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Deploying deep learning models in real-world certified systems requires the ability to provide confidence estimates that accurately reflect their uncertainty. In this paper, we demonstrate the use of the conformal prediction framework to construct reliable and trustworthy predictors for detecting railway signals. Our approach is based on a novel dataset that includes images taken from the perspective of a train operator and state-of-the-art object detectors. We test several conformal approaches and introduce a new method based on conformal risk control. Our findings demonstrate the potential of the conformal prediction framework to evaluate model performance and provide practical guidance for achieving formally guaranteed uncertainty bounds.

International audience; Nitrile butadiene rubbers (NBR) are widely used in sealing applications, such as O-rings inside the pneumatic system of the French high-speed train (TGV). In this application, a strong hardening of the NBRs alters the sealing function. Predicting the evolution of the mechanical properties during service life, especially the material hardening, is therefore a strategic issue to optimize time in maintenance operations of the TGV's pneumatic system. The main goal of this study is to reproduce the aging observed on a train's pneumatic system by performing thermo-oxidative accelerated aging tests with different commercial nitrile rubbers at several temperatures and up to 2016 h. After achieving similar degradation to specimens aged on trains, aging mechanisms and effects have been investigated through different characterization techniques: infrared spectroscopy, swelling tests, X-ray fluorescence spectrometry, differential scanning calorimetry, thermogravimetric analysis, and micro-hardness measurements. The results obtained enabled us to identify and to relate aging mechanisms to aging conditions and to precisely determine and quantify the effects of physicochemical state evolution on the mechanical properties of each NBR considered. Extra crosslinks and oxidative functionalities form in the different elastomers, making them hard and brittle, and thus impacting the sealing function.

Thanks to its new connected trains, Transilien is now able to measure the number of passengers boardings and alightings per train door in real time.Our research uses this unique dataset to contribute to a better synchronization of train and passenger flows during the operational stage of railway operations.We first evaluate several statistical learning models to estimate dwell time as a function of railway operation and passenger flows variables. These models allow us to isolate critical situations where passenger flows significantly impact dwell time. Our research forecasts each of the railway operation and passenger flows variables, one stop ahead, from bidirectional autoregressive models exploiting their recent past. We then simplify those models’ using patterns derived from timetables. Finally, we estimate the occupancy rate by zone of the open gangways rolling stocks in order to inform passengers about the comfort on board and build two models of on-board passenger movements., Grâce à ses rames connectées, Transilien mesure en temps réel le nombre de montées et de descentes par porte du train. Nous contribuons à une meilleure synchronisation en phase opérationnelle des flux de trains et de voyageurs à l’aide de ces données uniques. Nous évaluons plusieurs modèles d’apprentissage statistique afin d’estimer les temps de stationnement en fonction des variables d’exploitation ferroviaire et des flux de voyageurs. Ces modèles permettent d’isoler des situations critiques où les flux de voyageurs impactent les temps de stationnement. Nous prévoyons chacune des variables, à l’horizon d’un arrêt, à partir de modèles autorégressifs bidirectionnels exploitant leur passé proche. Ces modèles se simplifient grâce aux motifs issus de la grille horaire. Nous estimons enfin des taux d’occupation par zone des rames traversantes, afin d’informer les voyageurs sur le confort à bord et proposons deux modèles de déplacement des voyageurs à bord.

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

A multitude of explainability methods and associated fidelity performance metrics have been proposed to help better understand how modern AI systems make decisions. However, much of the current work has remained theoretical -- without much consideration for the human end-user. In particular, it is not yet known (1) how useful current explainability methods are in practice for more real-world scenarios and (2) how well associated performance metrics accurately predict how much knowledge individual explanations contribute to a human end-user trying to understand the inner-workings of the system. To fill this gap, we conducted psychophysics experiments at scale to evaluate the ability of human participants to leverage representative attribution methods for understanding the behavior of different image classifiers representing three real-world scenarios: identifying bias in an AI system, characterizing the visual strategy it uses for tasks that are too difficult for an untrained non-expert human observer as well as understanding its failure cases. Our results demonstrate that the degree to which individual attribution methods help human participants better understand an AI system varied widely across these scenarios. This suggests a critical need for the field to move past quantitative improvements of current attribution methods towards the development of complementary approaches that provide qualitatively different sources of information to human end-users.

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.

WOS:000470074400013; International audience; In France, most of the trains are electrical and some are fed by 1500V DC voltage. When a train is travelling, the power consumption creates a voltage drop. Therefore, below a certain limit, the train has to slow down its speed. This limitation prevents the increase of traffic in some area which needs an electric reinforcement. This paper proposes to use embedded storage systems to support the voltage of the catenary. The main issue is: how to size such an energy storage system? In this way, several aspects (e.g.: the energy management strategy) have to be taken into account. The paper deals with an optimization-based sizing method for the embedded energy storage systems. The proposed method takes into account the energy management strategy in the sizing by optimization. A comparison is made between the use of batteries or supercapacitors.

WOS:000458127000025; International audience; The increasing railway traffic and the environmental issue demand to find new solutions to provide energy to autonomous train (train with embedded energy sources such as diesel power supply). Using hybrid diesel train with embedded storage elements is an interesting technical solution but this kind of multisource system presents new scientific and methodology challenges. Thus, in the paper, the problematic focus on the design and the energy management of the different sources for this system. Moreover, these two fields are linked to each other. Indeed, there is a strong influence of the sizing on the energy management but the reverse is also true. The paper deals with a new optimization approach to perform the design of both the hybrid sources sizing and their energy management. The multi-sources system is represented by a power flow model and the energy management strategy is based on a frequency approach and on dynamic programming. This direct optimization problem is solved by the Sequential Quadratic Programming (SQP) algorithm. Thus, in the paper, this optimization approach is applied to a real railway study case. A comparison is made between two cases with different energy management methods.

International audience; Non-fluorinated coatings have recently emerged as a new approach to avoid contamination on transparent surfaces. This research work concerns the synthesis of highly transparent, hydrophobic, durable, and self-cleaning coatings as an alternative to fluorine-based coatings used on glass. The developed coating is produced by a chemical route (sol–gel method) using two silica-based precursors, hexamethyldisilazane, and tetraethoxysilane (HMDS/TEOS). Then, the prepared Gel HMDS/TEOS was deposited on glass slides by the dip-coating technique. Afterward, the effects of mechanical durability, water-drop testing, thermal cycling testing, and aging times on the hydrophobicity of the gel were analyzed. The properties of the coating were characterized by optical microscopy, scanning electron microscopy, UV-VIS-NIR spectrophotometry, and contact angle measurement. The results show that the organic modification of the gel by trimethylsilyl (-Si-(CH3)3) groups leads to a homogeneous and a hydrophobic surface. Sufficient dilution of this gel with ethanol has been found to provide a highly transparent coating without losing its hydrophobic property. In addition, the robust and durable coating exhibits high self-cleaning performance suitable for various industrial applications.

International audience; The speed profile of a train plays an important role in energy consumption and resulting costs. The industrial objective of this work is thus to develop a method to reduce the energy consumed by a train over a journey by playing on the driver commands (traction and braking forces) while respecting punctuality constraints. A coupling between measured data and simulation is proposed to solve this optimization problem. First, a rigid body approach (Lagrangian formalism) is introduced to characterize the dynamics of each element of the train and their interactions with their environment. In particular, the aerodynamic (including the wind effect), traction, and braking forces are taken into account, and a special attention is paid to the vertical and lateral characteristics of the track as they play a key role on the train dynamics. Secondly, a model for energy consumption and recovery (thanks to dynamic braking) is introduced. Experimental measurements of a high-speed line are then used to estimate the parameters on which the two previous models are based and to validate their predictive capacities. The optimization problem under constraints is finally solved using an evolutionary algorithm where the constraints are implemented using an augmented Lagrangian formalism. The performance of the proposed method in terms of speed optimization and energy consumption reduction is compared to measurements associated with commercial trains.

International audience; Several recent studies on dyadic human-human interactions have been done on conversations without specific business objectives. However, many companies might benefit from studies dedicated to more precise environments such as after sales services or customer satisfaction surveys. In this work, we place ourselves in the scope of a live chat customer service in which we want to detect emotions and their evolution in the conversation flow. This context leads to multiple challenges that range from exploiting restricted, small and mostly unlabeled datasets to finding and adapting methods for such context.We tackle these challenges by using Few-Shot Learning while making the hypothesis it can serve conversational emotion classification for different languages and sparse labels. We contribute by proposing a variation of Prototypical Networks for sequence labeling in conversation that we name ProtoSeq. We test this method on two datasets with different languages: daily conversations in English and customer service chat conversations in French. When applied to emotion classification in conversations, our method proved to be competitive even when compared to other ones.

International audience; The adjustment of rail traffic in the event of an electrical infrastructure disruption presents an important decision-making process for the smooth operation of the network. Railway systems are complex, and their analysis relies on expensive simulations, which makes incident management difficult. This paper proposes the use of sensitivity analysis in order to evaluate the influence of different traffic adjustment actions (e.g., spacing between trains and speed reduction) on the train supply voltage, which must never drop below the critical value prescribed by technical standards. Three global sensitivity analysis methods dedicated to black box, multivariate, nonlinear models are considered: generalized Sobol indices, energy distance-based indices, and regional sensitivity analysis. The three methods are applied to a simple traffic rescheduling test case and give similar results, but at different costs. Regional sensitivity analysis appears to be the most suitable method for the present application: it is easy to implement, rather fast, and accounts for constraints on the system output (a key feature for electrical incident management). The application of this method to a test case representative of a real rescheduling problem shows that it provides the information needed by the traffic manager to reschedule traffic in an efficient way. The same type of approach can be used for any power system optimization problem with the same characteristics.

To improve the design, Verification and Validation phases of Power Supply Equipment of the Electric Lines control systems at French Railway Company (SNCF for Societe nationale des chemins de fer francais), this paper proposes an integrated methodology, for automatic generation, formal verification and implementation of safe Programmable Logic Control (PLC) programs. The main objective is to save time and to improve the “overloaded” workflow of systems engineers. This methodology is compliant with the traditional engineering workflow. The first phase of the methodology focuses on the automatic generation of PLC programs, wiring diagrams, and test-based recipe books, based on reusing and adapting similar models of existing projects to the new specifications (corresponding to functional and safety requirements). The second phase is related to the application of formal verification and control synthesis techniques to guarantee the safety of the control installation. The first phase of the methodology has been successfully deployed at SNCF. The second phase is currently being evaluated.

In this thesis, we propose methods to approximate an anomaly score in order to detect abnormal parts in data streams. Two main problems are considered in this context. Firstly, the handling of the high dimensionality of the objects describing the time series extracted from the raw streams, and secondly, the low computation cost required to perform the analysis on-the-fly. To tackle the curse of dimensionality, we have selected the CFOF anomaly score, that has been proposed recently and proven to be robust to the increase of the dimensionality. Our main contribution is then the proposition of two methods to quickly approximate the CFOF score of new objects in a stream. The first one is based on safe pruning and approximation during the exploration of object neighbourhood. The second one is an approximation obtained by the aggregation of scores computed in several subspaces. Both contributions complete each other and can be combined. We show on a reference benchmark that our proposals result in important reduction of the execution times, while providing approximations that preserve the quality of anomaly detection. Then, we present our application of these approaches within the SNCF information system. In this context, we have extended the existing monitoring modules by a new tool to help to detect abnormal behaviours in the real stream of messages within the SNCF communication system., Dans cette thèse, nous proposons des méthodes de calcul approchées d'un score d'anomalie, pouvant être mises en oeuvre sur des flux de données pour détecter des portions anormales. La difficulté du problème est de deux ordres. D'une part, la haute dimensionnalité des objets manipulés pour décrire les séries temporelles extraites d'un flux brut, et d'autre part la nécessité de limiter le coût de détection afin de pouvoir la réaliser en continu au fil du flux. Concernant le premier aspect du problème, notre étude bibliographique a permis de sélectionner un score de détection d'anomalies proposé récemment, le score CFOF, qui est le seul pour lequel il existe des garanties formelles quant à son adéquation pour les données en haute dimensionnalité. Nos contributions ont alors porté sur la proposition de deux méthodes d'approximation du score CFOF pour permettre son usage en continu sur des flux. La première est une approche combinant élagage et approximation lors du parcours des voisinages dans l'espace de description des objets. Notre second apport est une approximation par agrégation de scores obtenus sur des sous-espaces, qui complète la première contribution et se combine avec elle. Nous avons montré sur une collection de jeux de données, utilisés comme cadre d'évaluation de référence dans le domaine, que nos méthodes permettaient des gains importants en temps de calcul, tout en fournissant des approximations qui préservent la qualité des détections. Enfin, nous présentons également l'application de ces approches au sein du système d'information de la SNCF dans lequel de nombreux flux sont collectés en temps réel, transformés et rediffusés. Dans ce contexte, nous avons étendu la supervision de bout-en-bout existante par la mise en oeuvre d'un outil d'aide à la détection d'anomalies sur le flux de messages entrant d'une des principales plateformes de traitement.

COMPRAIL 2020 : 17th International Conference on Railway Engineering Design & Operation, Berlin, ALLEMAGNE, 01-/07/2020 - 03/09/2020; In this paper we propose an experimental study aimed to assess a new paradigm for railway traffic management. In current practice and academic state of the art, train priority decisions in case of perturbation are made centrally. In this work, train traffic is modelled as a multi-agent system in which trains are decision makers. We implement a microscopic simulator for assessing the viability of this decentralized traffic management and for comparing it with the centralized one. Our results show that it is indeed possible to let trains decide their own priority and it is possible to achieve results which are comparable to simple centralized approaches in terms of passenger travel time in the mass-transit area of Paris, in France.

Mode I + II fatigue crack growth tests are run, following sequential or pseudo-sequential loading paths, representative of those computed for ”squat type” cracks in rails. Stereo DIC provides the near-tip displacements, from which Δ K I effective and Δ K II effective are derived. Compression while shearing extends coplanar growth, by slowing down the wear-induced rise of the effective mode mixity ratio, up to the critical value for which bifurcation occurs. The consideration of plasticity and contact and friction stresses improves crack paths predictions, compared to the maximum tangential stress criterion. The coplanar crack growth rate correlates well with a combination of Δ K I effective and Δ K II effective .

International audience

International audience

International audience; Structures composed of repetitions of multiple identical cells are common and have been the object of a large body of literature on waveguides, periodic media, and cyclic symmetry. Starting from a cell model, possibly with a large number of Degrees of Freedom both inside the cell and on its edges, the objective of this paper is to propose a Ritz-Galerkin reduction procedure retaining the standard second order model form and periodicity properties of the original model, while controlling accuracy in terms of model bandwidth and reproduction of the forced response to applied loads. The procedure is classically decomposed in two phases: subspace learning and basis generation. For the learning phase, Wave Finite Element (WFE) and periodic computations are presented as alternatives. The latter are then preferred for their easier control on the reduced model accuracy using classical modal synthesis and the simple choice of few target wavelengths. For the basis generation phase, constraints needed to generate a periodic superelement are defined and numerical procedures to generate a basis verifying those constraints are proposed. The validity of the reduction is demonstrated for the case of a cell with random elastic properties presenting bandgaps, local modes and wavemode crossing. Accurate predictions of modes and damped forced response are given for both the infinite and finite cases, using frequency and time simulations. The proposed analysis illustrates tracking of waveshapes, evaluation of significant waves by computation of the forced response in the frequency/wavenumber domain and interpretation of the relation between the infinite and finite forced responses. The case of a railway track with edges and transitions between multiple periodic zones is finally used to illustrate scalability issues.

This paper is about an integrated optimization approach for timetabling and rolling stock rotation planning in the context of passenger railway traffic. Given a set of possible passenger trips, service requirement constraints, and a fleet of multiple heterogeneous self-powered railcars, our method aims at producing a timetable and solving the rolling stock problem in such a way that the use of railcars and the operational costs are minimized. To solve this hard optimization problem, we design a mixed-integer linear programming model based on network-flow in an hypergraph. We use this models to handle effectively constraints related to coupling and decoupling railcars. To reduce the size of the model, we use an aggregation and disaggregation technique combined with reduced-cost filtering. We present computational experiments based on several French regional railway traffic case studies to show that our method scales successfully to real-life problems.

International audience; This paper presents numerical simulations and experimental studies on the frequency domain behavior of railway track below 100 Hz, focusing on the link between the substructure properties of the track and its global dynamic response. A numerical method in the frequency domain is first proposed and used to understand the frequency response of a railway track with a French High Speed Line (HSL) design. Then, low-frequency receptance measurements, performed in a specific HSL test site with different designs, are presented. These experimental results are used to characterize a change in the track substructure. Further analysis of the full track responses associated with peaks visible in the receptance test is conducted using numerical simulations. In the considered test case, these simulations demonstrate the existence of the superstructure and ballast resonance on relatively soft mats.

This work focuses on the detection of anomalies in a railway communication system. We present the detection method as well as the architecture of the system supporting it. The method is based on a preliminary stage to collect and aggregate the data. It then combines the use of a multidimensional indexation tree (i.e., iSax tree) to store time series, and the computation of an anomaly detection score (i.e., CFOF score). We propose a fast estimation of this anomaly score, based on the information stored in the indexation tree, so that the score can therefore be computed on the fly over the stream of messages in the communication system. We show by mean of experiments that this estimation is close to the exact score. We describe the platform that has been implemented, and we show that it effectively support abnormal behaviour detection in the real stream of messages within the communication system of the French Railway Company (SNCF).

ECML/PKDD - The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Würtzburg, ALLEMAGNE, 16-/09/2019 - 20/09/2019; The increase in the amount of data collected in the transport domain can greatly benefit mobility studies and help to create high value-added mobility services for passengers as well as regulation tools for operators. The research detailed in this paper is related to the development of an advanced machine learning approach with the aim of forecasting the passenger load of trains in public transport. Predicting the crowding level on public transport can indeed be useful for enriching the information available to passengers to enable them to better plan their daily trips. Moreover, operators will increasingly need to assess and predict network passenger load to improve train regulation processes and service quality levels. The main issues to address in this forecasting task are the variability in the train load series induced by the train schedule and the influence of several contextual factors, such as calendar information. We propose a neural network LSTM encoder-predictor combined with a contextual representation learning to address this problem. Experiments are conducted on a real dataset provided by the French railway company SNCF and collected over a period of one and a half years. The prediction performance provided by the proposed model are compared to those given by historical models and by traditional machine learning models. The obtained results have demonstrated the potential of the proposed LSTM encoder-predictor to address both one-step-ahead and multi-step forecasting and to outperform other models by maintaining robustness in the quality of the forecasts throughout the time horizon.

CAP 2019, Conférence sur l'Apprentissage Automatique, Toulouse, France, 03-/07/2019 - 05/07/2019; Les possibilités offertes en termes de collecte et de stockage de données permettent de renouveler les approches de modélisation dans le domaine du transport. L'exploitation croisée de différentes sources de données a pour vocation la création de services à forte valeur ajoutée pour l'usager. Les travaux détaillées dans cet article portent sur le développement de modèles de prévision a base de méthodes d'apprentissage notamment profond, pour la prévision court-terme de la charge (nombre de passagers) des trains. Cette prévision de l'affluence dans les trains peut servir à enrichir l'information voyageur à destination des usagers des transports collectifs qui peuvent ainsi mieux planifier leur déplacement. Elle peut également servir aux opérateurs de transport pour une régulation "à la demande" de l'offre de transport. La principale difficulté dans la prévision est liée à la variabilité intrinsèque des séries temporelles des charges à prédire, induite par l'influence de plusieurs paramètres dont ceux liés à l'exploitation (horaire, retard, type de mission...) et au contexte (information calendaire, grand évènement, météo,...). Nous proposons un modèle LSTM encodeur-prédicteur pour résoudre cette tâche de prévision. Plusieurs expérimentations sont menées sur des données réelles du réseau Transilien de la SNCF sur une durée d'un an et demi. Les résultats de prévision sont détaillées en vue de comparer les performances d'un tel modèle à plusieurs horizons temporels avec celles d'autres modèles plus classiques utilisées en prévision.

Lithium-ion batteries are getting larger due to the expansion of transportation and mass storage markets and they can now contain up to thousands of cells. However, a sole damaged cell can significantly impact the whole battery pack efficiency. Thus, the diagnosis of a single cell remains critical for those systems. Many methods exist in which the cell is considered homogeneous. We recently developed a heterogeneous equivalent circuit model that considers an internal resistance distribution to better represent a real single cell behaviour. This internal resistance distribution (IRD) may bring valuable information about a single cell internal quality, but only if it is determined with a sufficient accuracy. In this paper, we propose an algorithm that allows a responsive determination of the IRD. The results are compared to previous determination methods. This IRD, which is determined thanks to the preliminary construction of a homogenous model and a single discharge, is also valid for other operating conditions. The determination of a cell IRD can be used as a non-invasive diagnosis tool to track the internal degradations of a cell. The IRD of two different cell, one aged and one new are then compared, the IRD of the aged one being on average larger. This proves the relevance of the determination method and its use to get an insight into a cell. Differences in shape between the aged and the new cell IRD are discussed, as well as criteria that seems interesting. Although this work is developed for a single cell, its initial goal is to be used to detect a damaged cell inside a battery pack and may thus be applied to several cells connected in parallel.

Ballast grains in railway tracks progressively wear due to the efforts exerted by the continuous passage of trains and the periodic maintenance operations. The initial sharp edges and vertices of the grains tend to become smoother and the surface texture is removed. This change in morphology plays a key role on the proper behaviour of the ballast layer and, consequently, on the frequency maintenance required on the track. The object of this paper is to improve the understanding of the wear process by tracking the morphology of a sample of grains submitted to an accelerated ageing using the Micro-Deval standard test. To this goal, X-ray Computed Tomography is used to scan a sample of grains at different states of wear and the resulting images are compared using 3D image analysis. A description of morphology evolution at different scales is provided using scalar parameters and spherical harmonic analysis, proving that the general form is not significantly changed during a standard Micro-Deval test. Thus a detailed analysis at the asperity level is performed, showing the key role of the edge broadening and vertex smoothing phenomena on ballast wear.

International audience; Broadband acoustic attenuation produced by a three dimensional (3D) locally resonant sonic crystal (LRSC), exploiting both the multiple coupled resonances and the Bragg band gaps, is numerically and experimentally reported in this work. The LRSC is made of square cross-section scatterers arranged on a square lattice and periodically incorporating both quarter-wavelength and Helmholtz resonators along their heights. Local resonators of different types are combined with the periodicity of the system generating multiple coupled resonances at low frequencies and opening Bragg band gaps respectively. This twofold coupling produces a strong broadband attenuation: a large insertion loss (IL), with an average value of 16.8 dB, covering three and a half octaves from 350 Hz to 5000 Hz with a LRSC of 30 cm width. This frequency band corresponds to one of the several railway noise sources (rolling noise, traction auxiliaries , etc.). A simplified 2D LRSC is finally analyzed numerically in a real train-track configuration, showing the efficiency of the proposed design to attenuate the railway rolling noise.

International audience

National audience; La finalité de notre travail est la détection des anomalies dans les traces de fonctionnement de l'infrastructure de communication du Système d'Information (SI) de la SNCF. Deux techniques récentes et indépendantes semblentparticulì erement appropriées dans notre cas. Il s'agit d'une part du stockage et de l'indexation de séries temporelles dans un arbre appelé arbre iSAX, et d'autre part d'un score de détection d'anomalie nommé CFOF dont la robustesse au phénomène de concentration en haute dimension a ´ etéetéétablie de façon formelle. Dans cet article nous montrons qu'il est possible d'utiliser la structuration des in-formations dans l'arbre iSAX pour déterminer rapidement une approximation du score CFOF. La valeur obtenue est proche du score exact sur des données synthétiques et réelles. Les premiers retours d'expertises in-diquent que la méthode semble pertinente pour le déclenchement d'alarmes sur les données issues de trace d'activité du SI de la SNCF.

Proceedings of the ICA 2019 and EAA Euroregio : 23rd International Congress on Acoustics, integrating 4th EAA Euroregio 2019 : 9-13 September 2019, Aachen, Germany / proceedings editor: Martin Ochmann, Michael Vorländer, Janina Fels 23rd International Congress on Acoustics, integrating 4th EAA Euroregio 2019, ICA 2019, Aachen, Germany, 9 Sep 2019 - 13 Sep 2019; Aachen (2019)., Published by Aachen

Air pollution has become a major issue since it is responsible for several adverse health effects. Among pollutants, exposure to particle matter (PM) including ultrafine (UFP) and nanoparticles has been defined as particularly hazardous. It was shown that PM exposure increases the risks of cardiovascular and respiratory diseases. Particles size is strongly linked to their dangerousness. The smaller they are, the higher their deposition rate and their ability to penetrate respiratory and blood system are. Former studies carried out in underground train stations across the world revealed that particle concentration rates can reach higher levels than outdoor.Thus, commuting is one of the main contribution to daily personal exposure to particulate pollutants This work aims to increase our knowledge about particle dynamics issued from trains brake discs in the context of an underground station. Then, the influence of the flow surrounding a train passing a station at low speed is investigated. To assess the particle dynamics for these three specific size ranges, three-dimensional numerical simulations are conducted using the commercial code Fluent 19.2. The Eulerian-Lagrangian approach is used allowing a fine tracking of particles in the flow around the rolling stock. The turbulent mean flow is computed using Unsteady Reynolds-Averaged Navier-Stokes (URANS) through the RNG k-µ turbulence model. Particles dispersion from disks brakes to the station is computed with the Discrete Phase Model. Our methodology is described in the first part of this paper, followed by the results of flow computation. Train-induced flow influence on brake particle dispersion is discussed in the third section. This paper ends with our conclusions about regions of interested highlighted by the present study and perspective for further studies to assess properly particle dispersion in underground stations., La pollution de l'air est devenue un problème majeur puisqu'elle est responsable de plusieurs effets néfastes sur la santé. Parmi les polluants, l'exposition aux particules (PM), y compris les particules ultrafines (UFP) et les nanoparticules, a été définie comme particulièrement dangereuse. Il a été démontré que l'exposition aux particules augmente les risques de maladies cardiovasculaires et respiratoires. La taille des particules est fortement liée à leur dangerosité. Plus ils sont petits, plus leur taux de dépôt et leur capacité à pénétrer les voies respiratoires et le système sanguin sont élevés. Des études antérieures menées dans des gares souterraines à travers le monde ont révélé que les taux de concentration de particules peuvent atteindre des niveaux plus élevés qu'à l'extérieur, de sorte que les trajets domicile-travail constituent l'une des principales contributions à l'exposition quotidienne des personnes aux particules polluantes. Ensuite, l'influence de l'écoulement autour d'un train qui passe à faible vitesse devant une gare est étudiée. Pour évaluer la dynamique des particules pour ces trois plages de tailles spécifiques, des simulations numériques tridimensionnelles sont effectuées à l'aide du code commercial Fluent 19.2. L'approche eulérienne-lagrangienne permet un suivi fin des particules dans le flux autour du matériel roulant. Le débit moyen turbulent est calculé à l'aide du modèle de turbulence Unsteady Reynolds-Averaged Navier-Stokes (URANS) à l'aide du modèle RNG k-µ. La dispersion des particules des freins à disque vers la station est calculée à l'aide du modèle de phase discrète. Notre méthodologie est décrite dans la première partie de ce document, suivie des résultats du calcul des flux. L'influence du débit induit par le train sur la dispersion des particules de freinage est abordée dans la troisième section. Le présent document se termine par nos conclusions sur les régions d'intérêt mises en évidence par la présente étude et les perspectives d'autres études visant à évaluer correctement la dispersion des particules dans les stations souterraines.

International audience; This paper presents a Bayesian calibration method for a simulation-based model with stochastic functional input and output. The originality of the method lies in an adaptation involving the representation of the likelihood function by a Gaussian process surrogate model, to cope with the high computational cost of the simulation, while avoiding the surrogate modeling of the functional output. The adaptation focuses on taking into account the uncertainty introduced by the use of a surrogate model when estimating the parameters posterior probability distribution by MCMC. To this end, trajectories of the random surrogate model of the likelihood function are drawn and injected in the MCMC algorithm. An application on a train suspension monitoring case is presented.

Little research has examined the behavior of public transport users in response to a service disruption that has just occurred. This article aims to identify the different ways in which suburban train passengers cope with service interruptions or delays and to identify the factors involved in their decision-making process. We conducted a study mixing two methods: a revealed-preference questionnaire that asked 185 participants about their choices during the last major disruption they encountered and a diary study in which participants described all disruptions they experienced during a two-week period. Eighty disruptions were reported in detail by 38 users. We analyze our results using multiple correspondence analysis and ascending hierarchical clustering to construct eight suburban train passenger behavioral profiles. Additionally, we compare different cases of disruption (in a multiple-case study). We identify three categories of factors affecting suburban train user behavior: individual-specific factors, journey-specific factors and information-specific factors. The findings show that user expertise, car availability, perception of service recovery time, opinions on passenger information services, available transport services, time constraints, and the moment and place at which communication about the disruption is received influence user behavior.; Peu d'études ont examiné le comportement des usagers des transports publics face à une interruption de service qui vient de se produire. Cet article vise à identifier les différentes manières dont les voyageurs des trains de banlieue font face aux interruptions ou aux retards de service et à identifier les facteurs impliqués dans leur processus décisionnel. Nous avons mené une étude combinant deux méthodes : un questionnaire à préférence révélée qui interrogeait 185 participants sur leurs choix lors de la dernière perturbation majeure qu'ils ont rencontrée et une étude grâce à un journal de bord dans laquelle les participants décrivaient toutes les perturbations qu'ils ont vécues pendant une période de deux semaines. Quatre-vingts perturbations ont été signalées en détail par 38 utilisateurs. Nous analysons nos résultats à l'aide d'une analyse en correspondances multiples et d'une classification ascendante hiérarchique pour construire huit profils comportementaux de passagers de trains de banlieue. De plus, nous comparons différents cas de perturbation (dans une étude de cas multiples). Nous identifions trois catégories de facteurs affectant le comportement des usagers des trains de banlieue : les facteurs individuels, les facteurs spécifiques au trajet et les facteurs spécifiques à l'information. Les résultats montrent que l'expertise des utilisateurs, la disponibilité des véhicules, la perception du temps avant le rétablissement du service, les opinions sur les services d'information aux passagers, les services de transport disponibles, les contraintes de temps et le moment et le lieu où la communication concernant la perturbation est reçue influencent le comportement des utilisateurs.

This is the deliverable D4.5 Process Execution Report of the ERSAT GGC project. This document reports the execution of the track area classification process developed during the work package WP4 Track Survey and Track Classification along with the main outcomes and results obtained during this process. The goal of WP4 is to set up a standard measurement and classification system that is able to provide a prediction of the expected presence of GNSS hazard causes and degraded performance to be expected at a given track area, in order to enable the evaluation if the specific environment is suitable or not to place virtual balises or on the contrary should be discarded beforehand. In this document, we detail the results of applying the classification standard process, by means of the developed software Toolset to the measurement data collected in the three measurements campaigns during ERSAT GGC project. This execution report addressed the main important aspects that need to be carried out for the correct application of the classification process and the results obtained with the data collected.

International audience; This paper presents a novel method for the state health monitoring of high-speed train suspensions from in-line acceleration measurements by embedded sensors, for maintenance purposes. We propose a model-based method relying on a multibody simulation code. It performs the simultaneous identification of several suspension mechanical parameters. It is adapted to the introduction of uncertainties in the system and to the exploitation of numerous high-dimensional measurements. The novel method consists in a Bayesian calibration approach using a Gaussian process surro-gate model of the likelihood function. The method has been validated on numerical experiments. We demonstrate its ability to detect evolutions of the health state of suspension elements. It has then been tested on actual acceleration measurements to study the time evolution of the suspension parameters.

International audience; The ROADEF/EURO challenge is a contest jointly organized by the French Operational Research and Decision Aid society (ROADEF) and the European Operational Research society (EURO). The contest has appeared on a regular basis since 1999 and always concerns an applied optimization problem proposed by an industrial partner. The 2014 edition of the ROADEF/EURO challenge was led by the Innovation & Research department of SNCF, a global leader in passenger and freight transport services, and infrastructure manager of the French railway network. The objective of the challenge was to find the best way to store and move trains on large railway sites, between their arrivals and departures. Since trains never vanish and traffic continues to increase, in recent years some stations have been having real congestion issues. Train management in large railway sites is of high interest for SNCF, which is why it was submitted to the operations research community as the industrial problem for the 2014 edition of the ROADEF/EURO challenge. This paper introduces the special section of the Annals of Operations Research volume devoted to the ROADEF/EURO challenge 2014, as well as the methods of the finalist teams and their results.

International audience; Prior to renewal or maintenance works on a railway track it is required to perform a proper investigation of the stiffness thickness of each material constituting the track-bed. In practice, different techniques are used for this purpose. In this study, a comparison of two methods of determining the stiffness of track-bed materials (dynamic penetration dynamic plate load) is made for a representative French conventional railway line, aiming at optimising the use of different geotechnical auscultation techniques for the railway applications. Firstly, results from geo-endoscopic tests are analysed to define the thickness nature of the different materials found in the track. Then, dynamic penetration tests (PANDA tests) are performed to evaluate the stiffness of the different layers. Statistical distribution of soil stiffness is analysed for each layer. In addition, the elastic moduli of different materials are estimated from their stiffness using empirical equations. Secondly, dynamic plate load tests using a light weight deflectometer device (LWD) are carried out on surfaces (on tracks service paths) in order to estimate the dynamic static moduli of ballast subgrade. A statistical analysis of the obtained results shows a low dispersion rate a satisfactory repeatability. The static moduli estimated from LWD tests are found to be consistent with the elastic modulus estimated from PANDA tests, showing that different auscultation methods give complementary information about the mechanical properties of the materials constituting the conventional tracks. (C) 2016 The Japanese Geotechnical Society. Production hosting by Elsevier B.V. All rights reserved.

International audience; The influence of the track geometry on the dynamic response of the train is of great concern for the railway companies, because they have to guarantee the safety of the train passengers in ensuring the stability of the train. In this paper, the long-term evolution of the dynamic response of the train on a stretch of the railway track is studied with respect to the long-term evolution of the track geometry. The characterization of the long-term evolution of the train response allows the railway companies to start off maintenance operations of the track at the best moment. The study is performed using measurements of the track geometry, which are carried out very regularly by a measuring train. A stochastic model of the studied stretch of track is created in order to take into account the measurement uncertainties in the track geometry. The dynamic response of the train is simulated with a multibody software. A noise is added in output of the simulation to consider the uncertainties in the computational model of the train dynamics. Indicators on the dynamic response of the train are defined, allowing to visualize the long-term evolution of the stability and the comfort of the train, when the track geometry deteriorates.

International audience; This paper analyzes the dynamic behavior of a randomly-heterogeneous continuum model of a ballasted railway track through the numerical construction of dispersion curves along the axis of the track. At intermediate frequencies (50-100 Hz), the dispersion curves display a strong decrease of the group velocity. At higher frequencies (above 100 Hz), the wave are strongly localized and cannot propagate along the track. This behavior is coherent with experimental observations reported in the literature on ballasted railway tracks and other granular media. Additional simulations are performed to prove that the localization is not a consequence of the geometry of the track or the impedance mismatch between the ballast and the underlying soil, but is indeed Anderson localization due to the heterogeneity of the ballast.

IWSHM-RS 2018, 2nd International Workshop on Structural Health Monitoring for Railway Systems, Qingdao, CHINE, 17-/10/2018 - 19/10/2018; Track circuits play a major role in railway signaling. In some exceptional conditions, poor rail/wheel contact conditions may lead to a non-detection of the train on the zone. A presentation of the principle of detection by track circuits is proposed to introduce the existing detection criterion. The aim of the paper is first to present new detection approaches based on signal processing on an experiment with a dedicated train running on a track equipped with a track circuit. The second objective is to present a strategy to test new detection criteria on commercial zones over a long period of time (a few months) with the help of the PEGASE acquisition board. PEGASE has been developed by IFSTTAR and the presented work is the result of SNCF/IFSTTAR collaboration.

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International audience; La réorganisation du trafic ferroviaire en cas de panne sur infrastructure électrique est un processus de décision important pour une bonne exploitation du réseau. Le système étudié est complexe et son analyse repose sur des simulations coûteuses, ce qui rend difficile la gestion des incidents. Cet article propose d'utiliser l'analyse de sensibilité afin d'évaluer l'influence des différentes variables d'ajustement du trafic (espacement entre trains, vitesses de circulation...) sur la grandeur critique qu'est la tension d'alimentation des trains. Deux méthodes complémentaires sont mises en œuvre : l'analyse de sensibilité globale avec le calcul d'indices de sensibilité de Sobol, puis l'analyse régional avec du filtrage de Monte-Carlo associé à des comparaisons de densités de probabilité cumulées. L'approche proposée est illustrée sur un cas simple, pour lequel l'espacement entre trains se révèle avoir plus d'influence que la réduction de vitesse.

International audience; The present work addresses traffic rescheduling in case of electric infrastructure failure. The power available for train traction is restricted and the traffic must be reorganized according to this constraint. The system behaviour is computed using a dynamic multi-physics railway simulator which gives physi- cal quantities such as the train speed profiles, voltage along the catenary lines and temperatures. The rescheduling problem relies on this non-linear model, with a large number of continuous and discrete variables, constraints on dynamic outputs (typically voltage limits) and a high computation cost. We propose a rescheduling process based on sensitivity analysis in order to analyse the behaviour of this complex system and obtain information about the adjustment operations needed in order to reschedule the traffic in an optimal way. Our approach is based on statistics, with predefined variation ranges of the input parameters. In a first stage, variance decomposition-based sensitivity analysis (generalized Sobol indexes) is used for prioritization and fixing factors; then regional sensitivity analysis is used for factor mapping. The proposed approach has been tested on a simple case, with a nominal traffic running on a single-track line. The considered incident is the loss of a feeding power substation. The variables to be adjusted are the time interval between departure times and speed reduction in the vicinity of the faulty substation. The results show that increasing the time interval between trains is the most influential vari- able. Pareto-optimal fronts are also built in order to perform multi-criteria analysis according to travel- ling time, train delays and traction energy.

International audience; The present work addresses train traffic rescheduling that is needed when an electric incident limits the power available for train traction. This is a difficult process of prime importance and we propose a decision-support tool to help the operator. The railway network is a complex multi-physics dynamic system, with many operational constraints, and its simulation is expensive. This makes the management of an incident difficult. The proposed approached applies regional sensitivity in order to study the influence of the different adjustment variables (train delays, speed references ...) on traffic quality indicators and operational constraints (catenary voltage, for example). The analysis is divided in two parts: Monte Carlo filtering for factor mapping (qualitative analysis), and two samples Kolmogorov-Smirnov test for prioritization and fixing factors (quantitative analysis). The results provide information about the behavior of the system and the influence of the different traffic adjustment variables, and help reorganizing the train traffic in an optimal way. As a result of the analysis, we obtain a set of feasible solutions that are organized according to different performance criteria. Pareto-optimal front are plotted in order to guide the decisionmaker. The proposed approach is illustrated for a simple case representative of suburban traffic.

International audience; Rolling contact fatigue cracks in rails undergo non-proportional mixed-mode I+II+III, in variable proportionsalong their front. In order to determine the crack growth thresholds and kinetics in mixed-mode II/III, asymmetricfour point bending tests are run on a rail steel with different angles between the crack front and theshearing load, so as to vary the mode mixity ratio. For sufficiently high loading ranges, these tests give rise tocoplanar shear-mode crack growth. The effective stress intensity factors (SIFs) are derived by an inverse methodfrom the measured crack face relative displacements. It appears to be 10–70% lower than the nominal SIFs andto allow a reasonable correlation of the measured crack growth rates. The local application, ahead of the crackfront, of shear-driven or tension-driven fatigue damage models – after 3D elastic-plastic computations of localstress and strain ranges – allows a successful prediction of crack fronts paths and growth rates.