Dissertations / Theses on the topic 'Estimation du trafic'
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Ghorayeb, Ali. "Capteur catadioptrique pour le diagnostic du trafic routier urbain." Amiens, 2010. http://www.theses.fr/2010AMIE0101.
Full textIn this thesis we present an optimal omnidirectional visual sensor which can replace perspective camera network for traffic diagnosis. The proposed system has the advantage, by the number and the designed mirror, to generate a single view of the crown and junction ways of the crossroads by maximizing the number of useless pixels. So, the percentage of pixels used directly for subsequent phases of image processing is optimal. We describe the methodology used to design such a sensor. In addition, to assess our sensor, we also developed image processing methods that provide useful indicators for estimating the state of the traffic as the crossroads occupancy rate, the vehicle speed and the flow of vehicles. We compare this optimal sensor to the traditional ones that used parabolic, hyperbolic, spherical mirror or a mirror that has constant horizontal resolution to observe the scene. We prove that optimal sensor has better results than traditional ones
Martinet, Simon. "Estimation in-situ des facteurs d’émission des polluants du trafic routier." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSET006.
Full textUrban air pollution is a major issue for human health and the environment. Road traffic is the main source of pollution in urban areas and contributes significantly to air pollution in these areas despite improvements in pollution control technologies and engines. To measure and improve knowledge of pollutant emissions from road vehicles, different methods exist, each with its own advantages and limitations. For example, measurements on a test bench make it possible to study vehicle emissions according to their technology and with good reproducibility of test conditions. However, this approach remains limited, particularly for the representativeness of vehicle fleet emissions under real operating conditions. The limited knowledge of emissions of unregulated pollutants, such as BTEX, C9-22 alkanes, carbonyl compounds, particulate matter and soot carbon, which have adverse effects on health and the environment and are rarely measured due to the complexity of metrology, is a second area for further study of traffic emissions. The objective of this work is to estimate in-situ emission factors for unregulated pollutants from road traffic, under real vehicle traffic conditions and for fleets whose composition is precisely characterized. For this purpose, the work of this thesis has made it possible to develop and implement methodologies for in-situ measurement, in urban areas, of unregulated pollutant emissions from road traffic, and to estimate emission factors based on measurements made at different sites (open roadside site and confined site). These emission factors are established for unregulated pollutants, and for a precisely defined actual vehicle fleet (detailed knowledge of the composition of the vehicle fleet in use and local traffic conditions). The emission factors thus determined in-situ are compared with those derived from bench measurements in order to verify their consistency and analyse them according to the different measurement sites and the impact of the composition of the fleet on pollutant emissions. Three in-situ measurement campaigns were carried out, two roadside in urban areas (open sites) and one in a tunnel near an urban area (confined site). The concentrations of the targeted pollutants measured at these three sites, as well as the different fleet compositions and traffic conditions identified, were used to estimate emission factors per vehicle or for the entire fleet
Leon, Ojeda Luis. "Short-term multi-step ahead traffic forecasting." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENT081/document.
Full textThis dissertation falls within the domain of the Intelligent Transportation Systems (ITS). In particular, it is concerned with the design of a methodology for the real-time multi-step ahead travel time forecasting using flow and speed measurements from a instrumented freeway. To achieve this objective this thesis develops two main methodologies. The first one, a model-free, uses only speed measurements collected from the freeway, where a mean speed is assumed between two consecutive collection points. The travel time is forecasted using a noise Adaptive Kalman Filter (AKF) approach. The process noise statistics are computed using an online unbiased estimator, while the observations and their noise statistics are computed using the clustered historical traffic data. Forecasting problems are reformulated as filtering ones through the use of pseudo-observations built from historical data. The second one, a model-based, uses mainly traffic flow measurements. Its main appealing is the use of a mathematical model in order to reconstruct the internal state (density) in small road portions, and consequently exploits the relation between density and speed to forecast the travel time. The methodology uses only boundary conditions as inputs to a switched Luenberger state observer, based on the ``Cell Transmission Model'' (CTM), to estimate the road initial states. The boundary conditions are then forecasted using the AKF developed above. Consequently, the CTM model is run using the initial conditions and the forecasted boundaries in order to obtain the future evolution of densities, speeds, and finally travel time. The added innovation in this approach is the space discretization achieved: indeed, portions of the road, called ``cells'', can be chosen as small as desired and thus allow obtaining a finer tracking of speed variations. In order to validate experimentally the developed methodologies, this thesis uses as study case the Grenoble South Ring. This freeway, enclosing the southern part of the city from A41 to A480, consists of two carriageways with two lanes. For this study only the direction east-west was considered. With a length of about 10.5 km, this direction has 10 on-ramps, 7 off-ramps, and is monitored through the Grenoble Traffic Lab (GTL) that is able to provide reliable traffic data every 15 s, which makes it possible for the forecasting strategies to be validated in real-time. The results show that both methods present strong capabilities for travel time forecasting: considering the entire freeway, in 90% of the cases it was obtained a maximum forecasting error of 25% up to a forecasting horizon of 45 min. Furthermore, both methods perform as good as, or better than, the average historical. In particular, it is obtained that for horizons larger than 45 min, the forecasting depended exclusively on the historical data. For the dataset considered, the assessment study also showed that the model-based approach was more suitable for horizons shorter than 30 min
Kessaci, Abdellah. "Estimation en ligne et gestion des capacités pour la commande du trafic urbain." Toulouse, ENSAE, 1988. http://www.theses.fr/1988ESAE0010.
Full textKessaci, Abdellah. "Estimation en ligne et gestion des capacités pour la commande du trafic urbain." Grenoble 2 : ANRT, 1988. http://catalogue.bnf.fr/ark:/12148/cb376146037.
Full textMagne, Laurent. "Commande optimale décentralisée du trafic urbain." Toulouse, ENSAE, 2001. http://www.theses.fr/2001ESAE0001.
Full textPecot, Thierry. "Modélisation et estimation du trafic intracellulaire par tomographie de réseaux et microscopie de fluorescence." Phd thesis, Université Rennes 1, 2010. http://tel.archives-ouvertes.fr/tel-00541304.
Full textPécot, Thierry. "Modélisation et estimation du trafic intracellulaire par tomographie de réseaux et microscope de fluorescence." Rennes 1, 2010. https://tel.archives-ouvertes.fr/tel-00541304.
Full textThis thesis presents a new method for analyzing and simulating vesicular trafficking in fluorescence video-microscopy. Instead of tracking each individual vesicle, we have developed a global approach (network tomography) that is inspired from previous works on road traffic analysis and network telecommunication traffic analysis. This approach makes use of local countings of vesicles and a routing procedure to recover the global trajectories of vesicles on a whole image sequence. Contrary to the previous applications of network tomography, the local countings and the routing are also unknown in our case. In order to measure local countings of vesicles, we have developed a method for object and background estimation in fluorescence video-microscopy. This method exploits a non local detection term based on the similarity between image patches and considers the estimated background component as a reference to improve the detection. The routing procedure depends on vesicle countings for the traffic analysis, and is controlled by the user for the simulations. The generated synthetic image sequences enabled to evaluate quantitatively the vesicular trafficking estimation method. This method was also tested on real image sequences in the context of a study on the membranar transport and vesicular trafficking regulated by Rab6 isoforms
Fortuny, Cédric. "Estimation du trafic, planification et optimisation des ressources pour l'ingénierie des réseaux IP/MPLS." Toulouse 3, 2008. http://thesesups.ups-tlse.fr/1198/.
Full textIP networks have become critical systems in the last decade: service interruptions or even significant service degradations are less and less tolerable. Therefore, a new network engineering approach is required to help design, plan and control IP architectures on the basis of supervision information. Our contributions to this new approach are related to traffic matrix estimation from SNMP link loads, to IP routing weights optimization and to network dimensioning. The models and algorithms proposed in this thesis take into account many technological constraints in order to provide operational solutions
Ladino, lopez Andrés. "Traffic state estimation and prediction in freeways and urban networks." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAT016/document.
Full textCentralization of work, population and economic growth alongside continued urbanization are the main causes of congestion. As cities strive to update or expand aging infrastructure, the application of big data, new models and analytics to better understand and help to combat traffic congestion is crucial to the health and development of our smart cities of XXI century. Traffic support tools specifically designed to detect, forecast and alert these conditions are highly requested nowadays.This dissertation is dedicated to study techniques that may help to estimate and forecast conditions about a traffic network. First, we consider the problem Dynamic Travel Time (DTT) short-term forecast based on data driven methods. We propose two fusion techniques to compute short-term forecasts from clustered time series. The first technique considers the error covariance matrix and uses its information to fuse individual forecasts based on best linear unbiased estimation principles. The second technique exploits similarity measurements between the signal to be predicted and clusters detected in historical data and it performs afusion as a weighted average of individual forecasts. Tests over real data were implemented in the study case of the Grenoble South Ring, it comprises a highway of 10.5Km monitored through the Grenoble Traffic Lab (GTL) a real time application was implemented and open to the public.Based on the previous study we consider then the problem of simultaneous density/flow reconstruction in urban networks based on heterogeneous sources of information. The traffic network is modeled within the framework of macroscopic traffic models, where we adopt Lighthill-Whitham-Richards (LWR) conservation equation and a piecewise linear fundamental diagram. The estimation problem considers two key principles. First, the error minimization between the measured and reconstructed flows and densities, and second the equilibrium state of the network which establishes flow propagation within the network. Both principles are integrated together with the traffic model constraints established by the supply/demand paradigm. Finally the problem is casted as a constrained quadratic optimization with equality constraints in order to shrink the feasible region of estimated variables. Some simulation scenarios based on synthetic data for a manhattan grid network are provided in order to validate the performance of the proposed algorithm
Schettini, Frédéric. "Fusion de données pour la surveillance du trafic et l'information des usagers." Toulouse, ENSAE, 1998. http://www.theses.fr/1998ESAE0016.
Full textAmor, Yasmine. "Ιntelligent apprοach fοr trafic cοngestiοn predictiοn." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMR129.
Full textTraffic congestion presents a critical challenge to urban areas, as the volume of vehicles continues to grow faster than the system’s overall capacity. This growth impacts economic activity, environmental sustainability, and overall quality of life. Although strategies for mitigating traffic congestion have seen improvements over the past few decades, many cities still struggle to manage it effectively. While various models have been developed to tackle this issue, existing approaches often fall short in providing real-time, localized predictions that can adapt to complex and dynamic traffic conditions. Most rely on fixed prediction horizons and lack the intelligent infrastructure needed for flexibility. This thesis addresses these gaps by proposing an intelligent, decentralized, infrastructure-based approach for traffic congestion estimation and prediction.We start by studying Traffic Estimation. We examine the possible congestion measures and data sources required for different contexts that may be studied. We establish a three-dimensional relationship between these axes. A rule-based system is developed to assist researchers and traffic operators in recommending the most appropriate congestion measures based on the specific context under study. We then proceed to Traffic Prediction, introducing our DECentralized COngestion esTimation and pRediction model using Intelligent Variable Message Signs (DECOTRIVMS). This infrastructure-based model employs intelligent Variable Message Signs (VMSs) to collect real-time traffic data and provide short-term congestion predictions with variable prediction horizons.We use Graph Attention Networks (GATs) due to their ability to capture complex relationships and handle graph-structured data. They are well-suited for modeling interactions between different road segments. In addition to GATs, we employ online learning methods, specifically, Stochastic Gradient Descent (SGD) and ADAptive GRAdient Descent (ADAGRAD). While these methods have been successfully used in various other domains, their application in traffic congestion prediction remains under-explored. In our thesis, we aim to bridge that gap by exploring their effectiveness within the context of real-time traffic congestion forecasting.Finally, we validate our model’s effectiveness through two case studies conducted in Muscat, Oman, and Rouen, France. A comprehensive comparative analysis is performed, evaluating various prediction techniques, including GATs, Graph Convolutional Networks (GCNs), SGD and ADAGRAD. The achieved results underscore the potential of DECOTRIVMS, demonstrating its potential for accurate and effective traffic congestion prediction across diverse urban contexts
Lemarchand, Antoine. "Modélisation multi-modèle incertaine du trafic routier et suivi robuste de profils optimaux aux entrées des voies périurbaines." Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENT117/document.
Full textThis document synthesizes my Phd thesis work in Automatic Control in Grenoble-INP. This thesis has been prepared in the automatic control department of thelaboratory GIPSA-lab. This work is situated in the area of traffic systems control andsupervision. Our contributions are about modeling, supervision and local traffic control.The CTM traffic model has been extended with a model of uncertainties. Thisnews model allows us to take into account the uncertain parameters of the model, topropose new robust switched control law.In addition to this modeling approach, we propose some developments on supervisionof trafic systems. On one hand, we can estimate the operating mode of thesystem in real time and on the other hand to estimate some faults on the system. Thedynamical estimation of the operating mode allows us to know the state of congestion(or non congestion) of the road. We are able to estimate faults such as speed fall andcapacities drop that may appear.Finally, we propose two control laws based on switching systems control. The developedcontrollers adapt their geometry to the properties of the system. The purposeof these controllers is to be inserted in a hierarchic control scheme
Hofleitner, Aude. "Développement d'un modèle d'estimation des variables de trafic urbain basé sur l'utilisation des technologies de géolocalisation." Phd thesis, Université Paris-Est, 2012. http://tel.archives-ouvertes.fr/tel-00798239.
Full textBoulanger, Jérôme. "Estimation non-paramétrique et contributions à l'analyse de séquences d'images : modélisation, simulation et estimation du trafic intra-cellulaire dans les séquences de vidéo-microscopie." Rennes 1, 2007. ftp://ftp.irisa.fr/techreports/theses/2007/boulanger.pdf.
Full textIn this document, the problem of the restauration of videomicroscopy image sequences is first analyzed using an adaptive non-parametric estimation approach. A sequence of growing neighborhoods is thus design to control the bias-variance tradeoff of our estimator based on a weighted average of the data in a adapted neighborhood at the considered location. This procedure allows us to minimize the local quadratic risk in order to select the optimal extent of the neighborhood. The estimator selects points in this neighborhood using a similarity measure based on a distance computed between patches provides a way to better preserve the structures of the image. The analysis and the modelisation of the intracellular membrane trafficking is latter discussed distinguishing the slowy moving component and fast moving component of the sequence. A model based on the analogy between intracellular traffic and communication networks is used to capture the dynamic of the transport intermediates
Boulanger, Jérôme Bouthémy Patrick Kervrann Charles. "Estimation non-paramétrique et contributions à l'analyse de séquences d'images modélisation, simulation et estimation du trafic intra-cellulaire dans les séquences de vidéo-microscopie /." [S.l.] : [s.n.], 2007. ftp://ftp.irisa.fr/techreports/theses/2007/boulanger.pdf.
Full textGloaguen, Jean-Rémy. "Estimation du niveau sonore de sources d'intérêt au sein de mixtures sonores urbaines : application au trafic routier." Thesis, Ecole centrale de Nantes, 2018. http://www.theses.fr/2018ECDN0023/document.
Full textAcoustic sensor networks are being set up in several major cities in order to obtain a more detailed description of the urban sound environment. One challenge is to estimate useful indicators such as the road traffic noise level on the basis of sound recordings. This task is by no means trivial because of the multitude of sound sources that composed this environment. For this, Non-negative Matrix Factorization (NMF) is considered and applied on two corpuses of simulated urban sound mixtures. The interest of simulating such mixtures is the possibility of knowing all the characteristics of each sound class including the exact road traffic noise level. The first corpus consists of 750 30-second scenes mixing a road traffic component with a calibrated sound level and a more generic sound class. The various results have notably made it possible to propose a new approach, called ‘Thresholded Initialized NMF', which is proving to be the most effective. The second corpus created makes it possible to simulate sound mixtures more representatives of recordings made in cities whose realism has been validated by a perceptual test. With an average noise level estimation error of less than 1.3 dB, the Thresholded Initialized NMF stays the most suitable method for the different urban noise environments. These results open the way to the use of this method for other sound sources, such as birds' whistling and voices, which can eventually lead to the creation of multi-source noise maps
Charansonney, Luc. "Nouvelles méthodes de collecte des données de trafic : nouveaux enjeux pour les gestionnaires de voirie." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1013.
Full textRoad traffic evolves in a context which has undergone three major changes in the past two decades: first, a political change, reshaping the car's role in cities; second, a technical change, through which both vehicles and drivers emit and receive information independently of road authorities' roadside infrastructure; and finally, a financial change, as traffic management infrastructure has heavily relied on public funding which now becomes scarcer.From the perspective of a key road authority, the City of Paris, the Author, in charge of assessing the impact on traffic flow of major disruptive policies, addresses how new traffic data renews the road authority's knowledge of the traffic, on technical grounds.The Author has worked on Bluetooth travel-time and GPS based Floating Car Data datasets. He believes he makes two major contributions in the field.He first shows that traffic data and traffic information have always been at the core of the road authority's concerns, deeply related to the available technology, the missions of the road authority, and the theory attempting to bridge the gap between the two.Through the technical assessment of traffic-related policies (road closures, speed-limit reduction), based on two types of new traffic data (GPS speeds and Bluetooth travel-times), the Author analyzes the characteristics of the two datasets, the results they yield and how they complement legacy fixed-sensor based data. They allow the road authority to grasp user-perspective information whereas legacy data mostly offered a collective flow perspective. This, in turn, reshapes the decision-making process of road authorities
Godin, Olivier. "Information visuelle multirésolution pour l'estimation de la vitesse du trafic routier." Mémoire, Université de Sherbrooke, 2013. http://hdl.handle.net/11143/6583.
Full textEscamilla, Núñez Héctor. "Contribution au guidage des avions en trafic à haute densité." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30074/document.
Full textThis work is developed with the perspective of SESAR and Next-Gen projects, where new applications of Air Traffic Management (ATM) such as the Full 4D Management concept, are centered on Trajectory-Based Operations (TBO), deeply related with the extension of the flexibility in separation between aircraft, and hence, with the augmentation of air traffic capacity. Therefore, since a shift from fixed routes and Air Traffic Control (ATC) clearances to flexible trajectories is imminent, while relying on higher levels of onboard automation, the thesis hinges around topics that should enable or ease the transition from current systems to systems compliant with the new expectancies of Trajectory-Based Operations. The main axes of the manuscript can be summarized in three topics: 4D trajectory generation, 4D guidance, and mass estimation for trajectory optimization. Regarding the trajectory generation, the need of airspace users to plan their preferred route from an entry to an exit point of the airspace without being constrained by the existent configurations is considered. Thus, a particular solution for 4D smooth path generation from preexisting control points is explored. The method is based on Bezier curves, and is able to control the Euclidian distance between the given control points and the proposed trajectory. This is done by reshaping the path to remain within load factor limits, taking into account a tradeoff between path curvature and aircraft intended speed, representing a milestone in the road towards Trajectory-Based Operations. It is considered that accurate 4D guidance will improve safety by decreasing the occurrence of near mid-air collisions for planned conflict free 4D trajectories. In consequence, two autopilots and two guidance approaches are developed with the objective of diminishing the workload for air traffic controllers associated to a single flight. The backstepping and feedback linearization techniques are used for attitude control, while direct and indirect nonlinear inversion are adopted for guidance. Furthermore, the impact of inaccurate mass knowledge in trajectory guidance, with consequences in optimization, fuel consumption, and aircraft performance, has led to the implementation of an on-board aircraft mass estimation. The created approach is based on least squares, providing an initial mass estimation, and online computations of the current mass, both with enough accuracy to meet the objectives related to TBO. The methods proposed in this thesis are tested in a six degrees of freedom Matlab model with its parameters chosen similar to an aircraft type B737-200 or A320-200. The simulation is based on a full nonlinear modelling of transport aircraft dynamics under wind disturbances. Trained neural networks are used to obtain the aerodynamic coefficients corresponding the aircraft forces and moments
Blais, Philippe. "Élaboration d'un modèle de trafic à partir de données massives de trajectoires automobiles en assurance." Master's thesis, Université Laval, 2021. http://hdl.handle.net/20.500.11794/69181.
Full textHirayama, Yasuhiro, Hiraku Okada, Takaya Yamazato, and Masaaki Katayama. "An Access Control Protocol based on Estimation of Multimedia Trafic with an Adpative Algorithm in CDMA Packet Network." IEEE, 2002. http://hdl.handle.net/2237/7808.
Full textSchiper, Nicole. "Traffic data sampling for air pollution estimation at different urban scales." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSET008/document.
Full textRoad traffic is a major source of air pollution in urban areas. Policy makers are pushing for different solutions including new traffic management strategies that can directly lower pollutants emissions. To assess the performances of such strategies, the calculation of pollution emission should consider spatial and temporal dynamic of the traffic. The use of traditional on-road sensors (e.g. inductive sensors) for collecting real-time data is necessary but not sufficient because of their expensive cost of implementation. It is also a disadvantage that such technologies, for practical reasons, only provide local information. Some methods should then be applied to expand this local information to large spatial extent. These methods currently suffer from the following limitations: (i) the relationship between missing data and the estimation accuracy, both cannot be easily determined and (ii) the calculations on large area is computationally expensive in particular when time evolution is considered. Given a dynamic traffic simulation coupled with an emission model, a novel approach to this problem is taken by applying selection techniques that can identify the most relevant locations to estimate the network vehicle emissions in various spatial and temporal scales. This work explores the use of different statistical methods both naïve and smart, as tools for selecting the most relevant traffic and emission information on a network to determine the total values at any scale. This work also highlights some cautions when such traffic-emission coupled method is used to quantify emissions due the traffic. Using the COPERT IV emission functions at various spatial-temporal scales induces a bias depending on traffic conditions, in comparison to the original scale (driving cycles). This bias observed in our simulations, has been quantified in function of traffic indicators (mean speed). It also has been demonstrated to have a double origin: the emission functions’ convexity and the traffic variables covariance
Sainct, Rémi. "Étude des instabilités dans les modèles de trafic." Thesis, Paris Est, 2016. http://www.theses.fr/2016PESC1067/document.
Full textHighway traffic is known to be unstable when the vehicle density becomes too high, and to create stop-and-go waves, with an alternance of free flow and congested traffic. First-order traffic models can't reproduce these oscillations, but higher-order models can, both microscopic (car-following models) and macroscopic (systems of conservation laws).This thesis analyses the representation of unstable traffic states and oscillations in various traffic models. At the microscopic level, because of the flux concavity, the average flow of these oscillations is lower than the equilibrium flow for the same density. An algorithm is given to stabilize the flow with multi-anticipation, using an intelligent autonomous vehicle.At the macroscopic level, this work introduces averaged models, using the fact that the spatio-temporal scale of the oscillations is too small to be correctly predicted by simulations. The averaged LWR model, which consists of two conservation laws, enables a macroscopic representation of the density variance in a heterogeneous traffic, and gives the correct average flow of these states. A comparison with the ARZ model, also of order 2, shows that the averaged model can reproduce a capacity drop in a more realistic way.Finally, this thesis presents the SimulaClaire project of real-time traffic prediction on the ring road of Toulouse, and its parallelized parameter optimization algorithm
Djiéya, Nganchui Ferry. "Contributions à la conception de systèmes de contrôle de trafic et de gestion de ressources en ATM." Rennes 1, 2002. http://www.theses.fr/2002REN10150.
Full textRahme, Sandy. "Détection et estimation d'anomalies dans un réseau de communication." Phd thesis, Toulouse 3, 2011. http://thesesups.ups-tlse.fr/1589/.
Full textThe supervision domain particularly the anomaly detectionrepresents an important aspect of guaranteeing a Quality of Serviceto communication networks. A wide variety of disruptions designated as anomalies are often related to physical or technical problems such as power or file server failures, abrupt changes caused by legitimate traffic such as network congestion or flash crowds, and risky illegitimate behavior such as Denial-of-Service and Distributed Denial of Service (DoS/DDoS) attacks. We address the problem of anomalies detection and reconstruction in TCP/IP model based on control theory techniques. These anomalies are considered as fault signals in the mathematical model adopted for representing TCP/IP dynamics. For faults detection and according to our knowledge of the faults variations, the observers may be classified into known or unknown input observers. Our first contribution in terms of conceiving known input observers is limited to polynomial forms able to cover a wide range of anomalies. The anomaly and its derivatives are reconstructed by Luenberger observersafter introducing them in the state space of the system. The construction of these latter observers is limited in terms of specific anomaly profiles and constrained by the polynomial degree associated to the anomaly. Therefore, another detection approach dealing with completely unknown anomalies is proposed. The sliding modes first and higher orders are investigated to guarantee finite time convergence and robustness against parametric uncertainties and faults. Our proposals have been studied analytically by validating via Matlab/Simulinkand the Network SimulatorNS-2. Furthermore, in the context of NS-2, these approaches are integrated into a module for replaying traffic traces in order to test them on a TCP traffic captured in real environment
Rahme, Sandy. "Détection et estimation d'anomalies dans un réseau de communication." Phd thesis, Université Paul Sabatier - Toulouse III, 2011. http://tel.archives-ouvertes.fr/tel-00667420.
Full textVallet, Josselin. "Optimisation dynamique de réseaux IP/MPLS." Thesis, Toulouse, INSA, 2015. http://www.theses.fr/2015ISAT0006/document.
Full textThe high variability of traffic has become one of the major problems faced by network infrastructure managers . Under these conditions, flow route optimization based solely on an average busy hour traffic matrix is no longer relevant. The work done in this thesis aims to design dynamic routing optimization methods, adapting in real time the routes used by the flows to the actual network traffic conditions.We first study the problem of OSPF weight optimization for intra-domain routing in IP networks, where the traffic is routed along shortest paths, according to links weights. We propose an online scheme to dynamically reconfigure the OSPF weights and therefore the routes used, to respond to observed traffic variations and reduce the network congestion rate. The proposed approach is based on robust estimation of flow traffic demands from SNMP measurements on links loads. Experimental results, both on simulated and real traffic data show that the network congestion rate can be significantly reduced in comparison to a static weight configuration.On the same idea, we are also interested in optimizing MPLS networks that manage the available resource utilization by assigning a specific path for each LSP. We propose an algorithm inspired by game theory to determine the LSP placement optimizing a nonlinear performance criterion. We establish the convergence of the algorithm and obtain bounds on its approximation factor for several cost functions. As the main advantage of this technique is to offer good quality solutions in extremely reduced computation times, we are studying its use for dynamic reconfiguration of the LSP placement.The last part of this thesis is devoted to the design and development of a software solution for the deployment of a self-healing and self-optimizing network overlay between different cloud platforms. The solution is designed such that no change is required for client applications. By regularly measuring the quality of Internet links between data centers, it can quickly detect an IP route failure and switch the traffic to a backup path. It also allows to dynamically discover the paths in the overlay network that optimize a routing metric specific to the application. We describe the system architecture and implementation, as well as the experiments in both emulation and real platform composed of several data centers located in different countries
Clairais, Aurélien. "Calage en ligne d'un modèle dynamique de trafic routier pour l'estimation en temps réel des conditions de circulation." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSET004/document.
Full textTraffic models are of paramount importance for understanding and forecasting traffic dynamics. They represent a significant support for all the stages of traffic management. This thesis focuses on issues related to daily traffic management. For road network managers, four challenges are addressed. The speed refers to the choice of the scale of representation and formulation of the flow model. The selected model is the Lagrangian-Space LWR model. The reliability is associated to the integration of the model errors in the traffic conditions estimation process. The reactivity is described as the capacity of the method to take into account the prevailling traffic states in real time. Finally, the versatility refers to the capacity of the method parameters to evolve considering the observed traffic situations.The scientific challenges that the presented works aim are based on the four issues. The integration of the uncertainties into the flow model is a first challenge. Then, the production of operational indicators that account for the reliability of the results is discussed. Concerning the reactivity, the addressed scientific challenges are the establishment of a vehicle indexes based sequential data assimilation process and the calibration of the model's internal conditions. Finally, concerning the versatility, the associated scientific question is the online calibration of the parameters of the traffic flow model. A model for tracking the errors,assumed to be distributed following Gaussian mixtures, is developped. The error tracking is achieved thanks to an original perturbation method designed for multi-modal Gaussian mixtures. A sensitivity analysis is performed in order to establish a link between the designed method's robustness and the discretization of the network, the number of modes in the Gaussian mixture and the errors on the flow model's parameters. The data assimilation process enables to propagate traffic conditions in accordance with the observed situation in case of non-calibrated demand and supply. The posterior state is calculated by means of a Bayesian inference formulation knowing the prior and observed states. Two methods for model update have been tested. Facing model inconsistencies introduced by the method of substituting \textit{prior} states by \textit{posterior} states, the update acts also on the vehicles by means of addition, deletion, advancing and delaying of the passing times. The validation of the proposed solutions is achieved on a network composed of a simple homogeneous link without discontinuity. When the parameters of the traffic flow models are not calibrated, the data assimilation alone is not able to propagate the traffic states in accordance with the observed situation. The calibration of the parameters is addressed in an opening chapter in which several research avenues are proposed to resolve this last scientific question. The works in this thesis pave the way to perspectives in both research and operational domains. Indeed, it is interesting to quantify the reinforcement brought by model centered methods to usual data centered methods for the real time estimation and the short term forecasting of traffic conditions. Furthermore, the developed methods, associated to the cited research avenues, may represent a significant intake in the daily traffic management tools
Boudet, Céline. "Exposition du citadin aux particules fines en suspension : estimation de la part attribuable aux émissions automobiles : contribution à l'évaluation du risque sanitaire." Université Joseph Fourier (Grenoble), 1999. http://www.theses.fr/1999GRE18003.
Full textWerner, Stéphane. "Optimisation des cadastres d'émissions: estimation des incertitudes, détermination des facteurs d'émissions du "black carbon" issus du trafic routier et estimation de l'influence de l'incertitude des cadastres d'émissions sur la modélisation : application aux cadastres Escompte et Nord-Pas-de-Calais." Strasbourg, 2009. https://publication-theses.unistra.fr/public/theses_doctorat/2009/WERNER_Stephane_2009.pdf.
Full textEmissions inventories have a fundamental role in controlling air pollution, both directly by identifying emissions, and as input data for air pollution models. The main objective of this PhD study is to optimize existing emissions inventories, including one from the program ESCOMPTE « Experiments on Site to Constrain Models of Atmospheric Pollution and Transport of Emissions ». For that emissions inventory, two separate issues were developed: one designed to better assess the emissions uncertainties and the second to insert a new compound of interest in this inventory: Black Carbon (BC). Within the first issue, an additional study was conducted on the Nord-Pas-de-Calais emissions inventory to test the methodology of uncertainties calculation. The emissions uncertainties calculated were used to assess their influence on air quality modeling (model CHIMERE). The second part of the research study was dedicated to complement the existing inventory of carbon particulate emissions from road traffic sector by introducing an additional class of compounds: the BC. The BC is the raw carbonaceous atmospheric particles absorbing light. Its main source is the incomplete combustion of carbonaceous fuels and compounds. It can be regarded as a key atmospheric compound given its impact on climate and on health because of its chemical reactivity
Nguyen, Van Tri. "Adjoint-based approach for estimation & sensor location on 1D hyperbolic systems with applications in hydrology & traffic." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT063/document.
Full textThe thesis proposes a general framework for both state/parameters estimation and sensor placement in nonlinear infinite dimensional hyperbolic systems. The work is therefore divided into two main parts: a first part devoted to the optimal estimation and a second one to optimal sensor location. The estimation method is based on the calculus of variations and the use of Lagrange multipliers. The Lagrange multipliers play an important role in giving access to the sensitivities of the measurements with respect to the variables to be estimated. These sensitivities, described by the adjoint equations, are also the key idea of a new approach, so-called the adjoint-based approach, for the optimal sensor placement. Various examples, either based on some simulations with synthetic measurements or real data sets and for different scenarios, are also studied to illustrate the effectiveness of the developed approaches. Theses examples concern the overland flow systems and the traffic flow, which are both governed by nonlinear hyperbolic partial differential equations
Nguyen, Thai Phu. "Conception et application d'un modèle de l'information routière et ses effets sur le trafic." Phd thesis, Université Paris-Est, 2010. http://tel.archives-ouvertes.fr/tel-00626631.
Full textMichau, Gabriel. "Link Dependent Origin-Destination Matrix Estimation : Nonsmooth Convex Optimisation with Bluetooth-Inferred Trajectories." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEN017/document.
Full textOrigin Destination matrix estimation is a critical problem of the Transportation field since the fifties. OD matrix is a two-entry table taking census of the zone-to-zone traffic of a geographic area. This traffic description tools is therefore paramount for traffic engineering applications. Traditionally, the OD matrix estimation has solely been based on traffic counts collected by networks of magnetic loops. This thesis takes place in a context with over 600 Bluetooth detectors installed in the City of Brisbane. These detectors permit in-car Bluetooth device detection and thus vehicle identification.This manuscript explores first, the potentialities of Bluetooth detectors for Transport Engineering applications by characterising the data, their noises and biases. This leads to propose a new methodology for Bluetooth equipped vehicle trajectory reconstruction. In a second step, based on the idea that probe trajectories will become more and more available by means of new technologies, this thesis proposes to extend the concept of OD matrix to the one of link dependent origin destination matrix that describes simultaneously both the traffic demand and the usage of the network. The problem of LOD matrix estimation is formulated as a minimisation problem based on probe trajectories and traffic counts and is then solved thanks to the latest advances in nonsmooth convex optimisation.This thesis demonstrates that, with few hypothesis, it is possible to retrieve the LOD matrix for the whole set of users in a road network. It is thus different from traditional OD matrix estimation approaches that relied on successive steps of modelling and of statistical inferences
Alam, Muhammad Mahtab. "Power-Aware adaptive techniques for wireless sensor networks." Thesis, Rennes 1, 2013. http://www.theses.fr/2013REN1S049/document.
Full textWireless Sensor Networks (WSN) are a fast emerging technology with potential applications in various domains of daily-life, such as structural and environmental monitoring, medicine, military surveillance, robotic explorations etc. WSN devices are required to operate for a long time with limited battery capacity, therefore, the most important constraint in WSN is energy consumption. In this thesis, we propose algorithmic-level dynamic and adaptive optimization techniques for energy reduction in WSN. First, an accurate energy model is presented. This model relies on real-time power measurements of various scenarios that can occur during communication between sensor nodes. It is concluded that MAC layer plays a pivotal role for energy reduction. Then, a traffic-aware dynamic MAC protocol is presented which dynamically adapts the wake-up schedule of sensor nodes through traffic estimation. An adaptive algorithm is designed for this purpose that is heuristically modeled to understand the convergence behavior of algorithmic parameters. The proposed protocol is applied to body area networks and it outperforms other low-power MAC protocols in terms of latency as well as energy consumption and consequently increases the lifetime from three to six times. Finally, an SNR-based adaptive transmit power optimization technique is applied under time-varying channels. The output power is dynamically tuned to best power level under slow varying channel, which results in an average gain by two times
Majid, Hirsh. "Contribution à l'estimation et à la commande des systèmes de transport intelligents." Thesis, Artois, 2014. http://www.theses.fr/2014ARTO0203/document.
Full textThe works presented in this PhD dissertation fit into the framework of Intelligent TransportationSystems. Although the beginnings of these systems have started since the 60s, their development, basedon information and communication technologies, has reached maturity during the early 80s. The ITS usesthe intelligence of different systems (embedded systems, intelligents sensors, intelligents highways, etc.)in order to optimize road infrastructures performances and respond to the daily problems of congestions.The dissertation presents four contributions into the framework of road traffic flow and tackles theestimation and control problems in order to eliminate or at least reduce the “recurrent" congestionsphenomena. The first point treats the problem of traffic state estimation which is of most importance inthe field of ITS. Indeed, the implementation and performance of any control strategy is closely relatedto the ability to have all needed information about the traffic state describing the dynamic behavior ofthe studied system. Two estimation algorithms are then proposed. The first one uses the “metanet"model and high order sliding mode techniques. The second is based on the so-called Cell TransmissionModels. Several comparative studies with the Kalman filters, which are the most used in road traffic flowengineering, are established in order to demonstrate the effectiveness of the proposed approaches. Thethree other contributions concern the problem of traffic flow control. At first, the focus is on the isolatedramp metering using an algorithm based on the high order sliding mode control. The second contributiondeals with the dynamic traffic routing problem based on the high order sliding mode control. Such controlstrategy is enriched by introducing the concept of integration, in the third contribution. Indeed, integratedcontrol consists of a combination of several traffic control algorithms. In this thesis the proposed approachcombines an algorithm of on-ramp control with a dynamic traffic routing control. The obtained results arevalidated via numerical simulations. The validated results of the proposed isolated ramp metering controlare compared with the most used ramp metering strategy : ALINEA. Finally, the last contributiontreats the coordination problems. The objective is to coordinate several ramps which cooperate andchange information in order to optimize the highway traffic flow and reduce the total travel time in theapplied area. All these contributions were validated using real data mostly from French freeways. Theobtained results show substantial gains in term of performances such as travel time, energetic consumptiondecreasing, as well as the increasing in the mean speed. These results allow to consider several furtherworks in order to provide more interesting and efficient solutions in the ITS field
Laharotte, Pierre-Antoine. "Contributions à la prévision court-terme, multi-échelle et multi-variée, par apprentissage statistique du trafic routier." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSET013/document.
Full textThe maturity of information and communication technologies and the advent of Big Data have led to substantial developments in intelligent transportation systems (ITS) : from data collection to innovative processing solutions. Knowledge of current traffic states is available over most of the network range without the use of intrusive infrastructure-side collection devices, instead relying on wireless transmission of multi-source data. The increasing use of huge databases had a strong influence on traffic management, including forecasting methods. These approaches followed the recent trend towards innovative works on statistical learning. However, the prediction problem remains mainly focused on the local scale. The prediction for each road link relies on a dedicated, optimized and adapted prediction model. Our work introduces a traffic-forecasting framework able to tackle network scale problems. The study conducted in this thesis aims to present and evaluate this new “global” approach, in comparison to most-used existing works, and then to analyze its sensitivity to several factors. The traffic-forecasting framework, based on multi-variate learning methods, is detailed after a review of the literature on traffic flow theory. A multi-dimensional version of the k nearest-neighbors, a simple and sparse model, is evaluated through several use cases. The originality of the work stands on the processing approach, applied to data collected through new measurement process (e.g. Bluetooth, floating car data, connected vehicles). Then, the performance of our primary approach is compared to other learning-based methods. We propose an adaptation of kernel-based methods for the global prediction framework. The obtained results show that global approaches perform as well as usual approaches. The spatial and temporal specificities of the methods are highlighted according to the prediction accuracy. To improve the forecasting accuracy and reduce the computation time, we propose an identification and selection method targeting critical links. The results demonstrate that the use of a restricted subset of links is sufficient to ensure acceptable performances during validation tests. Finally, the prediction framework resilience is evaluated with respect to non-recurrent events as incidents or adverse weather conditions affecting the nominal network operations. The results highlight the impact of these non-recurrent conditions on real-time forecasting of short-term network dynamics. This enables the design of a further operational and resilient prediction framework. This perspective of forecasting matches the current applications relying on embedded systems and addressing the traffic network supervisor’s expectations
Sagnol, Guillaume. "Plans d'expériences optimaux et application à l'estimation des matrices de trafic dans les grands réseaux : programmation conique du second ordre et sous-modularité." Phd thesis, École Nationale Supérieure des Mines de Paris, 2010. http://pastel.archives-ouvertes.fr/pastel-00561664.
Full textBen, Cheikh Henda. "Evaluation et optimisation de la performance des flots dans les réseaux stochastiques à partage de bande passante." Thesis, Toulouse, INSA, 2015. http://www.theses.fr/2015ISAT0013/document.
Full textWe study queueing-theoretic models for the performance evaluation and optimization of bandwidth-sharing networks. We first propose simple and explicit approximations for the main performance metrics of elastic flows in bandwidth-sharing networks operating under balanced fairness. Assuming that an admission control mechanism is used to limit the number of simultaneous streaming flows, we then study the competition for bandwidth between elastic and streaming flows and propose performance approximations based on a quasi-stationary assumption. Simulation results show the good accuracy of the proposed approximations. We then investigate the energy-delay tradeoff in bandwidth-sharing networks in which nodes can regulate their speed according to the load of the system. Assuming that the network is initially congested, we investigate the rate allocation to the classes that drains out the network with minimum total energy and delay cost. We formulate this optimal resource allocation problem as a Markov decision process which proves tobe both analytically and computationally challenging. We thus propose to solve this stochastic problem using a deterministic fluid approximation. For a single link sharedby an arbitrary number of classes, we show that the optimal-fluid solution follows thewell-known cμ rule and give an explicit expression for the optimal speed. Finally, we consider cloud computing platforms under the SaaS model. Assuming a fair share of the capacity of physical resources between virtual machines executed concurrently, we propose simple queueing models for predicting response times of applications.The proposed models explicitly take into account the different behaviors of the different classes of applications (interactive, CPU-intensive or permanent applications). Experiments on a real virtualized platform show that the mathematical models allow to predict response times accurately
Macchi, Luigi. "Une approche de l'Ingénierie de la Résilience pour l'évaluation de la variabilité de la performance : développement et application de la Functional Resonance Analysis Method pour l'évaluation de la sécurité dans la gestion du trafic aérien." Phd thesis, École Nationale Supérieure des Mines de Paris, 2010. http://pastel.archives-ouvertes.fr/pastel-00589633.
Full textS, F. A. Batista Sérgio Filipe. "Dynamic traffic assignment for multi-regional transportation systems considering different kinds of users’ behavior." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSET009/document.
Full textThe population growth in urban areas represents an issue for transportation planning. This overload of urban transportation systems, leading to significant monetary costs and environmental issues. Policy measures are then needed to decrease the level of congestion and increase the efficiency of transportation systems. In a short term, traffic simulators might be a powerful tool that helps to design innovative solution. But, the classical traffic simulators are computationally demanding for large scale applications. Moreover, the set up of the simulation scenario is complex. An aggregated traffic modeling might be a good solution (Daganzo, 2007; Geroliminis and Daganzo, 2008). The city network is divided into regions where a well-defined Macroscopic Fundamental Diagram (MFD) regulates the traffic conditions inside each one. The MFD relates the average traffic flow and density inside a region. Despite the idea of aggregating the city network is simple, it brings several challenges that have not yet been addressed. Up to today, only Yildirimoglu and Geroliminis (2014) proposed a dynamic traffic assignment framework for regional networks and MFD models. This framework is based on the simple Multinomial Logit model and does not explicitly deal with trip length distributions. Moreover, their framework does not consider that users are different from each other and have different purposes and preferences for their travels. The goal of this PhD dissertation is to twofold. First, the influence of the users behavior on the global network performance is investigated. This analysis focus on the network mean speed and its internal and outflow capacities, comparing different models that account for different kinds of users behavior against the Deterministic and Stochastic User Equilibrium. Second, an innovative and complete dynamic traffic assignment framework for multi-regional MFD-based models is proposed. This framework is divided into several milestones and is based on the connections between the city and regional networks. In a first step, systematic scaling-up methods are proposed to gather the regional paths. In a second step, four methods are discussed to calculate the distributions of trip lengths that characterize these regional paths. In the third step, a network loading model that considers distributions of trip lengths that are explicitly calculated and the evolution of the regional mean speeds is proposed. Finally, this dynamic traffic assignment framework is extended to account for bounded rational and regret-averse users. This PhD is part of a European ERC project entitled MAGnUM: Multiscale and Multimodal Traffic Modeling Approach for Sustainable Management of Urban Mobility
Eum, Suyong, and suyong@ist osaka-u. ac jp. "Traffic Matrix Estimation in IP Networks." RMIT University. Electrical and Computer Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080215.155526.
Full textYue, Yang. "Spatial-temporal dependency of traffic flow and its implications for short-term traffic forecasting." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B35507366.
Full textRamakrishna, Sajja D. "An approach to predict traffic congestion." Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-09192009-040304/.
Full textHage, Ré-Mi. "Estimation du temps de parcours d’un réseau urbain par fusion de données de boucles magnétiques et de véhicules traceurs : Une approche stochastique avec mise en oeuvre d’un filtre de Kalman sans parfum." Phd thesis, Université de Nantes, 2012. http://tel.archives-ouvertes.fr/tel-00919589.
Full textHo, Hung-wai. "A continuum modeling approach to traffic equilibrium problems." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B31938267.
Full textZhu, Jiasong. "A self-learning short-term traffic forecasting system through dynamic hybrid approach." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B39634516.
Full textSanchez, Alex M. "Projection of truck traffic volumes at interstate permanent automatic traffic recorders." Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4472.
Full textTitle from document title page. Document formatted into pages; contains x, 111 p. : ill. (some col.), map (part col.). Includes abstract. Includes bibliographical references (p. 93-94).
Lance, Ryan. "Network state estimation via passive traffic monitoring." College Park, MD : University of Maryland, 2005. http://hdl.handle.net/1903/2429.
Full textThesis research directed by: Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Mao, Ruixue. "Road Traffic Density Estimation in Vehicular Network." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9467.
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