Search

International audience; Last few years have seen significant advances in the industrial application of autonomous road transport and has launched various actors in a technological race. Urban rail transport has a significant advantage in running fully automated commercial transport. Main line rail transport aims to also benefit from the advantages of automation. This work is done in the context of project TAS at IRT SystemX, with partners SNCF, Alstom Systra, and Université de Technologie de Compiègne. As part of the ongoing work this paper aims to present the context of automation for main line rail transport. The safety challenges in terms of the normative requirements, mainly for software-based solutions are discussed to identify developmental challenges. As a complement, risk analysis of some example functions for a full automation (GoA 3/4) is performed. The objective of this analysis is to identify the challenges associated with determining their safety targets, for subsequent development and demonstration to conformity.; Ces dernières années, le transport autonome a connu des avancées technologiques significatives. Le transport ferroviaire urbain présente un avantage significatif dans l'exploitation d'un transport commercial entièrement automatisé. Le transport ferroviaire de grandes lignes vise également à bénéficier des avantages de l'automatisation. Ce travail est réalisé dans le cadre du projet TAS à l'IRT SystemX, avec les partenaires SNCF, Alstom Systra et l'Université de Technologie de Compiègne. Ce papier présente le contexte de l'automatisation du transport ferroviaire pour les grandes lignes. Les défis de la démonstration de la sécurité principalement pour les logiciels applicatifs sont discutés. En complément, une analyse de risque de certaines fonctions d’une automatisation complète (GoA 3/4) est effectuée. L’objectif de cette analyse est d’identifier les défis de la détermination des objectifs de sécurité d’un tel système autonome les développements ultérieurs et l es démonstrations de la conformité.

International audience

International audience; —In the near future vehicles will be connected and able to communicate with their environment. Such technologies – commonly called Cooperative Intelligent Transportation Systems (C-ITS) – aim at improving road safety, traffic efficiency and drivers comfort. To this end the C-ITS community has proposed many different use cases. In this paper, we start by making an inventory of C-ITS use cases. We then extend this list by proposing new use cases mostly related to security and privacy aspects. Finally we propose a classification methodology based on K-means algorithm to classify the use cases according to criteria we defined. We apply the proposed methodology on our use cases list using security and technical criteria. The obtained results enable to extract a subset of representative use cases from the initial list. Such subset can then be used to apply any process/method (e.g. risk analysis) on it.

Cloud Radio Access Networks (C-RAN) are being adopted for future generations of access networks. 5G RAN services are expected to be built on cloud technologies, in combination with network function virtualization (NFV), leading to the use of virtual computing resources in RAN assets. Beyond standard RAN computing, many devices can benefit from this cloud and virtualization trend. 5G RAN monitoring middleboxes are particularly interesting to be implemented as virtual network functions (VNF) and run with C-RAN resources, in order to satisfy the technical requirements of 5G networks. In addition, there is a new paradigm shift in network monitoring, which leads to the adoption of stream processing. Therefore, we propose to investigate the design of RAN monitoring services as VNFs, provided as cloud services, built with stream processing platforms, to enable 5G network monitoring with fast data analysis. This paper describes a framework for 5G RAN monitoring with streams, and experiments with this framework.

International audience; Relation extraction (RE) between a pair of entity mentions from text is an important and challenging task specially for open domain relations. Generally, relations are extracted based on the lexical and syntactical information at the sentence level. However, global information about known entities has not been explored yet for RE task. In this paper, we propose to extract a graph of entities from the overall corpus and to compute features on this graph that are able to capture some evidences of holding relationships between a pair of entities. The proposed features boost the RE performance significantly when these are combined with some linguistic features.

International audience; Recent years have witnessed an increased awareness of the negative external impacts of freight transportation. The field of Operational Research (OR) has, particularly in the recent years, continued to contribute to alleviating the negative impacts through the use of various optimization models and solution techniques. This paper presents the basic principles behind and an overview of the existing body of recent research on 'greening' freight transportation using OR-based planning techniques. The particular focus is on studies that have been described for two heavily used modes for transporting freight across the globe, namely road (including urban and electric vehicles) and maritime transportation, although other modes are also briefly discussed.

International audience; During the last decades modelling and simulation technics has grown in importance in the product development context. For example, from an industrial point of view, simulation models seem to be an excellent alternative on vehicle construction and more specifically, in the decision making process. Nevertheless, the simulation activity becomes more difficult with the complexity of the product, highlighting more and more often a collaborative problem on the organization of the product development. But, how can this problem be defined? Several collaborative approaches have been proposed in this field. However, the majority of those approaches concern only one dimension of the problem. This paper introduces the Collaborative Modelling & Simulation System (CM&SS) from a systemic perspective in vehicle industry context. The systemic approach enables the definition of different dimensions of the system aiming at a successful performance of a collaborative simulation.

International audience

International audience; We present a visual analytics tool, MiningVis, to explore the long-term historical evolution and dynamics of the Bitcoin mining ecosystem. Bitcoin is a cryptocurrency that attracts much attention but remains difficult to understand. Particularly important to the success, stability, and security of Bitcoin is a component of the system called "mining.'' Miners are responsible for validating transactions and are incentivized to participate by the promise of a monetary reward. Mining pools have emerged as collectives of miners that ensure a more stable and predictable income. MiningVis aims to help analysts understand the evolution and dynamics of the Bitcoin mining ecosystem, including mining market statistics, multi-measure mining pool rankings, and pool hopping behavior. Each of these features can be compared to external data concerning pool characteristics and Bitcoin news. In order to assess the value of MiningVis, we conducted online interviews and insight-based user studies with Bitcoin miners. We describe research questions tackled and insights made by our participants and illustrate practical implications for visual analytics systems for Bitcoin mining.

International audience

Social Media usually provide streaming data access that enable dynamic capture of the social activity of their users. Leveraging such APIs for collecting social data that satisfy a given pre-defined need may constitute a complex task, that implies careful stream selections. With user-centered streams, it indeed comes down to the problem of choosing which users to follow in order to maximize the utility of the collected data w.r.t. the need. On large social media, this represents a very challenging task due to the huge number of potential targets and restricted access to the data. Because of the intrinsic non-stationarity of user's behavior, a relevant target today might be irrelevant tomorrow, which represents a major difficulty to apprehend. In this paper, we propose a new approach that anticipates which profiles are likely to publish relevant contents - given a predefined need - in the future, and dynamically selects a subset of accounts to follow at each iteration. Our method has the advantage to take into account both API restrictions and the dynamics of users' behaviors. We formalize the task as a contextual bandit problem with multiple actions selection. We finally conduct experiments on Twitter, which demonstrate the empirical effectiveness of our approach in real-world settings.

Massive data are surrounding us in our daily lives. Urban mobility generates a very high number of complex data reflecting the mobility of people, vehicles and objects. Transport operators are primary users who strive to discover the meaning of phenomena behind traffic data, aiming at regulation and transport planning. This paper tackles the question "How to design a supportive tool for visual exploration of digital mobility data to help a transport analyst in decision making?” The objective is to support an analyst to conduct an ex post analysis of train circulation and passenger flows, notably in disrupted situations. We propose a problem-solution process combined with data visualisation. It relies on the observation of operational agents, creativity sessions and the development of user scenarios. The process is illustrated for a case study on one of the commuter line of the Paris metropolitan area. Results encompass three different layers and multiple interlinked views to explore spatial patterns, spatio-temporal clusters and passenger flows. We join several transport network indicators whether are measured, forecasted, or estimated. A user scenario is developed to investigate disrupted situations in public transport.

International audience; We present a systematic review of visual analytics tools used for the analysis of blockchains-related data. The blockchain concept has recently received considerable attention and spurred applications in a variety of domains. We systematically and quantitatively assessed 76 analytics tools that have been proposed in research as well as online by professionals and blockchain enthusiasts. Our classification of these tools distinguishes (1) target blockchains, (2) blockchain data, (3) target audiences, (4) task domains, and (5) visualization types. Furthermore, we look at which aspects of blockchain data have already been explored and point out areas that deserve more investigation in the future.

International audience

Neural network classifiers (NNCs) are known to be vulnerable to malicious adversarial perturbations of inputs including those modifying a small fraction of the input features named sparse or $L_0$ attacks. Effective and fast $L_0$ attacks, such as the widely used Jacobian-based Saliency Map Attack (JSMA) are practical to fool NNCs but also to improve their robustness. In this paper, we show that penalising saliency maps of JSMA by the output probabilities and the input features of the NNC allows to obtain more powerful attack algorithms that better take into account each input's characteristics. This leads us to introduce improved versions of JSMA, named Weighted JSMA (WJSMA) and Taylor JSMA (TJSMA), and demonstrate through a variety of white-box and black-box experiments on three different datasets (MNIST, CIFAR-10 and GTSRB), that they are both significantly faster and more efficient than the original targeted and non-targeted versions of JSMA. Experiments also demonstrate, in some cases, very competitive results of our attacks in comparison with the Carlini-Wagner (CW) $L_0$ attack, while remaining, like JSMA, significantly faster (WJSMA and TJSMA are more than 50 times faster than CW $L_0$ on CIFAR-10). Therefore, our new attacks provide good trade-offs between JSMA and CW for $L_0$ real-time adversarial testing on datasets such as the ones previously cited. Codes are publicly available through the link https://github.com/probabilistic-jsmas/probabilistic-jsmas., Comment: Journal Machine Learning and Knowledge Extraction

The latest advances in the design of vehicles with the adaptive level of automation pose new challenges in the vehicle-driver interaction. Safety requirements underline the need to explore optimal cockpit architectures with regard to driver cognitive and perceptual workload, eyes-off-the-road time and situation awareness. We propose to integrate existing task analysis approaches into system architecture evaluation for the early-stage design optimization. We built the discrete-event simulation tool and applied it within the multi-sensory (sight, sound, touch) cockpit design industrial project.

This paper presents a methodology to disaggregate activity locations from zone-based activity chain data usually reported in the anonymized travel surveys. We propose an algorithm that aims to find a feasible sequence of activity locations, for each individual, that minimizes the maximum error of each trip’s Euclidean distance within the activity chain. The reconstructed activity locations are then used to create unchosen alternatives within the choice set for each individual. This is followed by the mode-choice model estimation. We test our approach on three large-scale travel surveys conducted in Switzerland, Île-de-France and São Paulo. We find that with our approach we can reconstruct activity locations that accurately match trip Euclidean distances, but with location errors that still provide location protection. The models estimated on the reconstructed locations perform similarly, in terms of goodness of fit and prediction, to the ones obtained on the original activity locations., Arbeitsberichte Verkehrs- und Raumplanung, 1650

International audience; On-demand ride-pooling has been studied frequently in recent literature. However, real-world applications of large-scale ride-pooling systems are rare and the breakthrough of comprehensive systems is yet to come. In this study, we simulate a ride-pooling service based on real-world demand data from Hamburg, Germany and evaluate the impact of traffic uncertainty on the system performance using a state-of-the-art ride-pooling algorithm. We propose strategies to remedy customer constraint violations on wait and travel times due to wrongly estimated travel times. We show that uncertainty leads to a trade-off between violating customer constraints and rejecting requests after prior acceptance.

We present an exploratory analysis of gender representation among the authors, committee members, and award winners at the IEEE Visualization (VIS) conference over the last 30 years. Our goal is to provide descriptive data on which diversity discussions and efforts in the community can build. We look in particular at the gender of VIS authors as a proxy for the community at large. We consider measures of overall gender representation among authors, differences in careers, positions in author lists, and collaborations. We found that the proportion of female authors has increased from 9% in the first five years to 22% in the last five years of the conference. Over the years, we found the same representation of women in program committees and slightly more women in organizing committees. Women are less likely to appear in the last author position, but more in the middle positions. In terms of collaboration patterns, female authors tend to collaborate more than expected with other women in the community. All non-gender related data is available on https://osf.io/ydfj4/ and the gender-author matching can be accessed through https://nyu.databrary.org/volume/1301.

International audience

This paper investigates the Electric Autonomous Dial-A-Ride Problem (E-ADARP), which consists in designing a set of minimum-cost routes that accommodates all customer requests for a fleet of Electric Autonomous Vehicles (EAVs). Problem-specific features of the E-ADARP include: (i) the employment of EAVs and a partial recharging policy; (ii) the weighted-sum objective function that minimizes the total travel time and the total excess user ride time. In this work, we propose a Deterministic Annealing (DA) algorithm and provide the first heuristic results for the static E-ADARP. Partial recharging (i) is handled by an exact route evaluation scheme of linear time complexity. To tackle (ii), we propose a new method that allows effective computations of minimum excess user ride time by introducing a fragment-based representation of paths. These two methods compose an exact and efficient optimization of excess user ride time for a generated E-ADARP route. To validate the performance of the DA algorithm, we compare our algorithm results to the best-reported Branch-and-Cut (B\&C) algorithm results on existing instances. Our algorithm provides 25 new best solutions and 45 equal solutions on 84 existing instances. To test the algorithm performance on larger-sized instances, we establish new instances with up to 8 vehicles and 96 requests, and we provide 19 new solutions for these instances. Our final investigation extends the state-of-the-art model and explores the effect of allowing multiple visits to recharging stations. This relaxation can efficiently improve the solution's feasibility and quality.

International audience; Cryptography has been used from time immemorial for preserving the confidentiality of data/information in storage or transit. Thus, cryptography research has also been evolving from the classical Caesar cipher to the modern cryptosystems, based on modular arithmetic to the contemporary cryptosystems based on quantum computing. The emergence of quantum computing poses a major threat to the modern cryptosystems based on modular arithmetic, whereby even the computationally hard problems which constitute the strength of the modular arithmetic ciphers could be solved in polynomial time. This threat triggered post-quantum cryptography research to design and develop post-quantum algorithms that can withstand quantum computing attacks. This paper provides an overview of the various research directions that have been explored in post-quantum cryptography and, specifically, the various code-based cryptography research dimensions that have been explored. Some potential research directions that are yet to be explored in code-based cryptography research from the perspective of codes is a key contribution of this paper.

The full benefit from using AI to generate value for businesses, societal wellbeing and the environment is still to be fully realised. To lower adoption barriers of Industrial AI, challenges on multiple levels (technical complexity, trustworthiness, industrialisation, data frameworks and infrastructures, etc.) need to be addressed to benefit from its full socio economic potential for economy, society and welfare. It is now time to foster the development of "industrial and trustworthy AI" and nurture European innovation and sovereignty ambitions and benefit society and leading European industries. In this position paper, a comprehensive industrial and trustworthy AI framework that clusters the priority area for AI research, innovation and deployment is introduced. It covers tools and methodologies that support the design, test, validation, verification, and maintainability of AI based functions and systems and addresses the development of AI based process and systems to demonstrate its integration into new products and services. Conformity assessment schemes, balancing innovation, business and European perspectives, are considered to connect risk management, functional andtrustworthiness requirements to industrial processes. In addition adequate standards supporting industrial AI and trustworthiness will play a central role. Implementing the industrial and trustworthy AI framework will require resources beyond the means of any European private stakeholders. Therefore, strong support from ecosystems, governments, and Europe might not be an option but necessary.

International audience; Simulation is an essential tool to evaluate a complex system's behavior. Simulation reuse can potentially improve simulation quality, cost, and delivery. However, identifying reusable simulations is a difficult task, often manual and based on limited information. This paper presents a method to facilitate the reuse of specific parts of past simulations. The system to simulate is compared to systems which have already been simulated. The comparison, which permits to identify similar chains of components in the systems' block diagrams, is formalized as inexact graph matching. The comparison takes into account standardized tags as well as block properties defined by a name, a value, and a unit. Similar systems are identified with a limited computational cost. When two systems are similar, a mapping between their components and interactions can be obtained at a higher computational cost. A software prototype is implemented to perform the necessary computations, visualize the results, and accordingly select the simulation parts to reuse. The software prototype is tested with the block diagram of an autonomous electric car. Similar chains of components are successfully identified in the powertrain of a non-autonomous electric car represented in a past simulation. The corresponding part of the past simulation can then be selected for reuse.

A high penetration of electric vehicles (EVs) will deeply impact the management of electric power systems. To avoid costly grid reinforcements and the risk of load curtailment due to EV charging, indirect load control via adapted economic signals is a solution proposed by many utilities. Charging costs can be reduced with a domestic tariff applied only to EV charging using a dedicated load measurement method while enhancing the flexibility offered by EVs. We develop a game-theoretical model expressed and treated as a mathematical programme with equilibrium constraints (MPEC) to capture the interaction between a national regulatory authority (NRA) designing these tariffs and heterogeneous agents. First, we analyse the conditions in which EV-only tariffs can be applied for domestic slow charging sessions by comparing different energy profiles. Second, we study the impact of EV charging on different tariff structures to identify the most efficient way of recovering network costs. Submetering with a pure volumetric tariff can bring yearly gains varying from $64 to $110 compared to a flat rate. This depends on the share of investment in grid reinforcement that remains to be made. Finally, we derive policy implications from the results and earmark more sophisticated tariff designs for further investigation.

International audience

National audience

International audience; Empowering sustainable urban futures is a key task for practitioners and policymakers alike. A focus lies today on co-creative practices to represent stakeholders' interests and encourage inclusivity and sustainability. Nevertheless, the multitude of associated concepts and tools complicates the realisation thereof. Thus, we present a taxonomy that structures existing knowledge and proposes a conceptual foundation for an applicable framework to co-create sustainable futures.

International audience; Progress in digitalization opens opportunities to capture accurate transportation logistics data and provide advanced decision support, which leads into the question of how to efficiently exploit this progress in order to improve solution quality in transportation services. Here we address this issue in the context of a dynamic and stochastic patient transportation problem, where besides considering new events, we also incorporate stochastic information about future events. We propose different anticipatory algorithms and investigate which algorithm performs best according to the given settings in a real-world application. We therefore address different types of dynamic events, appropriate response times, and the synchronization of real-world data with the plan. In order to test and analyze how the algorithms behave and perform, we apply the concept of a digital twin. The implemented anticipatory algorithms compared here are a sample scenario planning approach and two waiting strategies. The question of the value of more sophisticated algorithms compared to algorithms with less computational effort is investigated. The experimental results show that solution quality benefits from incorporating information about future requests, and that simple waiting strategies prove most suitable for such a highly dynamic environment. We find that in highly stochastic environments, a rescheduling should be done whenever a new event occurs, whereas in less stochastic environments it is better to let the optimization engine run a bit longer and not start reoptimization after every new event.

This paper presents a synthetic travel demand for the Greater Sao Paulo Metropolitan Region of Brazil, entirely based on open data and representative of the observed travel demand. The open-source and extendable pipeline creates a path from raw data to the synthetic travel demand and, further, to the downstream agent-based mobility simulation. An advantage of this approach is that it enables the reproduction of the synthetic travel demand and, therefore, provides the foundation of repeatability of downstream studies. Furthermore, as the methodology is based on open data, the study's outcomes are easily accessible to the broad research and practice-oriented community., Regional Studies, Regional Science, 8 (1), ISSN:2168-1376

International audience; To overcome the challenge of the architectural design of smart grid as complex system of systems, severalSmart Grid Reference Architectures (SGRAs) have been developed. These architectures not only guide the design andimplementation of smart grid, but also address the critical underlying issues like security and interoperability. However, existing SGRAs are neither lexically aligned, nor structured alike. This impedes their understanding, transferability to otherprojects, and hence dissemination, essentially in the absence of a unified framework for analyzing them. To fill this researchgap, the current paper proposes a standard-based framework for comparing SGRAs. By clarifying the similarities and differences, such frameworkwould be actionable for bothacademics and practitioners who are interested in, but not acquainted with the SGRAs. Moreover, being standard-based, the proposed framework paves the way for improving and aligning legacy and future SGRAs. Hence, it yields a first step towards a unified framework for SGRAs comparison. For the sake of validity, the framework is applied to two well-known SGRAs.

International audience; Agent-based transport models demand that the daily activity patterns of artificial agents are described in great detail. While established choice models for residential locations or work places exist, only few approaches are available to find locations for highly constrained secondary activities such as grocery shopping or recreation. The paper describes a novel data-driven approach of assigning viable locations to such secondary locations while maintaining consistency with homes, work places and other fixed points in an artificial traveller’s daily plan. Two use cases for Switzerland and Île-de-France are presented which show that the algorithm is able to assign locations while providing realistic distance distributions that are consistent with mode-specific travel times.

International audience; We consider Initial Public Offering (IPO) on blockchains while preserving privacy using Secure Multiparty Computation (MPC), which allows participants to perform a computation on secret data. We provide "MPC as a service", where users requiring a computation distributes shares of their data to MPC workers who run an MPC protocol on the shares and return the result. Previous work by Benhamouda et al. considered IPO over Hyperledger Fabric. We improve by providing a tighter and easier integration of MPC protocol in Fabric using the MPC library SCALE-MAMBA. We explain the obtained security benefits and experimental results are provided.

The main contribution of this thesis is the theoretical and the numerical study shape and topology optimization for nonlinear phenomena, like contact, plasticity, and fracture using the level-set method. One application of the contact boundary condition for an idealized-bolt model is also proposed.The governing equations of the three physics dealt with in this thesis: contact, plasticity and damage, are theoretically not shape-differentiable. In each case, we construct an approximation by penalization, regularization or a combination of the two. The approximations for contact and plasticity are shown to be well-posed and to admit solutions that converge to the exact solution. For each physics, the shape sensitivity analysis is performed on the approximate model and the resultant adjoint problem is shown to be well-posed under technical assumptions. The shape optimization is implemented numerically using a level-set method with body-fitted remeshing, which captures the boundary of the shapes while allowing for topology changes. Numerical results are presented in 2D and 3D. We also discuss high-performance computing for linear elasticity and for fracture model, and present a few 3D results.; La contribution principale de cette thèse est l'étude théorique et numérique de l'optimisation topologique pour des phénomènes non-linéaires, comme le contact, la plasticité, et l’endommagement en utilisant la méthode des lignes de niveaux. Une application de la condition de contact dans un modèle de vis idéalisée est aussi proposée. Les trois modèles physiques traités - le contact, la plasticité et l'endommagement - ne sont pas différentiables théoriquement par rapport la forme. Pour chaque modèle nous construisons une approximation par pénalisation, régularisation, ou une combinaison des deux. Les problèmes approchés sont alors bien posés (sauf dans le cas de l'endommagement) et convergent vers les solutions des problèmes initiaux. Pour chaque physique, nous faisons une analyse de sensibilité sur le modèle approché et nous démontrons que le problème d'adjoint est bien posé sous quelques hypothèses. L'optimisation de forme est implémentée numériquement avec une méthode de ligne de niveaux, qui capte le bord des formes, en permettant les changements topologiques. Les résultats numériques sont présentés en 2D et en 3D. Nous abordons le calcul haute performance (HPC) pour l'élasticité linéaire et le modèle de fracture et nous présentons quelques résultats numériques.

International audience; Industrial systems are subject to faults and failures on their components, which can lead to the unavailability of the systems and increase costs due to interventions. A suitable maintenance strategy is therefore a good solution so to reduce such costs, and also to increase the availability of the system. Combination of different kinds of maintenance policies on components of a system can be a good solution. Nevertheless such a combination has to be finely analyzed, so to get the optimal maintenance strategies on the system according to specified criteria (e.g. availability, cost, etc.).In this publication, we illustrate how the combination of a simulation tool, based on stochastic discrete event systems, and an optimization algorithm can be used to find (one of) the best strategy of maintenances. The simulations are led by an optimization algorithm. We propose a solution that optimizes system availability, and cost with system-maintenance constraints using an exact mathematical formulation. A stochastic simulator performs calculations according to parameters provided by an optimization algorithm, which plans preventive maintenance schedules. The optimization algorithm provides the optimum maintenance scenario defined by the kind of maintenances to apply and the suitable schedules. The experiments show that the simulation based optimization algorithm gives more flexibility to the decision maker.

International audience; Markov chains are one of the modeling formalisms used in reliability engineering. Even if it is powerful, from a mathematical point of view, one of its big issue is the design of models for large scale systems. In fact, designing a Markov chain of a system with several components, each one may be in several states, is an important amount of job. There are no structural constructs to efficiently design such a model (e.g. composition, synchronization, etc.). In this publication, we present how the AltaRica 3.0 modeling language can be used to design efficiently large continuous time Markov chains. We consider an example of a system composed of combinations of series-parallel components, combining different states for components and different modes for parts of the system. We show that the design of the model is very efficient thanks to the advanced structural constructs of the AltaRica 3.0 modeling language. Finally, we use assessment tools available for AltaRica 3.0, e.g. the stochastic simulator, to evaluate the model of the system.

International audience; The safety analysis of a system is a classical field of engineering, with its well-known processes and tools (e.g. fault trees, event trees, reliability block diagrams or a combination among them). Nevertheless, the increasing complexity of systems has to be handled with appropriate approaches and tools: for example the so called Model-Based Safety Assessment approach. In this publication, we present the modelling and assessment of an insulin pump system. Also called “artificial pancreas”, this system is a medical device and a typical example of a cyber-physical system. It contributes to reduce the constraints of diabetic patients caused by their illness. The system is composed of three main parts in close loop. The safety assessment of this system has to be finely analyzed since failures may result in severe harms to the patient. We use the AltaRica 3.0 modeling language, which is such a solution of the Model-Based Safety Assessment approach, to realize the safety assessment of the Insulin Pump System.

International audience; This paper proposes a generic shared mobility simulator based on an agent-based model - MATSim. The implemented module can simulate both human and engine powered vehicles, docked/station and dockless/free-floating services, and multiple operators. The module is employed in a case study based in the San Francisco Bay Area that focuses on potential benefits of docked bike-sharing services to serve as a first/last mile solution for public transit. The initial results indicate that the current BayWheels docked bike-sharing service can potentially reduce travel time for 1049 765 trips through intermodality compared to current transit travel times. By focusing our analysis on these trips that could benefit from bike-sharing as a first/last mile solution we present the quality of service indicators for different levels of adoption and two fleet sizes.

International audience; Demand estimation in public transport is critical for transport stakeholders. Thanks to the emerging technologies in recent years, many sources of mobility data are available to model passengers flow in public transport network. One of the most added-value mobility data is smart card Origin-Destination (OD) data. These data could inform us on when, where and how flows transit within the network. The OD matrix used in this work is obtained from smart card data collected by Automated Fare Collection (AFC) system in the Greater Paris Area which is called Navigo Pass. Despite its immense value, this matrix doesn't cover the entire passenger flow. This is due to fraud, other types of tickets (e.g. the standard paper ticket) and uncertainties in destination estimates. In this paper we propose a two-step approach for correcting and scaling smart card OD matrix based on adapted Growth Factor model considering the complexity caused by temporal variation of the OD matrix. In the first step we map all the OD pairs in the OD matrix over our area of study to infer their departure and arrival stations and time. In the second step we exploit passengers' counting data and use growth factor model to scale the OD matrix to obtain a new corrected matrix which can present the real flow in the transit network. We apply our proposed methodology to scale an OD matrix constructed only from smart card validation data which presents between 40% to 65% of the overall flow. For this purpose, passengers' counting data are exploited.

User experience (UX) focused business needs to survive and plan its new product development (NPD) activities in a highly turbulent environment. The latter is a function of volatile UX and technology trends, competition, unpredictable events, and user needs uncertainty. To address this problem, the concept of design roadmapping has been proposed in the literature. It was argued that tools built on the idea of design roadmapping have to be very flexible and data-driven (i.e., be able to receive feedback from users in an iterative manner). At the same time, a model-based approach to roadmapping has emerged, promising to achieve such flexibility. In this work, we propose to incorporate design roadmapping to model-based roadmapping and integrate it with various user testing approaches into a single tool to support a flexible data-driven NPD planning process.

International audience

International audience; This research aims to identify the current place of walking as a utilitarian active mode in MaaS solutions in the context of the French capital region. The appraisal of value creation mechanisms out of active modes represents a current challenge and a research gap to be filled to develop sustainable MaaS solutions and new business models. This study represents the first stage of a long-term research aiming to appraise the integration of active modes in MaaS solutions deployed in France. To do so, we analyze its context, characteristics, business model, and the current state of integration of pedestrian mobility they propose. A comparative analysis of the information provided for pedestrian itineraries was carried out to identify the type of information provided and the mode hierarchy in each MaaS solution. Firstly, we describe the territorial context, and the mobility dynamics present, including the modal share of active modes and a walkability index developed by the Institut Paris Region (2018). Then, we explore the institutional context of each of the entities having taken the role as MaaS operators. We identify and give an overview of their business models and policies, which directly impact the direction they decide to take with their MaaS solutions. Lastly, we start a discussion to synthesize the main findings and propose future directions for the following stages of this research. One of our research assumptions was that the mode hierarchy responds to the objectives and policies of the project carrier. This was found to be partially valid in the case of the studied MaaS solutions.This research can contribute to the scientific production emphasizing the role of MaaS solutions as catalyzers of sustainable modal shifts among specific users (Sarasini, Sochor and Arby, 2017; Pangbourne et al., 2020a). It also stresses the importance of incorporating better, high-quality information for active modes to enable enhanced intermodality and sustainable MaaS solutions.

International audience; Designing and managing system transitions towards sustainable urban futures are crucial to fulfilling the Paris Agreement and the United Nations (UN) Sustainable Development Goals (SDGs). However, the complexity and multidisciplinarity of urban systems pose challenges to the holistic conceptualisation, design, and management of such transitions. Utilising complex system theory as the theoretical foundation, we apply transition concepts from Complex Adaptive Systems (CAS), Multi-Level Perspective (MLP), Social Practice Theory (SPT), and Future Scenario Methods (FSM) to explore the frameworks’ potentials and shortcomings when applied to the urban mobility system transition (UMST) of Paris and its region Île-de-France (ÎdF) between 2001 and 2021.Our leading and supporting research questions are:RQ1: To what extent can current transition frameworks represent the UMST?RQ1a: What are the key transition drivers (TD) of Paris’ UMST between 2001 and 2021? RQ1b: What are the key transition frameworks that have been applied to UMST?RQ1c: How adequately can these frameworks represent the UMST of Paris?To answer these questions, we introduce the challenge and the underlying complex system theory, followed by the case study description. Afterwards, each framework is described, applied to the case, and discussed. This is followed by a literature-driven analysis and discussion of complements, commonalities, and mismatches between the frameworks. Finally, the initial outcomes are discussed, and a perspective for the next steps building on this preparatory work is drawn.Key outcomes of the paper are:• A descriptive case study of the UMST of Paris between 2001 and 2021;• An interdisciplinary overview of UMST frameworks applied to the case study;• A discussion of complementary, overlapping, and competing properties of the transition frameworksthat will feed the next steps of the research towards the development of a holistic, heuristic transition framework for UMST.

International audience; Public transport route planning is of growing interest in smart cities and especially in metropolitan areas where congestions and traffic jams are frequently recorded. The availability of multiple data sources, such as passenger load in trains or ticketing logs, provides an interesting opportunity to develop decision support tools to help passengers better plan their trips around the city and to enhance their travel experience. We present, in this paper, a multi-criteria journey planner that incorporates train load predictions as criteria. To this end, on the one hand, we enrich the proposed routes with predictive indicators of passenger flow such as the load on board the trains. These indicators are computed for each section of the itinerary using machine learning algorithms. On the other hand, we design a journey planner that incorporates the predicted load in its search criteria.

International audience; Deep Learning algorithms have recently received a growing interest to learn from examples of existing solutions and some accurate approximations of the solution of complex physical problems, in particular relying on Graph Neural Networks applied on a mesh of the domain at hand. On the other hand, state-of-the-art deep approaches of image processing use different resolutions to better handle the different scales of the images, thanks to pooling and up-scaling operations. But no such operators can be easily defined for Graph Convolutional Neural Networks (GCNN). This paper defines such operators based on meshes of different granularities. Multi-resolution GCNNs can then be defined. We propose the MGMI approach, as well as an architecture based on the famed U-Net. These approaches are experimentally validated on a diffusion problem, compared with projected CNN approach and the experiments witness their efficiency, as well as their generalization capabilities.

International audience; Hybrid Automatic ReQuest (HARQ) protocol enables reliable communications in wireless systems. Usually, several parallel streams are sent in successive timeslots following a time-sharing approach. Recently, multi-layer HARQ has been proposed by superposing packets within a timeslot. In this paper, we evaluate the potential of this multi-layer HARQ by playing with some design parameters. We show that a gain in throughput is only obtained at mid-Signal-to-Noise Ratio (SNR).

The increase in the amount of data collected in the transport domain can greatly benefit mobility studies and create high value-added mobility information for passengers, data analysts, and transport operators. This work concerns the detection of the impact of disturbances on a transport network. It aims, from smart card data analysis, to finely quantify the impacts of known disturbances on the transportation network usage and to reveal unexplained statistical anomalies that may be related to unknown disturbances. The mobility data studied take the form of a multivariate time series evolving in a dynamic environment with additional contextual attributes. The research mainly focuses on contextual anomaly detection using machine learning models. Our main goal is to build a robust anomaly score to highlight statistical anomalies (contextual extremums), considering the variability within the time series induced by the dynamic context. The robust anomaly score is built from normalized forecasting residuals. The normalization of the residuals is carried out using the estimated contextual variance. Indeed, there are complex dynamics on both the mean and the variance in the ridership time series induced by the flexible transportation schedule, the variability in transport demand, and contextual factors such as the station location and the calendar information. Therefore, they should be considered by the anomaly detection approach to obtain a reliable anomaly score. We investigate several prediction models (including an LSTM encoder–decoder of the recurrent neural network deep learning family) and several variance estimators obtained through dedicated models or extracted from prediction models. The proposed approaches are evaluated on synthetic data and real data from the smart card riderships of the Quebec Metro network. It includes a basis of events and disturbances that have impacted the transport network. The experiments show the relevance of variance normalization on prediction residuals to build a robust anomaly score under a dynamic context.

International audience; In this chapter, we investigate the potential integration of innovative mobility modes with urban public transport. We emphasize the design of future autonomous on-demand transport systems in the future and the interactions of these systems with public transport. These new modes pose several implementation challenges for public transport system design, including strategic, tactical, and operational choices when projecting the implementation. The impact on the overall urban transport systems is considered along with sustainability. We will review the current literature and state of the art, investigate technological developments, and finally develop some visions and research perspectives on the future integration of on-demand transport in public transport systems.

International audience; In this chapter, we are interested in the design and innovation processes of mobility solutions from the viewpoint of designers (vehicle providers, start-up companies developing digital mobility applications, mobility operators...). It is becoming increasingly important to take the door-to-door experience of travelers, and of other users of mobility systems, into account. Taking a step back on the research contributions of the Anthropolis chair (2016–2019), we advocate that user-centered insights are useful at different stages of solution development. When mobility solutions are not yet on the market, trials are mostly conducted to evaluate technological maturity (of autonomous shuttles for instance). Testers in these trials can say more about their experience and the value of the solution if asked to. When mobility solutions are on the market, there is a need to capture the experience of travelers and uncover their door-to-door problems through a systemic mobility diagnosis. This type of problem diagnosis can be used to feed innovation approaches, reveal value buckets for companies, and bring meaningful solutions for both travelers and the other users. Finally, we discuss the applicability and the potential of the recommendations for different mobility stakeholders