Literatura académica sobre el tema "Donnés ferroviaires"
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Artículos de revistas sobre el tema "Donnés ferroviaires"
Fize, Jacques, Lucile Sautot, Martin Lentschat, Laurence Dujourdy, Ludovic Journaux y Mohamed Hilaf. "Extraction et mise en contexte spatial des propositions relatives au transport dans le Grand Débat National". Revue Internationale de Géomatique 31, n.º 3-4 (julio de 2022): 329–54. http://dx.doi.org/10.3166/rig31.329-354.
Texto completoMuriset, Rosalie y Anne Vuilleumier. "nformatisation du trafic ferroviaire. Perception de la dimension surveillante de l’application «Mobilbonus» par ses utilisateurs". Géo-Regards 7, n.º 1 (2014): 89–101. http://dx.doi.org/10.33055/georegards.2014.007.01.89.
Texto completoCao, Huhua, Vincent Roy y Sylvain Lacombe. "Dynamique de l’implantation des services de garde à l’enfance dans la région urbaine de Moncton, 1990-2001". Notes de recherche 35, n.º 2 (15 de marzo de 2005): 185–202. http://dx.doi.org/10.7202/010649ar.
Texto completoYaba, Olatounde Alexandre, Fabrice Emeriault, Orianne Jenck, Jean-François Ferellec y Amine Dhemaied. "Évaluation des apports de géogrilles dans une structure d’assise ferroviaire en conditions opérationnelles". Revue Française de Géotechnique, n.º 172 (2022): 4. http://dx.doi.org/10.1051/geotech/2022017.
Texto completoCastagnino, Florent. "Surveiller par les bases de données : construction et gestion des faits de sécurité et de sûreté dans le milieu ferroviaire". Sociologie du travail 58, n.º 3 (30 de septiembre de 2016): 273–95. http://dx.doi.org/10.4000/sdt.1157.
Texto completoCastagnino, Florent. "Surveiller par les bases de données : construction et gestion des faits de sécurité et de sûreté dans le milieu ferroviaire". Sociologie du Travail 58, n.º 3 (julio de 2016): 273–95. http://dx.doi.org/10.1016/j.soctra.2016.06.015.
Texto completoMaltais, Danielle. "Personnes âgées ayant des incapacités et désastres naturels : vulnérabilité des aînés et post-trauma". Développement Humain, Handicap et Changement Social 22, n.º 1 (16 de febrero de 2022): 119–30. http://dx.doi.org/10.7202/1086385ar.
Texto completoGautier, Axel. "Numéro 22 - juin 2004". Regards économiques, 12 de octubre de 2018. http://dx.doi.org/10.14428/regardseco.v1i0.16053.
Texto completoGautier, Axel. "Numéro 22 - juin 2004". Regards économiques, 12 de octubre de 2018. http://dx.doi.org/10.14428/regardseco2004.06.01.
Texto completoMuller, M., Marc Papinutti y Christian Reynaud. "TGV spread effects analysis". Les Cahiers Scientifiques du Transport - Scientific Papers in Transportation 15-16 | 1987 (30 de junio de 1987). http://dx.doi.org/10.46298/cst.11847.
Texto completoTesis sobre el tema "Donnés ferroviaires"
Lenart, Marcin. "Sensor information scoring for decision-aid systems in railway domain". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS157.
Texto completoIn this thesis, the problem of assessing information quality produced by sensors is investigated. Indeed, sensors, usually used in networks, do not always provide correct information and the scoring of this information is needed. An approach is proposed that deals with some of the major limitations in the literature by providing a model designed to be sensor-generic, not dependent on ground truth and dependent only on easy-to-access meta-information, exploiting only attributes shared among the majority of sensors. The proposed model is called ReCLiC from the four dimensions that it considers: Reliability, Competence, Likelihood and Credibility. The thesis discusses in depth the requirements of these dimensions and proposes motivated definitions for each of them. Furthermore, it proposes an implementation of the generic ReCLiC definition to a real case, for a specific sensor in the railway signalling domain: the form of the four dimensions for this case is discussed and a formal and experimental study of the information scoring behaviour is performed, analysing each dimension separately. The proposed implementation of the ReCLiC model is experimentally validated using realistic simulated data, based on an experimental protocol that allows to control various quality issues as well as their quantity, Finally, the ReCLiC model is used to analyse a real datasetapplying a new visualisation method that, in addition, allows to study the notion of trust dynamic
Dimanche, Vincent. "Compréhension fine du comportement des lignes des réseaux métro, RER ettramway pour la réalisation des études d’exploitabilité". Thesis, Reims, 2018. http://www.theses.fr/2018REIMS010.
Texto completoDense railway networks face significant saturation. And the balance between the theoretical offer and the growing demand imposes strong operability constraints. An imbalance will generate conflicting points such as bottlenecks with the effect of delays on the following trains. As the human factor influences the operation performance; taking it into account more accurately should improve understanding and modeling of railway lines to increase capacity without reducing passenger comfort. To fulfill this objective, we are working on an adapted visualization of the operating data and on their automated mining. These two solutions have been adapted and applied to the railway sector, particularly to the lines of rail networks operated by RATP. The "Visual Analytics" process, implemented in our work to meet these needs, encompasses the steps required to value the data, going from the preparation of the data to the expert analysis. This expert analysis is made through graphic representation and the use of data mining algorithms. Among these data mining algorithms, CorEx and Sieve allowed us to analyze operating data and then extract characteristics human behavior thanks to unsupervised learning based on a multivariate mutual information measure to. Finally, we propose an intuitive visualization of a large amount of data allowing their global integration and facilitating the overall diagnosis of the railway lines behavior
Rachedi, Nedjemi Djamel Eddine. "Modélisation et surveillance de systèmes Homme-Machine : application à la conduite ferroviaire". Thesis, Valenciennes, 2015. http://www.theses.fr/2015VALE0009.
Texto completoThe scope of the thesis is the monitoring of human-machine systems, where the operator is the driver of rail-based transportation system. Our objective is to improve the security of the system preventing and avoiding factors that increase the risk of a human error. Two major problems are identified: characterization, or how to determine indicative and discernible phases of driver's activity and representation, or how to describe and codify driver's actions and its repercussions on the rail system in a mathematical formalism that will allow unequivocal analysis. In order to bring a solution to those problems, we propose, first-of-all, a behavioral model of the human operator representing his control behavior in continuous-time. To consider inter- and intra-individual differences of human operators and situation changes, we propose a transformation of the latter behavioral model in a new space of representation. This transformation is based on the theory of Hidden Markov Models, and on an adaptation of a special pattern recognition technique. Then, we propose a discrete-time behavioral modeling of the human operator, which represents his actions and takes account of errors and unexpected events in work environment. This model is inspired by cognitive models of human operators. These two aspects allow us to interpret observables with respect to reference situations in order to characterize the overall human operator state. Different information sources are considered; as a result the data are heterogeneous and subject to measuring uncertainties, needing a robust data fusion approach that is performed using a Bayesian Network. Finally, the proposed modeling and fusion methodologies are used to design a reliable and unintrusive vigilance system. This system can interpret driving behaviors and to detect driver’s risky states in order to prevent drowsiness. The theoretical study was tested in simulation to check the validity. Then, a feasibility study was conducted using data obtained during experiments on the LAMIH laboratory railroad platform “COR&GEST”. These results allowed us to plan and implement experiments to be conducted on the future multimodal driving simulator “PSCHITT-PMR”
Tea, Céline. "Retour d'expérience et données subjectives : quel système d'information pour la gestion des risques ?" Phd thesis, Paris, ENSAM, 2009. http://pastel.archives-ouvertes.fr/pastel-00005574.
Texto completoLe, Nguyen Minh Huong. "Online machine learning-based predictive maintenance for the railway industry". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT027.
Texto completoBeing an effective long-distance mass transit, the railway will continue to flourish for its limited carbon footprint in the environment. Ensuring the equipment's reliability and passenger safety brings forth the need for efficient maintenance. Apart from the prevalence of corrective and periodic maintenance, predictive maintenance has come into prominence lately. Recent advances in machine learning and the abundance of data drive practitioners to data-driven predictive maintenance. The common practice is to collect data to train a machine learning model, then deploy the model for production and keep it unchanged afterward. We argue that such practice is suboptimal on a data stream. The unboundedness of the stream makes the model prone to incomplete learning. Dynamic changes on the stream introduce novel concepts unseen by the model and decrease its accuracy. The velocity of the stream makes manual labeling infeasible and disables supervised learning algorithms. Therefore, switching from a static, offline learning paradigm to an adaptive, online one is necessary, especially when new generations of connected trains continuously generating sensor data have already been a reality. We investigate the applicability of online machine learning for predictive maintenance on typical complex systems in the railway. First, we develop InterCE as an active learning-based framework that extracts cycles from an unlabeled stream by interacting with a human expert. Then, we implement a long short-term memory autoencoder to transform the extracted cycles into feature vectors that are more compact yet remain representative. Finally, we design CheMoc as a framework that continuously monitors the condition of the systems using online adaptive clustering. Our methods are evaluated on the passenger access systems on two fleets of passenger trains managed by the national railway company SNCF of France
Debiolles, Alexandra. "Diagnostic de systèmes complexes à base de modèle interne, reconnaissance des formes et fusion d’informations : Application au diagnostic des Circuits de Voie ferroviaires". Compiègne, 2007. http://www.theses.fr/2007COMP1672.
Texto completoThis works presents different diagnosis methods that aim at detecting and estimating defects appearing on a system made up of several spatially related subsystems. The application deals with the diagnosis of track circuits. A first approach based on an internal model is layed out; it allows to detect and assess the graveness of ail the system defects, by optimizing a local physical model of the system. But this method is dependant from the good estimation of several parameters of the model. The second method that is set out is an external approach based on classical pattern recognition. A classifier is associated to each subsystem. Their outputs are combined within the framework of belief functions in order to manage possible conflicts among the classifiers. This method is very efficient, but it can only detect one defect without assessing its graveness. Finally a last approach is presented, that combines the two previous ones in order to both detect several defects, and assess them
Saint-Marc, Cécile. "Formalisation et géovisualisation d'événements historiques issus de risques naturels pour la compréhension des dynamiques spatiales : application aux inondations ayant touché le système ferroviaire français". Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAS024/document.
Texto completoThis research was led in an industrial partnership with SNCF Réseau. In the field of geovisualization of spatio-temporal information, it focuses on developing cartographical visualization methods adapted to the analysis of the impacts of floods on the railway system.Historical events are of great help to understand and manage natural risks. Cartography became a key tool to analyze risks in their territorial contexts. But making maps which remain legible while showing all the complexity of risk processes that occurred during natural disaster is not easy. The main challenges are the need to represent both the temporal and the spatial dimensions of risk events, the need to visualize domino-effect, because they often lead to worsen damages, and the will to adapt representations to the cognitive capacities of users.This research resulted in four contributions. The first one is the formalization of flood narratives in a domain ontology, which describes flood events, domino-effects, their impacts on the railway system and also response measures to restore the system. Five case studies of historical floods were instantiated in the model. Resulting from their study, the second contribution consists in generic semiology principles to visualize the narratives of floods on maps. The third contribution is a geovisualization interface, which includes original graphical representations to visualize the temporal features associated with flood events. This geovisualization interface was tested in an experiment with expert users of the railway field. Results confirmed the proposals of representation of time but disconfirm proposals of visualization of domino effects. The analysis of results led to the fourth contribution, which consists in a reusable model of an experimental procedure that is adapted to test geovisualization interfaces
Cornet, Sélim. "Formalisation et résolution du problème de construction de grilles horaires robustes pour les réseaux ferrés denses". Thesis, Lille, 2020. https://pepite-depot.univ-lille.fr/LIBRE/EDSPI/2020/50376-2020-Cornet.pdf.
Texto completoDue to the greater concentration of economic activities around big cities, transportation facilities are facing an increasing demand. In order to answer it, transport companies seek to provide an adequate offer. However, they are constrained by the progressive saturation of transportation networks. Concerning railway transportation, the increasing numbers of passengers and trains result in a higher number of disturbances occuring during operations, as well as a higher tendency of those to amplify and spread over the network. The final consequence is a lower quality of service for passenger and financial penalties to operating companies. There are two levels of action to prevent these phenomena or limit their reach : taking control actions during operations, and anticipate by building robust transportation plans that will make easier to cope with small random disturbances. The work presented in this thesis focuses mainly on this last point. After presenting railway operations and its specificities in a Mass Transit context and defining what we call ``small disturbances'', we provide a review of existing work on similar topics. Most of conceptual frames designed for robust train timetabling are inadequate when it comes to applying them to the specific case of dense traffic areas. That is why we propose a new model given under the form of a stochastic program. We present then a three-step approach for solving it. In the first step, we use data about operations to estimate the probability distributions of disturbances. In the second one, we use these distributions in a stochastic simulation tool that allows us to compute the performance of a given timetable. Finally, this tool is used as a fitness function in a simheuristic, based on simulated annealing algorithm, that aims at computing automatically some robust train timetables
Sammouri, Wissam. "Data mining of temporal sequences for the prediction of infrequent failure events : application on floating train data for predictive maintenance". Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1041/document.
Texto completoIn order to meet the mounting social and economic demands, railway operators and manufacturers are striving for a longer availability and a better reliability of railway transportation systems. Commercial trains are being equipped with state-of-the-art onboard intelligent sensors monitoring various subsystems all over the train. These sensors provide real-time flow of data, called floating train data, consisting of georeferenced events, along with their spatial and temporal coordinates. Once ordered with respect to time, these events can be considered as long temporal sequences which can be mined for possible relationships. This has created a neccessity for sequential data mining techniques in order to derive meaningful associations rules or classification models from these data. Once discovered, these rules and models can then be used to perform an on-line analysis of the incoming event stream in order to predict the occurrence of target events, i.e, severe failures that require immediate corrective maintenance actions. The work in this thesis tackles the above mentioned data mining task. We aim to investigate and develop various methodologies to discover association rules and classification models which can help predict rare tilt and traction failures in sequences using past events that are less critical. The investigated techniques constitute two major axes: Association analysis, which is temporal and Classification techniques, which is not temporal. The main challenges confronting the data mining task and increasing its complexity are mainly the rarity of the target events to be predicted in addition to the heavy redundancy of some events and the frequent occurrence of data bursts. The results obtained on real datasets collected from a fleet of trains allows to highlight the effectiveness of the approaches and methodologies used
Milliet, De Faverges Marie. "Développement et implémentation de modèles apprenants pour l’exploitation des grandes gares". Electronic Thesis or Diss., Paris, CNAM, 2020. http://www.theses.fr/2020CNAM1283.
Texto completoThis thesis deals with uncertainty and robustness in decision problems, with the case of the train platforming problem subject to delays. A two-part methodology is proposed to address this problem. First, delay records are used to build models predicting probability distributions conditionnaly to a set of explanatory variables. A methodology to validate and evaluate these predictions is proposed to ensure their reliability for decision-making. As the train platforming problem can be seen as a weighted clique problem, these predicted distributions are used in a second part to add weights on edges to penalize risk of conflict. Local search algorithms are used and experiments show a significant decrease in conflicts
Libros sobre el tema "Donnés ferroviaires"
Les chemins de fer de Nantes et Châteaubriant à Saint-Nazaire: Deux siècles d'histoire ferroviaire, Nantes, Chantenay, Couëron, Indre, Saint-Etienne-de-Montluc, Cordemais, Savenay, Donges, Montoir-de-Bretagne, Saint-Nazaire, Besné-Pontchâteau, Campbron, Bouvron, Blain, Le Gâvre, Vay Nozay, Tréffieux, Saint-Vincent-des-Landes, Louisfert, Châteaubriant. Pornichet: JPN, 2005.
Buscar texto completoCapítulos de libros sobre el tema "Donnés ferroviaires"
"Questionnaire de recueil d’informations et de données concernant les pays du TER et leur réseau ferroviaire". En Chemin de fer transeuropéen à grande vitesse étude du plan directeur: Phase 2, 325–26. United Nations, 2023. http://dx.doi.org/10.18356/9789210054966c010.
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