Adverse weather has a direct effect on traffic congestion and the time delay on roads. Weather conditions today are changing rapidly and are more likely to have a severe effect on traffic in the future. Although different measures have been taken to mitigate these conditions, it is important to study the impact of these events on road conditions and traffic flow. For example, the surface of a road is affected by snow, compacted snow and ice. The objective of this thesis is to characterize the effect of road surface conditions on traffic flow. To date, traffic flow under adverse weather conditions has not been characterized. A macroscopic traffic flow model based on the transition velocity distribution is proposed which characterizes traffic behavior during traffic alignment under adverse weather conditions. The model proposed realistically characterizes the traffic flow based on snow, compacted snow, and ice. Results are presented which show that this model provides a more accurate characterization of traffic flow behavior than the well known Payne-Whitham model. / Graduate

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High-speed transmission over contemporary communication networks has drawn many research efforts. Traffic scheduling schemes which play a critical role in managing network transmission have been pervasively studied and widely implemented in various practical communication networks. In a sophisticated communication system, a variety of applications co-exist and require differentiated Quality-of-Service (QoS). Innovative scheduling schemes and hybrid scheduling disciplines which integrate multiple traditional scheduling mechanisms have emerged for QoS differentiation. This study aims to develop novel analytical models for commonly interested scheduling schemes in communication systems under more realistic network traffic and use the models to investigate the issues of design and development of traffic scheduling schemes. In the open literature, it is commonly recognized that network traffic exhibits self-similar nature, which has serious impact on the performance of communication networks and protocols. To have a deep study of self-similar traffic, the real-world traffic datasets are measured and evaluated in this study. The results reveal that selfsimilar traffic is a ubiquitous phenomenon in high-speed communication networks and highlight the importance of the developed analytical models under self-similar traffic. The original analytical models are then developed for the centralized scheduling schemes including the Deficit Round Robin, the hybrid PQGPS which integrates the traditional Priority Queueing (PQ) and Generalized Processor Sharing (GPS) schemes, and the Automatic Repeat reQuest (ARQ) forward error control discipline in the presence of self-similar traffic. Most recently, research on the innovative Cognitive Radio (CR) techniques in wireless networks is popular. However, most of the existing analytical models still employ the traditional Poisson traffic to examine the performance of CR involved systems. In addition, few studies have been reported for estimating the residual service left by primary users. Instead, extensive existing studies use an ON/OFF source to model the residual service regardless of the primary traffic. In this thesis, a PQ theory is adopted to investigate and model the possible service left by selfsimilar primary traffic and derive the queue length distribution of individual secondary users under the distributed spectrum random access protocol.

Si la pertinence de la prise en compte de la météorologie dans la gestion du trafic est bien admise, son intégration dans les outils d'aide à la décision et les stratégies de contrôle représente encore un enjeu réel pour les gestionnaires d'infrastructures. En effet, cette avancée semble légitimée par les effets significatifs d'une météorologie dégradée sur la sécurité des usagers et le comportement des conducteurs. Ainsi, au niveau de la sécurité, un sur-risque d'accident a été mis en évidence par de nombreux travaux. Or, l'étiologie de ce risque augmenté ne permet pas seulement de démontrer l'impact d'évènements météorologiques extrêmes et ponctuels (ex : tempêtes de neige), mais égalementcelui de phénomènes récurrents (ex : la pluie). La baisse de la sécurité des conducteurs se traduit concrètement par un changement de comportements des usagers (vitesses, temps inter-véhiculaires) mais aussi du flot de véhicules en général (vitesse, débit, concentration), ceci influant de manière conséquente sur la mobilité. De fait, la pluie représente ainsi la seconde cause de congestion ponctuelle.Pourtant, malgré ces enjeux indéniables, les effets de la météorologie sur le trafic demeurent mal quantifiés et ne sont guère intégrés à la modélisation ou l'estimation du trafic. Ce travail de thèse se propose ainsi de contribuer à une meilleure compréhension des effets météorologiques sur le trafic, en se focalisant sur des phénomènes de précipitations en milieu interurbain. Partant d'un état de l'art de l'impact météorologique sur le trafic, il nous est apparu que les études existantes, par leurs carences, soulèvent le besoin de fonder une méthodologie d'analyse plus rigoureuse. Cette méthodologie, une fois clairement définie, a ensuite été appliquée à des données opérationnelles. Elle nous a permis de quantifier les effets de la pluie à plusieurs niveaux selon l'échelle de représentation abordée : au niveau microscopique, considérant le comportement individuel des conducteurs, les analyses statistiques mettent en lumière des effets sur les vitesses, les temps et les distances inter-véhiculaires. Ces effets se reflètent au niveau macroscopique (celui du flot de véhicules) avec des variations de débits, de vitesses du flot et, de façon générale, de l'ensemble des paramètres formant le diagramme fondamental du trafic. Les résultats empiriques nous semblent ainsi ouvrir la voie à l'intégration du phénomène météorologique à la modélisation du trafic.Partant, nous avons développé, à ce stade de notre travail, une contribution théorique à la modélisation du trafic se fondant sur une formulation Vlasov qui permet de dériver un modèle macroscopique à deux équations à partir d'une formulation cinétique. Le modèle ainsi proposé offre un cadre propice à l'intégration d'un paramètre météorologique. La discrétisation numérique du modèle s'effectue à l'aide d'une méthode à pas fractionnaire qui permet de traiter successivement le terme source et la partie homogène du système. Pour la partie homogène du système, nous avons fait l'usage d'un schéma de type Lagrange+remap. Le comportement du modèle, couplé à une équation de transport sur les temps inter-véhiculaires, a ensuite été illustré à travers une série d'expérimentations numériques qui ont mis en évidence ses aptitudes face à des conditions météorologiques changeantes.Dans un ultime volet, notre travail s'est orienté vers de futures applications en temps réel qui se placeraient dans un cadre bayesien d'assimilation de données. Le défi à relever est celui de l'estimation en ligne du vecteur d'état du trafic au fur et à mesure de l'arrivée de nouvelles observations. Une méthode de filtrage particulaire (Monte Carlo séquentielle) nous a paru judicieuse à mobiliser, car elle s'adapte bien à la problématique du trafic routier. Plusieurs scénarios fondés sur des données opérationnelles permettent ensuite de montrer les bénéfices de l'intégration du phénomène météorologique à de telles approches. Une meilleure connaissance du phénomène météorologique doit ainsi mener à son insertion dans les modèles de trafic qui forment le substrat des outils d'aide à la décision destinés aux gestionnaires.Le travail proposé ouvre donc des perspectives pour le développement de stratégies de gestion de trafic météo-sensibles. / The integration of the weather effects into decision support tools and real time traffic management strategies represents a critical need for all road operators. The motivations are clear because of the significant effects of adverse weather on road safety and drivers' behaviors. At a safety level, the increase of the crash frequency and severity has been highlighted by several studies. This increase of the crash risk does not concern only extreme weather events, such as winter storms, but also recurring events like rain. The changes in drivers' behaviors (decrease of speeds, headways) andtraffic flow dynamics (speed, flow, density) lead to significant consequences from a mobility point of view : thus, rain represents the second largest cause of non recurring congestion (15 \%) after incidents.In spite of this context, the effects of adverse weather on traffic are not well quantified and, above all, not integrated into traffic modelling and estimation. The presented thesis research aims at contributing to a better understanding of the meteorological effects on traffic by focusing on precipitation events at interurban sections. From a literature review of the meteorological impact on traffic, we have underlined a need of a standardized methodology. Such a standardized methodology for the rain impact quantification is proposed and applied to real data. It enables aquantification of the rain effects at different levels, according to the scale of representation : at a microscopic level, the statistical analyses highlight changes in drivers speeds, time headways. Those effects reflect on the macroscopic level of traffic flow with changes in speed, flows, and, in a general way, in all the parameters composing the fundamental diagram of traffic. Hence, the empirical results pave the way for integrating the meteorological phenomenon into traffic modelling.Next, we propose a theoretical contribution to traffic modelling, based on a Vlasov formulation, which enables the derivation of a two equations macroscopic model. The proposed model offers a relevant framework for the integration of a meteorological parameter. Regarding the numerical discretization, we propose a fractionnal step method allowing to deal successively with the source terme and the homogeneous part of the system. We develop a Lagrange+remap scheme for the homogeneous part of the system. The model behaviour is illustrated through several numerical experiments which highlight the model features faced with changing meteorological conditions.In the last chapter, an effort towards future online applications is put forward. Within a Bayesian framework for data assimilation, the goal resides in the online estimation of the traffic state vector given current measurements. Based on real world data, some scenarios show the benefits of the integration of the meteorology into such approaches. Thus, a better knowledge of the weather impact on traffic leads to its integration into traffic models and will enable the improvement of decision support tools for road operators. The proposed work opens perspectives for the development ofweather-responsive traffic management strategies.

Differential-algebraic equations (DAEs) result in many areas of science and engineer- ing. In this thesis, numerical methods for solving DAEs are compared for two prob- lems, energy-economic models and traffic flow models. An energy-economic model is presented based on the Hubbert model of oil production and is extended to include economic factors for the first time. Using numerical methods to simulate the DAE model, the resulting graphs break the symmetry of the traditional Hubbert curve. For the traffic flow models, a numerical method is developed to solve the steady-state flow pattern including the linearly unstable regime, i.e. solutions which cannot be found with an initial value solver. / UOIT

Differential-algebraic equations (DAEs) result in many areas of science and engineer- ing. In this thesis, numerical methods for solving DAEs are compared for two prob- lems, energy-economic models and traffic flow models. An energy-economic model is presented based on the Hubbert model of oil production and is extended to include economic factors for the first time. Using numerical methods to simulate the DAE model, the resulting graphs break the symmetry of the traditional Hubbert curve. For the traffic flow models, a numerical method is developed to solve the steady-state flow pattern including the linearly unstable regime, i.e. solutions which cannot be found with an initial value solver.

This thesis describes the design and implementation of a traffic generator that can simulate the traffic of tens of thousands of networking devices from a given traffic model. It is designed to handle traffic models created with Unified Traffic Modelling. The traffic generator is then evaluated and different solutions are compared in an effort to find the best solution for each issue. This thesis is meant to serve as a guideline for future development of traffic generators by providing insight into the problems faced during the development of one.

High-speed transmission over contemporary communication networks has drawn many research efforts. Traffic scheduling schemes which play a critical role in managing network transmission have been pervasively studied and widely implemented in various practical communication networks. In a sophisticated communication system, a variety of applications co-exist and require differentiated Quality-of-Service (QoS). Innovative scheduling schemes and hybrid scheduling disciplines which integrate multiple traditional scheduling mechanisms have emerged for QoS differentiation. This study aims to develop novel analytical models for commonly interested scheduling schemes in communication systems under more realistic network traffic and use the models to investigate the issues of design and development of traffic scheduling schemes. In the open literature, it is commonly recognized that network traffic exhibits self-similar nature, which has serious impact on the performance of communication networks and protocols. To have a deep study of self-similar traffic, the real-world traffic datasets are measured and evaluated in this study. The results reveal that selfsimilar traffic is a ubiquitous phenomenon in high-speed communication networks and highlight the importance of the developed analytical models under self-similar traffic. The original analytical models are then developed for the centralized scheduling schemes including the Deficit Round Robin, the hybrid PQGPS which integrates the traditional Priority Queueing (PQ) and Generalized Processor Sharing (GPS) schemes, and the Automatic Repeat reQuest (ARQ) forward error control discipline in the presence of self-similar traffic. Most recently, research on the innovative Cognitive Radio (CR) techniques in wireless networks is popular. However, most of the existing analytical models still employ the traditional Poisson traffic to examine the performance of CR involved systems. In addition, few studies have been reported for estimating the residual service left by primary users. Instead, extensive existing studies use an ON/OFF source to model the residual service regardless of the primary traffic. In this thesis, a PQ theory is adopted to investigate and model the possible service left by selfsimilar primary traffic and derive the queue length distribution of individual secondary users under the distributed spectrum random access protocol.

In this dissertation, we study macroscopic traffic flow modeling for intelligent transportation systems. Based on the characteristics of traffic flow evolution, and the requirement to realistically predict and ameliorate traffic flow in high traffic regions, we consider traffic flow modeling for intelligent transportation systems. Four major traffic flow modeling issues, that is, accurately predicting the spatial adjustment of traffic density, the traffic behavior on a long infinite road and on a road having egress and ingress to the flow, affect of driver behavior on traffic flow, and the route merit are investigated. The spatial adjustment of traffic density is investigated from a velocity adjustment perspective. Then the traffic behavior based on the safe distance and safe time is studied on a long infinite road for a transition and uniform flow. The traffic flow transition behavior is also investigated for egress and ingress to the flow having a regulation value which characterizes the driver response. The variation of regulation value refines the traffic velocity and density distributions according to a slow or aggressive driver response. Further, the influence of driver behavior on traffic flow is studied. The driver behavior includes the physiological and psychological response. In this dissertation, route merits are also developed to reduce the trip time, pollution and fuel consumption. Performance results of the proposed models are presented. / Graduate / 0543, 0544, 0548 / khanz@uvic,ca