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The multi-agent environment is now widely recognised as a key design abstraction for constructing multi-agent systems, equally important as the agents. An explicitly designed environment may have several roles, such as the inter-mediation between agents, the support for interaction, the embodiment of rules and constraints, etc. Mobility applications fit perfectly with a design in the form of a multi-agent system with an explicit environment model. Indeed, in these applications, the components of the system are autonomous and intelligent (drivers, travellers, vehicles, etc.), and the transportation network is a natural environment that they perceive and on which they act. However, the concept of the multi-agent environment may be profitably used beyond this specific geographical context. This paper discusses the relevance of the multi-agent environment in mobility applications and describes different use cases in simulation and optimisation.

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.

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.

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.

Mes travaux de recherche portent sur les systèmes multi-agents et leur application aux problèmes et applications de transport dynamiques. Je suis intéressé par le développement de modèles, de simulations et d'algorithmes pour aider les acteurs du transport multimodal à résoudre les problèmes complexes induits par les changements continus dans l'offre et la demande de services de transport. Je propose un certain nombre de modèles et de simulations aidant les acteurs du transport à observer, estimer et optimiser l'état des réseaux de transport. Mon travail contribue à une meilleure compréhension des problèmes soulevés par les nouveaux services de mobilité (numérotation, covoiturage, systèmes de recherche de parkings urbains, etc.) et à une meilleure conception des systèmes d'exploitation et des systèmes d'information du voyageur, intégrant ces nouveaux services., My research work deals with multi-agent systems and their application to dynamic transportation problems and applications. I am interested in the development of models, simulations and algorithms to support the multimodal transportation actors solving the complex problems induced by the continuous changes in the supply and demand of transportation services. I propose a number of models and simulations supporting the transportation actors in observing, estimating and optimizing the state of the transportation networks. My work contributes to a better understanding of the problems raised by the new mobility services (dial a ride, ride sharing, urban parking search systems, etc.) and to a better design of operating systems and traveler information systems, integrating these new services.

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.

KES-AMSTA 2017, 11th KES International Conference Agent and Multi-Agent Systems: Technology and Applications, Vilamoura, PORTUGAL, 21-/06/2017 - 23/06/2017; Dynamic transportation applications have long been a domain of choice for the multiagent paradigm. Indeed, the presence of distributed entities, the highly dynamic character of these applications and the often presence of human actors in the system makes it very suitable for a multiagent design. This paper advocates for the primary consideration of multiagent environment design when dealing with such dynamic transportation applications. Transportation applications can greatly benefit from the use of the multiagent environment since most of them consider a dynamic geographical positioning of the system components. Indeed, the simultaneous consideration of the time and space dimensions makes the environment, which is shared and accessed by all the agents of the system, a candidate of choice to capture the dynamics of the application. The environment design can be envisioned at several levels of the system construction. It can be used as a medium for interaction between distributed entities. It could be used as a coordination entity of the system components. It can finally be designed as a mental model for the agents that they can use in their reasoning. We illustrate the possible uses of the environment with two transportation applications dealing with traveler information.

JFSMA'16 - 24èmes Journées Francophones sur les Systèmes Multi-Agents, Rouen, FRANCE, 05-/10/2016 - 07/10/2016; With the generalization of real-time traveler information, the behavior of modern transport networks becomes harder to analyze and to predict. It is now critical to develop simulation tools for mobility policies makers, taking into account this new information environment. In this paper, we present a multiagent simulation model to measure the impact of information provision on the quality of passengers travels. This impact is measured by simulating different scenarios in function of the percentage of connected travelers, represented as agents. These simulated scenarios are analyzed following their impact on the average travel times of the travelers. Results show that the number of connected travelers has a positive impact on overall travel times up until a certain threshold before becoming relatively negative.; Avec la généralisation de l'information temps-réel des voyageurs, le comportement des réseaux de transport modernes devient de plus en plus difficile à analyser et à prévoir. Il est devenu important de développer des outils de simulation pour les décideurs de politiques de mobilité, prenant en compte ce nouvel environnement informationnel. Dans ce papier, nous présentons une simulation multi-agent pour mesurer l'impact de la fourniture d'informations sur la qualité des voyages en transports en commun. Cet impact est mesuré par la simulation de scénarios différents en fonction du pourcentage de voyageurs connectés, représentés par des agents. Ces scénarios sont analysés selon leur impact sur les temps de parcours moyens des voyageurs. Les résultats montrent que le nombre de voyageurs connectés a un impact positif sur les temps de parcours jusqu'à un certain seuil après lequel il devient relativement négatif.

Transport systems are increasingly complex and are made of more and more connected entities. It becomes critical to develop micro-simulation tools to understand the new transport systems dynamics. However, the data for building mobility simulation are quite hard to get, and simulations on new areas are not easy to set up. In this paper, we propose methods for building a realistic data environment for multimodal mobility simulators. We also propose a method to integrate travel patterns (patterns of travelers' origins and destinations). The methods presented in this paper can be used when dealing with new areas for which we have few and incomplete data.

JFSMA'15 - 23es Journées Francophones sur les Systèmes Multi-Agents, RENNES, FRANCE, 29-/06/2015 - 01/07/2015; With the generalization of real-time traveler information, the behavior of modern transport networks becomes harder to analyze and to predict. It is now critical to develop simulation tools for mobility policies decision makers, taking into account this new information environment. Indeed, the spread of individualized information may highly influence the traffic network. However, existing mobility multiagent and micro-simulations can only consider a sample of the real volumes of travelers, especially for large areas. With distributed simulations, it would become possible to analyze and predict the status of current and future networks, with informed and connected travelers.; Avec la généralisation de l'information voyageurs en temps réel, la dynamique des réseaux de transport est de plus en plus diffcile à analyser et à prévoir. Il devient nécessaire de développer des outils de simulation pour les décideurs de politiques de mobilité, tenant compte de ce nouvel environnement informationnel. En effet, informer un très grand nombre de voyageurs guidés individuellement peut avoir des conséquences importantes sur l'état du trafic. Il est utile d'évaluer cet impact par la simulation. Or, les simulations multi-agents existantes pour la mobilité de voyageurs ne peuvent prendre en considération qu'une partie du volume réel de voyageurs. En distribuant ces simulations, il serait possible de prendre en compte des volumes réels de voyageurs connectés et d'analyser et de prévoir l'état des réseaux de transport. Dans cet article, nous proposons une comparaison entre deux méthodes pour la distribution des simulations multi-agents de mobilité des voyageurs, permettant la prise en compte de flux réalistes et de zones géographiques étendues.

With the generalization of real-time traveler information, the behavior of modern transport networks becomes harder to analyze and to predict. It is now critical to develop simulation tools for mobility policies makers, taking into account this new information environment. Information is now individualized, and the interaction of a huge population of individually guided travelers have to be taken into account in the simulations. However, existing mobility multiagent and micro-simulations can only consider a sample of the real volumes of travelers, especially for big regions. With distributed simulations, it would be easier to analyze and predict the status of nowadays and future networks, with informed and connected travelers. In this paper, we propose a comparison between two methods for distributing multiagent travelers mobility simulations, allowing for the consideration of realistic travelers flows and wide geographical regions.