Search

International audience; The availability of transportation scenarios from open data introduces new research motivations, such as route pricing and pollution. Unfortunately, these data are often anonymized to protect the privacy of the survey participants. In this paper, we present a generation of a mobility simulation from anonymized data based on a modal choice model. We show how to reconstruct missing information such as the municipalities of residence and activities and the coordinates of the origins/destinations (O/D) of trips necessary for the estimation of the model. This model integrates an inter-modal alternative mixing car and public transport (pt) for commutes through park and ride (PR) facilities. A first application of the simulation model was made on the perimeter of the European Metropolis of Lille (MEL) where the activities zones were blurred and the O/D coordinates of trips were withdrawn from the household trip survey. The preliminary results obtained show that the simulation is capable of reproducing the behaviors of trips performed by combining car and pt. Therefore, this work can serve as a basis for testing inter-modality incentive policies (designated by “car + pt”) such as intra-urban tools or the creation of PR facilities.

International audience; Le domaine de la déconstruction des bâtiments produit un grand nombre de déchets industriels qui ne sont, pour l'heure, que peu valorisés, alors qu'ils pourraient être soit recyclés, soit directement réutilisés dans d'autres chantiers.L'objectif de cette étude est de fournir un outil permettant de modéliser et d'optimiser les différents flux de matériaux issus des déconstructions pour une meilleure réutilisation de ces derniers, ou le cas échéant un meilleur recyclage.Pour cela, des techniques de modélisations à base d'agents seront utilisées afin de visualiser les dynamiques des différents acteurs de la filière, ainsi que des techniques de métaheuristiques afin d'optimiser l'ordonnancement des actions de ces acteurs.

National audience

International audience; Introduced in 2013, the A.L.P.E.S. approach (AgiLe aPproaches in higher Education Studies) aims to apply agile practices to teaching. Agile approaches are project management practices for IT development. More pragmatic than traditional methods, they allow to be closer to the applicant and to involve him/her as much as possible. They offer a great reactivity and a good adaptation to best meet the needs. They are used today in a large part of IT companies. Largely inspired by agile approaches, the A.L.P.E.S. approach allows the teaching of project management in a transverse way to a main course. It makes teaching more flexible and more adapted to the students. In this article, we describe the approach. We describe the tools, the process of creating a course, and the process of running a course.

International audience; Today, large cities and peri-urban areas experience problems in the mobility of their population. Faced withthis problem, decision-makers must have reliable tools to help them to build and evaluate their policies of mobility. Computer simulations especially traffic simulation tools are, therefore, the solution to better understand(study) the problem and test different resolution scenarios. Unfortunately, there are numerous simulation toolsand the choice can be very difficult for traffic modelers. In this paper, we present, based on a generic method,a comparison of the most popular traffic simulation tools in two steps: 1) a comparison part using a weightedsystem of evaluation criteria to automatically select the candidate simulators. 2) a deeper study of the candidate simulators according to a simulation scenario corresponding to the study case. Finally, this paper presentsan application of this method for the selection of a simulator for the study of intermodal mobility behaviorswhere MATSIM and SUMO were studied in deeper.

International audience; Coordination is required in order to solve a multi robot navigation problem and allow an efficient and fast search of a solution while avoiding any possible collisions. Planning with a fleet of robots can rely on Multi-agent Markovian Decision Processes (MMDPs) model This assumes that it is possible to share the local perceptions of robots every time. However the computation of a distributed policy is not necessarily distributable between robots as with multiple path planning, where the movement of one robot depends on all the other's paths. The global search space would be of exponential size (in the number of robots) in most of multi-robot scenarios. Distributed planning over the robots would allow each robot to plan its own policy while taking advantage of parallel computing. In this paper, this problem is addressed by presenting an approach consisting in starting from a simplified model which can be distributed, then by adding robots interactions constraints while maintaining the model distributable. The results of the experimentations with different configurations highlight some of the strength and limitations of the current approach.

Traffic flow measurement is very important for traffic management systems. However, the existing traditional measurement approaches are highly time-consuming and expensive to continuously gather the required data and to maintain the corresponding equipment, such as loop detectors and video cameras. On the other hand, many services on the web propose to estimate automobile travel time taking into account traffic conditions thanks to crowd sourced data (Floating Car Data). This work proposes to reconstruct, from estimated travel time, traffic flows using machine learning method. In particular, we evaluate the capacity of Gaussian Process Regressor (GPR) to address this issue. After obtaining estimated travel time on a given route, a clustering process shows that travel duration profiles in each day can be associated to different “types of day”. Then, different regressors are trained in order to estimate traffic flows from travel duration. In the “multi-model” variant, we trained a Regressor for each type of day. Conversely, in the “single model” variant, only one Regressor is trained (the type of day is not taken into account). This is an innovative work to estimate and reconstruct the traffic flow in transportation networks with machine learning method from aggregated Floating Car Data (FCD). A series of experiments are conducted to compare the estimated traffic flows, obtained by the proposed single model and multi-model, and the real ones from actual sensors. The obtained results show that both single model and multi-models can capture the tendency of real traffic flows. Furthermore, the performance can be improved by regulating parameters in GPR machine learning model, such as half width of sample window and sample size (a whole week or only weekdays), and multi-models can highly increase the performance compared with the single model. Therefore, the proposed GPR machine learning and FCD based new method can replace those traditional loop detectors for the measurement of traffic flow.

La gestion des reseaux de voies navigables devrait subir des modifications notables en raison d'une volonte d'accroitre le trafic fluvial dans un contexte de changement climatique. Ces nouvelles contraintes vont imposer une gestion adaptative et resiliente de la ressource en eau conduisant a une planification de sa repartition sur l'ensemble de ces reseaux. Une approche basee sur les MDP est proposee afin de repondre a cette problematique. Elle permet la coordination de plusieurs entites sur plusieurs pas de temps. Son utilisation sur un sous-reseau de voies navigables conduit a une minimisation de l'impact negatif lors d'evenements de montees des eaux ou de secheresse. Malgre des premiers resultats prometteurs, les mecanismes de passage a l'echelle permettant de considerer une application reelle ne sont pas totalement definis. Differentes approches proposees dans la litterature pour le passage a l'echelle sont discutees en degageant leurs avantages et limitations. Parmi elles, une modelisation distribuee est privilegiee et un nouvel algorithme de resolution est propose. Il est teste sur un sous-reseau de 7 biefs.

Vector-based map formats such as PolyMap are more compact in terms of memory footprint compared with regular occupancy grid map formats. The sparse nature of a PolyMap along with the explicit representation of traversable space and frontiers makes it suitable for mono and multi-robot autonomous exploration. In this paper, we propose a PolyMap-based navigation stack suitable for autonomous exploration. This navigation stack internally relies on a topological graph that is automatically computed from a PolyMap. This graph is critical to significantly reduce the search space when computing a global path compared with traditional occupancy grid maps. We also discuss two optimizations to speed up path planning computation on this graph. Our experiments demonstrate the efficiency of our PolyMap-based navigation stack.

Indoor SLAM and exploration is an important topic in robotics. Most solutions today work with a 2D grid representation as map model, both for the internal data format and for the output of the algorithm. While this is convenient in several ways, it also brings its own limitations, in particular because of the memory requirements of this map format. In this paper we introduce PolySLAM, a SLAM algorithm that produces PolyMaps. Our PolyMap utilizes polygons built from vectors to model the environment, and as such this is a special case of vector-based SLAM algorithms. The results of our experiments both in simulation and on real world datasets show that this SLAM is promising. First, maps are more compact which reduces memory consumption compared to standard grid-maps making it attractive for map sharing over wireless connection such as in multi-robots systems. Second, the quality of maps produced by PolySLAM is already good for localization despite the lack of global optimization or loop closure.

International audience

Route planning system in bus network is very important on providing passengers a better experience for public transportation services. This work presents a new on-line dynamic path planning algorithm in a stochastic and time-dependent bus network, with the objective of least expected travel time. Firstly, an initial optimal path is found when a passenger leaves from the starting location at time t for the destination location. Secondly, when the passenger reaches a bus stop, the real time traffic condition is checked to decide whether the optimal path should be modified and re-calculated. If so, the shortest path algorithm is re-applied to find the new path. The simulation scenario is executed to show the performance and efficiency of the proposed dynamic path planning system.

In recent years, due to road congestion issues and environmental concerns, multimodal transport has received an increased attention from policymakers. Fortunately, they can now rely on advanced computer tools. In particular, there are many tools for optimizing travel time in a multimodal context. In this article, we defend the thesis that the simulation tool remains essential to define an appropriate transport policy. Indeed, there are many profiles of public transport users and not all of them rely only on time to build their itinerary. Other qualitative criteria such as the cleanliness of the vehicle, the number of visitors to the stations, safety, etc. are also taken into account. The integration of user profiles in a multimodal simulation is not trivial. This article compares different traffic simulators and simulation platforms for multimodal scenarios in urban environments. In particular, the focus of the study is on two candidates: SUMO and GAMA.

At the European scale, inland navigation waterway transport is considered as a competitive and environment friendly alternative to road and rail transport. This transport mode is promoting in north of France thanks to the building of the Canal Seine-Nord. The number of boats and the navigation scheduling will increase significantly. A big raise of the required water volume is waited. Overcoming this future navigation demand will be particularly challenging in a climate change context. Hence, a crucial step consists in investigating the resilience of the inland navigation networks considering the future navigation demand and the climate change impacts. The main objective of this paper is to present the tools dedicated to the resilience determination that is required to design adaptive management strategies of these networks. An integrated model is proposed to model the network and to identify and quantify water supplies and water intakes. Then, a generalized flow-based network is used to model the water volume dispatching. The water dispatching has to be optimized according to the network resilience against the increase of navigation demand, and against the decrease of available water resource particularly during drought periods. Due to the complex structure of the networks, uncertainties that are linked to the daily number of boat and to the available water resource, an interesting approach will consist in defining a distributed problem. Local virtual agents will be designed to manage each waterway section, and a global coordination process will guarantee the efficient management of the network. The tools are designed and tested considering the inland navigation network in north of France.

The T-Ten European program aims at optimizing the transport logistics in Europe by promoting alternative transport modes. Navigation transport offers a competitive and environmentally friendly alternative. Hence, it is foresaw an increase of the navigation transport demand that it will be necessary to accommodate. This will be very challenging particularly in a global change context where less available water resource is expected. A constraint satisfaction problem based on flow graph is proposed in this paper to study the resilience of inland navigation networks against increase of the navigation demand and extreme events. Drought and flood scenarios are simulated considering an network composed of five interconnected navigation reaches. The results show that the designed tools are well adapted to the resilience study of inland navigation networks.

Route planning systems in bus networks play a key role in providing a better experience to passengers for public transportation services. The existing systems satisfy the multicriteria in two ways. One way is firstly to find the k-shortest paths, then these paths are compared according to the given multi-criteria to select the ultimate path. Another way is to search for a set of Pareto paths with multi-criteria. However, both of the above systems only consider users' preferences qualitatively but not quantitatively. As a result, they can not integrate multicriteria as a single criterion based on users' quantitative preferences. Therefore, in this paper, a new integrated multi-criteria route planning method is proposed for searching the optimal path in a bus network according to passengers' quantitative references. Multi objectives being considered in this work are arrival time, walking time and number of bus transfers. A set of simulation scenarios is executed to show the performance of proposed new system.

In this work, we provide an insight study of various 2D Simultaneous Localization and Mapping (SLAM) solutions available in Robotic Operating System (ROS) in the context of autonomous exploration of unknown indoor environment. We are interested in verifying which SLAM packages works out-of-the box for such system. Beside of validating the resulting SLAM’s maps quality, it is necessary to validate also the online mapping reliability and stability of the SLAMs as well as their robustness in environment variation. To be able to conclude the performance of each tested SLAM, a series of tests in both simulation and real world experiment has been conducted on different kinds of terrain. Based on our metrics, we show that Karto SLAM produces the best result when doing autonomous online mapping of unknown indoor environments. We also show that Cartographer can achieve better results than Karto SLAM but this requires offline post-processing to tune the parameters specifically for a given terrain.

International audience; In this work, we set the bases of the integration of ambient intelligence (AmI) with mobile robot teams (MRT), aiming to enhance ambient assisted living services addressing a variety of tasks. We argue that people with reduced mobility can benefit from a synergy between AmI and MRT in various aspects. Towards this direction, we identify principal function-alities such an integrated system should provide in connection to relevant previous works and the way by which synergy could be accomplished, from low-level behavioural to higher-level task planning of a multi-layered system architecture.

Autonomous exploration is an important tasks in many robotic fields such as disaster response scenarios. In time critical situations, the use of multiple robots can reduce the time to create a complete map of the environment. However among the most popular map formats in use today, none are ideal for the multi-robot autonomous indoor localization. In terms of memory usage, visualization, and usability in navigation and exploration tasks, all formats have some strengths and weaknesses.