Dissertations / Theses: 'Trafic spatial'

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Tanzi, Tullio. "Systeme spatial temps réel d'aide a la décision, application aux risques autoroutiers : D.E.S.T.IN : dispositif d'études et de surveillance du trafic et des incidents." Lyon, INSA, 1998. http://www.theses.fr/1998ISAL0058.

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L'objectif de ces travaux est de concevoir un système temps réel d'analyse de risque afin de compléter les systèmes autoroutiers d'aide à l'exploitation. Plutôt que de s'attacher à la révision d'accidents, le système repose sur l'analyse de l'évolution des conditions de trafic, afin de caractériser les situations à haut risque. Cette anticipation des accidents potentiels doit permettre la mise en œuvre d'actions préventives et une meilleure gestion de la crise. Ces travaux ont permis la définition d'indicateurs de risque dans le contexte routier. Un prototype original a été développé et testé en vraie grandeur. Les indicateurs ont été testés sur un jeu de données de l'autoroute du réseau ESCOT A. Grace à notre approche, les techniques classiques d'analyses spatiales, telles que nous les connaissons dans le monde des systèmes d'information géographiques permettront à la fois de produire des informations quantitatives en temps réel, tels que des distances, des temps prévus d'arrivée, ou encore des périmètres de sécurité, mais aussi de mieux maîtriser l'événement en permettant la simulation de ses phénomènes. Cela nécessite la prise en compte de l'acheminement de l'information au sein du système d'information global. L'originalité de ces travaux peut être résumée en deux points principaux : • Un nouveau concept de système d'information temps réel pour l'analyse des risques. A ce niveau il s'agit de nouveaux indicateurs, • Un nouveau concept qui émerge : la TéléGéomatique, terme fédérateur de la géomatique et des télécommunications, dont l'importance est justifiée par ces travaux
The objective of these works is to specify a real-time system of risk analysis in order to complete systems for motorways to help the exploitation of motorways. Instead of focusing on accidents, the system relies on the analysis of the evolution of traffic conditions, in order to characterise high risk situations. The aim of this potential accident anticipation is to elaborate preventive actions and to allow a better management of the crisis. These works have permitted the definition of risk indicators in the road context. An original prototype has been developed and has been tested in real situations. Indicators have been tested on samples ofdata ofthe freeway of the ESCOT A network. Thanks to our approach, the classic techniques of spatial analyses, as we know them in the world of the geographical information systems will permit to produce sorne quantitative information in real-time, as distances, predicted times of arrivai or security perimeters, but also to better manage the event using phenomena simulations. It requires to take into ac-count the routing of information within the global information system. The originality ofthese works can be summarised in two main points: • A new concept of real-time information system for the analysis of risks (it is about new indicators) A new emerging concept: TéléGéomatique, term based on geomatics and telecommunications, whose importance is justified by these works

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Schiper, Nicole. "Traffic data sampling for air pollution estimation at different urban scales." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSET008/document.

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La circulation routière est une source majeure de pollution atmosphérique dans les zones urbaines. Les décideurs insistent pour qu’on leur propose de nouvelles solutions, y compris de nouvelles stratégies de management qui pourraient directement faire baisser les émissions de polluants. Pour évaluer les performances de ces stratégies, le calcul des émissions de pollution devrait tenir compte de la dynamique spatiale et temporelle du trafic. L’utilisation de capteurs traditionnels sur route (par exemple, capteurs inductifs ou boucles de comptage) pour collecter des données en temps réel est nécessaire mais pas suffisante en raison de leur coût de mise en oeuvre très élevé. Le fait que de telles technologies, pour des raisons pratiques, ne fournissent que des informations locales est un inconvénient. Certaines méthodes devraient ensuite être appliquées pour étendre cette information locale à une grande échelle. Ces méthodes souffrent actuellement des limites suivantes : (i) la relation entre les données manquantes et la précision de l’estimation ne peut être facilement déterminée et (ii) les calculs à grande échelle sont énormément coûteux, principalement lorsque les phénomènes de congestion sont considérés. Compte tenu d’une simulation microscopique du trafic couplée à un modèle d’émission, une approche innovante de ce problème est mise en oeuvre. Elle consiste à appliquer des techniques de sélection statistique qui permettent d’identifier les emplacements les plus pertinents pour estimer les émissions des véhicules du réseau à différentes échelles spatiales et temporelles. Ce travail explore l’utilisation de méthodes statistiques intelligentes et naïves, comme outil pour sélectionner l’information la plus pertinente sur le trafic et les émissions sur un réseau afin de déterminer les valeurs totales à plusieurs échelles. Ce travail met également en évidence quelques précautions à prendre en compte quand on calcul les émissions à large échelle à partir des données trafic et d’un modèle d’émission. L’utilisation des facteurs d’émission COPERT IV à différentes échelles spatio-temporelles induit un biais en fonction des conditions de circulation par rapport à l’échelle d’origine (cycles de conduite). Ce biais observé sur nos simulations a été quantifié en fonction des indicateurs de trafic (vitesse moyenne). Il a également été démontré qu’il avait une double origine : la convexité des fonctions d’émission et la covariance des variables de trafic
Road traffic is a major source of air pollution in urban areas. Policy makers are pushing for different solutions including new traffic management strategies that can directly lower pollutants emissions. To assess the performances of such strategies, the calculation of pollution emission should consider spatial and temporal dynamic of the traffic. The use of traditional on-road sensors (e.g. inductive sensors) for collecting real-time data is necessary but not sufficient because of their expensive cost of implementation. It is also a disadvantage that such technologies, for practical reasons, only provide local information. Some methods should then be applied to expand this local information to large spatial extent. These methods currently suffer from the following limitations: (i) the relationship between missing data and the estimation accuracy, both cannot be easily determined and (ii) the calculations on large area is computationally expensive in particular when time evolution is considered. Given a dynamic traffic simulation coupled with an emission model, a novel approach to this problem is taken by applying selection techniques that can identify the most relevant locations to estimate the network vehicle emissions in various spatial and temporal scales. This work explores the use of different statistical methods both naïve and smart, as tools for selecting the most relevant traffic and emission information on a network to determine the total values at any scale. This work also highlights some cautions when such traffic-emission coupled method is used to quantify emissions due the traffic. Using the COPERT IV emission functions at various spatial-temporal scales induces a bias depending on traffic conditions, in comparison to the original scale (driving cycles). This bias observed in our simulations, has been quantified in function of traffic indicators (mean speed). It also has been demonstrated to have a double origin: the emission functions’ convexity and the traffic variables covariance

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Tchappi, haman Igor. "Dynamic Multilevel and Holonic Model for the Simulation of a Large-Scale Complex System with Spatial Environment : Application to Road Traffic Simulation." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA004.

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De nos jours, avec l’émergence d’objets et de voitures connectés, les systèmes de trafic routier deviennent de plus en plus complexes et présentent des comportements hiérarchiques à plusieurs niveaux de détail. L'approche de modélisation multiniveaux est une approche appropriée pour représenter le trafic sous plusieurs perspectives. Les modèles multiniveaux constituent également une approche appropriée pour modéliser des systèmes complexes à grande échelle comme le trafic routier. Cependant, la plupart des modèles multiniveaux de trafic proposés dans la littérature sont statiques car ils utilisent un ensemble de niveaux de détail prédéfinis et ces représentations ne peuvent pas commuter pendant la simulation. De plus ces modèles multiniveaux considèrent généralement seulement deux niveaux de détail. Très peu de travaux se sont intéressés à la modélisation dynamique multiniveau de trafic.Cette thèse propose un modèle holonique multiniveau et dynamique du trafic à grande échelle.La commutation dynamique des niveaux de détail lors de l’exécution de la simulation permet d’adapter le modèle aux contraintes liées à la qualité des résultats ou aux ressources de calcul disponibles.La proposition étend l'algorithme DBSCAN dans le contexte des systèmes multi-agents holoniques. De plus, une méthodologie permettant la commutation dynamique entre les différents niveaux de détail est proposée. Des indicateurs multiniveaux basés sur l'écart type sont aussi proposés afin d'évaluer la cohérence des résultats de la simulation
Nowadays, with the emergence of connected objects and cars, road traffic systems become more and more complex and exhibit hierarchical behaviours at several levels of detail. The multilevel modeling approach is an appropriate approach to represent traffic from several perspectives. Multilevel models are also an appropriate approach to model large-scale complex systems such as road traffic. However, most of the multilevel models of traffic proposed in the literature are static because they use a set of predefined levels of detail and these representations cannot change during simulation. Moreover, these multilevel models generally consider only two levels of detail. Few works have been interested on the dynamic multilevel traffic modeling.This thesis proposes a holonic multilevel and dynamic traffic model for large scale traffic systems. The dynamic switching of the levels of detail during the execution of the simulation allows to adapt the model to the constraints related to the quality of the results or to the available computing resources.The proposal extends the DBSCAN algorithm in the context of holonic multi-agent systems. In addition, a methodology allowing a dynamic transition between the different levels of detail is proposed. Multilevel indicators based on standard deviation are also proposed in order to assess the consistency of the simulation results

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Chen, Guangshuo. "Human Habits Investigation : from Mobility Reconstruction to Mobile Traffic Prediction." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLX026/document.

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La capacité à prévoir les activités humaines a des implications essentielles dans de nombreux aspects des réseaux cellulaires. En particulier, la haute disponibilité de la prédiction de la mobilité peut permettre différents scénarios d'application; une meilleure compréhension de la demande de trafic de données mobiles peut aider à améliorer la conception de solutions pour l'équilibrage de la charge du réseau. Bien que de nombreux chercheurs aient étudié le sujet de la prédiction de la mobilité humaine, il y a eu peu de discussions sur l'anticipation du trafic de données mobiles dans les réseaux cellulaires.Pour comprendre la mobilité humaine, les ensembles de données de téléphones mobiles, consistant en des enregistrements de données de taxation (CDR), constituent un choix pratique d'empreintes humaines. Comme le déploiement du réseau cellulaire est très irrégulier et que les fréquences d'interaction sont généralement faibles, les données CDR sont souvent caractérisées par une parcimonie spatio-temporelle qui, à son tour, peut biaiser les analyses de mobilité basées sur de telles données et provoquer la perte de trajectoires individuelles.Dans cette thèse, nous présentons de nouvelles solutions de reconstruction de trajectoires individuelles et de prédiction de trafic de données mobiles individuelles. Nos contributions abordent les problèmes de (1) surmonter l'incomplétude des informations de mobilité pour l'utilisation des ensembles de données de téléphonie mobile et (2) prédire la future demande de trafic de données mobiles pour le support des applications de gestion de réseau.Premièrement, nous nous concentrons sur la faille de l'information sur la mobilité dans les ensembles de données de téléphones mobiles. Nous rapportons une analyse en profondeur de son effet sur la mesure des caractéristiques de mobilité individuelles et l'exhaustivité des trajectoires individuelles. En particulier, (1) nous fournissons une confirmation des résultats antérieurs concernant les biais dans les mesures de mobilité causées par la rareté temporelle de la CDR; (2) nous évaluons le décalage géographique provoqué par la cartographie des emplacements des utilisateurs vers les tours cellulaires et révélons le biais causé par la rareté spatiale de la CDR; (3) nous fournissons une estimation empirique de l'exhaustivité des données des trajectoires CDR individuelles. (4) nous proposons de nouvelles solutions de complétion CDR pour reconstruire incomplète. Nos solutions tirent parti de la nature des modèles de mouvements humains répétitifs et des techniques d'inférence de données de pointe et surpassent les approches précédentes illustrées par des simulations axées sur les données.Deuxièmement, nous abordons la prédiction des demandes de trafic de données mobiles générées par les abonnés individuels du réseau mobile. Sur la base de trajectoires complétées par nos solutions développées et nos historiques de consommation de données extraites d'un ensemble de données de téléphonie mobile à grande échelle, (1) nous étudions les limites de prévisibilité en mesurant la prévisibilité maximale que tout algorithme peut atteindre. les approches de prédiction du trafic de données mobiles qui utilisent les résultats de l'analyse théorique de la prévisibilité. Notre analyse théorique montre qu'il est théoriquement possible d'anticiper la demande individuelle avec une précision typique de 75% malgré l'hétérogénéité des utilisateurs et avec une précision améliorée de 80% en utilisant la prédiction conjointe avec des informations de mobilité. Notre pratique basée sur des techniques d'apprentissage automatique peut atteindre une précision typique de 65% et avoir un degré d'amélioration de 1% à 5% en considérant les déplacements individuels.En résumé, les contributions mentionnées ci-dessus vont dans le sens de l'utilisation des ensembles de données de téléphonie mobile et de la gestion des opérateurs de réseau et de leurs abonnés
The understanding of human behaviors is a central question in multi-disciplinary research and has contributed to a wide range of applications. The ability to foresee human activities has essential implications in many aspects of cellular networks. In particular, the high availability of mobility prediction can enable various application scenarios such as location-based recommendation, home automation, and location-related data dissemination; the better understanding of mobile data traffic demand can help to improve the design of solutions for network load balancing, aiming at improving the quality of Internet-based mobile services. Although a large and growing body of literature has investigated the topic of predicting human mobility, there has been little discussion in anticipating mobile data traffic in cellular networks, especially in spatiotemporal view of individuals.For understanding human mobility, mobile phone datasets, consisting of Charging Data Records (CDRs), are a practical choice of human footprints because of the large-scale user populations and the vast diversity of individual movement patterns. The accuracy of mobility information granted by CDR depends on the network infrastructure and the frequency of user communication events. As cellular network deployment is highly irregular and interaction frequencies are typically low, CDR data is often characterized by spatial and temporal sparsity, which, in turn, can bias mobility analyses based on such data and cause the loss of whereabouts in individual trajectories.In this thesis, we present novel solutions of the reconstruction of individual trajectories and the prediction of individual mobile data traffic. Our contributions address the problems of (1) overcoming the incompleteness of mobility information for the use of mobile phone datasets and (2) predicting future mobile data traffic demand for the support of network management applications.First, we focus on the flaw of mobility information in mobile phone datasets. We report on an in-depth analysis of its effect on the measurement of individual mobility features and the completeness of individual trajectories. In particular, (1) we provide a confirmation of previous findings regarding the biases in mobility measurements caused by the temporal sparsity of CDR; (2) we evaluate the geographical shift caused by the mapping of user locations to cell towers and reveal the bias caused by the spatial sparsity of CDR; (3) we provide an empirical estimation of the data completeness of individual CDR-based trajectories. (4) we propose novel solutions of CDR completion to reconstruct incomplete. Our solutions leverage the nature of repetitive human movement patterns and the state-of-the-art data inference techniques and outperform previous approaches shown by data-driven simulations.Second, we address the prediction of mobile data traffic demands generated by individual mobile network subscribers. Building on trajectories completed by our developed solutions and data consumption histories extracted from a large-scale mobile phone dataset, (1) we investigate the limits of predictability by measuring the maximum predictability that any algorithm has potential to achieve and (2) we propose practical mobile data traffic prediction approaches that utilize the findings of the theoretical predictability analysis. Our theoretical analysis shows that it is theoretically possible to anticipate the individual demand with a typical accuracy of 75% despite the heterogeneity of users and with an improved accuracy of 80% using joint prediction with mobility information. Our practical based on machine learning techniques can achieve a typical accuracy of 65% and have a 1%~5% degree of improvement by considering individual whereabouts.In summary, the contributions mentioned above provide a step further towards supporting the use of mobile phone datasets and the management of network operators and their subscribers

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Fimbel, Amaury. "Origami à base de matériaux électroactifs pour des applications spatiales." Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0071.

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Ce projet de thèse s’inscrit dans le cadre d’une collaboration Cifre entre le LGEF et l’entreprise ArianeGroup. La fluctuation de forme de structures complexes à l'aide de polymères électroactifs est le sujet principal de cette étude. Les matériaux électroactifs, qui, de par leurs structures peuvent réaliser une conversion électromécanique de l’énergie, prouvent progressivement leur potentiel de percée technologique dans de nombreux domaines. En plus de l'hypothèse qu'ils pourraient éventuellement remplacer les capteurs et actionneurs actuellement utilisés, les nouvelles capacités de ces matériaux tant au niveau des performances que des capacités de couplage multiphysique sont une sérieuse source d’espoir pour aborder et résoudre des problèmes issus de secteurs émergents. Ces innovations technologiques potentielles peuvent affecter particulièrement le domaine de l'aérospatial. La combinaison d'une faible masse volumique et d'une densité d'énergie mécanique considérable pour un polymère semble apporter une réponse attrayante à la mise au point de dispositifs innovants, compacts et modulables. Mais certains points restent à explorer pour démontrer tout le potentiel applicatif de cette technologie et aboutir au développement de systèmes intelligents. Une grande partie de ce travail de recherche va donc se concentrer sur cette problématique. Ce projet se focalise ainsi sur l'élaboration et la caractérisation d'un composite à haute performance pour l'actionnement électrostatique et sa tenue en vieillissement en milieu spatial. Les objectifs de l'étude mécanique des structures origami sont de trouver des solutions concernant la compréhension et le développement de systèmes complexes et modulables. L’association de ces deux axes ouvre la voie à la création de structures mécaniques très légères pilotables par un champ électrique. Cette thèse concerne les applications spatiales mais peut tout à fait s’ancrer dans un enjeu sociétal plus large comme par exemple le médical, la robotique ou encore le domaine des transports
This thesis project is part of a Cifre collaboration between the Electrical Engineering and Ferro Electricity Laboratory and ArianeGroup. The main subject of this study is the shape shifting of complex structures by using electroactive polymers. Electroactive materials, whose internal conformations are capable of electromechanical energy conversion, are gradually proving their potential for technological breakthroughs in many fields. In addition to the hypothesis that they could eventually replace actual sensors and actuators, the new capabilities of these materials in terms of both performance and multiphysics coupling capacities are a serious source of hope for tackling and solving problems in emerging fields. These potential technological innovations may be of particular interest for aerospace industry. Combination of low density and high mechanical energy density in a polymer seems to offer an attractive answer to the development of innovative, compact and modular devices. However, some parts remain to be explored in order to demonstrate the full application potential of this technology and lead to the development of smart systems. A large part of this research work will focus on this issue. This project will deal with the development and characterization of a high-performance composite for electrostatic actuation and its resistance to ageing in a space environment. The objectives of the mechanical study of origami structures are to find solutions for understanding and developing complex, modular systems. The combination of these two lines opens the way to the creation of very light mechanical structures that can be controlled by an electric field. This thesis concerns space applications, but can also be applied to a wider societal issue, such as medical, robotics or transport sectors

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Manout, Ouassim. "Spatial aggregation issues in traffic assignment models." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE2014/document.

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Les villes sont des systèmes complexes que les modèles urbains peuvent aider à comprendre. Des modèles les plus simplistes aux modèles les plus sophistiqués, la modélisation urbaine a permis de mieux comprendre la question urbaine et ses implications sociétales. Dans ce contexte, les modèles peuvent avoir une valeur-ajoutée appréciable dans le processus de décision publique. Encore faut-il que ces modèles deviennent pratiques et répondent aux contraintes opérationnelles de la chaîne de décision. Dans ce sens, peu de recherches s’est intéressée à la question de praticité des modèles urbains et leur utilisation en situation opérationnelle. À ce jour, les modèles urbains standard qui reposent sur une description agrégée de l’espace sont parmi les approches de modélisation les plus opérationnelles et aussi les plus répandues. De par sa relative praticité, cette approche standard est attractive et simple à mettre en oeuvre. Toutefois, l’agrégation spatiale peut aussi être une source de biais statistiques préjudiciables à la qualité de la modélisation. C’est en particulier, le cas des modèles intégrés Transport-Urbanisme ou des modèles de transport à quatre étapes.La présente thèse a pour objectif d’étudier la question de l’agrégation spatiale dans les modèles transport et plus particulièrement dans les modèles d’affectation des déplacements. Les modèles d’affectation servent à calculer les temps de parcours et les conditions de déplacement sous congestion, présents et futurs, des personnes et des marchandises. Ils servent aussi à calculer les accessibilités nécessaires aux modèles d’usage des sols dont les modèles de choix de localisation des ménages et des entreprises. Toute erreur ou biais dans l’affectation des déplacements peut compromettre la validité et la qualité globales de la modélisation. Dans ce cadre, une attention particulière doit être allouée au problème d’agrégation spatiale dans les modèles d’affectation. Dans ces modèles, l’agrégation spatiale consiste à regrouper les observations individuelles enutilisant une description agrégée de l’espace, i.e. des zones. Par nature, l’utilisation d’une description agrégée à la place d’une représentation continue engendre une omission de l’information et de sa variabilité et donc un biais statistique dans la modélisation. C’est le cas par exemple avec l’utilisation des connecteurs de zones ou avec l’omission des trafics intrazones dans les modèles d’affectation.En reposant sur les zones comme unité spatiale de base, les modèles de transport recourent à l’utilisation des connecteurs de zones pour relier les centroïdes de zones au réseau de transport. Les connecteurs sont des liens fictifs qui modélisent les conditions moyennes d’entrée et de sortie du réseau de transport. Pour ce faire, la majorité des modèles de transport reposent sur une méthode simpliste sujette au problème d’agrégation spatiale. La présente thèse examine en détail l’impact de cette description simpliste sur les résultats et la qualité d’un modèle d’affectation des déplacements en transports en commun. Cette thèse propose aussi une nouvelle méthode de modélisation des connecteurs de zones afin de s’affranchir partiellement du biaisd’agrégation spatiale dans la modélisation des conditions d’accès au réseau des transports en commun.L’utilisation des zones comme unité spatiale de base a aussi pour conséquence l’omission des trafics intrazones de l’affectation des déplacements. Les trafics intrazones ont pour origine et pour destination la même zone et de ce fait ne sont pas pris en compte par les modèles standard d’affectation. Cette omission a souvent été ignorée et son impact sur la qualité de la modélisation demeure non évalué. Cette thèse développe une méthode stochastique pour l’évaluation de cet impact
Cities are complex systems that urban models can help to comprehend. From simplistic models to more sophisticated ones, urban models have pushed forward our understanding the urban phenomenon and its intricacies. In this context, models can be of great value to policy makers providing that these tools become practical. In this regard, research has put little emphasis on the practicality of urban models and their use under operational conditions.To date, urban models which rely on spatial aggregation are the closest possibility to come to practical models. For this reason, the spatially aggregated modeling framework is widely used. This framework is relatively practical when compared to other modeling frameworks like microsimulation. Nevertheless, spatial aggregation is a serious source of bias in these models. This is especially the case of Land-Use and Transport Interaction (LUTI) models and more particularly of Four Step Models.The current PhD is committed to the study of spatial aggregation issues in traffic assignment models. Traffic assignment is responsable for the computation of travel times and travel conditions of present and future travel demand. Accessibility measurement, which is at the core of LUTI models, is tightly dependent on traffic assignment modeling and outcomes. Any bias in traffic assignment is likely to corrupt the overall modeling framework. In this context, a special attention is to be paid to spatial aggregation in traffic assignment models.In traffic assignment, spatial aggregation consists in grouping observations using zones or traffic analysis zones instead of using a continuous representation of space. By design, aggregation bears an implicit omission in data variability and thus a potential bias if this omission is not random. This is the case with the definition of centroid connectors and the omission of intrazonal demand in traffic assignment. With the use of zones as the basic spatial units, transport models require the use of centroid connectors to attach zones to the transportation network. Centroid connectors are introduced to model average access and egress conditions to and from the network. Nevertheless, average accessibility conditions are found to be too crude to render accurately accessibility conditions as encountered by trip makers. The current PhD explores the extent of the impact of this spatial aggregation bias in the case of transit models and suggests a new modeling strategy to overcome such modeling errors.The use of zones as spatial units induces a loss of intrazonal data. The omission of intrazonal trips in traffic assignment models is an example of such omission. This research introduces an uncertainty framework to study the statistical impact of ignoring intrazonal trips in traffic assignment models. Findings from this research are used to design new assignment strategies that are more robust towards the omission bias and more generally towards the spatial aggregation bias

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Lenkei, Zsolt. "Crowdsourced traffic information in traffic management : Evaluation of traffic information from Waze." Thesis, KTH, Transportplanering, ekonomi och teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239178.

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The early observation and elimination of non-recurring incidents is a crucial task in trafficmanagement. The performance of the conventional incident detection methods (trafficcameras and other sensory technologies) is limited and there are still challenges inobtaining an accurate picture of the traffic conditions in real time. During the last decade,the technical development of mobile platforms and the growing online connectivity made itpossible to obtain traffic information from social media and applications based on spatialcrowdsourcing. Utilizing the benefits of crowdsourcing, traffic authorities can receiveinformation about a more comprehensive number of incidents and can monitor areaswhich are not covered by the conventional incident detection systems. The crowdsourcedtraffic data can provide supplementary information for incidents already reported throughother sources and it can contribute to earlier detection of incidents, which can lead tofaster response and clearance time. Furthermore, spatial crowdsourcing can help to detectincident types, which are not collected systematically yet (e.g. potholes, traffic light faults,missing road signs). However, before exploiting crowdsourced traffic data in trafficmanagement, numerous challenges need to be resolved, such as verification of the incidentreports, predicting the severity of the crowdsourced incidents and integration with trafficdata obtained from other sources.During this thesis, the possibilities and challenges of utilizing spatial crowdsourcingtechnologies to detect non-recurring incidents were examined in form of a case study.Traffic incident alerts obtained from Waze, a navigation application using the concept ofcrowdsourcing, were analyzed and compared with officially verified incident reports inStockholm. The thesis provides insight into the spatial and temporal characteristics of theWaze data. Moreover, a method to identify related Waze alerts and to determine matchingincident reports from different sources is presented. The results showed that the number ofreported incidents in Waze is 4,5 times higher than the number of registered incidents bythe Swedish authorities. Furthermore, 27,5 % of the incidents could have been detectedfaster by using the traffic alerts from Waze. In addition, the severity of Waze alerts isexamined depending on the attributes of the alerts.

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Wan, Kin-yung. "Biham-middleton-levine traffic model in different spatial dimensions /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20128538.

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Yue, Yang. "Spatial-temporal dependency of traffic flow and its implications for short-term traffic forecasting." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B35507366.

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Yue, Yang, and 樂陽. "Spatial-temporal dependency of traffic flow and its implications for short-term traffic forecasting." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B35507366.

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溫建勇 and Kin-yung Wan. "Biham-middleton-levine traffic model in different spatial dimensions." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B3122183X.

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Katzouraki, Antonia. "Communication Networks : Dynamic Traffic Distribution and Spatial Diffusion Disruptions." Thesis, Imperial College London, 2008. http://hdl.handle.net/10044/1/4374.

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This thesis concerns robust load allocation in communication networks. The main goal of this work is to avoid the situation in which the failure of a node (or nodes) causes a cascade of failures through an entire network, with a sequence of healthy nodes becoming overloaded and failing from picking up the slack from previously failed nodes. The network should remain functional even after some of the nodes have failed. In the dissertation we present a new methodology for dynamically distributing the load across a network so as to avoid the overloading of any of the networks' nodes. A numerical solution is proposed to solve this model and build a simulation tool. This numerical method adjusts the classic explicit form of Runge-Kutta 4th order in order to integrate graph principles and produce synchronized numerical solutions for each network element. Unlike most solutions in the literature, as for example, Motter et al. [2002], Motter and Adilson [2004], Liang et al. [2004], Schafer et al. [2006], Wang and Kim [2007] and Pinget et al. [2007] our methodology is generic in the sense that it works on any network topology. This means not only that it is applicable to a large range of networks, but also that it continues to be relevant after failure has destroyed part of a network, thereby changing the topology. In particular, geographical catastrophes can be of both random and intended types, taking place within a heterogeneous physical environment, on a civil (populated) area. Unlike most fault methodologies in the literature our methodology is generic in the sense that it simulates real-world geographic failure propagation towards any type of network which can be embedded to a two dimenional metric system [Chen and He, 2004], Liu et al. [2000], Callaway et al. [2000], Albert and Barabasi [2000]. It describes how physical one dimensional catastrophic waves spread in heterogeneous environments and how built-in resilience, within each network element determines its percentage of damage. We have tested our system on various randomly generated graphs with faults injected according to a model we have developed that simulates real-world geographic failure propagation. We present results from our dynamic traffic distribution methodology applied to networks, which have been either under attack or not. Throughout our case studies we prove that as soon as the topology is assigned the appropriate resources comparing to the load that it is to serve, our methodology successfully redistributes the load across the network and prevents a potential cascade failure. We either prevent the propagation of cascading failures or suggest recovery strategies after an unavoidable failure. Therefore, our methodology is instrumental in designing and testing reliable and robust networks.

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Munir, Said. "Spatial-temporal analysis of traffic-related ground level ozone." Thesis, University of Leeds, 2013. http://etheses.whiterose.ac.uk/5504/.

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Prolonged exposure to elevated levels of ozone (O3) has been proven to adversely affect human health, agricultural crops and building materials. Therefore, ground level O3 is currently regarded as one of the most harmful air pollutants, and identified as a priority pollutant in almost all national and international air quality legislations. Historically, O3 has been regarded as a rural issue because relatively high levels of O3-depleting species like NO from traffic and other combustion sources have helped to reduce its impact in urban area. However, more recently, and most likely as the result of increasingly more aggressive and effective management of NOx, urban O3 levels have begun to rise much more rapidly than ‘background O3’ in rural areas. As a result O3 pollution is becoming an increasing important urban issue. Therefore, the main aim of this PhD project is to characterise the temporal - spatial variability of ground level ozone. As in other studies, rural time-series of O3 and meteorological data and spatial parameters are considered as part of the analysis. However, here the analysis is extended using recently collected urban data, most notably co-located O3 and traffic related pollutant time-series and road traffic characteristics (from double loop counters), to provide more insight into the mechanisms driving traffic-related O3 trends. Traditionally, many researchers have used linear, mean-centred (and parametric) statistical methods to ozone. However, here, in light of the non-linear association between ozone and its potential covariates, the focus has been on non-parametric methods. In particular two methods have been extensively used, namely Quantile Regression and Generalised Additive Modelling. Such approaches provide a much more comprehensive description of O3 trends because they are better able to represent the distinctly different behaviour of O3 at concentration extremes. Statistical analysis shows that on average ozone concentrations are about 26 % higher in rural areas than in urban areas. The urban decrement varies at different quantiles of ozone concentrations and ranges from 10.5 μg/m3 (25%) to 21.56 μg/m3 (30%) at quantile 0.1 and 0.99, respectively. The results of quantile regression model show that up to 90 % of ozone variations between rural and urban sites can be explained with the help of road traffic characteristics. It is shown that the strength and nature (positive or negative) of the association between ozone and its covariates change at different levels of ozone. The model result shows that ozone levels increase towards north and decrease towards east in the UK. Ozone concentration appears to be negatively correlated with the distance from the coast within a range of 0 to 60 km, a trend associated with relatively less dry deposition of ozone molecules on water surfaces. Furthermore, there is a positive association between the altitude of monitoring site and ozone level, most likely due to local topographical effects. Ozone temporal trends show significant variability at different statistical metrics (e.g., mean, median, maximum and selected quantiles). Urban and rural trends have different rates and indicate that urban decrement has been decreasing over the period. Ozone trends during 1993 to 2011 and 2004 to 2011 showed different patterns, i.e., average ozone trends at both urban and rural sites are positive for the former and negative for the latter case. NOx trends have been stabilised during the last 8 years in urban areas and could have caused the ozone trends to change from positive to negative.

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Gebresilassie, Mesele Atsbeha. "Spatio-temporal Traffic Flow Prediction." Thesis, KTH, Geoinformatik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-212323.

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The advancement in computational intelligence and computational power and the explosionof traffic data continues to drive the development and use of Intelligent TransportSystem and smart mobility applications. As one of the fundamental components of IntelligentTransport Systems, traffic flow prediction research has been advancing from theclassical statistical and time-series based techniques to data–driven methods mainly employingdata mining and machine learning algorithms. However, significant number oftraffic flow prediction studies have overlooked the impact of road network topology ontraffic flow. Thus, the main objective of this research is to show that traffic flow predictionproblems are not only affected by temporal trends of flow history, but also by roadnetwork topology by developing prediction methods in the spatio-temporal.In this study, time–series operators and data mining techniques are used by definingfive partially overlapping relative temporal offsets to capture temporal trends in sequencesof non-overlapping history windows defined on stream of historical record of traffic flowdata. To develop prediction models, two sets of modeling approaches based on LinearRegression and Support Vector Machine for Regression are proposed. In the modelingprocess, an orthogonal linear transformation of input data using Principal ComponentAnalysis is employed to avoid any potential problem of multicollinearity and dimensionalitycurse. Moreover, to incorporate the impact of road network topology in thetraffic flow of individual road segments, shortest path network–distance based distancedecay function is used to compute weights of neighboring road segment based on theprinciple of First Law of Geography. Accordingly, (a) Linear Regression on IndividualSensors (LR-IS), (b) Joint Linear Regression on Set of Sensors (JLR), (c) Joint LinearRegression on Set of Sensors with PCA (JLR-PCA) and (d) Spatially Weighted Regressionon Set of Sensors (SWR) models are proposed. To achieve robust non-linear learning,Support Vector Machine for Regression (SVMR) based models are also proposed.Thus, (a) SVMR for Individual Sensors (SVMR-IS), (b) Joint SVMR for Set of Sensors(JSVMR), (c) Joint SVMR for Set of Sensors with PCA (JSVMR-PCA) and (d) SpatiallyWeighted SVMR (SWSVMR) models are proposed. All the models are evaluatedusing the data sets from 2010 IEEE ICDM international contest acquired from TrafficSimulation Framework (TSF) developed based on the NagelSchreckenberg model.Taking the competition’s best solutions as a benchmark, even though different setsof validation data might have been used, based on k–fold cross validation method, withthe exception of SVMR-IS, all the proposed models in this study provide higher predictionaccuracy in terms of RMSE. The models that incorporated all neighboring sensorsdata into the learning process indicate the existence of potential interdependence amonginterconnected roads segments. The spatially weighted model in SVMR (SWSVMR) revealedthat road network topology has clear impact on traffic flow shown by the varyingand improved prediction accuracy of road segments that have more neighbors in a closeproximity. However, the linear regression based models have shown slightly low coefficientof determination indicating to the use of non-linear learning methods. The resultsof this study also imply that the approaches adopted for feature construction in this studyare effective, and the spatial weighting scheme designed is realistic. Hence, road networktopology is an intrinsic characteristic of traffic flow so that prediction models should takeit into consideration.

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Nordlöv, Anna, and Niklas Lindqvist. "Network based spatial analysis of traffic accidents in Stockholm, Sweden." Thesis, KTH, Geoinformatik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188515.

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Rafferty, Paula S. "Spatial Analysis of North Central Texas Traffic Fatalities 2001-2006." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc33195/.

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A traditional two dimensional (planar) statistical analysis was used to identify the clustering types of North Central Texas traffic fatalities occurring in 2001-2006. Over 3,700 crash locations clustered in ways that were unlike other researched regions. A two dimensional (x and y coordinates) space was manipulated to mimic a one dimensional network to identify the tightest clustering of fatalities in the nearly 400,000 crashes reported from state agencies from 2003-2006. The roadway design was found to significantly affect crash location. A one dimensional (linear) network analysis was then used to measure the statistically significant clustering of flow variables of after dark crashes and daylight crashes. Flow variables were determined to significantly affect crash location after dark. The linear and planar results were compared and the one dimensional, linear analysis was found to be more accurate because it did not over detect the clustering of events on a network.

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Hussain, Etikaf. "Transit spatial gap identification: Exploiting big transit and traffic data." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/229971/1/Etikaf_Hussain_Thesis.pdf.

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This PhD exploits the big datasets from the road transport network to accurately model the transit supply and demand, the knowledge for which is used to identify evidence-based transit gaps. The effectiveness of the proposed novel modelling is demonstrated with the case study on the Brisbane network. The developed tool assists transit stakeholders in identifying regions where transit services can be significantly improved, leading to efficient and reliable transit services.

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Arnaud, Raphaëlle. "Méthodologie pour l'analyse d'une répartition modale et spatiale plus durable du trafic : application au trafic marchandises transalpin en Suisse /." [S.l.] : [s.n.], 2007. http://library.epfl.ch/theses/?nr=3700.

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Karimpour, Marzieh. "Ecological impacts of traffic noise - A national spatial assessment for Sweden." Thesis, KTH, Miljöbedömning och -förvaltning, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-99360.

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Increased utilization of terrestrial vehicles requires devotion of huge efforts to provide road infrastructure for increased accessibility. Meanwhile, there is no doubt that transportation infrastructure has major impacts on ecosystems, ecological processes and biodiversity at a landscape scale. These impacts result from habitat loss, fragmentation and degradation, spreading of invasive species, noise pollution, road mortality but also more unknown impacts such as altered isolation patterns. This study aims to picture probable impacts of noise in the current transportation infrastructure at a national scale in Sweden. The study involves mapping of potential conflict zones between roads and ecological networks by defining road-effect zones around roads based on traffic volume, surrounding vegetation type, land cover type and consequently noise level for each segment of the roads. Focal to this study are impacts of noise on areas of importance for nature conservation like key habitat areas, meadows, pastures and wetlands. A method called "Calculation of Road Traffic Noise" (CRTN) was selected for calculation of noise generated by traffic flows in Sweden .The analysis was performed by using data on roads from the National Road Database and SAMPERS, land cover data, data on biodiversity conservation, and national databases concerning nature values. The analysis was done with a spatial prediction model based on a Geographic Information System (GIS), using Sweden as a case study. According to the results, 813 ha of nature values are exposed to noise levels higher than 55 dB; same wise, 2190 ha of outlined areas with nature values are exposed to noise higher than 45 dB. The study provided baseline information and a broad prediction of main impacts on biodiversity at landscape and regional scales. Impact prediction of alternative planning scenarios will be facilitated using the GIS-based prediction models and assist decision making on allocation of future roads. Using such methods when discussing future localization of road infrastructure will be useful for integrated sustainability assessment and strategic environmental assessment. Thus, it provides possibilities for minimizing adverse impacts of infrastructure on biodiversity at an early planning stage.

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Oberoi, Kamaldeep Singh. "Modélisation spatio-temporelle du trafic routier en milieu urbain." Thesis, Normandie, 2019. http://www.theses.fr/2019NORMR075/document.

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Le domaine de la modélisation du trafic routier vise à comprendre son évolution. Dans les dernières années, plusieurs modèles du trafic ont été proposés dans l’objectif de géolocaliser les embouteillages au sein du trafic, détecter des motifs dans le trafic routier, estimer l’état du trafic etc. La plupart des modèles proposés considèrent le trafic routier en termes de ses constituants ou comme une entité agrégée en fonction de l’échelle choisie et expliquent l’évolution du trafic quantitativement en tenant compte des relations entre les variables de trafic comme le flot, la densité et la vitesse. Ces modèles décrivent le trafic en utilisant des données très précises acquises par différents capteurs. La précision des données rend son calcul coûteux en termes de ressources requises. Une des solutions à ce problème est la représentation qualitative du trafic routier qui réduit le nombre de ressources de traitement nécessaires. Puisque le trafic routier est un phénomène spatio-temporel, les modèles proposés pour représenter ce type de phénomène pourraient être appliqués dans le cas du trafic routier. Les modèles spatio-temporels, proposés par la communauté de l’Analyse Spatio-Temporelle, ont comme objectif la représentation d’un phénomène tant du point de vue qualitatif que quantitatif. Certains de ces modèles proposent une discrétisation des phénomènes modélisés en considérant un phénomène comme constitué d’entités. Appliquée au trafic routier, cette notion permet d’identifier différentes entités, comme les véhicules, les piétons, les bâtiments etc., qui le constituent. Ces entités influent sur l’évolution du trafic. Les modèles spatio-temporels qualitatifs définissent l’effet des différentes entités les unes sur les autres en terme de relations spatiales. L’évolution spatio-temporelle du phénomène modélisé est représenté par la variation temporelle de ces relations. La prise en compte des entités du trafic et des relations spatiales formalise une structure qui peut être représentée en utilisant un graphe, où les nœuds modélisent des entités et les arcs des relations spatiales. Par conséquent, l’évolution du trafic, modélisée via ce graphe, devient l’évolution du graphe et peut être représenté en terme de la variation de la structure du graphe ainsi que celle des attributs de ses nœuds et de ses arcs. Dans cette thèse, nous proposons une modélisation du trafic routier de ce type basée sur la théorie des graphes. Une des applications à la modélisation du trafic routier est la détection des motifs pertinents au sein du trafic. Dans les modèles du trafic existants, les motifs détectés sont statistiques et sont représentés en utilisant des caractéristiques numériques. Le modèle que nous pro posons dans cette thèse met en avant la structure représentant le trafic routier et peut donc être utilisé pour définir des motifs structurels du trafic qui prennent en compte des différentes entités du trafic et leurs relations. Ces motifs structurels sont sous-jacents à une modélisation sous forme de graphe dynamique. Dans cette thèse, nous proposons un algorithme pour détecter ces motifs structurels du trafic dans le graphe spatio-temporel représentant le trafic routier. Ce problème est formalisé comme celui de l’isomorphisme de sous-graphe pour des graphes dynamiques. L’algorithme proposé est évalué en fonction desdifférents paramètres de graphes
For past several decades, researchers have been interested in understanding traffic evolution, hence, have proposed various traffic models to identify bottleneck locations where traffic congestion occurs, to detect traffic patterns, to predict traffic states etc. Most of the existing models consider traffic as many-particle system, describe it using different scales of representation and explain its evolution quantitatively by deducing relations between traffic variables like flow, density and speed. Such models are mainly focused on computing precise information about traffic using acquired traffic data. However, computation of such precise information requires more processing resources. A way to remedy this problem is to consider traffic evolution in qualitative terms which reduces the required number of processing resources. Since traffic is spatio-temporal in nature, the models which deal with spatio-temporal phenomenon can be applied in case of traffic. Such models represent spatio-temporal phenomenon from qualitative as well as quantitative standpoints. Depending on the intended application, some models are able to differentiate between various entities taking part in the phenomenon, which proves useful in case of traffic since different objects like vehicles, buildings, pedestrians, bicycles etc., directly affecting traffic evolution, can be included in traffic models. Qualitative spatio-temporal models consider the effects of different entities on each other in terms of spatial relations between them and spatio-temporal evolution of the modeled phenomenon is described in terms of variation in such relations over time. Considering different traffic constituents and spatial relations between them leads to the formation of a structure which can be abstracted using graph, whose nodes represent individual constituents and edges represent the corresponding spatial relations. As a result, the evolution of traffic, represented using graph, is described in terms of evolution of the graph itself, i. e. change in graph structure and attributes of nodes and edges, with time. In this thesis, we propose such a graph model to represent traffic. As mentioned above, one of the applications of existing traffic models is in detecting traffic patterns. However, since such models consider traffic quantitatively, in terms of acquired traffic data, the patterns detected using such models are statistical (a term employed by Pattern Recognition researchers) in the sense that they are represented using numerical description. Since graph-based traffic model proposed in this thesis represents the structure of traffic, it can be employed to redefine the meaning of traffic patterns from statistical to structural (also a term from Pattern Recognition community). Structural traffic patterns include different traffic constituents and their inter-links and are represented using time-varying graphs. An algorithm to detect a given structural traffic pattern in the spatio-temporal graph representing traffic is proposed in this thesis. It formalizes this problem as subgraph isomorphism for time-varying graphs. In the end, the performance of the algorithm is tested using various graph parameters

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Hashimoto, Tomoyuki. "Spatial analysis of pedestrian accidents." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001072.

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Yao, Shenjun, and 姚申君. "Advances in spatial analysis of traffic crashes: the identification of hazardous road locations." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B50434445.

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The identification of hazardous road locations is important to the improvement of road safety. However, there is still no consensus on the best method of identifying hazardous road locations. While traditional methods, such as the hot spot methodology, focus on the physical distances separating road crashes only, the hot zone methodology takes network contiguity into consideration and treats contiguous road segments as hazardous road locations. Compared with the hot spot method, hot zone methodology is a relatively new direction and there still remain a number of methodological issues in applying the method to the identification of hazardous road locations. Hence, this study aims to provide a GIS-based study on the identification of crash hot zones as hazardous road locations with both link-attribute and event-based approaches. It first explores the general procedures of the two approaches in identifying traffic crash hot zones, and then investigates the characteristics of the two approaches by conducting a range of sensitivity analysis on defining threshold value and crash intensity with both simulated and empirical data. The results suggest that it is better to use a dissolved road network instead of a raw-link-node road network. The segmentation length and the interval of reference points have great impacts on the identification of hot zones, and they are better defined as 100 meters considering the stabilities of the performance. While employing a numerical definition to identify hot zones is a simple and effort-saving approach, using the Monte Carlo method can avoid selection bias in choosing an appropriate number as the threshold value. If the two approaches are compared, it is observed that the link-attribute approach is more likely to cause false negative problem and the event-based approach is prone to false positive problem around road junctions. No matter which method is used, the link-attribute approach requires less computer time in identifying crash hot zones. When a range of environmental variables have to be taken into consideration, the link-attribute approach is superior to the event-based approach in that it is easier for the link-attribute approach to incorporate environmental variables with statistical models. By investigating the hot zone methodology, this research is expected to enrich the theoretical knowledge of the identification of hazardous road locations and to practically provide policy-makers with more information on identifying road hazards. Further research efforts have to be dedicated to the ranking of hot zones and the investigation of false positive and false negative problems.
published_or_final_version
Geography
Doctoral
Doctor of Philosophy

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Razzaghi, Hesham M. "Spatial Analysis of Alcohol-related Injury and Fatal Traffic Crashes in Ohio." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1493979849390008.

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Silva, Patricia Maria de Oliveira e. "Contrôle spatio-temporel de la croissance filamenteuse chez Candida albicans." Thesis, Université Côte d'Azur (ComUE), 2018. http://www.theses.fr/2018AZUR4030.

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Candida albicans est un pathogène fongique opportuniste de l’Homme, qui peut causer des infections superficielles mais aussi systémiques chez les patients immunodéprimés. Sa virulence est associée à sa capacité de changer d’une forme bourgeonnante à une forme hyphale. La petite GTPase de type Rho, Cdc42, est critique pour la croissance filamenteuse et, sous forme activée, sa localisation est restreinte à l’extrémité des hyphes. J’ai utilisé un système photoactivable, constitué des domaines d’Arabidopsis thaliana Cry2PHR-CibN, pour contrôler le recrutement de Cdc42 constitutivement actif à la membrane plasmique. J'ai déterminé comment le photo-recrutement de Cdc42 constitutivement actif perturbe la croissance filamenteuse et où, quand et comment une nouvelle croissance filamenteuse est ré-initiée. Mes résultats démontrent que, lors du photo-recrutement de Cdc42 constitutivement actif, l'extension du filament cesse puis un nouveau site de croissance s’établit dans la cellule. La localisation de ce nouveau site de croissance est corrélée à la longueur du filament. J'ai étudié les mécanismes moléculaires qui sous-tendent le désassemblage du site de croissance initial et l'emplacement spécifique du nouveau site de croissance filamenteuse. Dans les hyphes en croissance, un «cluster» de vésicules, appelé Spitzenkörper, est localisé à l'extrémité du filament. Lors du photo-recrutement de Cdc42 constitutivement actif, un nouveau «cluster» de vésicules, de composition similaire à celui du Spitzenkörper initial, apparaît dans la cellule mère. J'ai suivi la dynamique du Spitzenkörper et la localisation de Cdc42 sous forme activée, des sites d'endocytose, des vésicules de sécrétion et des câbles d’actine suite à la perturbation du site de croissance initial dans le filament. Dans l’ensemble, mes résultats indiquent qu'il existe une compétition pour la croissance entre le Spitzenkörper et le «cluster» de vésicules qui se forme immédiatement après le photo-recrutement de Cdc42 constitutivement actif et qu'un axe de polarité dynamique peut être établi en l'absence de croissance directionnelle
Candida albicans is a fungal human pathogen that can cause life-threatening infections in immunocompromised patients, in part, due to its ability to switch between an oval budding form and a filamentous hyphal form. The small-Rho GTPase Cdc42 is crucial for filamentous growth and, in its active form, localizes as a tight cluster at the tips of growing hyphae. I have used a light-activated membrane recruitment system comprised of the Arabidopsis thaliana Cry2PHR-CibN domains to control the recruitment of constitutively active Cdc42 to the plasma membrane. I have determined how photorecruitment of constitutively active Cdc42 perturbs filamentous growth and where, when and how new filamentous growth is subsequently initiated. My results demonstrate that, upon photorecruitment of constitutively active Cdc42, filament extension is abrogated and a new growth site can be established in the cell. Location of a new filamentous growth site correlates with the length of the initial filament. I have investigated the molecular mechanisms that underlie the disassembly of an initial growth site and the specific location of the new filamentous growth site. In growing hyphae a cluster of vesicles, referred to as a Spitzenkörper, is localized at the tip of the filament. Upon photorecruitment of constitutively active Cdc42, a new cluster of vesicles, with a composition similar to that of the initial Spitzenkörper, appears in the mother cell. I have followed the dynamics of the Spitzenkörper, active Cdc42, sites of endocytosis, secretory vesicles and actin cables subsequent to disruption of the initial growth site in the filament. Taken together, my results suggest that there is competition for growth between the Spitzenkörper and the cluster of vesicles that forms immediately after the photorecruitment of constitutively active Cdc42 and that a dynamic polarity axis can be established in the absence of directional growth

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Li, Linhua. "A GIS-based Bayesian approach for analyzing spatial-temporal patterns of traffic crashes." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1766.

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Sener, Ipek Nese. "An Innovative Model Integrating Spatial And Statistical Analyses For A Comprehensive Traffic Accident Study." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606148/index.pdf.

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The negative social and economic results of traffic accidents are the most serious problems within the concept of traffic safety. Every year, unfortunately, a huge number of traffic accidents result in destructive losses. Especially, when the holiness of human life is concerned, traffic safety has an invaluable role for the traffic improvement strategies. In this manner, Turkey places one of the highest ranks regarding the growing rate and severity of traffic accidents that should be immediately taken under control. In this study, an innovative model that constructs a hybrid between the spatial and statistical analyses is developed in order to examine the importance of enhancing statistical analysis with georeferenced data and so location-based studies in traffic accident analysis. Meanwhile, the effects of road characteristic and environment are considered for exploring the integral role of roadway factor to the occurrence of accidents, and consequently for emphasizing easily applicable and controllable engineering safety measures. Because of the rare and random distribution of traffic accident data, logistic regression is used for the statistical part of the study in order to find the pairwise risk factors among the roadway and environmental parameters. After unifying these relative risk factors with the logic of Analytic Hierarchy Process, the finalized accident risk factors are attached to the digitized road characteristics map through Geographic Information Systems (GIS). The abilities of GIS in mapping, displaying and overlaying different data sets ensure to visualize high risked accident areas with their corresponding potential causal factors. The integration of statistical and spatial analyses is essential for developing appropriate and effective precautions in addition to its easily understandable, applicable and modifiable structure. Finally, the model is proven to be appropriate for both interpreting the existing traffic accident problem or potential future accidents and also developing comprehensive and reliable location-based safety studies.

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Oris, William Nathan. "Spatial Analysis of Fatal Automobile Crashes in Kentucky." TopSCHOLAR®, 2011. http://digitalcommons.wku.edu/theses/1119.

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Fatal automobile crashes have claimed the lives of over 33,000 people each year in the United States since 1995. As in any point event, fatal crash events do not occur randomly in time or space. The objectives of this study were to identify spatial patterns and hot spots in FARS (Fatal Analysis Reporting System) fatal crash events based on temporal and demographic characteristics. The methods employed included 1) rate calculation using FARS points and average daily traffic flow; 2) planar kernel density estimation of FARS crash events based on temporal and demographic attributes within the data; and 3) two case studies using network kernel density estimation along roadways to determine hot spots fatal crashes in Jefferson County and Warren County. Rate calculation analyses revealed that travel on roads with high speed limits and winding topography led to the highest number of crashes and highest rate of fatal crashesper 1,000 daily vehicles. Planar kernel density estimation results showed temporalpatterns, revealing that ‘hot spots’ and fatalities were highest in the summer, and typically occurred from 2pm-6pm on the weekends. Further, the 16 to 25 year age group was responsible for the most significant ‘hot spots’ and the most fatal accidents. Also showing that the most significant hot spots involving alcohol occurring in close proximity to meeting places such as bars and restaurants. Finally, results from the network kernel density estimation revealed that most hot spots were in high traffic areas of where majorr oads converged with secondary roads.

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Casapietra, Edoardo [Verfasser]. "Spatial Road Representation for Driving in Complex Scenes by Interpretation of Traffic Behavior / Edoardo Casapietra." Bielefeld : Universitätsbibliothek Bielefeld, 2019. http://d-nb.info/1176701444/34.

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Wang, Carter (Carter Robert Ren-Deh). "Urban transportation networks : analytical modeling of spatial dependencies and calibration techniques for stochastic traffic simulators." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82859.

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Thesis (S.M. in Transportation)--Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 79-82).
Exact numerical evaluation of the stationary joint queue-length distribution of a Markovian finite capacity network with arbitrary size and topology can be obtained numerically. Nonetheless, the main challenge to such an approach remains the dimensionality of the joint distribution, which is exponential in the number of queues. This thesis proposes an analytical approximation of the joint distribution with a dimension that is linear in the number of queues. The method decomposes the network into overlapping subnetworks. The state of each subnetwork is described aggregately, i.e. in terms of a reduced state space, while ensuring consistency with the disaggregate, i.e., full state space, distribution. This aggregation-disaggregation technique is proposed for the analysis of Markovian tandem finite capacity queueing networks. The model is validated. We present its use to address an urban traffic control problem, and show the added value of accounting for higher-order spatial between-queue dependency information in the control of congested networks. A second, distinct goal of this thesis is to examine the calibration of route choice parameters in microscopic traffic simulators. Automatically calibrating simulators using traffic counts requires describing the relationship between route choice and traffic flows. This thesis proposes an analytical finite capacity queueing model that accounts for the relationship between route choice and traffic flows. The method is embedded in a simulation-based optimization framework and applied to a calibration problem.
by Carter Wang.
S.M.in Transportation

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Räntilä, Maria, and Louise Tillander. "Hållbart resande i Malmö : En kvalitativ textanalys." Thesis, Linköpings universitet, Tema Miljöförändring, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157175.

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Urbaniseringen ökar globalt och med den ökar även bilanvändningen. Städer står inför problematiken att bilanvändningen har en negativ klimatpåverkan och försämrar livskvalitén för invånarna i staden. I och med detta måste biltransporter minska och de hållbara färdsätten öka. Avsikten med studien är att undersöka hur Malmö stad arbetar med hållbara transporter. Malmö är utvald då staden har påbörjat omställningen till mer hållbara resor och är i framkant i Sverige. Genom kvalitativ textanalys har fyra dokument angående det strategiska arbetet kring hållbara resor analyserats. Staden arbetar på olika sätt för att möta utmaningar med hållbara färdmedel såsom cykel, gång och kollektivtrafik. De hållbara transportmedlen ska prioriteras genom stadens utformning och beteendeändringar. Studien har visat på att Malmö arbetar med social, ekonomisk och miljömässig hållbarhet med en övervägande del på de sociala aspekterna. Staden prioriterar cykel som det främsta transportmedlet där tillgänglighet och närhet ska vara ledordet för staden. Det som anses saknas efter genomförandet av studien är fokus på samtliga stadsdelar och klimataspekten i relation till hållbara resor. Malmö stad vill vara en framstående stad i frågan om hållbara resor, men är det mest för att visa upp en grön sida av staden?
Urbanization is increasing globally and with it, the use of cars. Cities are now faced with the problem that cars have a negative climate impact and reduce the quality of life for the inhabitants of the city. Therefore, the use of cars must decrease and sustainable mobility increase. The purpose of the study is to investigate how Malmö city works with sustainable mobility. Malmö is selected due to being ahead in the transition to more sustainable mobility in Sweden. A qualitative text analysis on four documents regarding sustainable mobility has been conducted. Malmö works in various ways to meet the challenges with sustainable transportation means for example cycling, walking and public transport. The sustainable means of transport must be prioritized through the city's design and behavior changes. The study has shown that Malmö works with social, economic and environmental sustainability, with more focus on the social aspects. The city prioritized the bicycle as the main means of transport, where accessibility and proximity should be leading. What is considered missing after the implementation of the study is focus on all neighborhoods and the climate aspect in relation to sustainable mobility.

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Nassir, Neema. "Optimal Integrated Dynamic Traffic Assignment and Signal Control for Evacuation of Large Traffic Networks with Varying Threat Levels." Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/297042.

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This research contributes to the state of the art and state of the practice in solving a very important and computationally challenging problem in the areas of urban transportation systems, operations research, disaster management, and public policy. Being a very active topic of research during the past few decades, the problem of developing an efficient and practical strategy for evacuation of real-sized urban traffic networks in case of disasters from different causes, quickly enough to be employed in immediate disaster management scenarios, has been identified as one of the most challenging and yet vital problems by many researchers. More specifically, this research develops fast methods to find the optimal integrated strategy for traffic routing and traffic signal control to evacuate real-sized urban networks in the most efficient manner. In this research a solution framework is proposed, developed and tested which is capable of solving these problems in very short computational time. An efficient relaxation-based decomposition method is proposed, implemented for two evacuation integrated routing and signal control model formulations, proven to be optimal for both formulations, and verified to reduce the computational complexity of the optimal integrated routing and signal control problem. The efficiency of the proposed decomposition method is gained by reducing the integrated optimal routing and signal control problem into a relaxed optimal routing problem. This has been achieved through an insight into intersection flows in the optimal routing solution: in at least one of the optimal solutions of the routing problem, each street during each time interval only carries vehicles in at most one direction. This property, being essential to the proposed decomposition method, is called "unidirectionality" in this dissertation. The conditions under which this property exists in the optimal evacuation routing solution are identified, and the existence of unidirectionality is proven for: (1) the common Single-Destination System-Optimal Dynamic Traffic Assignment (SD-SODTA) problem, with the objective to minimize the total time spent in the threat area; and, (2) for the single-destination evacuation problem with varying threat levels, with traffic models that have no spatial queue propagation. The proposed decomposition method has been implemented in compliance with two widely-accepted traffic flow models, the Cell Transmission Model (CTM) and the Point Queue (PQ) model. In each case, the decomposition method finds the optimal solution for the integrated routing and signal control problem. Both traffic models have been coded and applied to a realistic real-size evacuation scenario with promising results. One important feature that is explored is the incorporation of evacuation safety aspects in the optimization model. An index of the threat level is associated with each link that reflects the adverse effects of traveling in a given threat zone on the safety and health of evacuees during the process of evacuation. The optimization problem is then formulated to minimize the total exposure of evacuees to the threat. A hypothetical large-scale chlorine gas spill in a high populated urban area (downtown Tucson, Arizona) has been modeled for testing the evacuation models where the network has varying threat levels. In addition to the proposed decomposition method, an efficient network-flow solution algorithm is also proposed to find the optimal routing of traffic in networks with several threat zones, where the threat levels may be non-uniform across different zones. The proposed method can be categorized in the class of "negative cycle canceling" algorithms for solving minimum cost flow problems. The unique feature in the proposed algorithm is introducing a multi-source shortest path calculation which enables the efficient detection and cancellation of negative cycles. The proposed method is proven to find the optimal solution, and it is also applied to and verified for a mid-size test network scenario.

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Touhbi, Saâd. "Élaboration d’un modèle multi-agents pour la génération synthétique de trafic : application à la mobilité urbaine de la ville de Marrakech." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS326.

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La génération de trafic est un processus indispensable dans la simulation du trafic. Ce processus permet de définir une série d’arrivées des véhicules dans une route. Cette série est prouvée aléatoire depuis 1930. Toutefois, sa distribution reflète à un niveau le comportement de conduite. L’objectif de notre thèse est de mettre en place un outil de génération de trafic permettant de définir le point d’arrivée du véhicule dans une route, identifier le choix de type du véhicule, définir son temps d’arrivée, et lui affecter une vitesse initiale. Une première étape faite dans ce sens est la modélisation d’arrivée, particulièrement le TIV, en mettant en place un protocole d’analyse de cette variable en quatre étapes : traitement des données d’arrivée des véhicules et calcul de la variable TIV, définir des échantillons représentant différents niveaux de trafic, le choix des modèles probabilistes et estimation des paramètres, comparaison des modèles estimés et les données empiriques. Une application numérique est effectuée sur quatre routes à trois voies chacune interrompue par un feu de signalisation à la ville de Marrakech. On en conclut que Le modèle de Pareto IV est non rejeté pour les différents niveaux de trafic. Le modèle Exponentiel est prouvé inadéquat dans les différents niveaux de trafic. Le modèle Log-Normal et le modèle de Pearson type III sont adéquats pour des niveaux de trafic intermédiaire et élevé. Ces résultats ont permis de tester l’utilisabilité de notre outil de génération de trafic sous différentes modalités. L’outil permet de générer un niveau trafic cohérent avec celui désiré. Une première intégration a été faite pour la simulation MarrakAir permettant d’estimer les polluants automobiles en se basant sur le trafic. Ceci afin d’éliminer la nécessité d’avoir des données ponctuelles sur l’arrivée des véhicules
Traffic generation is an important process in traffic simulation. It defines a series of vehicle arrival at a road. This series is proven to be random since 1930. However, its distribution explains at a level the behavior of the motorists. The goal of this thesis is to establish a traffic generation toolbox to produce a series of vehicle arrival characterized by their entry point, their type, arrival time and their initial speed. A first step was to establish an analysis protocol for the time headway (TH) of the arriving vehicles consisting of four steps : (i) data processing and calculating TH, (ii) sampling of data according to different levels of traffic, (iii) model choice and estimation, (iv) and the comparison of the estimated models and the empirical data. The application of the latter on four roads with three lanes interrupted by a signalized intersection showed that : The Pareto IV model is not rejected for all levels of traffic in these roads, the Exponential model is proven to be inadequate for all levels of traffic, the Log-Normal and Pearson III are best suited for intermediate to high levels of traffic. These results helped in testing the traffic generation toolbox proving that it generates coherent level of traffic compared to the desired configuration. The latter was integrated in the simulation MarrakAir that estimated the level of pollution based on traffic. This was done to eliminate the necessity to have punctual data from sensors about vehicle arrivals

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Cisse, Yahya Ibrahima. "Trafic aérien de passagers au Canada : analyse exploratoire d'un modèle origine-destination avec interactions spatiales." Master's thesis, Université Laval, 2015. http://hdl.handle.net/20.500.11794/26457.

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Ce mémoire revisite à l’aide de méthodes d’économétrie spatiale le modèle gravitaire PODM (Passenger Origin-Destination Model) que Transports Canada utilise pour prédire le trafic aérien domestique de passagers. Différents modèles spatiaux de panel sont estimés par maximum de vraisemblance et par la méthode des moments. Les résultats montrent que l’approche traditionnelle ne détecte pas d’effets de la distance entre l’origine et la destination sur le volume du trafic intérieur de passagers. Ce sont les caractéristiques de la région d’origine et de destination (PIB, revenu disponible, population) et les caractéristiques du trajet (prix moyen du billet, nombre de vols offerts) qui sont les déterminants les plus importants des flux de passagers. Dans les modèles spatiaux explorés, les interactions spatiales se révèlent d’importants déterminants aux côtés des caractéristiques locales. La prise en compte de ces effets spatiaux pourrait permettre d’améliorer les prévisions de trafic de passagers au Canada. Mots clés : interactions spatiales, données de panel, maximum de vraisemblance, méthodes des moments.

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Weimer, Silke. "Particle emission of traffic and wood combustion and its impact on spatial distributions of submicron particulate matter /." Zürich : ETH, 2008. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17995.

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Ekram, Al-Ahad Mohammad Yaseen. "Reduced visibility related crashes in Florida crash characteristics, spatial analysis and injury severity /." Orlando, Fla. : University of Central Florida, 2009. http://purl.fcla.edu/fcla/etd/CFE0002903.

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Dunn, Sarah, and Sean M. Wilkinson. "Increasing the resilience of air traffic networks using a network graph theory approach." Elsevier, 2015. https://publish.fid-move.qucosa.de/id/qucosa%3A72825.

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Air traffic networks are essential to today’s global society. They are the fastest means of transporting physical goods and people and are a major contributor to the globalisation of the world’s economy. This increasing reliance requires these networks to have high resilience; however, previous events show that they can be susceptible to natural hazards. We assess two strategies to improve the resilience of air traffic networks and show an adaptive reconfiguration strategy is superior to a permanent re-routing solution. We find that, if traffic networks have fixed air routes, the geographical location of airports leaves them vulnerable to spatial hazard.

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Nugent, Ayres Michelle V. "Spatial and Geochemical Techniques to Improve Exposure Assessment of Manganese in Windsor, Ontario." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20276.

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This study was conducted to investigate the urban geochemistry of the city of Windsor (Ontario) and to provide added source apportionment information to work being carried out by the Canadian government. The goal of this study was to investigate the distribution, spatial variation and sources of manganese in urban Windsor soil. The literature indicates that human exposure to high levels of manganese, via inhalation, can cause respiratory and/or neurological effects. At the outset of the present study it was first hypothesized that vehicular traffic was the dominant source of anthropogenic manganese. An alternative hypothesis was that there were multiple anthropogenic sources of manganese in Windsor. The sample collection scheme was designed to determine (1) the current and background soil concentrations of manganese in Windsor, (2) the spatial distribution of manganese in order to reveal sources of manganese, and (3) the manganese content of moss-sequestered airborne particles, which can potentially deposit onto the soil surface, using low-technology biomonitoring. The first phase of the study consisted of a preliminary soil survey which identified elevated areas of soil manganese concentrations. During this survey, the field efficiency of a field portable X-ray fluorescence (FPXRF) instrument, as well as sample preparation methods were evaluated. Efficiency of the FPXRF was determined by comparison to ICP-MS, a traditional trace element analysis method. The preliminary soil survey identified several areas of elevated (ranging from 884 to 2390 ppm) soil manganese which were further investigated during the second, more complete, soil survey. The moss biomonitoring technique of using moss bags was used to collect airborne particles for semi-quantitative analysis. Analysis of soil samples included total manganese and other trace elements, pH, moisture and carbon content, and manganese speciation. Urban Windsor soil manganese distribution revealed both natural and anthropogenic sources of soil manganese and three distinct soil sample types, transect, baseline and natural. In general, manganese in Windsor had a west-to-east trend of decreasing levels in soil and moss-sequestered airborne particles. The latter showed a modern-day elemental signature while the former (collocated soil) a legacy elemental signature. It was concluded that both the FPXRF instrument and the moss biomonitoring technique can be useful screening tools in studies of urban environments.

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DESHPANDE, NITIN PRAKASH. "SPATIAL DISTRIBUTION AND CHARACTERISTICS OF FATAL & INJURY CRASHES AT WORK ZONES IN OHIO'S INTERSTATES." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1115864347.

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Anbaroglu, B. "Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1408826/.

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Non-Recurrent Congestion events (NRCs) frustrate commuters, companies and traffic operators because they cause unexpected delays. Most existing studies consider NRCs to be an outcome of incidents on motorways. The differences between motorways and urban road networks, and the fact that incidents are not the only cause of NRCs, limit the usefulness of existing automatic incident detection methods for identifying NRCs on an urban road network. This thesis contributes to the literature by developing an NRC detection methodology to support the accurate detection of NRCs on large urban road networks. To achieve this, substantially high Link Journey Time estimates (LJTs) on adjacent links that occur at the same time are clustered. Substantially high LJTs are defined in two different ways: (i) those LJTs that are greater than a threshold, (ii) those LJTs that belong to a statistically significant Space-Time Region (STR). These two different ways of defining the term ‘substantially high LJT’ lead to different NRC detection methods. To evaluate these methods, two novel criteria are proposed. The first criterion, high-confidence episodes, assesses to what extent substantially high LJTs that last for a minimum duration are detected. The second criterion, the Localisation Index, assesses to what extent detected NRCs could be related to incidents. The proposed NRC detection methodology is tested for London’s urban road network, which consists of 424 links. Different levels of travel demand are analysed in order to establish a complete understanding of the developed methodology. Optimum parameter settings of the two proposed NRC detection methods are determined by sensitivity analysis. Related to the first method, LJTs that are at least 40% higher than their expected values are found to maintain the best balance between the proposed evaluation criteria for detecting NRCs. Related to the second method, it is found that constructing STRs by considering temporal adjacencies rather than spatial adjacencies improves the performance of the method. These findings are applied in real life situations to demonstrate the advantages and limitations of the proposed NRC detection methods. Traffic operation centres could readily start using the proposed NRC detection methodology. In this way, traffic operators could be able to quantify the impact of incidents and develop effective NRC reduction strategies.

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Desjardins, Pauline. "L'organisation spatiale du corridor du Canal de Lachine au 19e siècle." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ47606.pdf.

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Fischer, Manfred M., and Sucharita Gopal. "Neural Network Models and Interregional Telephone Traffic. Comparative Performance Comparisons between Multilayer Feedforward Networks and the Conventional Spatial Interaction Model." WU Vienna University of Economics and Business, 1992. http://epub.wu.ac.at/4206/1/WSG_DP_2792.pdf.

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42

Patterson, Joanne Louise. "Urban scale modelling of traffic and cycling flow using spatial analysis and an assessment of factors that influence cyclist behaviour." Thesis, Cardiff University, 2014. http://orca.cf.ac.uk/66970/.

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To understand and facilitate modal shift to more sustainable modes of transport there is a need to model accessibility and connectivity at an urban scale using data collection and modelling procedures that require less data and specialist input than traditional transport models. This research has used spatial analysis modelling procedures based on space syntax to investigate the potential to model aggregate traffic flows at an urban scale, and to investigate the potential to apply the same methodology to model both aggregate and individual cycle flows. Cyclist behaviour has been investigated through a questionnaire to support modelling work. The research has demonstrated that spatial analysis modelling is an effective means of representing urban scale motor traffic network, however, modifications to the model were required to achieve a correlation between modelled and measured motor traffic flow comparable to other modelling procedures. Boundary weighting was found to be effective at representing traffic crossing the boundary of an isolated urban sub-area, but was not so effective at an urban scale. Road weighting was found to be effective in improving model performance by representing traffic flows along routes according to a national classification scheme. It was demonstrated that these modelling principles could be used to represent an urban bicycle network and that the impact of the modification of infrastructure on relative flows of both cyclists and motor traffic could be accommodated. The modelling approach has the potential to be extremely useful at an early planning stage to represent changes to flows across the network. A survey of behaviour identified that cyclists modify their journey to use cycling facilities such as on-road lanes and off-road paths, or to avoid particular areas perceived to be less favourable for cyclists and that analysis indicates that it is difficult to predict (25% from survey) individual route choice. Results indicate that there were more opportunities related to route characteristics that could be influenced by infrastructure changes for occasional cyclists than for frequent/everyday cyclists.

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Chen, Yulu. "Spatial Temporal Analysis of Traffic Patterns during the COVID-19 Epidemic by Vehicle Detection using Planet Remote Sensing Satellite Images." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1609843145639886.

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Zhang, Yong. "Use of spatial models and the MCMC method for investigating the relationship between road traffic pollution and asthma amongst children." Thesis, University of Greenwich, 2000. http://gala.gre.ac.uk/8247/.

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This thesis uses two datasets: NCDS (National Child Development Study) and Bartholomew's Digital road map to investigate the relationship between road traffic pollution and asthma amongst children. A pollution exposure model is developed to provide an indicator of road traffic pollution. Also, a spatially driven logistic regression model of the risk of asthma occurrence is developed. The relationship between asthma and pollution is tested using this model. The power of the test has been studied. Because of the uncertainty of exact spatial location of subjects, given a post-code, we have considered error-in-variable model, otherwise known as measurement error model. A general foundation is presented. Inference is attempted in three approaches. Compared with models without measurement error, no improvement on log-likelihood is made. We suggest the error can be omitted. We also take a Bayesian approach to analyse the relationship. A discretized MCMC (Markov Chain Monte Carlo) is developed so that it can be used to estimate parameters and to do inference on a very complex posterior density function. It extends the simulated tempering method to 'multi-dimension temperature' situation. We use this method to implement MCMC on our models. The improvement in speed is remarkable. A significant effect of road traffic pollution on asthma is not found. But the methodology (spatially driven logistic regression and discretized MCMC) can be applied on other data.

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Rui, Zhu. "Moving Object Trajectory Based Intelligent Traffic Information Hub." Thesis, KTH, Geodesi och geoinformatik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-134944.

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Congestion is a major problem in most metropolitan areas and given the increasingrate of urbanization it is likely to be an even more serious problem in the rapidlyexpanding mega cities. One possible method to combat congestion is to provide in-telligent trafﬁc management systems that can in a timely manner inform drivers aboutcurrent or predicted trafﬁc congestions that are relevant to them on their journeys. Thedetection of trafﬁc congestion and the determination of whom to send in advance no-tiﬁcations about the detected congestions is the objective of the present research. Byadopting a grid based discretization of space, the proposed system extracts and main-tains trafﬁc ﬂow statistics and mobility statistics from the grid based recent trajectoriesof moving objects, and captures periodical spatio-temporal changes in the trafﬁc ﬂowsand movements by managing statistics for relevant temporal domain projections, i.e.,hour-of-day and day-of-week. Then, the proposed system identiﬁes a directional con-gestion as a cell and its immediate neighbor, where the speed and ﬂow of the objectsthat have moved from the neighbor to the cell signiﬁcantly deviates from the histori-cal speed and ﬂow statistics. Subsequently, based on one of two notiﬁcation criteria,namely, Mobility Statistic Criterion (MSC) and Linear Movement Criterion (LMC),the system decides which objects are likely to be affected by the identiﬁed conges-tions and sends out notiﬁcations to the corresponding objects such that the numberof false negative (missed) and false positive (unnecessary) notiﬁcations is minimized.The thesis discusses the design and DBMS-based implementation of the proposedsystem. Empirical evaluations on realistically simulated trajectory data assess the ac-curacy of the methods and test the scalability of the system for varying input sizes andparameter settings. The accuracy assessment results show that the MSC based systemachieves an optimal performance with a true positive notiﬁcation rate of 0.67 and afalse positive notiﬁcation rate of 0.05 when min prob equals to 0.35, which is superiorto the performance of the LMC based system. The execution time of- and the spaceused by the system scales linearly with the input size (number of concurrently movingvehicles) and the methods mutually dependent parameters (grid resolution r and RTlength l) that jointly deﬁne a spatio-temporal resolution. Within the area of a large city (40km by 40km), assuming a 60km/h average vehicle speed, the system, runningon a commodity personal computer, can manage the described congestion detectionand three-minute-ahead notiﬁcation tasks within real-time requirements for 2000 and20000 concurrently moving vehicles for spatio-temporal resolutions (r=100m, l=19)and (r=2km, l=3), respectively.

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El, Joubari Oumaima. "Mobility and traffic models for VANETs." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG018.

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La congestion routière devient de plus en plus un problème auquel il faut s'attaquer rapidement. Parmi les conséquences néfastes de la congestion figure les accidents de la route, l'empreinte environnementale, la vitesse commerciale des transports publics et les embouteillages. Il est donc nécessaire de développer un système de transport capable d'assurer la sécurité des usagers de la route et d'améliorer l'infrastructure de transport. Les systèmes de transport intelligents ont gagné d'importance comme une solution prometteuse pour réduire la congestion. Ces systèmes intègrent des technologies avancées pour fournir des services intelligents et robustes qui visent à prévenir les incidents routiers susceptibles de menacer la vie des passagers. L'une des exigences les plus rigoureuses de ces applications est la transmission fiable et sans risque des paquets de données. VANET est conçu spécifiquement pour fournir une infrastructure de communication sans fil pour que les véhicules et les équipements routiers puissent échanger des données de trafic. La caractéristique particulière de ce réseau est la forte mobilité des nœuds du réseau qui entraîne des changements fréquents dans la topologie et la densité. Ceci peut impacter les réseaux et par conséquent ne pas répondre aux exigences des applications de sécurité. Dans cette thèse, nous traitons spécifiquement les méthodes d'accès pour les VANETs qui sont basées sur la méthode TDMA. TDMA s'est avérée être la technique d'accès la plus appropriée pour ces réseaux car elle permet à un seul nœud d'accéder au canal à tout moment. Cependant, les protocoles classiques TDMA ne sont pas adaptés aux réseaux fortement dynamiques et leur utilisation dans un environnent véhiculaire peut engendrer des collisions d'accès et une utilisation inéquitable des ressources. Par conséquent, une bonne compréhension de la mobilité permettra de concevoir et d'évaluer des méthodes d'accès qui soient efficaces même dans un environnement mobile. Bien que l'on trouve des modèles de mobilité fidèles dans la littérature, ils ne parviennent pas à reproduire avec précision certains aspects de la mobilité des véhicules. Le comportement du trafic est influencé par plusieurs facteurs tels que l'architecture de la route, les limitations de vitesse, les règles de circulation et le comportement individuel des véhicules. Ceux-ci doivent être inclus dans un modèle de mobilité afin d'obtenir des résultats fidèles. Dans ce contexte, nous développons dans cette thèse des modèles stochastiques à base de chaînes de Markov basés sur des traces réelles de véhicules collectées par les RSUs en utilisant la communication V2X pour émuler le comportement des véhicules sur des routes urbaines et autoroutières. Les modèles proposés présentent le double avantage de modéliser et de prévoir le trafic. En utilisant une technique de résolution numérique, la densité du trafic, la longueur des files d'attente, le temps du trajet et le temps d'attente sont estimés. La densité de trafic estimée est ensuite exploitée pour développer une méthode d'accès basé sur TDMA appelée TA-TDMA qui a pour but principal de réduire les collisions d'accès et améliorer l'utilisation des ressources à travers la prédiction de mobilité et le clustering. Pour évaluer la performance de la méthode proposée, un outil de simulation de trafic basé sur les files d'attente a été développé à l'aide du logiciel SimEvents. Ce modèle de simulation permet de générer des mesures synthétiques importantes pour l'évaluation des performances des réseaux routiers. Le protocole TA-TDMA a ensuite été implémenté et comparé avec le protocole VeMAC, en considérant différents scénarios et environnements. La solution proposée a montré de meilleurs résultats par rapport au protocole VeMAC en termes d'efficacité et de robustesse face aux changements topologiques
The ever-growing traffic congestion is becoming a serious issue that needs to be urgently addressed. The growing number of traffic accidents, the environmental footprint of transport, commercial speed of public transportation and traffic jams are few examples of the adverse consequences of congestion. This calls for an upgrade of the transport system in order to ensure road users safety and effectively enhance the transportation infrastructure. ITS have grown in importance as a promising solution to the congestion issue. These systems rely on the most advanced technologies to provide intelligent and robust safety services that seek to prevent road incident that may threaten the life of passengers. One of the most stringent requirements of these applications is an error-free and reliable transmission of data packets. VANET were designed specifically to provide a wireless communication infrastructure to allow vehicles and road equipment to exchange traffic data. The particular feature of this network is the highly dynamic mobility which results in frequent changes in the topology and density of the network. This has negative effects on the network performance which does not allow to cater safety applications requirements. In this thesis, we address specifically channel access methods for VANET that are based on TDMA method. TDMA has been proven the most suitable access technique for VANET as it allows a single node to access the channel at any time slot. However, conventional TDMA-based protocols might encounter difficulties in a dynamic networks such as access collision and unfair use of resources. Hence, a good understanding of mobility will allow the design and evaluation of channel access methods that are efficient and robust even in a mobile environment. Although faithful mobility models are found in the literature, they fail to accurately capture some aspects of vehicular mobility. The traffic behaviour is influenced by several factors such as road layout, speed limits, traffic rules and individual vehicle’s behaviour. Consequently, it is compulsory to include all these features in a mobility model for accurate results. In this context, we develop in this thesis stochastic Markov chain models based on real vehicle traces collected by RSUs using V2X communication to emulate vehicular behaviour in both urban and highway roads. The proposed models have the twofold benefit of modelling and predicting traffic. Using a direct numerical resolution technique, traffic density, waiting queue lengths, travel times and delays are predicted. The predicted traffic density is then exploited to design a Traffic-aware TDMA channel access method that aims to reduce access collisions and enhance resource utilization through mobility prediction and clustering. To evaluate the performance of the proposed method, a queue-based mobility simulation framework was developed using the SimEvents toolbox. The simulation framework allows the generation of synthetic measures relevant to the assessment of road network performance. The TA-TDMA MAC protocol was then implemented and compared with an existing MAC protocol called VeMAC, under different scenarios and environments. The proposed solution has shown better results than the VeMAC protocol in terms of efficiency and robustness against topological changes

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Lacerda, Cléber Jean. "ANÁLISE DE DADOS GEORREFERENCIADOS PARA OBTER A DISTRIBUIÇÃO ESTATÍSTICA ESPACIAL DAS VÍTIMAS FATAIS EM ACIDENTES DE TRÂNSITO EM GOIÂNIA." Pontifícia Universidade Católica de Goiás, 2014. http://localhost:8080/tede/handle/tede/2456.

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Made available in DSpace on 2016-08-10T10:40:23Z (GMT). No. of bitstreams: 1 Cleber Jean Lacerda.pdf: 3814885 bytes, checksum: b263f1cb09d2491d541c4fc217a2f7ff (MD5) Previous issue date: 2014-09-16
The purpose of this search work is to analyze spatially traffic lethal accidents in Goiânia-City, from a geo-referenced database using geographic information system with spatial statistics tools systems, such as intensity estimator of Rosenblatt-Parzen, the Moran`s global autocorrelation index and local. With as spatial analysis aims to verify the existence of spatial autocorrelation of traffic accidents and the presence of data grouped (clusters).
O propósito deste trabalho é analisar espacialmente os acidentes de trânsito com vítimas fatais, no município de Goiânia, a partir de uma base de dados georreferenciada utilizando sistemas de informação geográfica, associados a ferramentas de estatística espacial, como o estimador de intensidade de Rosenblatt-Parzen, os índices de autocorrelação global e local de Moran. Com tais técnicas de análise espacial, pretende- -se verificar a existência de autocorrelação espacial dos acidentes de trânsito e a presença de agrupamentos (clusters).

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Chen, Yan. "Spatial Analysis of Fatal Automobile Crashes in Nashville, TN, 2001-2011." TopSCHOLAR®, 2013. http://digitalcommons.wku.edu/theses/1300.

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With increasing levels of motor vehicle ownership, automobile crashes have become a serious public issue in the U.S. and around the world. Knowing when, where, and how traffic accidents happen is critical in order to ensure road safety and to plan for adequate road infrastructure. There is a rich body of literature pertaining to time-related fatal crashes, most of which focuses on non-spatial factors such as a driver’s visibility at night, drinking and drug use, and road conditions. These studies provide a theoretical basis for understanding the causes of crashes from a non-spatial perspective, and a number of traffic laws and policies consequently have been enacted to minimize the impacts of non-spatial factors. Over the past few years, advances in Geographic Information Systems (GIS) have greatly enhanced our ability to analyze traffic accidents from a spatial perspective. This study aims to fill a void in traffic safety studies by comparing and analyzing the differences in the spatial distribution of fatal crashes based on temporal factors, specifically in three periods: 1) day and night; 2) A.M. rush hours and P.M. rush hours; and 3) weekdays and weekends. With the Nashville Metropolitan Area as the study area, the research utilized a number of spatial point-pattern analysis (SPPA) methods, including planar KDE, planar global auto K function, network global cross K functions, and network local cross K functions. All fatal crashes in the Nashville area were found to be clustered and generally follow the patterns of average daily traffic flow. All time-based subtypes of fatal crashes also were found to be concentrated within the central urban area of Nashville, mostly along major roads, and especially near major road intersections and highway interchanges. No notable spatial differences were detected among the subtypes of fatal crashes when applying network global cross K function. However, with the help of the network local cross K function, some localized spatial differences were identified. Some specific locations of hotspots of nighttime and P.M. rush hour fatal crashes were found not to be at the same locations as those at of daytime and A.M. rush hour fatal crashes, respectively. The approach adopted in this study not only provides a new way to analyze spatial distribution of spatial point events such as fatal crashes, but it also can be applied readily to real-world applications. A good understanding of where these spatial differences are should help various agencies practice effective measures and policies in order to improve road conditions, reduce traffic accidents, and ensure road safety.

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Chilakamarri, Venkata Srinivasa Ravi Chandra. "SPATIO-TEMPORAL ANALYSES FOR PREDICTION OF TRAFFIC FLOW, SPEED AND OCCUPANCY ON I-4." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3508.

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Traffic data prediction is a critical aspect of Advanced Traffic Management System (ATMS). The utility of the traffic data is in providing information on the evolution of traffic process that can be passed on to the various users (commuters, Regional Traffic Management Centers (RTMCs), Department of Transportation (DoT), ... etc) for user-specific objectives. This information can be extracted from the data collected by various traffic sensors. Loop detectors collect traffic data in the form of flow, occupancy, and speed throughout the nation. Freeway traffic data from I-4 loop detectors has been collected and stored in a data warehouse called the Central Florida Data Warehouse (CFDW[trademark symbol]) by the University of Central Florida for the periods between 1993-1994 and 2000 - 2003. This data is raw, in the form of time stamped 30-second aggregated data collected from about 69 stations over a 36 mile stretch on I-4 from Lake Mary in the east to Disney-World in the west. This data has to be processed to extract information that can be disseminated to various users. Usually, most statistical procedures assume that each individual data point in the sample is independent of other data points. This is not true to traffic data as they are correlated across space and time. Therefore, the concept of time sequence and the layout of data collection devices in space, introduces autocorrelations in a single variable and cross correlations across multiple variables. Significant autocorrelations prove that past values of a variable can be used to predict future values of the same variable. Furthermore, significant cross-correlations between variables prove that past values of one variable can be used to predict future values of another variable. The traditional techniques in traffic prediction use univariate time series models that account for autocorrelations but not cross-correlations. These models have neglected the cross correlations between variables that are present in freeway traffic data, due to the way the data are collected. There is a need for statistical techniques that incorporate the effect of these multivariate cross-correlations to predict future values of traffic data. The emphasis in this dissertation is on the multivariate prediction of traffic variables. Unlike traditional statistical techniques that have relied on univariate models, this dissertation explored the cross-correlation between multivariate traffic variables and variables collected across adjoining spatial locations (such as loop detector stations). The analysis in this dissertation proved that there were significant cross correlations among different traffic variables collected across very close locations at different time scales. The nature of cross-correlations showed that there was feedback among the variables, and therefore past values can be used to predict future values. Multivariate time series analysis is appropriate for modeling the effect of different variables on each other. In the past, upstream data has been accounted for in time series analysis. However, these did not account for feedback effects. Vector Auto Regressive (VAR) models are more appropriate for such data. Although VAR models have been applied to forecast economic time series models, they have not been used to model freeway data. Vector Auto Regressive models were estimated for speeds and volumes at a sample of two locations, using 5-minute data. Different specifications were fit--estimation of speeds from surrounding speeds; estimation of volumes from surrounding volumes; estimation of speeds from volumes and occupancies from the same location; estimation of speeds from volumes from surrounding locations (and vice versa). These specifications were compared to univariate models for the respective variables at three levels of data aggregation (5-minutes, 10 minutes, and 15 minutes) in this dissertation. For data aggregation levels of [less than]15 minutes, the VAR models outperform the univariate models. At data aggregation level of 15 minutes, VAR models did not outperform univariate models. Since VAR models were used for all traffic variables reported by the loop detectors, this made the application of VAR a true multivariate procedure for dynamic prediction of the multivariate traffic variables--flow, speed and occupancy. Also, VAR models are generally deemed more complex than univariate models due to the estimation of multiple covariance matrices. However, a VAR model for k variables must be compared to k univariate models and VAR models compare well with AutoRegressive Integrated Moving Average (ARIMA) models. The added complexity helps model the effect of upstream and downstream variables on the future values of the response variable. This could be useful for ATMS situations, where the effect of traffic redistribution and redirection is not known beforehand with prediction models. The VAR models were tested against more traditional models and their performances were compared against each other under different traffic conditions. These models significantly enhance the understanding of the freeway traffic processes and phenomena as well as identifying potential knowledge relating to traffic prediction. Further refinements in the models can result in better improvements for forecasts under multiple conditions.
Ph.D.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Civil Engineering PhD

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Ouyang, Xutong. "Exploring the attributes relevant to accidents between vehicles and unprotected road users, taking Stockholm as an example." Thesis, KTH, Geoinformatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278990.

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Traffic accidents is one of the major causes of fatalities and economic loss around the world. Thus, there is an urgent need for a better understanding about the factors that contribute to accidents so that the accidents can be prevented in the future. The research objective of this thesis is to analyze the traffic accidents between vehicles and unprotected road users (pedestrians and bicycles) in Stockholm, finding spatial distribution patterns, related attributes and examining relationships between accidents and a number of vehicle flows. The data is first analyzed with general statistical analysis to examine the basic characteristics. There is no apparent trend of change among the number of accidents per year, while the numbers of accidents happening from May to October is higher than the rest of the year except for July due to less traffic during holiday period. Most traffic accidents occur in overcast weather, on a dry road surface, or during the day. In the spatial analysis part of the thesis, Global Moran’s I is used to detect whether there is an attribute-related spatial distribution pattern. Hot spot analysis is then applied on the clustered attributes to find significant hot and cold spots over the study area. The conclusions are that road surface conditions and occurrence time during day/night are two related factors that influence traffic accidents while weather is not considered a related attribute since the accidents distribute randomly in terms of weather, of which it is difficult to obtain temporally-aligned, detailed local information for further analysis. Different parameters are selected and discussed during the process. When calculating the distance between two accidents in traffic accident analysis, Manhattan distance is more appropriate than Euclidean distance since traffic accidents are restricted to the road network. The distance band determines scales of analysis tools, with 50 meters on an intersection and 500 meters for a larger region in Stockholm. Most hot spots arise at intersections and roundabouts where different types of traffic flows meet each other. The result of the relationships between traffic accidents and different types of vehicle flows shows that the correlation coefficients between number of traffic accidents and traffic flows are low, meaning that there is no obvious correlation between them, which is also proved by the scatter plots. Poisson regression model is applied on the traffic accident data. As a result, high-risk and low-risk areas in Stockholm are pointed out. Some are consistent with the hot-spot analysis result.

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