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Статті в журналах з теми "Estimation du trafic"
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Beuthe, Michel, and Anne-Sophie De Saint Martin. "Les coûts et bénéfices du Canal du Centre." Recherches économiques de Louvain 56, no. 1 (1990): 79–112. http://dx.doi.org/10.1017/s0770451800003572.
Повний текст джерелаАнотація:
RésuméCet article présente une analyse coûts-bénéfices des travaux du canal du Centre. Les coûts des expropriations, de construction et d'opération du canal sont comparés aux économies de coûts de transport. Leur calcul est basé sur une estimation des trafics futurs du canal et sur une analyse détaillée des coûts d'opération des bateaux. Divers scénarios d'évolution des coûts et du trafic sont examinés. L'article conclut que ces investissements ne sont pas rentables. Il serait même plus économique de ne pas terminer les travaux et de fermer l'ancien canal existant.
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Cariñena Balaguer, Rafael, and Andrés Díaz Borrás. "La colonia genovesa en Valencia durante la guerra civil catalana: el secuestro de sus bienes en 1472." Anuario de Estudios Medievales 24, no. 1 (April 2, 2020): 131. http://dx.doi.org/10.3989/aem.1994.v24.968.
Повний текст джерелаАнотація:
Le commerce génois à Valence a été considéré historiquement comme une des activités les plus dynamiques de l'économie locale. Jusqu’à maintenant on estimait que le trafic ligure, qui avait établi des maisons commerciales dans la ville de Valence, de même que le trafic local étaient énormes. Cependant, on ne disposait pas d'un paramètre assez ajusté pour évaluer la véracité de cette affirmation. Grâce à la confiscation des biens génois, qui eut lieu en 1472, en raison des soupçons du roi Juan de Navarre concernant l'appui ligure aux rebelles catalans pendant la Guerre Civile, nous avons pu avanceroune estimation, partielle mais significative, de l'importance commerciale des maisons génoises établies à Valence au XVème siècle. Au contraire de l'opinion générale, le trafic génois ne devait pas être aussi puissant qu'il a été pretendu dans les études historiques citées, tant sur le plan des marchandises disponibles que sur celui du nombre de maisons commerciales établies à Valence.
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KOENIGUER, Elise, Jean-Marie Nicolas, Béatrice Pinel-Puyssegur, Jean-Michel Lagrange, and Fabrice Janez. "Visualisation des changements sur séries temporelles radar : méthode REACTIV évaluée à l'échelle mondiale sous Google Earth Engine." Revue Française de Photogrammétrie et de Télédétection, no. 217-218 (September 21, 2018): 99–108. http://dx.doi.org/10.52638/rfpt.2018.409.
Повний текст джерелаАнотація:
Cet article présente une méthode de visualisation d'une pile temporelle d'images SAR, appelée REACTIV, qui permet de faire ressortir en couleur les zones ayant subi des changements sur la période de temps observée. Cette méthode a été testée à grande échelle grâce à la plateforme Google Earth Engine. Elle est basée sur l'espace de visualisation HSV et n'exploite que des estimations dans le domaine temporel, sans aucune estimation spatiale. La saturation des couleurs est codée par le coefficient de variation temporel, dont plusieurs propriétés statistiques sont explicitées. Les limites de l'utilisation de la plateforme Google Earth Engine sont évaluées, et plusieurs cas d'applications sont proposés : agriculture, dynamique urbaine, trafic maritime.
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Jourquin, Bart. "Estimation de l'impact de l'internalisation des coûts externes du trafic de fret interurbain en Belgique." Reflets et perspectives de la vie économique XLIII, no. 4 (2004): 77. http://dx.doi.org/10.3917/rpve.434.0077.
Повний текст джерела
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Chanut, S., and E. Chevallier. "Estimation des impacts atmosphériques des projets de gestion de trafic : de l’application des modèles théoriques sur des cas concrets*." Recherche Transports sécurité 2012, no. 01 (February 2013): 1–14. http://dx.doi.org/10.1007/s13547-011-0018-4.
Повний текст джерела
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Ozguven, Eren Erman, and Kaan Ozbay. "Nonparametric Bayesian Estimation of Freeway Capacity Distribution from Censored Observations." Transportation Research Record: Journal of the Transportation Research Board 2061, no. 1 (January 2008): 20–29. http://dx.doi.org/10.3141/2061-03.
Повний текст джерелаАнотація:
Previous studies have been made of the usefulness and effectiveness of survival analysis in transportation and traffic engineering studies with incomplete data in which the Kaplan–Meier estimate is proposed for determining traffic capacity distribution. However, well-known estimators like Kaplan–Meier and Nelson–Aalen have several disadvantages that make it difficult to obtain the traffic capacity distribution. First, neither estimator is defined for all values of traffic flows possible. That is, the maximum flow followed by a breakdown defines the final point of the estimated distribution curve. Therefore, parametric fitting tools have to be applied to obtain the remaining portion of the curve. Moreover, the discontinuity and nonsmoothness of the Kaplan–Meier and Nelson–Aalen estimates make it difficult to ensure the robustness of the estimation. In this paper the Kaplan–Meier and Nelson–Aalen nonparametric estimators are used to obtain the traffic capacity function of four freeway sections. Then a Bayesian nonparametric estimator, which is shown to be a Bayesian extension of the Kaplan–Meier estimator, is introduced for estimating the capacity distribution. This estimator assumes a Dirichlet process prior for the survival function under the minimization of a squared-error loss function. The results indicate that the curves obtained by using the Bayesian estimation method are smoother than those obtained with the other estimator. This smoothness also ensures the continuity in the vicinity of censored observations. Furthermore, the Bayesian estimates can be obtained for any traffic flow value regardless of the availability of data only for certain ranges of observations (including censored data).
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Bresch, M., J. Shi, and R. Kokozinski. "Employing beam-forming for estimating the direction of arrival in a multi-path propagation environment." Advances in Radio Science 3 (May 12, 2005): 151–55. http://dx.doi.org/10.5194/ars-3-151-2005.
Повний текст джерелаАнотація:
Abstract. Due to recent researches on traffic accidents with vulnerable road users (VRUs), several measures revealed a great opportunity of reduction. However, all measures applied so far failed to reduce the number of traffic accidents if there is no line-of-sight. Therefore, a transponder signal is utilized to make the VRU visible. The motor vehicle carries a mobile receiver for VRU detection and location. The receiver employs digital beam-forming for estimating the direction of arrival (DOA) with an antenna array for RF ISM band. A sequence of DOA estimations is used for location and motion estimation purposes.
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Tao, Tao, Greg Lindsey, Raphael Stern, and Michael Levin. "The use of crowdsourced mobile data in estimating pedestrian and bicycle traffic: A systematic review." Journal of Transport and Land Use 17, no. 1 (February 1, 2024): 41–65. http://dx.doi.org/10.5198/jtlu.2024.2315.
Повний текст джерелаАнотація:
To address the need for better non-motorized traffic data, policymakers and researchers are collaborating to develop new approaches and methods for estimating pedestrian and bicyclist traffic volumes. Crowdsourced mobile data, which has higher spatial and temporal coverage and lower collection costs than data collected through traditional approaches, may help improve pedestrian and bicyclist traffic estimation despite their limitations or biases. This systemic literature review documents how researchers have used crowdsourced mobile data to estimate pedestrian and bicyclist traffic volumes. We find that one source of commercial fitness application data (i.e., Strava) has been used much more frequently than other crowdsourced mobile data, and that most studies have used crowdsourced mobile data to estimate bicyclist volumes. Comparatively few studies have estimated pedestrian volumes. The most common approach to the use of crowdsourced counts is as independent variables in direct demand models. Variables constructed from crowdsourced mobile data not only have significant correlations with observed counts in statistical models but also have larger relative importance than other factors in machine learning models. Studies also show that including crowdsourced mobile data can significantly improve estimation performance. Future research directions include application of crowdsourced mobile data in more pedestrian traffic estimations, comparison of the performance of different crowdsourced mobile data, incorporation of multiple data sources, and expansion of the methods using crowdsourced mobile data for non-motorized traffic estimation.
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Suyama, Emilio, Roberto C. Quinino, and Frederico R. B. Cruz. "Simple and Yet Efficient Estimators for Markovian Multiserver Queues." Mathematical Problems in Engineering 2018 (December 25, 2018): 1–7. http://dx.doi.org/10.1155/2018/3280846.
Повний текст джерелаАнотація:
Estimators for the parameters of the Markovian multiserver queues are presented, from samples that are the number of clients in the system at arbitrary points and their sojourn times. As estimation in queues is a recognizably difficult inferential problem, this study focuses on the estimators for the arrival rate, the service rate, and the ratio of these two rates, which is known as the traffic intensity. Simulations are performed to verify the quality of the estimations for sample sizes up to 400. This research also relates notable new insights, for example, that the maximum likelihood estimator for the traffic intensity is equivalent to its moment estimator. Some limitations of the results are presented along with a detailed numerical example and topics for future developments in this research area.
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Somplak, Radovan, Zlata Smidova, Veronika Smejkalova, and Vlastimir Nevrly. "Statistical Evaluation of Large-Scale Data Logistics System." MENDEL 24, no. 2 (December 21, 2018): 9–16. http://dx.doi.org/10.13164/mendel.2018.2.009.
Повний текст джерелаАнотація:
Data recording is struggling with the occurrence of errors, which worsen the accuracy of follow-up calculations. Achievement of satisfactory results requires the data processing to eliminate the influence of errors. This paper applies a data reconciliation technique for mining of data from ecording movement vehicles. The database collects information about the start and end point of the route (GPS coordinates) and total duration.The presented methodology smooths available data and allows to obtain an estimation of transportation time through individual parts of the entire recorded route. This process allows obtaining valuable information which can be used for further transportation planning. First, the proposed mathematical model is tested on simplifled example. The real data application requires necessary preprocessing within which anticipated routes are designed. Thus, the database is supplemented with information on the probable speed of the vehicle. The mathematical model is based on weighted least squares data reconciliation which is organized iteratively. Due to the time-consuming calculation, the linearised model is computed to initialize the values for a complex model. The attention is also paid to the weight setting. The weighing system is designed to reflect the quality of specific data and the dependence on the frequency of trafic. In this respect, the model is not strict, which leaves the possibility to adapt to the current data. The case study focuses on the GPS data of shipping vehicles in the particular city in the Czech Republic with several types of roads.
Дисертації з теми "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.
Повний текст джерелаАнотація:
Nous proposons, dans cette thèse, un capteur visuel catadioptrique optimal en vue d'une application au diagnostic du trafic routier permettant de remplacer profitablement un réseau de caméras perspectives. Le système proposé a l'avantage, par le nombre et la conception des miroirs calculés, de générer une vue unique de la couronne du carrefour et de ses voies en maximisant le nombre de pixels utiles. Il en résulte une image où le pourcentage de pixels directement exploitables pour les phases ultérieures de traitement d'image est optimal. Nous décrivons la méthodologie utilisée pour concevoir un tel capteur. Afin de tester notre capteur, nous avons également développé les traitements d'images, qui fournissent les indicateurs utiles à l'estimation de l'activité du carrefour, à savoir, le taux d'occupation, la vitesse des véhicules et le débit. Le capteur ainsi conçu a été comparé avec des caméras utilisant différentes formes standard de miroirs (sphérique, parabolique, hyperbolique et miroirs ayant la résolution constante horizontale). Cette validation a montré que ce capteur est plus robuste et plus précisIn 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
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Martinet, Simon. "Estimation in-situ des facteurs d’émission des polluants du trafic routier." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSET006.
Повний текст джерелаАнотація:
La pollution atmosphérique en milieu urbain est un enjeu majeur pour la santé humaine et l’environnement. Le trafic routier représente la principale source de pollution dans les zones urbaines et contribue fortement à cette pollution atmosphérique dans ces zones malgré des améliorations de technologies de dépollution et de motorisations. Pour mesurer et approfondir les connaissances des émissions de polluants des véhicules routiers, différentes méthodes existent, avec chacune ses avantages et ses limites. Par exemple, les mesures sur un banc d’essai permettent d’étudier les émissions des véhicules selon leur technologie et avec une bonne reproductibilité des conditions de test. Cependant, cette approche reste limitée notamment pour la représentativité des émissions d’un parc de véhicules dans des conditions réelles de fonctionnement. La connaissance limitée des émissions des polluants non-réglementés, tels que les BTEX, les alcanes de C9 à C22, les composés carbonylés, les particules en nombre et le carbone suie qui ont des effets néfastes sur la santé et l’environnement et qui sont rarement mesurés en raison de la complexité de la métrologie, constitue un second axe d’approfondissement des émissions du trafic. L’objectif de ce travail est d’estimer in-situ des facteurs d’émission des polluants non-réglementés du trafic routier, dans des conditions réelles de circulation des véhicules et pour des parcs dont la composition est caractérisée avec précision. Dans ce but, les travaux de cette thèse ont permis de développer et de mettre en œuvre des méthodologies de mesure in-situ, en zone urbaine, des émissions de polluants non-réglementés du trafic routier, d’estimer des facteurs d’émission à partir des mesures effectuées sur différents sites (site ouvert en bord de route et site confiné). Ces facteurs d’émission sont établis pour des polluants non-réglementés, et pour un parc automobile réel précisément défini (connaissance détaillée de la composition du parc automobile en circulation et des conditions locales de circulation). Les facteurs d’émission ainsi déterminés in-situ sont comparés avec ceux dérivés des mesures sur banc à rouleau afin d’en vérifier la cohérence et analyser en fonction des différents sites de mesure et de l’impact de la composition du parc sur les émissions de polluants. Trois campagnes de mesure in-situ ont été menées, deux en bord de route dans des zones urbaines (sites ouverts) et une dans un tunnel proche d’une zone urbaine (site confiné). Les concentrations des polluants ciblés mesurées sur ces trois sites, ainsi que les différentes compositions de parc et les conditions de circulation relevées ont été utilisées pour estimer des facteurs d’émission par véhicule ou pour l’ensemble d’un parcUrban 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
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Leon, Ojeda Luis. "Short-term multi-step ahead traffic forecasting." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENT081/document.
Повний текст джерелаАнотація:
Dans le cadre des systèmes de transport intelligents (ITS), cette thèse concerne la conception d'une méthodologie de prédiction, en temps réel et pour différents horizons, du temps de parcours à partir des données de vitesse et de débit d'une route instrumentée. Pour atteindre cet objectif, deux approches sont considérées dans cette thèse. La première approche, dite « sans modèle », utilise exclusivement des mesures de vitesse. Grâce à l'utilisation astucieuse des données historiques, nous avons résolu le problème de prédiction comme étant un problème de filtrage. Pour ce faire, des données historiques sont utilisées pour construire des pseudo-observations qui alimentent un filtre de Kalman adaptatif (AKF). Sous une hypothèse de Gaussianité, les statistiques du bruit de processus sont estimées en temps-réel, tandis que les statistiques du pseudo-bruit d'observation sont déduites des données historiques adéquatement classées. La seconde approche, dite ‘'basée-modèle'', utilise principalement des mesures de débit et de vitesse. Contrairement à la précédente approche où la résolution spatiale est fixée par l'emplacement des capteurs, une discrétisation spatiale plus fine est considérée. Celle-ci s'avère possible grâce à l'utilisation du modèle CTM (Cell Transmission Model). Un observateur d'état commuté, de type Luenberger, permet d'estimer les états internes (densités des cellules). En utilisant uniquement les prédictions des débits des conditions frontières via une approche de type AKF similaire à celle développée dans la première approche, le modèle CTM contraint permet de prédire les densités des cellules et d'en déduire les vitesses et le temps de parcours. Les méthodes développées ont été validées expérimentalement en considérant la rocade sud grenobloise comme cas d'étude. Les résultats montrent que les deux méthodes présentent de bonnes performances de prédiction. Les méthodes proposées performent mieux que celles basées sur une utilisation directe des moyennes historiques. Pour l'ensemble des données considérées, l'étude a également montré que l'approche ‘'basée modèle‘' est plus adaptée pour des horizons de prédictions de moins de 30 minThis 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
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Kessaci, Abdellah. "Estimation en ligne et gestion des capacités pour la commande du trafic urbain." Toulouse, ENSAE, 1988. http://www.theses.fr/1988ESAE0010.
Повний текст джерелаАнотація:
PRODYN est une méthode de régulation des feux en temps réel, actuellement en cours de validation sur le site. Restent à résoudre des problèmes d'adaptativité de la commande aux variations du trafic et d'autre part, PRODYN devient inadapté à l'approche de la saturation. Cette thèse propose des nouveaux algorithmes d'estimation en ligne des pourcentages directionnels et des débits de saturation, ainsi qu'une méthode de gestion de la capacité.
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Kessaci, 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.
Повний текст джерела
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Magne, Laurent. "Commande optimale décentralisée du trafic urbain." Toulouse, ENSAE, 2001. http://www.theses.fr/2001ESAE0001.
Повний текст джерелаАнотація:
Ce mémoire traite de la commande du trafic routier par les feux. À partir de données sur les flux, les méthodes hors ligne construisent des plans de feux caractérisés par un cycle, des durées de vert et des décalages entre carrefours. Les méthodes en ligne peuvent adapter ces paramètres en fonction de mesures. Cette thèse applique des techniques d’estimation d’état, de prédiction et d’optimisation, à la commande décentralisée du trafic urbain. Le modèle proposé vise à estimer l’état et à prédire le temps perdu sur un horizon. On montre que, si on surestime les queues, la stabilité de l’estimation est assurée par l’optimisation de la commande. Les arrivées sont calculées, à partir de données communiquées par des carrefours amonts et de mesures pouvant être filtrées en tenant compte des incertitudes de vitesse et de destination. Les départs tiennent compte des conséquences, sur l’écoulement de chaque voie, des conditions de trafic en aval. L’optimisation d’un critère qui considère les temps perdus des chaînons pondérés en fonction de leur saturation ou de la présence de véhicule prioritaire, est réalisé en deux temps : En regroupant les feux en phases et en utilisant que les phases permettant l’obtention du cycle minimal, l’espace de recherche est réduit avec une faible perte d’optimalité ; Profitant de la commande obtenue à la période d’échantillonnage précédente et d’une propagation des contraintes, un algorithme par séparation – évaluation rend possible l’optimisation en temps réel. Des essais en simulation dans différentes configurations, montrent que le modèle est robuste vis-à-vis des incertitudes. Ainsi, l’effet d’une erreur de l’ordre de 20% sur les pourcentages directionnels ou de 10km/h sur la vitesse libre est corrigé par filtrage. La modélisation des élargissements permet de réduire de 2 à 22% les temps de parcours. Pour des réseaux la stabilité de l’estimation est assurée même en conditions saturées et les véhicules prioritaires peuvent être favorisés.
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Pecot, 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.
Повний текст джерелаАнотація:
Cette thèse traite de l'analyse et de la simulation du trafic vésiculaire sur des séquences d'images de microscopie de fluorescence. À contre-courant des approches habituelles exploitant un suivi individuel des vésicules, nous avons développé une approche globale (tomographie de réseaux) inspirée de travaux antérieurs sur l'analyse du trafic routier et l'analyse du trafic sur des réseaux de télécommunications. Cette approche repose sur l'utilisation de comptages locaux de vésicules couplés à une procédure de routage qui permettent d'estimer les trajectoires globales des vésicules sur l'ensemble d'une séquence d'images. Contrairement aux précédentes applications de la tomographie de réseaux, les comptages et le routage sont également des inconnues du problème. Afin de mesurer les comptages locaux de vésicules, nous avons développé une méthode de séparation des composantes “objet” et “fond” dans des séquences de microscopie de fluorescence. Cette méthode exploite un terme de détection non local reposant sur la similarité entre motifs de l'image et utilise la composante “fond” estimée comme “référence” pour améliorer la détection des vésicules. Par ailleurs, la procédure de routage dépend des données observées. Dans le cas de l'estimation du trafic, le routage est établi à partir du comptage des vésicules ; dans le cas de simulations, le routage est contrôlé par l'utilisateur. La génération de séquences synthétiques a permis d'évaluer quantitativement la méthode d'estimation du trafic vésiculaire. Cette méthode a égale- ment été évaluée sur des séquences d'images réelles de microscopie dans le cadre d'une étude précise sur le transport membranaire et le trafic vésiculaire régulé par des isoformes de la protéine Rab6.
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Pécot, 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.
Повний текст джерелаАнотація:
Cette thèse traite de l’analyse et de la simulation du trafic vésiculaire sur des séquences d’images de microscopie de fluorescence. À contre-courant des approches habituelles exploitant un suivi individuel des vésicules, nous avons développé une approche globale (tomographie de réseaux) inspirée de travaux antérieurs sur l’analyse du trafic routier et l’analyse du trafic sur des réseaux de télécommunications. Cette approche repose sur l’utilisation de comptages locaux de vésicules couplés à une procédure de routage qui permettent d’estimer les trajectoires globales des vésicules sur l’ensemble d’une séquence d’images. Contrairement aux précédentes applications de la tomographie de réseaux, les comptages et le routage sont également des inconnues du problème. Afin de mesurer les comptages locaux de vésicules, nous avons développé une méthode de séparation des composantes “objet” et “fond” dans des séquences de microscopie de fluorescence. Cette méthode exploite un terme de détection non local reposant sur la similarité entre motifs de l’image et utilise la composante “fond” estimée comme “référence” pour améliorer la détection des vésicules. Par ailleurs, la procédure de routage dépend des données observées. Dans le cas de l’estimation du trafic, le routage est établi à partir du comptage des vésicules ; dans le cas de simulations, le routage est contrôlé par l’utilisateur. La génération de séquences synthétiques a permis d’évaluer quantitativement la méthode d’estimation du trafic vésiculaire. Cette méthode a également été évaluée sur des séquences d’images réelles de microscopie dans le cadre d’une étude précise sur le transport membranaire et le trafic vésiculaire régulé par des isoformes de la protéine Rab6This 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
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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/.
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Les réseaux IP sont devenus des systèmes réellement critiques, l'interruption du service fourni par le réseau ou même une dégradation significative de la qualité de service étant de moins en moins tolérables. Une nouvelle approche de l'ingénierie des réseaux devient des lors nécessaire, pour concevoir, planifier et contrôler les architectures IP sur la base des informations de supervision. Nos contributions a cette nouvelle approche portent sur l'estimation du trafic a partir des mesures de charges SNMP, sur l'optimisation des métriques de routage IP et sur le dimensionnement d'infrastructures. Les modèles et algorithmes développés prennent en compte de nombreuses contraintes technologiques dans le but de fournir des solutions opérationnellesIP 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
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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.
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La centralisations du travail, la croissance économique et celle de la population autant que l’urbanisation continue sont les causes principales de la congestion. Lors que les villes s’efforcent pour mettre à jour leurs infrastructures du trafic, l’utilisation de nouvelles techniques pour la modélisation, l’analyse de ces systèmes ainsi que l’intégration des mega données aux algorithmes aident à mieux comprendre et combattre les congestions, un aspect crucial pour le bon développement de nos villes intelligentes du XXIe siècle. Les outilsd’assistance de trafic spécialement conçus pour détecter, prévoir et alerter des conditions particulières sont très demandés dans nos jours.Cette recherche est consacrée au développement des algorithmes pour l’estimation et la prédiction sur des réseaux de trafic routier. Tout d’abord, nous considérons le problème de prévision à court terme du temps de trajet dynamique basé sur des méthodes pilotées par les données. Nous proposons deux techniques de fusion pour calculer les prévisions à court terme. Dans un première temps, nous considérons la matrice de covariance d’erreur et nous utilisons ses informations pour fusionner les prévisions individuelles créées á partir de clusters. Dans un deuxième temps, nous exploitons les mesures de similarité parmi le signal á prédire et des clusters dans l’histoire et on propose une fusion en tant que moyenne pondérée des sorties des prédicteurs de chaque cluster. Les résultats des deux méthodes on été validés dans le Grenoble Traffic Lab, un outil en temps réel qui permet la récupération de données d’une autoroute d’environ (10.5Km) qui couvre le sud de Grenoble.Postérieurement nous considérons le problème de reconstruction de la densité / et le débit de façon simultanée à partir de sources d’information hétérogènes. Le réseau de trafic est modélisé dans le cadre de modèles de trafic macroscopique, où nous adoptons l’équation de conservation Lighthill-Whitham-Richards avec un diagramme fondamental linaire par morceaux. Le problème d’estimation repose sur deux principes clés. Dans un premier temps, nous considérons la minimisation des erreurs entre les débits et les densités mesurés et reconstruits. Finalement, nous considérons l’état d’équilibre du réseau qui établit la loi de propagation des flux entrants et sortants dans le réseau. Tous les principes sont intégrés et le problème est présenté comme une optimisation quadratique avec des contraintes d’égalité a fin de réduire l’espace de solution des variables à estimer. Des scénarios de simulation basés sur des données synthétiques pour un réseau de manhattan sont fournis avec l’objectif de valider les performances de l’algorithme proposéCentralization 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
Книги з теми "Estimation du trafic"
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Johnson, Dennis L. 20-year traffic forecasting factors. Pierre, SD: South Dakota Dept. of Transportation, Office of Research, 2000.
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Ltd, Works Consultancy Services, and Transit New Zealand, eds. National traffic database. Wellington, N.Z: Transit New Zealand, 1996.
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Afghani, Abdus Sattar. Traffic growth in Karachi: Projections 1990 : presented at XIII Conference of the Mayors of the World's Major Cities on "problems of urban circulation in the 90's", at Milan. [Karachi]: Karachi Metropolitan Corporation, 1985.
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Schiffer, Robert G. Long-Distance and Rural Travel Transferable Parameters for Statewide Travel Forecasting Models. WASHINGTON, D.C: TRANSPORTATION RESEARCH BOARD, 2012.
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Institution of Highways and Transportation., ed. Guidelines for traffic impact assessment. London: Institution of Highways & Transportation, 1994.
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Tittemore, Lawrence H. Highland Avenue/Needham Street corridor traffic study: Existing conditions. [Boston, Mass.]: Central Transportation Planning Staff, 1986.
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Steffens, William T. Highland Avenue/Needham Street corridor traffic study: Future conditions. [Boston, Mass.]: Central Transportation Planning Staff, 1987.
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(Japan), Nagoya-shi. Dai 4-kai Chūkyō toshiken pāson torippu chōsa hōkokusho. [Nagoya-shi]: Nagoya-shi, 2002.
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New York (State). Dept. of Transportation. Planning Division and New York (State). Dept. of Transportation. Data Services Bureau, eds. Revised quick response procedure to forecast rural traffic: Revised methodology and users guide. [Albany, N.Y.]: New York State Dept. of Transportation, Planning Division, 1990.
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Steffens, William T. Bridge Street bypass in Salem and Beverly: Traffic forecasts. [Boston, Mass.]: Central Transportation Planning Staff, 1987.
Знайти повний текст джерелаЧастини книг з теми "Estimation du trafic"
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Treiber, Martin, and Arne Kesting. "Travel Time Estimation." In Traffic Flow Dynamics, 367–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32460-4_19.
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de Lima, Alexandre Barbosa, and José Roberto de Almeida Amazonas. "Modeling of Internet traffic." In Internet Teletraffic Modeling and Estimation, 127–50. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338666-5.
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Bickel, Peter, Chao Chen, Jaimyoung Kwon, John Rice, Pravin Varaiya, and Erik van Zwet. "Traffic Flow on a Freeway Network." In Nonlinear Estimation and Classification, 63–81. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21579-2_5.
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Westendorf, M., S. Thal, T. Ahrenhold, and R. Henze. "Accuracy Requirements of Camera-Based Depth Estimation for Urban Automated Driving." In Lecture Notes in Mechanical Engineering, 158–63. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_23.
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AbstractFor autonomous driving in urban areas higher accuracy requirements for localization of surrounding traffic participants become apparent. The use of cost-efficient camera sensors shows potential for a performant depth estimation and can supplement perception systems to achieve redundancy. Current research focuses on improving the algorithms towards better performance whereas the application-oriented analysis of present estimation errors in relation to urban traffic scenarios is often neglected. Based on stereo and mono camera images, a benchmark analysis of rule- and deep learning-based depth estimation approaches is conducted in this work. The error-prone estimation results are then analyzed against braking distances of urban traffic scenarios simulated by a two-track model to analyze the criticality of different depth estimation approaches. The application-oriented evaluation shows that current approaches could already be used in real automated driving systems and enable the definition of requirements.
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de Lima, Alexandre Barbosa, and José Roberto de Almeida Amazonas. "The fractal nature of network traffic." In Internet Teletraffic Modeling and Estimation, 25–45. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338666-2.
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Conti, Pier Luigi, Livia De Giovanni, and Maurizio Naldi. "Estimation of Traffic Matrices for LRD Traffic." In Contributions to Statistics, 91–107. Milano: Springer Milan, 2012. http://dx.doi.org/10.1007/978-88-470-2871-5_8.
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Beckmann, K. J., and G. Rindsfüser. "Dynamic Estimation of Transport Demand: Solutions — Requirements — Problems." In Traffic and Mobility, 15–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60236-8_2.
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Salter, R. J. "Traffic speed distributions and estimation." In Highway Traffic Analysis and Design, 131–45. London: Macmillan Education UK, 1996. http://dx.doi.org/10.1007/978-1-349-13423-6_14.
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Afanasyeva, Larisa, and Ekaterina Bulinskaya. "Estimation of Transport Systems Capacity." In Traffic and Granular Flow '11, 63–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39669-4_7.
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Cao, Jin, William S. Cleveland, Dong Lin, and Don X. Sun. "Internet Traffic Tends Toward Poisson and Independent as the Load Increases." In Nonlinear Estimation and Classification, 83–109. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21579-2_6.
Повний текст джерелаТези доповідей конференцій з теми "Estimation du trafic"
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Kim, Changhun, Jiyeon Lee, Minji Koh, and Seung-Neo Son. "ANALYSIS OF ERROR FACTORS IN REGIONAL GHG ESTIMATION BASED ON FUEL CONSUMPTION: FOCUS ON THE ROAD TRANSPORT SECTOR." In SGEM International Multidisciplinary Scientific GeoConference, 249–56. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024v/6.2/s26.31.
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The climate crisis due to global warming is intensifying, and international cooperation to curb temperature rise is underway. South Korea has set GHG reduction targets, along with systems for implementation and assessment to achieve these goals. South Korea currently applies the IPCC-Tier2 method based on fuel consumption to estimate GHG emissions at the national level. Additionally, starting in 2024, it has begun estimating GHG emissions at the level of 17 local cities. However, applying the fuel consumption-based GHG estimation method at the local level can lead to errors, as it cannot accurately estimate GHG emissions from cross-regional traffic. Accordingly, this study conducted the following analyses to identify the error factors when applying the fuel consumption-based GHG estimation method at the local level. First, GHG emissions were estimated for 25 districts in Seoul, South Korea, based on fuel consumption and VKT (Vehicle Kilometers Traveled). After identifying the differences in emissions based on the estimation method, a regression analysis was conducted to determine the factors influencing these differences. The analysis showed that the errors in the fuel consumption-based GHG estimation method result from GHG emissions generated by commercial vehicles and freight trucks. Since commercial vehicles and freight trucks travel across multiple regions, it is likely that their refueling areas differ from their areas of operation, which may explain this outcome. This study analyzed the limitations and error factors when estimating GHG emissions at the local level based on fuel consumption. The analysis results suggest a necessity to develop methodologies for accurately estimating carbon emissions at the local level.
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Zhao, Xiaowei, Guoyu Zhang, and Lin Yang. "A Strategy for Estimating State-of-Charge and State-of-Health of Li-Ion Batteries in Electric and Hybrid Electric Vehicles." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-87324.
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A task that has to be solved for the application of batteries in vehicles with an electric drive train is the determination of the actual state-of-health (SOH) and state-of-charge (SOC) of the battery cells. In this paper, an on board strategy for estimating SOC and SOH of Li-ion batteries is proposed. The equivalent circuit model is used for both SOC and SOH estimations. In SOH algorithm, the estimated value of battery capacity not only reflects the aging degree of battery pack, but also provides information for SOC estimation. Meanwhile, the extended Kaiman filtering is used in SOC estimation. Because the performance of the equivalent circuit model will be better at small currents than at high currents, extended Kaiman filtering is substituted by Ampere-Hour counting when the absolute value of current is greater than a calibration value. The Digatron battery tester was used to evaluate the proposed estimation method, and results show that the estimation method has high accuracy and efficiency at ordinary temperatures.
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Abdulrahman, Hassan Shuaibu, and Gabriella Amarachi Ekuma. "Extraction of Traffic Flow Models Using Typical Traffic Information from Google." In 8th International Students Science Congress. ULUSLARARASI ÖĞRENCİ DERNEKLERİ FEDERASYONU (UDEF), 2024. https://doi.org/10.52460/issc.2024.018.
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One way to control or reduce congestion is by sharing traffic information through traffic models. These models include the estimation of travel time from a desired place of origin-destination, the speed-flow-density parameters help to calculate the travel time. This thesis extracted traffic flow models by utilizing the extensive collection of real-time traffic information made available through Google Maps Application at a cost-efficient rate despite its representation of traffic conditions through color codes without the attachment of traffic models compared to the traditional methods of data collection to reduce traffic congestions. Therefore, the objective of this research includes data collection variables from Google Maps traffic information using real-time and historical traffic data, data processing by estimating vehicle speed, flow and density from Google traffic information through congestion color codes and model selection, development, and evaluation through the use of trendline equations. To create dynamic and precise traffic models, better understanding of traffic pattern is expected to lead to more efficient urban planning, traffic control, and infrastructure opti-mization.
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di Leo, Giuseppe, Antonio Pietrosanto, and Paolo Sommella. "Estimating Measurement Uncertainty of Traffic Monitoring Systems." In 2007 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement. IEEE, 2007. http://dx.doi.org/10.1109/amuem.2007.4362587.
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Antelis, Andres, and Claudia Moreno. "Convolutional neural network regression for estimating physical parameters of astrophysical binary black hole (BBH) systems." In LatinX in AI at International Conference on Machine Learning 2023. Journal of LatinX in AI Research, 2023. http://dx.doi.org/10.52591/lxai202307236.
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In this paper we propose the use of a deep learning based model for inferring astrophysical information of binary black hole (BBH) systems from observed gravitational wave (GW) signals. We focused in estimating the total mass of BBH systems MT using a convolutional neural network regression (CNNR) model. We built a large dataset of 2Dimages representing the time-frequency evolution of BBH GW signals which are embedded in noise, where for each generated image the real total mass is known. A hold-out cross-validation procedure was performed to train and evaluate five architectures of CNNR models with different number and sizes of kernels The results indicate that the proposed deep neural network models for regression provide reliable point-parameter estimations with high accuracy. This estimation parameter approach can be easily extended to reconstruct more parameters from astrophysical sources directly from obseved GW events.
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Figueroa, J., P. Baraldi, I. Chouybat, F. Ursini, E. Vignati, and E. Zio. "Estimation of Real-Time Bottomhole Parameters in CO2 Injection Wells During Operations by Means of an Ensemble of Neural Networks." In SPE Europe Energy Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/220048-ms.
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Abstract Effective management of CO2 storage operations in subsurface reservoirs demands real-time estimation of well performance to identify deviations from expected operating conditions and ensure compliance with the requirements of Carbon Capture and Storage monitoring plans. To overcome the limitations of analytical tools in accurately estimating bottomhole flowing parameters, particularly during two-phase flow, this work aims to develop an empirical mapping between monitored parameters at various well sections and the bottomhole flowing parameters. Specifically, an ensemble of neural networks (NNs) is developed for the estimation of the bottom hole pressure (FBHP) and temperature (FBHT) in a CO2 injection well during operations. A feature selection approach based on trial-and-error is employed for the selection of the model inputs among the monitored parameters. The data used to train the NNs are generated using a thermo-fluid dynamic numerical simulator. Diversity between the NNs is achieved during the training phase by using different subsets of data and different weight initializations. The final FBHP and FBHT estimations are the median of the individual NN outcomes. The methodology has been applied to several cases in all flow regimes and different regions of the pure CO2 phase diagram, including single-phase, supercritical and two-phase transition. The proposed ensemble of NNs achieves accurate estimations of FBHP and FBHT, with root mean square errors (RMSE) below 1 bar and 0.1 °C, respectively, significantly outperforming an individual NN. This is due to the diversity among the NNs of the ensemble, which allows obtaining individual models that compensate for their errors, and the use of the median for the aggregation of the NN estimations, which make the ensemble robust towards the presence of possible outlier estimations provided by individual NNs. To our knowledge, this is the first application of machine learning solutions to real-time estimate bottomhole properties in CO2 injection wells. The satisfactory performance achieved on simulated data in a wide range of operating conditions, including two-phase transition, paves the way for the deployment of an ensemble of NNs for field application during operations.
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Gao, Ruipeng, Xiaoyu Guo, Fuyong Sun, Lin Dai, Jiayan Zhu, Chenxi Hu, and Haibo Li. "Aggressive Driving Saves More Time? Multi-task Learning for Customized Travel Time Estimation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/234.
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Estimating the origin-destination travel time is a fundamental problem in many location-based services for vehicles, e.g., ride-hailing, vehicle dispatching, and route planning. Recent work has made significant progress to accuracy but they largely rely on GPS traces which are too coarse to model many personalized driving events. In this paper, we propose Customized Travel Time Estimation (CTTE) that fuses GPS traces, smartphone inertial data, and road network within a deep recurrent neural network. It constructs a link traffic database with topology representation, speed statistics, and query distribution. It also uses inertial data to estimate the arbitrary phone's pose in car, and detects fine-grained driving events. The multi-task learning structure predicts both traffic speed at public level and customized travel time at personal level. Extensive experiments on two real-world traffic datasets from Didi Chuxing have demonstrated our effectiveness.
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Wang, Yifan, Jannes Iatropoulos, Silvia Thal, and Roman Henze. "Enhancing Urban AEB Systems: Simulation-Based Analysis of Error Tolerance in Distance Estimation and Road-Tire Friction Coefficients." In 2024 Stuttgart International Symposium. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2992.
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<div class="section abstract"><div class="htmlview paragraph">AEB systems are critical in preventing collisions, yet their effectiveness hinges on accurately estimating the distance between the vehicle and other road users, as well as understanding road conditions. Errors in distance estimation can result in premature or delayed braking and varying road conditions alter road-tire friction coefficients, affecting braking distances. The integration of advanced sensors like LiDARs has significantly enhanced distance estimation. Cameras and deep neural networks are also employed to estimate the road conditions. However, AEB systems face notable challenges in urban environments, influenced by complex scenarios and adverse weather conditions such as rain and fog. Therefore, investigating the error tolerance of these estimations is essential for the performance of AEB systems. To this end, we develop a digital twin of our test vehicle in the IPG CarMaker simulation environment, which includes realistic driving dynamics and sensor models. Our simulated test vehicle is equipped with a distance estimation algorithm and AEB system designed for eventual deployment in its real-world counterpart. We test the vehicle in various simulated test scenarios. This approach facilitates accurate measurement and adjustment of distance and road-tire friction coefficients. The testing protocol begins with the European New Car Assessment Programme (EU NCAP) AEB Car-to-Pedestrian standard. Additionally, our simulation encompasses realistic urban scenarios, featuring complex traffic conditions and diverse weather scenarios, including rain, fog, and varying road surfaces like dry, wet, snow-covered, and icy. Finally, we have determined the error tolerances for various conditions. The simulation process and results reveal that the major challenges involve creating critical scenarios, modeling environments and sensors, and constructing digital twins of test vehicles. Recommendations and insights derived from these findings are also provided.</div></div>
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Zarembski, Allan M., and Pradeep Patel. "Estimating Maintenance Costs for Mixed Higher Speed Passenger and Freight Rail Corridors." In 2010 Joint Rail Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/jrc2010-36036.
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In order to reduce the cost of new intercity passenger rail corridors, the operation of higher speed passenger networks on existing freight corridors is being examined and considered. The issues to be addressed in such operations include the one-time upgrade of the track to allow for this higher speed passenger traffic and the ongoing maintenance costs necessary to maintain this track for the mixed higher speed passenger and freight operations. This latter issue is usually addressed in the access agreements for the corridor, and must include how these costs are to be shared. A recent US Federal Railroad Administration study specifically addressed the issue of “steady state” maintenance costs for mixed use corridors consisting on this class of higher speed passenger operations and concurrent freight operations, to include heavy axle load freight operations. The result of that study was a “planner’s handbook” for estimating these track maintenance costs, as part of the overall analysis of the feasibility and cost of operating higher speed passenger traffic on existing freight corridors. This paper presents the methodology used in the development of the methodology for estimating maintenance costs for mixed higher speed passenger and freight rail corridors (Classes 4, 5 and 6). Specifically, it addresses the estimation of these “steady state” infrastructure maintenance costs for a range of operating scenarios with different combination of passenger and freight traffic densities and operating speeds. These infrastructure costs include track, bridge and building (B&B), and communications and signal (C&S) costs. The resulting costs are presented as a set of cost matrices both in terms of a total cost per track mile and in terms of cost per passenger train mile. The cost matrices cover a range of combinations of traffic and track configuration, with minimum and maximum costs developed for each cell in the cost matrices. The minimum costs are based on maintenance standards geared to typical Class I freight railroad practice, such as where passenger trains currently operate on a freight railroad right of way, while the maximum costs reflect maintenance practices on existing high speed railroad track. This paper provides a description of the analytic models used to generate the costs, and the process by which those models were calibrated to actual cost data to develop costs for a wide range of traffic and track combinations. Sample application of the methodology to include several proposed mixed use corridors is also presented.
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Martínez-Díaz, Margarita, and Ignacio Pérez Pérez. "An algorithm for the estimation of road traffic space mean speeds from double loop detector data." In CIT2016. Congreso de Ingeniería del Transporte. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/cit2016.2016.3208.
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Most algorithms trying to analyze or forecast road traffic rely on many inputs, but in practice, calculations are usually limited by the available data and measurement equipment. Generally, some of these inputs are substituted by raw or even inappropriate estimations, which in some cases come into conflict with the fundamentals of traffic flow theory. This paper refers to one common example of these bad practices. Many traffic management centres depend on the data provided by double loop detectors, which supply, among others, vehicle speeds. The common data treatment is to compute the arithmetic mean of these speeds over different aggregation periods (i.e. the time mean speeds). Time mean speed is not consistent with Edie’s generalized definitions of traffic variables, and therefore it is not the average speed which relates flow to density. This means that current practice begins with an error that can have negative effects in later studies and applications. The algorithm introduced in this paper enables easily the estimation of space mean speeds from the data provided by the loops. It is based on two key hypotheses: stationarity of traffic and log-normal distribution of the individual speeds in each time interval of aggregation. It could also be used in case of transient traffic as a part of any data fusion methodology.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3208
Звіти організацій з теми "Estimation du trafic"
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Snyder, Corey, Daniel Gonzales, Minh Do, and Tian Ma. Congestion Estimation Using Traffic Cameras. Office of Scientific and Technical Information (OSTI), May 2019. http://dx.doi.org/10.2172/1762323.
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2
Yang, Yu, Hen-Geul Yeh, and Cesar Ortiz. Battery Management System Development for Electric Vehicles and Fast Charging Infrastructure Improvement. Mineta Transportation Institute, July 2024. http://dx.doi.org/10.31979/mti.2024.2325.
Повний текст джерелаАнотація:
The electric vehicle (EV) has become increasingly popular due to its being zero-emission. However, a significant challenge faced by EV drivers is the range anxiety associated with battery usage. Addressing this concern, this project develops a more efficient battery management system (BMS) for electric vehicles based on a real-time, state-of-charge (SOC) estimation. The proposed study delivers three modules: (1) a new equivalent circuit model (ECM) for lithium-ion batteries, (2) a new SOC estimator based on the moving horizon method, and (3) an on-board FPGA implementation of the classical Coulomb counting method for SOC estimation. The research team extends the traditional ECM by incorporating more functional features through the least absolute shrinkage and selection operator (LASSO). Then the first-order transfer function model identification and LASSO are iteratively executed to minimize the data fitting error. Given this model, the research team employs the moving horizon estimator (MHE) to determine the SOC by fitting the measured terminal voltage. Subsequently, the research team implements the SOC estimation scheme on an FPGA board. This hardware-in-the-loop simulation is demonstrated in this report step by step. The proposed research has broad societal impacts. It aligns with SB1 objectives in several ways. First, EVs with a more efficient BMS can improve their cruise range, reducing energy consumption and traffic congestion. Second, the resulting BMS can be applied in the solar-power and battery-assisted charging stations to make more reliable infrastructure in an age of sustainable transportation.
3
Tarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru, and Xueqian Shi. Guidelines for Evaluating Safety Using Traffic Encounters: Proactive Crash Estimation on Roadways with Conventional and Autonomous Vehicle Scenarios. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317587.
Повний текст джерелаАнотація:
With the expected arrival of autonomous vehicles, and the ever-increasing levels of automation in today’s human driven vehicles, road safety is changing at a rapid pace. This project aimed to address the need for an efficient and rapid method of safety evaluation and countermeasure identification via traffic encounters, specifically traffic conflicts that are considered useful surrogates of crashes. Recent research-delivered methods for estimating crash frequencies based on these events were observed in the field. In this project we developed a method for observing traffic encounters with two LiDAR-based traffic monitoring units, called TScan, which were recently developed in JTRP-funded projects SPR-3831 and SPR-4102. The TScan units were deployed in the field for several hours to collect data at selected intersections. These large data sets were used to improve object detection and tracking algorithms in order to better assist in detecting traffic encounters and conflicts. Consequently, the software of the TScan trailer-based units was improved and the results generated with the upgraded system include a list of potential encounters for further analysis. We developed an engineering application for analyzing the trajectories of vehicles involved in the pre-selected encounters to identify final traffic encounters and conflicts. Another module of the engineering application visualized the traffic encounters and conflicts to inspect the spatial patterns of these events and to estimate the number of crashes for the observation period. Furthermore, a significant modeling effort resulted in a method of producing factors that expand the conflict-based crash estimates in short observation periods to an entire year. This report provides guidelines for traffic encounters and conflicts, the user manuals for setting up and operating the TScan research unit. and manuals for the engineering applications mentioned above.
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Hunter, Margaret, Jijo K. Mathew, Ed Cox, Matthew Blackwell, and Darcy M. Bullock. Estimation of Connected Vehicle Penetration Rate on Indiana Roadways. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317343.
Повний текст джерелаАнотація:
Over 400 billion passenger vehicle trajectory waypoints are collected each month in the United States. This data creates many new opportunities for agencies to assess operational characteristics of roadways for more agile management of resources. This study compared traffic counts obtained from 24 Indiana Department of Transportation traffic counts stations with counts derived by the vehicle trajectories during the same periods. These stations were geographically distributed throughout Indiana with 13 locations on interstates and 11 locations on state or US roads. A Wednesday and a Saturday in January, August, and September 2020 are analyzed. The results show that the analyzed interstates had an average penetration of 4.3% with a standard deviation of 1.0. The non-interstate roads had an average penetration of 5.0% with a standard deviation of 1.36. These penetration levels suggest that connected vehicle data can provide a valuable data source for developing scalable roadway performance measures. Since all agencies currently have a highway monitoring system using fixed infrastructure, this paper concludes by recommending agencies integrate a connected vehicle penetration monitoring program into their traditional highway count station program to monitor the growing penetration of connected cars and trucks.
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Saldivar-Carranza, Enrique D., and Darcy M. Bullock. Deriving a Purdue Probe Diagram from Connected Vehicle Waypoint Data. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317763.
Повний текст джерелаАнотація:
The provided video shows the transition from raw connected vehicle (CV) geospatial trajectory data to a Purdue Probe Diagram (PPD). PPDs allow for the estimation of delay and operational traffic signal performance measures, such as arrivals on green, split failures, and downstream blockage. These performance measures can be used to identify locations where retiming or capital investment could improve operations.
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Nasr, Elhami, Tariq Shehab, Nigel Blampied, and Vinit Kanani. Estimating Models for Engineering Costs on the State Highway Operation and Protection Program (SHOPP) Portfolio of Projects. Mineta Transportation Institute, November 2024. http://dx.doi.org/10.31979/mti.2024.2365.
Повний текст джерелаАнотація:
The State Highway Operation and Protection Program (SHOPP) is crucial for maintaining California’s 15,000-mile state highway system, which includes projects like pavement rehabilitation, bridge repair, safety enhancements, and traffic management systems. Administered by Caltrans, SHOPP aims to preserve highway efficiency and safety, supporting economic growth and public safety. This research aimed to develop robust cost-estimating models to improve budgeting and financial planning, aiding Caltrans, the California Transportation Commission (CTC), and the Legislature. The research team collected and refined comprehensive data from Caltrans project expenditures from 1983 to 2021, ensuring a high-quality dataset. Subject matter experts validated the data, enhancing its reliability. Two models were developed: a statistical model using exponential regression to account for non-linear cost growth, and an AI model employing neural networks to handle complex relationships in the data. Model performance was evaluated based on accuracy and reliability through repeated testing and validation. Key findings indicated that the new models significantly improved the precision of cost forecasts, reducing the variance between predicted and actual project costs. This advancement minimizes budget overruns and enhances resource allocation efficiency. Additionally, leveraging historical data with current market trends refined the models’ predictive power, boosting stakeholder confidence in project budgeting and financial planning. The study’s innovative approach, integrating machine learning and big data analytics, transforms traditional estimation practices and serves as a reference for other state highway programs. Continuous improvement and broader application of these models are recommended to further enhance cost estimation accuracy and support informed decision-making in transportation infrastructure management.
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Lin, Pei-Sung. Coordinated Pre-Preemption of Traffic Signals to Enhance Railroad Grade Crossing Safety in Urban Areas and Estimation of Train Impacts to Arterial Travel Time Delay. Tampa, FL: University of South Florida, January 2004. http://dx.doi.org/10.5038/cutr-nctr-rr-2014-06.
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Chien, Stanley, Yaobin Chen, Lauren Christopher, Mei Qiu, and Zhengming Ding. Road Condition Detection and Classification from Existing CCTV Feed. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317364.
Повний текст джерелаАнотація:
The Indiana Department of Transportation (INDOT) has approximately 500 digital cameras along highways in populated areas of Indiana. These cameras are used to monitor traffic conditions around the clock, all year round. Currently, the videos from these cameras are observed one-by-one by human operators looking for traffic conditions and incidents. The main objective of this research was to develop an automatic, real-time system to monitor traffic conditions and detect incidents automatically. The Transportation and Autonomous Systems Institute (TASI) of the Purdue School of Engineering and Technology at Indiana University-Purdue University Indianapolis (IUPUI) and the Traffic Management Center of INDOT developed a system that monitors the traffic conditions based on the INDOT CCTV video feeds. The proposed system performs traffic flow estimation, incident detection, and classification of vehicles involved in an incident. The research team designed the system, including the hardware and software components added to the existing INDOT CCTV system; the relationship between the added system and the currently existing INDOT system; the database structure for traffic data extracted from the videos; and a user-friendly, web-based server for showing the incident locations automatically. The specific work in this project includes vehicle-detection, road boundary detection, lane detection, vehicle count over time, flow-rate detection, traffic condition detection, database development, web-based graphical user interface (GUI), and a hardware specification study. The preliminary prototype of some system components has been implemented in the Development of Automated Incident Detection System Using Existing ATMS CCT (SPR-4305).
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Chien, Stanley, Lauren Christopher, Yaobin Chen, Mei Qiu, and Wei Lin. Integration of Lane-Specific Traffic Data Generated from Real-Time CCTV Videos into INDOT's Traffic Management System. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317400.
Повний текст джерелаАнотація:
The Indiana Department of Transportation (INDOT) uses about 600 digital cameras along populated Indiana highways in order to monitor highway traffic conditions. The videos from these cameras are currently observed by human operators looking for traffic conditions and incidents. However, it is time-consuming for the operators to scan through all video data from all the cameras in real-time. The main objective of this research was to develop an automatic and real-time system and implement the system at INDOT to monitor traffic conditions and detect incidents automatically. The Transportation and Autonomous Systems Institute (TASI) of the Purdue School of Engineering and Technology at Indiana University-Purdue University Indianapolis (IUPUI) and the INDOT Traffic Management Center have worked together to research and develop a system that monitors the traffic conditions based on the INDOT CCTV video feeds. The proposed system performs traffic flow estimation, incident detection, and the classification of vehicles involved in an incident. The goal was to develop a system and prepare for future implementation. The research team designed the new system, including the hardware and software components, the currently existing INDOT CCTV system, the database structure for traffic data extracted from the videos, and a user-friendly web-based server for identifying individual lanes on the highway and showing vehicle flowrates of each lane automatically. The preliminary prototype of some system components was implemented in the 2018–2019 JTRP projects, which provided the feasibility and structure of the automatic traffic status extraction from the video feeds. The 2019–2021 JTRP project focused on developing and improving many features’ functionality and computation speed to make the program run in real-time. The specific work in this 2021–2022 JTRP project is to improve the system further and implement it on INDOT’s premises. The system has the following features: vehicle-detection, road boundary detection, lane detection, vehicle count and flowrate detection, traffic condition detection, database development, web-based graphical user interface (GUI), and a hardware specification study. The research team has installed the system on one computer in INDOT for daily road traffic monitoring operations.
10
Takubo, Nobuaki, and Shinichi Yoshida. An Estimation of Effects of Active Safety Devices by Hayashi's Quantification Theory Analysis of Traffic Accident Data. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0062.
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