232 research outputs found
Estimation des émissions instantanées de polluants provenant du transport routier par couplage de la modélisation du trafic mésoscopique et de la génération de profils de vitesse
No full textLa pollution de l'air urbain, principalement causée par les émissions des véhicules, reste un problème crucial pour les grandes villes européennes qui s'efforcent de respecter les normes de qualité de l'air ambiant de l'Union européenne. Bien que des politiques environnementales plus strictes soient essentielles, leur mise en œuvre entraîne souvent des coûts économiques et sociaux importants. Par conséquent, des modèles et des simulations fiables sont indispensables pour évaluer l'efficacité de ces stratégies, garantissant qu'elles atteignent les réductions souhaitées des concentrations de polluants sans imposer de charges excessives à la société.Les approches traditionnelles pour estimer les émissions de polluants liés au trafic routier dans des scénarios prospectifs reposent sur des modèles de trafic et d'émissions à différentes échelles, chacun ayant des limites spécifiques en contexte urbain. Les modèles macroscopiques utilisent une approche agrégée qui manque de granularité pour capturer les pics d'émissions au sein du réseau. Les modèles microscopiques, bien qu'ils fournissent des analyses détaillées, sont limités par des exigences de données importantes et une complexité de calcul élevée.Cette thèse présente deux nouvelles méthodologies qui améliorent la modélisation des émissions à grande échelle grâce à l'intégration de l'apprentissage profond dans le flux de travail. La première méthodologie, SPG-M, comble le fossé entre les modèles de trafic mésoscopiques et les modèles d'émissions microscopiques, une combinaison auparavant jugée irréalisable en raison des problèmes de compatibilité des données. L'innovation clé réside dans un générateur de profils de vitesse basé sur l'apprentissage profond, entrainé sur des données de conduite réelles, qui transforme les résultats des modèles de trafic mésoscopiques en profils de vitesse instantanés nécessaires au fonctionnement des modèles d'émissions microscopiques. Cette intégration permet d'estimer les émissions de manière très détaillée au niveau local en prenant en compte toutes les variations de vitesse sur un tronçon, tout en fournissant des estimations globales précises.La deuxième méthodologie combine un modèle de trafic mésoscopique avec un modèle d'émissions mésoscopique basé sur l'apprentissage profond. Ce modèle d'émissions est entrainé sur des données d'émissions synthétiques générées par un modèle d'émissions microscopique. Cette méthodologie offre une précision comparable au SPG-M tout en améliorant considérablement l'efficacité du calcul.Les deux méthodologies ont été appliquées avec succès dans la région Île-de-France, qui compte plus de 12 millions d'habitants, pour prédire les émissions de CO2 et de NOx, démontrant leur capacité de mise à l'échelle et leur aptitude à maintenir une grande granularité dans les grandes zones urbaines. Le processus de validation a consisté à évaluer chaque composant des méthodologies proposées séparément. Enfin, les émissions ont été comparées aux modèles d'émissions macroscopiques bien établis, à savoir HBEFA et COPERT, ainsi qu'aux campagnes de mesure des émissions. Les résultats montrent que, bien que les émissions globales de CO2 soient comparables entre tous les modèles, les émissions de NOx sont sous-estimées par les modèles macroscopiques. Des écarts sont également observés au niveau local, les modèles macroscopiques ne parvenant pas à capturer les zones de fortes et faibles accélérations, responsables respectivement des zones de fortes et faibles émissions.Urban air pollution, predominantly caused by vehicular emissions, remains a critical issue for major European cities striving to meet European Union ambient air quality standards. While stricter environmental policies are essential, implementing them often incurs substantial economic and social costs. Therefore, reliable models and simulations are essential to evaluate the effectiveness of these strategies, ensuring they achieve the desired reductions in pollutant concentrations without imposing undue burdens on society.Traditional approaches to estimating road traffic pollutant emissions for prospective scenarios rely on traffic and emission models at various scales, each with distinct limitations in urban contexts. Macroscopic models employ an aggregated approach that lacks the granularity needed to capture emission peaks within the network. Microscopic models, while providing detailed analyses, are constrained by extensive data requirements and high computational complexity.This thesis presents two novel methodologies that enhance large-scale emission modeling by integrating deep learning into the workflow. The first methodology, SPG-M, bridges the gap between mesoscopic traffic models and microscopic emission models, a combination previously considered unfeasible due to data compatibility issues. The key innovation is a deep learning-based speed profile generator, trained on real-world driving data, which transforms mesoscopic traffic model outputs into the instantaneous speed profiles required by microscopic emission models. This integration enables highly detailed emission estimations at the local level by accounting for all speed variations within a link while providing accurate global estimates.The second methodology combines a mesoscopic traffic model with a deep learning-based mesoscopic emission model. The emission model is trained on synthetic emission data generated by a microscopic emission model. This methodology offers accuracy comparable to SPG-M while significantly improving computational efficiency.Both methodologies were successfully applied in the Île-de-France region, home to over 12 million inhabitants, to predict CO2 and NOx emissions, demonstrating their scalability and capacity to maintain high granularity in large urban areas. The validation process involved evaluating each component of the proposed methodologies separately. Finally, emissions were compared to well-established macroscopic emission models, namely HBEFA and COPERT, as well as to data from emission measurement campaigns. Results indicate that while global CO2 emissions are at comparable levels across all models, macroscopic models underestimate NOx emissions. Discrepancies are also observed at local levels, as macroscopic models fail to capture high- and low-acceleration zones, which are responsible for high- and low-emission zones, respectively
Traffic simulation datasets supporting a Digital Twin of Milos Island road network
No full textThis dataset contains traffic simulation inputs and outputs used to develop and validate a Digital Twin of the road network of Milos Island, Greece. The data support scenario testing of congestion mitigation and sustainability interventions using SUMO, including traffic counts, synthetic demand, route files, and simulation-derived performance indicators such as travel time loss, fuel consumption, and CO₂ emissions© the author</p
Historical fiction and post-modernism. View of the older Czech history in the works of Milos Urban
Full text link(in English) The aim of this work is especially the post-modern art, its symbols and manifestations. The main area of this research is post-modern literature and effort to concretise post-modern symbols in literature. This work is also trying show the influence of post-modern on historiographical approach. The solution of this work goes from general manifestations of post-modern to particular manifestation in post-modern literature. The main source represented the set of monographs about this theme. The significantly part is about publications of Czech author Milos Urban, and especially the interview of author this thesis with him. On the particular symbols was able to improved, that Milos Urban really writing literature with influence of post-modernism. The post-modern symbols in literature and also the influence of post-modern on historiography were managed to described and instanced
Bourgeois Balkans: world-building in Belgrade and Sofia 1830-1912
Full text linkThis dissertation examines the transformation of urban life in the Balkan capitals of Belgrade and Sofia between 1830 and 1912. In the nineteenth century, mayors, planners, doctors and intellectuals envisioned a new, urban society in which progressive social transformation could emerge through a combination of political and economic institutions based on expertise. I explore the ambitions and limits of this “bourgeois world-building” through three constitutive processes: the production of space, the gendered transformation of intimate labor, and the re-calibration of state violence. With the advent of autonomous rule, the Balkan capitals were reconstructed as “European” cities through dispossession, real-estate speculation and municipal corruption. For architects, merchant capitalists, and municipal officials, the post-Ottoman city appeared as a landscape of accumulation, a vision often frustrated by its failure to materialize in full. Medical professionals and police officials envisioned the city as a space of managed, commodified intimacy, yet found limits in expanding institutional control over sex workers, domestic servants, and other working women. Activists and state actors were likewise frustrated in their attempts to create productive urban subjects through scientific policing and prison labor. Ultimately, the application of bourgeois visions was both intensive and costly, limited by the scope of elite ambitions and the struggle of those who were excluded from them.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-12-01The student, Milos Jovanovic, accepted the attached license on 2016-12-02 at 13:57.The student, Milos Jovanovic, submitted this Dissertation for approval on 2016-12-02 at 13:58.This Dissertation was approved for publication on 2016-12-02 at 14:26.DSpace SAF Submission Ingestion Package generated from Vireo submission #10419 on 2017-02-28 at 14:43:05Made available in DSpace on 2017-03-01T17:02:01Z (GMT). No. of bitstreams: 2
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Problems of translation Milos Macourek's fairy-tales into Polish
Full text linkSummery This thesis deals with the issue of translation Milos Macourek's fairy tales into Polish. The starting point for the analysis is a comparison of two versions of a translation of selected texts. The author of the first one is Marie Erhardtowa, the second version is translated by Herman Grzeszczyk and Andrzej Kulikowski. The first part of the thesis is a theoretical preparation which explains the basic questions of literary translation. The main goal of the practical part of this thesis is to determine the target groups of readers, followed by an analysis of the particular translation issues.The conclusion contains a summary of findings and an assessment of individual translation in terms of adequacy
Integration and Laplace Transformation of Orthogonal Series
No full textTitle: Integration and Laplace Transformation of Orthogonal Series, Author: Milos Novotny, Location: ThodeIn the first part of this thesis the basic
properties of classical orthogonal polynomials are derived from a unified
theory. Then sufficient conditions for term-by-term integration and
Laplace transformation of Fourier expansions in terms of an orthogonal
system with respect to a weight function on a bounded or unbounded interval
are given, and successively applied to Fourier expansions in terms of the
trigonometric system, the classical orthogonal polynomials, the Haar system
and eigenfunction expansions for ordinary linear differential equations with
boundary conditions on both ends of a compact interval as well as in limit
point and limit circle cases for an infinite interval. Finally a necessary
and sufficient condition for representation of a complex function as a
Laplace interval of f ε L(0,2π) with period 2π is proved.ThesisDoctor of Philosophy (PhD
Synthetic population for the state of California based on open-data: examples of San Francisco Bay area and San Diego County
No full textInternational audienceThis paper describes the steps to create a synthetic population of any region in California. By using only open data, and an open-source population synthesis pipeline, we ensure that the whole process can be easily repeated by others. This not only ensures reproducibility and transparency of the synthesis process, but also allows that studies using this population can be easily replicated. As agent-based models are gaining in popularity in recent times due to the rapid developments in the transportation sector, the need for convenient ways to generate synthetic individuals and their daily patterns has grown as well. We present our approach for two regions: nine-county San Francisco Bay area and San Diego County. The validation results show that the methodology used is suitable to replicate socio-demograpahics and activity patterns of the population. However, it also points to some limitations due to the lack of data and the methods used. Nevertheless, the approach could be a good complement to the local and regional transportation models, as it allows easy access and can be readily used in agent-based models
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