The results from our approach are presented in two distinct simulation frameworks. The first one is an experimentation prototype aiming at highlighting the low-level behaviors in a simplified environment. In this context, we show that the model is able to combine efficiently several individual concurrent representations in order to build a high-level representation that can be adapted to several contexts. The second framework is the traffic simulator where the results lead to some insights about the potential of our approach for such realistic applications., Dans un proche futur, les véhicules connectés et autonomes remplaceront nos véhicules actuels, et il sera nécessaire de repenser intégralement la mobilité. Le conducteur, avec ses lacunes, sera de plus en plus assisté, et un jour détrôné par un système embarqué, capable d'agir plus rapidement, tout en ayant une représentation plus précise et fiable de son environnement de navigation. Pourtant, il reste encore du chemin à parcourir avant d'arriver à un tel stade de maturité : l'environnement du véhicule est complexe, imprévisible et conflictuel, car partagé avec d'autres acteurs de la mobilité.Ce travail de thèse vise à anticiper l'arrivée de ces nouveaux véhicules afin de proposer des comportements coopératifs au niveau des véhicules et de l'infrastructure tout en permettant un contrôle décentralisé de ce type de système complexe. Dans le cadre d'une approche multi-agents et d'une architecture distribuée, nous proposons d'abord une modélisation par couplage des dynamiques physique et communicationnelle, auxquelles s'ajoute une intégration de la fiabilité de l'information (confiance).L'étape suivante a été de développer un cadre de simulation propice à l'implémentation de nos modèles dans le cas des véhicules connectés. Nous introduisons un nouveau simulateur de trafic, construit comme une extension d'une plateforme existante, au sein duquel les flux d'informations entre les véhicules et avec l'infrastructure prennent la forme d'échanges de messages. Toutes les informations du système proviennent de capteurs, et toutes les entités, modélisées comme des agents, sont autonomes dans leur prise de décision. De nouvelles formes de contrôle sont désormais envisageablesen utilisant des consignes transmises par l'infrastructure communicante. Le couplage des dynamiques assure la cohérence et l'inter-dépendance des différents modèles dans le simulateur. Nous montrons en simulation que, grâce à l'intégration d'informations supplémentaires via la communication, les véhicules, modélisés par un modèle microscopique multi-anticipatif bilatéral, sont capables de réduire l'effet de perturbations propagées au sein d'un flux. En termes de stratégies de contrôle, une des problématiques principales est de garantir une forme de contrôle qui s'adaptera aux différentes phases de déploiement des systèmes coopératifs. L'analogie avec des problèmes de l'IA (problème de cognition) nous a mené à traiter le problème de manière plus abstraite : comment permettre à un système autonome de contrôler son environnement. Les approches constructivistes, que nous avons retenues, modélisent le processus de cognition comme un phénomène de construction itératif. Pour le trafic coopératif, l'avantage est de disposer d'un système capable de générer ses propres stratégies, en utilisant ou non des connaissances expertes, et de les faire évoluer au cours du temps pour s'adapter aux véhicules composant le flux. Les résultats de notre approche sont présentés dans deux cadres de simulation. Le premier est un prototype visant à illustrer les comportements de bas niveau dans un environnement simplifié. Nous montrons que le modèle est capable dans ce cadre de combiner différentes représentations individuelles pour construire une représentation et de s'adapter à différents contextes en les recombinant dynamiquement. Puis, dans le cadre de simulation du trafic coopératif, les résultats laissent entrevoir le potentiel de notre approche dans des applications réelles.

In the last decade, humanity has faced many different pandemics such as SARS, H1N1, and presently novel coronavirus (COVID-19). On one side, scientists have developed vaccinations, and on the other side, there is a need to propose models that can help in understanding the spread of these pandemics as it can help governmental and other concerned agencies to be well prepared, especially for pandemics, which spreads faster like COVID-19. The main reason for some epidemic turning into pandemics is the connectivity among different regions of the world, which makes it easier to affect a wider geographical area, often worldwide. Also, the population distribution and social coherence in the different regions of the world are non-uniform. Thus, once the epidemic enters a region, then the local population distribution plays an important role. Inspired by these ideas, we propose two versions of our mobility-based SIR model, (i) fully-mixed and (ii) for complex networks, which especially takes into account real life interactions. To the best of our knowledge, this model is the first of its kind, which takes into account the population distribution, connectivity of different geographic locations across the globe, and individuals' network connectivity information. In addition to presenting the mathematical proof of our models, we have performed extensive simulations using synthetic data to demonstrate the generalization capability of our models. Finally, to demonstrate the wider scope of our model, we applied our model to forecast the COVID-19 cases at county level (Estonia) and regional level (Rhône-Alpes region in France).

Railway operations in dense traffic areas are very sensitive even to small disturbances, and thus require careful planning and real-time management. Dwell times in stations are in particular subject to a high variability and are hard to predict; this is mostly due to the interactions between passengers and the railway system during the dwelling process. This paper presents a data-driven approach for assessing the influence of the numbers of alighting, boarding and on board passengers on the dwell time. We propose to split the dwell time into a deterministic component depending on the passenger flow, called the Minimum Dwell Time, and a random component. A method for estimating the minimum dwell time is provided. Based on the knowledge of this value, observations can be classified according to the main determinant of dwell time, namely timetable constraints or passenger exchange. The latter observations are used for estimating the conditional distribution of dwell time given passenger flows. Numerical experiments are carried out on stations located inside the dense traffic area of Paris suburban network. The obtained results indicate that the presented method can be used for a variety of applications, such as capacity assessment or stochastic simulation.

hEART 2020, 9th Symposium of the European Association for Research in Transportation - Virtual conference, Lyon, FRANCE, 03-/02/2021 - 04/02/2021; This work focuses on dynamic sustainable system optimum (SSO). When carrying out dynamic SSO assignment, instead of minimizing their own travel cost of each user, we aim to define an SSO status to minimize the on-road traffic emissions of the whole system. The method is based on an system-optimization program. This work proposes a simple, intuitive, simulation-based methodological framework for solving the SSO problem. The main contribution of this paper is to propose a statistically reliable learning process of link marginals that permits to derive link performance function from any DTA simulator. Then the obtained functions can be used to compute network equilibrium including SSO. A test case is carried out on a toy network. Numerical results show that with the help of the proposed method, we can reduce 5.79% of carbon monoxide (CO) emission on the whole system under SSO equilibrium, when compared with UE.

TRB 2021, Transportation Research Board 100th Annual Meeting - A Virtual Event, Washington DC, ETATS-UNIS, 05-/01/2021 - 29/01/2021; The design of network-wide schemes requires computationally efficient traffic models. The Macroscopic Fundamental Diagram (MFD) is a promising tool for such an application. Unfortunately, current semi-analytical approaches require an inaccurate network reduction to a corridor. Our methodology allows us to estimate the MFD for general networks, without the information loss induced by the reduction of networks to corridors. The model is based on the method of cuts, but can account for different demand patterns, and determine the upper bound of network flow. Thereby, we consider flow conservation and the effects of spillbacks at the network level. Furthermore, we propose an overall framework which decomposes the network into a set of corridors, and then applies our model to each of them while taking into account the dependencies across corridors (e.g. due to turning flows and spillbacks). Aggregating the results leads then to a network MFD. The proposed framework applies to any general network, and we show a proof of concept using a simple but sufficient case study. We compare the results to the original method of cuts, and a ground truth derived from a microscopic simulation. Analysis reveals a strongly reduced error of the estimated MFD based on our method, compared to the currently applied approach. The potential of this methodology lies in its implementation simplicity and reduced computational cost.

This paper investigates the accuracy of mean density estimation from direct sensing at link and network levels. Different calculation methods are compared depending on sensor type, probe vehicles or loop detectors, and availability to quantify the magnitude of expected errors. Probe data are essential to reduce the error but accurate density estimation requires high penetration rates, which is hardly true in practice. We enhance the fishing rate method, i.e. using the ratio of probes detected at the loop locations over the loop flow, to estimate density. Accurate density estimation at the link level can only be obtained when probes and loop data are available in real-time. At the network level, accurate density estimations can be obtained when combining loop and probe observations, even if few links capture both data sources. It requires applying the proper analytical formulation to aggregate the local observations, i.e. carefully defining fishing rates at this scale.

Urban road transportation performance is the result of a complex interplay between the network supply and the travel demand. Fortunately, the framework around the macroscopic fundamental diagram (MFD) provides an efficient description of network-wide traffic performance. In this paper, we show how temporal patterns of vehicle traffic define the performance of urban road networks. We present two high-resolution traffic datasets covering a year each. We introduce a methodology to quantify the similarity of macroscopic traffic patterns. We do so by using the concepts of the MFD and a dynamic time warping (DTW) based algorithm for time series. This allows us to derive a few representative MFD clusters that capture the essential macroscopic traffic patterns. We then provide an in-depth analysis of traffic heterogeneity in the network which is indicative of the previously found clusters. Thereupon, we define a parsimonious classification approach to predict the expected MFD clusters early in the morning with high accuracy.

hEART 2020, 9th Symposium of the European Association for Research in Transportation - Virtual conference, Lyon, FRANCE, 03-/02/2021 - 04/02/2021; This paper investigates the accuracy of accumulation estimation at different urban scales, mainly link and network levels. Different methods are compared depending on sensor types, probe vehicles or loop detectors, and historical data availability. Probe vehicles can provide an accurate estimation of the average travel times but only for a given vehicle sample. So, we resort to loops to calculate the fishing rate and scale up the observations to all vehicles. The fishing rate is the ratio of probes that are detected at the loop locations over the loop flow. Accurate accumulate estimation at the link level can only be obtained when probes and loop data are available in real-time. Methods based on spatially aggregated fishing rates when probe data are available but not loops show better results, but the errors are still significant at the link level. It is possible to obtain accurate accumulation estimations at the network level when combining loop and probe observations, even if few links capture both data sources. It requires applying proper aggregation technics and a careful definition of fishing rates at this scale.

Nowadays a large amount of data is originated by complex systems, such as social networks, transportation systems, computer and service networks. These systems can be modeled by using graphs and studied by exploiting graph metrics, such as Betweenness Centrality (BC), a popular metric to analyze node centrality of graphs. In spite of its great potential, this metric requires long computation time, especially for large graphs. In this paper, we present a very fast algorithm to compute BC of undirected graphs by exploiting clustering. The algorithm leverages structural properties of graphs to find classes of equivalent nodes: by selecting one representative node for each class, we are able to compute BC by significantly reducing the number of single-source shortest path explorations adopted by Brandes' algorithm. We formally prove the graph properties that we exploit to define the algorithm and present an implementation based on Scala for both sequential and parallel map-reduce executions. The experimental evaluation of both versions, conducted with synthetic and real graphs, reveals that our solution largely outperforms Brandes' algorithm and significantly improves known heuristics.

This study reviews existing computational methods to calculate simulation-based dynamic network equilibrium. We consider a trip-based multi-modal approach for the dynamic network loading. Mode and path choices are carried out at the same level; therefore, travel times depend on the travel path and the mode attributes of travelers. This study develops a multi-class model with several parameters per class. Two different categories of algorithms (heuristic and meta-heuristic) are considered in order to solve the discrete DTA problem. Finally, we analyze the equilibrium in a large-scale multi-modal DTA test case (Lyon 6th + Villeurbanne) in order to investigate the performance of different optimization approaches to solve trip-based DTA. The results show that, in a multimodal and heterogeneous setting, the meta-heuristic methods provide better solutions than the heuristic methods in terms of optimality and computation time. These improvements are even more significant than in a homogeneous setting.

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.

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.

Analysing the properties of a network equilibrium can help to have a better view about network state, robustness, and the effect of any variation in the network. This study investigated the impacts of network design history on day-to-day multimodal user equilibrium. In particular, we analyze the long-term evolution of the network, including opening new multimodal options and its impacts on the final network equilibrium. First, the analysis focuses on static network loading with different successive configurations. Then, a more realistic setting is studied by simulation. A large-scale multimodal network with the flexible opening over time of three possible transport facilities shows that the final equilibrium is not unique; more importantly, significant differences can be observed in public transportation occupancy, while user equilibrium is enforced in all situations. Some solutions prove to be better from the collective viewpoint (shorter total travel time), thus giving new insight into public transport planning.

The estimation of trip lengths has been proven to be a key feature for the application of aggregated traffic models based on the Macroscopic Fundamental Diagram. The paths and distances to be traveled by vehicles in regional networks vary over time, due to changes in the traffic conditions. In this paper, we develop a methodological framework to explicitly determine traffic-dependent regional paths and estimate their travel distances. This framework is incorporated into a dynamic traffic assignment module designed to target the Deterministic and Stochastic User Equilibrium in regional networks. We first discuss how regional paths and their characteristic trip lengths are influenced by changes in the regional traffic dynamics. We then test the proposed methodology for estimating traffic-dependent travel distances on small and medium-sized networks, considering a simulation environment. We show that our methodology provides good estimations of the traffic-dependent trip lengths. Our results also shed light on the importance of how time-dependent trip lengths influence the traffic dynamics in the regions.

4th webinar Ectri Traffic Management Thematic Group, Bron, FRANCE, 08-/10/2020 - 08/10/2020; We realized an analysis of the CACC data of the Volpe laboratory. We observe that the response times distributions are spreading when the distance to the leader and the position in the platoon increase. This leads us to question the connectivity inside those platoons.

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.

IEEE ITSC 2020, 23rd IEEE International Conference on Intelligent Transportation Systems, Rhodes, GRECE, 20-/09/2020 - 23/09/2020; In order to provide a satisfying quality of service to passengers, companies operating suburban trains seek to implement timetables that perform well despite the occurrence of random disturbances. However, due to some specificities of Mass Transit, the standard approaches for robust train timetabling do not apply in that context. In this paper, we present a data-based stochastic simulation method for assessing the efficiency of train timetables for dense traffic areas. We describe models for the random variabilities that occur daily on such networks, and use them in a stochastic simulation algorithm. Results are presented on a saturated line of Paris suburban network.

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

Coupling a traffic microsimulation with an emission model is a means of assessing fuel consumptions and pollutant emissions at the urban scale. Dealing with congested states requires the efficient capture of traffic dynamics and their conditioning for the emission model. Two emission models are investigated here: COPERT IV and PHEM v11. Emission calculations were performed at road segments over 6 min periods for an area of Paris covering 3 km2. The resulting network fuel consumption (FC) and nitrogen oxide (NOx) emissions are then compared. This article investigates: (i) the sensitivity of COPERT to the mean speed definition, and (ii) how COPERT emission functions can be adapted to cope with vehicle dynamics related to congestion. In addition, emissions are evaluated using detailed traffic output (vehicle trajectories) paired with the instantaneous emission model, PHEM. COPERT emissions are very sensitive to mean speed definition. Using a degraded speed definition leads to an underestimation ranging from -13% to -25% for fuel consumption during congested periods (from -17% to -36% respectively for NOx emissions). Including speed distribution with COPERT leads to higher emissions, especially under congested conditions (+13% for FC and +16% for NOx). Finally, both these implementations are compared to the instantaneous modeling chain results. Performance indicators are introduced to quantify the sensitivity of the coupling to traffic dynamics. Using speed distributions, performance indicators are more or less doubled compared to traditional implementation, but remain lower than when relying on trajectories paired with the PHEM emission model.; Le couplage d'une microsimulation de trafic avec un modèle d'émissions est un moyen d'évaluer les consommations de carburant et les émissions polluantes à l'échelle urbaine. Le traitement des états congestionnés nécessite l'intégration efficace de la dynamique du trafic et son conditionnement pour le modèle d'émissions. Deux modèles d'émissions sont étudiés ici: COPERT IV et PHEM v11. Des calculs d'émissions ont été réalisés sur des tronçons routiers sur des périodes de 6 minutes pour une zone de Paris de 3 km2. La consommation de carburant (FC) et les émissions d'oxydes d'azote (NOx) du réseau qui en résultent sont ensuite comparées. Cet article analyse : (i) la sensibilité de COPERT à la définition de la vitesse moyenne, et (ii) la manière dont les fonctions d'émission de COPERT peuvent être adaptées pour faire face à la dynamique des véhicules liée à la congestion. De plus, les émissions sont évaluées à l'aide de données détaillées sur le trafic (trajectoires des véhicules) jumelées au modèle des émissions instantanées, PHEM. Les émissions de COPERT sont très sensibles à la définition de la vitesse moyenne. L'utilisation d'une définition de vitesse dégradée conduit à une sous-estimation allant de -13% à -25% pour la consommation de carburant en période de congestion (de -17% à -36% respectivement pour les émissions NOx). L'inclusion de la distribution de vitesse avec COPERT entraîne une augmentation des émissions, surtout en conditions de congestion (+13% pour le FC et +16% pour le NOx). Enfin, ces deux implémentations sont comparées aux résultats de la chaîne de modélisation instantanée. Des indicateurs de performance sont introduits pour quantifier la sensibilité du couplage à la dynamique du trafic. En utilisant des distributions de vitesse, les indicateurs de performance sont plus ou moins doublés par rapport à la mise en oeuvre traditionnelle, mais restent inférieurs à ceux qui reposent sur des trajectoires couplées au modèle d'émission PHEM.

This paper extends a trip-based aggregate dynamic traffic model to account for on-street parking search. The trip-based approach for a road network defined as a reservoir characterizes the internal traffic states by a macroscopic fundamental diagram (MFD) in speed while individualizing all vehicle travel distances. This paper first investigates distances to park for on-street parking based on real data in Lyon (France) and stochastic numerical experiments. An updated formulation compared to the existing literature is proposed for the relation between such distances and the parking occupancy. This new formulation is then incorporated into an event-based numerical scheme that solves the trip-based MFD model.

TRB 2020, 99th Annual Meeting Transportation Research Board, Washington, ETATS-UNIS, 12-/01/2020 - 16/01/2020; This work focuses on the estimation ofMacroscopic Fundamental Diagram (MFD) and trip lengths for the city scale network using mobile phone data for the city of Dallas. The data used in the present work has different sampling intervals and in order to have an accurate estimation of travelled distance, a trip enhancement method is proposed based on map-matching scheme. Data from peak hour OD matrix and loop-detectors are fused together to obtain a typical full day OD matrix of Dallas city. Using the enhanced trip set and OD matrix data, penetration rates that depend on OD pair and time are estimated. The macroscopic variables computed from the data are expanded using the penetration rates. Finally, MFDs and trip lengths are estimated for the partitioned net work. An analysis on the trip lengths is presented to demonstrate the differences in trip length distributions that depend on the OD pair and macro-path.

Although emission models have been designed using vehicle data over driving cycles of a few minutes, they are often applied at large scale to estimate total emission (inventories). In between, there is a range of scales in use in traffic and environmental studies (road sections, sub-areas, etc.). Coupling a traffic microsimulation with COPERT emission factors at different scales reveals scaling biases. We compare network fuel consumption (FC) and nitrogen oxide (NOx) emissions resulting from emission calculations based on different spatial decompositions. The results show that for an area of Paris covering 3 km2, the differences due to the aggregation scale for emissions range from 5 to 17% depending on the pollutant, spatial partitioning and traffic conditions. These discrepancies can be reduced using a distance-weighted mean speed, which is not a scale-consistent definition of mean travel speed. They can almost be cancelled by using a correction term derived analytically in this paper, thus consistency can be guaranteed between emissions assessed at different scales. Finally, a case study shows that it is possible to evaluate FC and NOx emissions on a large-scale network from a sample of traffic data (probes), and obtain the corrective term to be applied to remove scaling bias. The most critical step is the accurate estimation of the total travel distance. The gaps were successfully reduced to a maximum of 8% in congestion for a penetration rate of about 20%.

In this paper, we revisit the concept of Prospect-basedUser Equilibrium in a dynamic context. We consider a mesoscopic Lighthill-Whitham-Richards(LWR) traffic model to determine time-dependent route costs. We propose a solution algorithm to determine the network equilibria. Monte Carlo simulations are used to account for the travel time distributions. We analyze the dynamic Prospect-basedUser Equilibrium compared to the benchmarks Deterministic and Stochastic User Equilibrium, on a synthetic Manhattan network. We set four endogenous reference points. We show that the setting of the reference point plays a very important role in the route flow patterns and on the network performance at an aggregated level, i.e. in terms of vehicles mean speed as well as internal and outflow capacities. Our results also enhance that the Prospect-basedUser Equilibrium is more sensitive to a change in the reference point than in the calibration of the users' risk-aversionand risk seekingparameters.

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).

This work attempts to validate the dynamic outputs of bi-modal MFD-based models, also referred to as 3D-MFD models, using empirical data. A previous study (Loder et al., 2017) gathered cars and public transport vehicles data in two different regions of Zurich city and showed that a well-defined 3D-MFD exists by proposing a functional form relating overall travel production to the accumulations of cars and public transport vehicles. This work aims to go one step further with the same data set to investigate if 3D-MFD embedded in dynamic conservation laws can predict the time evolution of the network traffic states. Two different approaches to estimate the inflow demand using outflow and mean speed evolutions are presented. The mean trip lengths are estimated using a network exploration technique. Accumulation-based, trip-based and accumulation-based with outflow delay models are considered in the validation study. It is concluded that a single bi-linear 3D-MFD fit is insufficient to predict the evolution of traffic states accurately. The current work proposes multi bi-linear 3D-MFD fits segregated depending on the time of the day. The proposed approach significantly improved the simulation results, where good correspondence with empirical data is obtained. Finally, it is shown that in multi-modal networks like the city of Zurich, it is essential to consider the effect of public transport vehicles, when considering aggregated simulations. It is also shown that using a 2D-MFD by treating public transport vehicles and private cars alike, result in poor accordance with the field observations.

The aim of this study is to solve the large?scale dynamic traffic assignment (DTA) model using a simulation?based framework, which is computationally a challenging problem. Many studies have been performed on developing an efficient algorithm to solve DTA. Most of the existing algorithms are based on path?swapping descent direction methods. From the computational standpoint, the main drawback of these methods is that they cannot be parallelized. This is because the existing algorithms need to know the results of the last iteration to determine the next best path flow for the next iteration. Thus, their performance depends on the single initial or intermediate solution, which means they exploit a solution that satisfies the equilibrium conditions more than explore the solution space for the optimal solution. More specifically, the goal of this study is to overcome the drawbacks of serial algorithms by using meta?heuristic algorithms known to be parallelizable and that have never been applied to the simulation?based DTA problem. This study proposes two new solution methods: a new extension of the simulated annealing and an adapted genetic algorithm. With parallel simulation, the algorithm runs more simulations in comparison with existing methods, but the algorithm explores the solution space better and therefore obtains better solutions in terms of closeness to the optimal solution and computation time compared to classical methods.

TRB 2020, Transportation Research Board Annual Meeting, Washington, ETATS-UNIS, 12-/01/2020 - 16/01/2020; Heavy-duty vehicles or trucks contribute in a significant share with global green house emissions and energy consumption in transportation systems. Truck platoon coordination strategies have introduced new technologies for better coordination and management of those intelligent transportation systems (ITS) to account for environmental efficiency. Current control strategies focus their attention on specific factors such as fuel efficiency or safety while 6 neglecting the intrinsic combined effect when interactions exist with conventional vehicles. In this work, a study considering fuel consumption and environmental effects is presented when platooning strategies are operated near merging areas (on-ramps), a consumption model that considers dynamic conditions on each one of the vehicles is calibrated from real data, and a suitable adaptation is provided to the platooning case to taking into drag effect. Different scenarios at the control level are studied to analyze the impact of splitting maneuvers

Aggregated traffic flow models based on the Macroscopic Fundamental Diagram (MFD), also known as multi-reservoir or multi-region MFD models, have been developed for more than a decade for various applications. While being very appealing for simulating traffic dynamics at a city level, the outputs of these models were rarely confronted with real data measurements. Thus, this paper focuses on calibration and validation of an MFD simulation for a city partitioned into multiple reservoirs. The traffic predictions from the MFD simulation (total accumulation and mean speed) are compared with real data from loop and probe sensors. The questions addressed in this study include the influence of the city partitioning, the MFD and average trip length estimation, and the path flow distribution among reservoirs. This study is carried on the network of Lyon, France, composed by around 27,000 links that extend over an urban area of 80 km2. Two different partitioning cases are defined and compared, with respectively 5 and 10 reservoirs. Our results notably show that the proper estimation of three elements is critical for accurate traffic state prediction: (i) the total ?active? network length of each reservoir, (ii) the regional trip lengths in the reservoirs, and (iii) the path flow distribution at the regional network level. While the network equilibrium found in the 5-reservoir partitioning can be roughly approximated with Wardrop's principle, the 10-reservoir case is more complex and requires to design ad-hoc optimization process to derive regional path flow distributions that fit the data. The global equilibrium found in this latter case turns out to be hardly predictable with any traffic equilibrium principle.

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.

TRB 2020, 99th Annual Meeting Transportation Research Board, Washington DC, ETATS-UNIS, 12-/01/2020 - 16/01/2020; Since the development of the Macroscopic Fundamental Diagram (MFD), many traffic control strategies and algorigthms have been developed to implement perimeter control over a specific urban region. In most cases, the typical assumption is to have a common average trip length for all vehicles traveling within the same region. However, the proper calibration of the trip length has been proved to be of crucial importance for ensuring accurate traffic predictions from MFD models. In this paper, we propose to investigate how the calibration of these travel distances influences the traffic dynamics and the performance of a perimeter control strategy using a Model Predictive Control (MPC) scheme. In particular, we quantify the effects of an improper estimation of the travel distances inMPC applications. We also propose a methodological framework based on the Unsected Kalmann Filter (UKF), to correct for these improper estimations of trip lengths and improve the efficiency of the controller. We perform the simulation tests using an accumulation based MFD model on a medium-sized real city network.

Mobil.TUM 2019, International Scientific Conference on Mobility and Transport , Munich, ALLEMAGNE, 11-/09/2019 - 12/09/2019; Since the development of the Macroscopic Fundamental Diagram (MFD), many traffic control strategies and algorigthms have been developed to implement perimeter control over a specific urban region. In most cases, the typical assumption is to have a common average trip length for all vehicles traveling within the same region. However, the proper calibration of the trip length has been proved to be of crucial importance for ensuring accurate traffic predictions from MFD models. In this paper, we propose to investigate how the calibration of these travel distances influences the traffic dynamics and the performance of a perimeter control strategy using a Model Predictive Control (MPC) scheme. In particular, we quantify the effects of an improper estimation of the travel distances inMPC applications. We also propose a methodological framework based on the Unsected Kalmann Filter (UKF), to correct for these improper estimations of trip lengths and improve the efficiency of the controller. We perform the simulation tests using an accumulation based MFD model on a medium-sized real city network.

hEART 2019, 8th Symposium of the European Association for Research in Transportation, Budapest, HONGRIE, 04-/09/2019 - 06/09/2019; Dynamic Traffic Assignment (DTA) refers to the procedure of assigning trips to paths in a given transportation system considering the Origin-Destination pair (OD) flow demand and the network dynamic traffic states. This problem can be represented as a fixed-point problem. In a large-scale and trip-based setting, it is not possible to guarantee that fixed point algorithms converge towards the optimal. From a computational point of view, the main drawback of these methods for addressing DTA on large-scale networks is that they cannot be parallelized. This is because the existing algorithms need to know the last iteration results to determine the next best path flow for the next iteration. The goal of this study is to overcome the drawbacks of serial algorithms by using meta-heuristic algorithms that are known to be parallelizable. This study proposes two new solution methods: the new extension of the Simulated Annealing and adaptive Genetic Algorithm. With parallel simulation, the algorithm is going to run more simulations in comparison with existing methods, but it is expected to carry out a better exploration of the solution space and consequently achieve better solutions in terms of closeness to the optimal solution and computation time in comparison with classic methods.

hEART 2019, 8th Symposium of the European Association for Research in Transportation, Budapest, HONGRIE, 04-/09/2019 - 06/09/2019; Traffic state prediction models based on 3D or bi-modal Macroscopic Fundamental Diagram (MFD) is discussed in this work. The well known MFD-based models namely, accumulationbased and trip-based models are extended to the case of multimodal traffic. In addition, accumulation-based with outflow delay, which is studied in the context of link level traffic flow dynamics is investigated. The results from MFD-based models is verified with the solution of continuum space-time model, which is based on hyperbolic conservation equations. It is concluded that accumulation-based model with outflow delay address the limitations of conventional accumulation-based model and trip-based solution is most accurate among all the considered MFD-based models.

hEART 2019, 8th Symposium of the European Association for Research in Transportation, Budapest, HONGRIE, 04-/09/2019 - 06/09/2019; One of the key questions for Macroscopic Fundamental Diagram traffic models lies in the calibration of the trip lengths, i.e. the travel distances of vehicles in the regions. Few studies in the literature have attempted to propose methodological frameworks to calibrate the trip lengths. Batista et al. (2019) propose a framework to explicitly calculate trip length distributions. However, they do not consider the influence of the traffic conditions on the trip lengths. In this paper, we propose to extend their methodological framework to determine time-varying trip length distributions according to the changes in the traffic conditions.

hEART 2019, 8th Symposium of the European Association for Research in Transportation, Budapest, HONGRIE, 04-/09/2019 - 06/09/2019; This work investigates network-related trajectory features to unravel trips that the most contribute to the system under-performance. When such trips are identified, features analysis also permits to identify the best alternatives in terms of routes to make the system to its optimum. First, data mining is carried out on trajectories obtained from reference dynamic traffic assignment (DTA) simulations in a real-world network, based on User-Equilibrium (UE) and System-Optimum (SO). This helps us (i) to target the trajectories to be changed, and (ii) to identify their main features (trip lengths, experienced travel time, path marginal costs, and network-related features such as betweenness centrality and traffic light parameters, etc.). Similarity analysis based on Longest Common Subsequence, Principle Component Analysis are the main methods that are performed to carry out descriptive analysis of trajectories. Supported Vector Machine is then used to determinate the features with regards to their contribution to better network performance.

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.

WCTS 2019, 1st IFAC Workshop on Control of Transportation Systems , Haifa, ISRAEL, 30-/06/2019 - 01/07/2019; In this paper, we propose a model-based perimeter gating control approach to improve network-wide emissions in an urban traffic network. An accumulation-based Macroscopic Fundamental Diagram (MFD) model of a single reservoir city is developed to describe the evaluation of traffic flows in a network. Moreover, a path flow distribution scheme using Dynamic User Equilibrium (DUE) discipline is designed to reproduce driver's adaptation to controlled flow. Perimeter gating control scheme using the Nonlinear Model Predictive Controller (NMPC) is developed to track the optimal green routing coefficient which will indirectly track the network-wide emission levels by manipulating the traffic flows into a reservoir. Simulation results show the effectiveness of the proposed scheme in improving the traffic emissions inside and outside the perimeter. Comparative analysis of nocontrol and NMPC shows that the proposed NMPC-based network-wide emission control strategy outperforms.

TRISTAN X, The Tenth Triennial Symposium on Transportation Analysis, Hamilton Island, AUSTRALIE, 17-/06/2019 - 21/06/2019; In the recent past, the Macroscopic Fundamental Diagram (MFD) proved to be an attractive alternative to describe the traffic states at the network level. But although complex formulations were proposed for MFD-based simulation in the literature, very few detailed validations were carried on real networks. Hence, this work focuses on a thorough validation of MFD simulators on the real field. In particular, we aim at (i) investigating the accuracy of the MFD multi-reservoir trip-based and accumulation-based models for a real large scale network by comparing the simulation results with real traffic data; and (ii) going a step further in defining proper calibration methods for the key parameters of MFD models. The network studied is the city of Lyon, which has the second greatest urban area of France, with more than 2 million inhabitants. This area is manually split into 5 reservoirs exhibiting a well defined MFD. The demand scenario was estimated for a typical weekday in a previous study based on household trip surveys and socio-demographic data. The traffic data consists of GPS trajectories of taxi fleets in Lyon, and all the loop detectors available in the area. The taxi trip data allows to determine the mean speed of each reservoir. The loop data provides a measure of the mean flow of each equipped link, which is then scaled up to each reservoir level by assuming that the measured mean flow is also representative of the non-observed links (homogeneity assumption). The average distances traveled are estimated by using shortest path calculations on the empty network (a method proposed by some of the authors in another study). A first comparison between the accumulation-based MFD simulation and the real data is presented with the evolution of accumulation in each reservoir. While providing a reliable estimation of the accumulation level in reservoir 1, the MFD simulation under-estimates the number of circulating vehicles in the other reservoirs. Different sources of errors are identified, but more investigations are still required for a better calibration of the MFD, the trip lengths and the distribution of flows.

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.

TRISTAN X, The Tenth Triennial Symposium on Transportation Analysis, Hamilton island, AUSTRALIE, 17-/06/2019 - 21/06/2019; Dynamic Traffic Assignment (DTA) refers to the procedure of assigning trips to paths in a given transportation system considering the Origin-Destination pair (OD) flow demand and the network dynamic traffic states. This problem can be represented as a fixed-point problem. In a large-scale and trip-based setting, it is not possible to guarantee that fixed point algorithms converge towards the optimal. From a computational point of view, the main drawback of these methods for addressing DTA on large-scale networks is that they cannot be parallelized. This is because the existing algorithms need to know the last iteration results to determine the next best path flow for the next iteration. The goal of this study is to overcome the drawbacks of serial algorithms by using meta-heuristic algorithms that are known to be parallelizable. This study proposes two new solution methods: the new extension of the Simulated Annealing and adaptive Genetic Algorithm. With parallel simulation, the algorithm is going to run more simulations in comparison with existing methods, but it is expected to carry out a better exploration of the solution space and consequently achieve better solutions in terms of closeness to the optimal solution and computation time in comparison with classic methods.

In a city-scale network, trips are made in thousands of origin-destination (OD) pairs connected by multiple routes, resulting in a large number of alternatives with diverse characteristics that influence the route choice behaviour of the travellers. As a consequence, to accurately predict user choices at full network scale, a route choice model should be scalable to suit all possible configurations that may be encountered. In this article, a new methodology to obtain such a model is proposed. The main idea is to use clustering analysis to obtain a small set of representative OD pairs and routes that can be investigated in detail through computer route choice experiments to collect observations on travellers behaviour. The results are then scaled-up to all other OD pairs in the network. It was found that 9 OD pair configurations are sufficient to represent the network of Lyon, France, composed of 96,096 OD pairs and 559,423 routes. The observations, collected over these nine representative OD pair configurations, were used to estimate three mixed logit models. The predictive accuracy of the three models was tested against the predictive accuracy of the same models (with the same specification), but estimated over randomly selected OD pair configurations. The obtained results show that the models estimated with the representative OD pairs are superior in predictive accuracy, thus suggesting the scaling-up to the entire network of the choices of the participants over the representative OD pair configurations, and validating the methodology in this study.

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.

Demand estimation is an important step for multiple applications in urban studies. However, the level of accuracy required depends on the objective of the study. In dynamic traffic microsimulation, the estimation of demand needs to be accurate, as it is intended to describe individuals' trips on a very small-scale. In this case, poor estimation of trip initiation, path, and endings could result in the wrong estimation of the city-block scale's traffic state. Estimating demand at a large-scale with high-resolution is not only very challenging because it requires a large volume of data from multiple sources, but the underlying mathematical problem is considerable and thus hard to solve. In this paper, we address the issue of trip starts and ends when modeling large perimeters. We propose to enhance the location of trip initiation and termination by merging heterogeneous and large public datasets. To do so, we develop a series of algorithms that identify fine-mesh areas where trips could reliably start or end and we share the estimated demand within these sub-areas, following the distribution of trip purposes (Home, Work, Shop, etc.). The method is deployed in Lyon city, France, and validated on an extraction of it. Micro-simulation results show that the demand, once accurately distributed, changes the overall network's performance, confirming the significant influence of trip endings and starts on the overall traffic dynamics.

TRB 2019, Transportation Research Board 98th Annual Meeting, Washington, ETATS-UNIS, 13-/01/2019 - 17/01/2019; Calculating dynamic network equilibrium is a challenging problem. This study first analyzes the different frameworks for the simulation-based User Equilibrium (UE) in the literature. Most of them are based on solving a fixed-point problem by iterative methods. Two elements have to be determined: the path set between all origin-destination pairs and the optimal path flow distribution. The most advanced solution methods expressed the problem with two-layers, the outer and the inner loop, that tackle each element respectively. The goal of this study is to improve the inner loop, i.e. the path flow calculation. The first improvement is keeping the best assignment pattern during the inner loop iterations and send it to the outer loop. It improves the solution quality in trip-based traffic assignment because flows are then discretized into units of user, which makes the point-fix calculation much harder. This study proposes three new swapping methods and two new methods for the step size initialization that is used in each descent iteration. Moreover, this study proposes two alternative methods to initialize the path flow distribution at the beginning of the outer loop. Two quality indicators are considered to examine the quality of UE solution and then the methods are applied to three networks with different sizes (small, medium and large). The results show that the performance of different components of the solution algorithm is sensitive to the network size. Finally, the best configurations of the solution algorithms are recommended for all network sizes with a particular focus on the large-scale.

TRB 2019, 98th Annual Meeting Transportation Research Board, Washigton, D.C., ETATS-UNIS, 13-/01/2019 - 17/01/2019; Large urban areas are vital for the economic and social development of modern cities. The mobility in these areas strongly depends on a seamless and available multi-modal transport infrastructure network. Since many of these networks operates at capacity limits, any disruptions of a single edge can have negative effects on the mobility. Hence, modeling and quantifying the resilience of large-scale urban transport networks will allow cities guaranteeing higher quality of service, even in the presence of unpredictable disruptive events. This paper aims to develop a new methodology linking together topological data with traffic dynamics for resilience analysis. To do so, we adopt a dynamic weighted graph approach where weights are generated according to prevailing traffic conditions. Weight-extended centrality measures are leveraged in order to capture the evolution of vulnerabilities. The paper also aims at studying the impact of area-wide disruptions, such as snow storms or floods, insufficiently explored in the literature. This is based on a stress testing approach. Our graph-based approach is also supported by other analyses performed in the paper by leveraging more traditional traffic indicators. Results demonstrate the importance of both the spatial and temporal dimension in the assessment of transport network criticality, thus stressing the importance of jointly considering the topological properties and traffic dynamics in the study of transport resilience. Consequences of area-wide disruptions are also proved to be different with respect to single-link failures, therefore deserving a deeper characterization in the study of transport resilience

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.