In this work, we focus on two important goals of the transmission of scalable video over the Internet. The first goal is to provide high quality video to end users and the second one is to properly design networks and predict network performance for video transmission based on the characteristics of existing video traffic. Rate-distortion (R-D) based schemes are often applied to improve and stabilize video quality; however, the lack of R-D modeling of scalable coders limits their applications in scalable streaming. Thus, in the first part of this work, we analyze R-D curves of scalable video coders and propose a novel operational R-D model. We evaluate and demonstrate the accuracy of our R-D function in various scalable coders, such as Fine Granular Scalable (FGS) and Progressive FGS coders. Furthermore, due to the time-constraint nature of Internet streaming, we propose another operational R-D model, which is accurate yet with low computational cost, and apply it to streaming applications for quality control purposes. The Internet is a changing environment; however, most quality control approaches only consider constant bit rate (CBR) channels and no specific studies have been conducted for quality control in variable bit rate (VBR) channels. To fill this void, we examine an asymptotically stable congestion control mechanism and combine it with our R-D model to present smooth visual quality to end users under various network conditions. Our second focus in this work concerns the modeling and analysis of video traffic, which is crucial to protocol design and efficient network utilization for video transmission. Although scalable video traffic is expected to be an important source for the Internet, we find that little work has been done on analyzing or modeling it. In this regard, we develop a frame-level hybrid framework for modeling multi-layer VBR video traffic. In the proposed framework, the base layer is modeled using a combination of wavelet and time-domain methods and the enhancement layer is linearly predicted from the base layer using the cross-layer correlation.

The majority of current internet traffic is based on TCP. With the emergence of new applications, especially new multimedia applications, however, UDP-based traffic is expected to increase. Furthermore, multimedia applications have sparkled the development of protocols responding to congestion while behaving differently from TCP. As a result, network traffc is expected to become more and more diverse. The increasing link capacity further stimulates new applications utilizing higher bandwidths of future. Besides the traffic diversity, the network is also evolving around new technologies. These trends in the Internet motivate our research work. In this dissertation, modeling and estimation techniques of heterogeneous traffic at a router are presented. The idea of the presented techniques is that if the observed queue length and packet drop probability do not match the predictions from a model of responsive (TCP) traffic, then the error must come from non-responsive traffic; it can then be used for estimating the proportion of non-responsive traffic. The proposed scheme is based on the queue length history, packet drop history, expected TCP and queue dynamics. The effectiveness of the proposed techniques over a wide range of traffic scenarios is corroborated using NS-2 based simulations. Possible applications based on the estimation technique are discussed. The implementation of the estimation technique in the Linux kernel is presented in order to validate our estimation technique in a realistic network environment.

The majority of current internet traffic is based on TCP. With the emergence of new applications, especially new multimedia applications, however, UDP-based traffic is expected to increase. Furthermore, multimedia applications have sparkled the development of protocols responding to congestion while behaving differently from TCP. As a result, network traffc is expected to become more and more diverse. The increasing link capacity further stimulates new applications utilizing higher bandwidths of future. Besides the traffic diversity, the network is also evolving around new technologies. These trends in the Internet motivate our research work. In this dissertation, modeling and estimation techniques of heterogeneous traffic at a router are presented. The idea of the presented techniques is that if the observed queue length and packet drop probability do not match the predictions from a model of responsive (TCP) traffic, then the error must come from non-responsive traffic; it can then be used for estimating the proportion of non-responsive traffic. The proposed scheme is based on the queue length history, packet drop history, expected TCP and queue dynamics. The effectiveness of the proposed techniques over a wide range of traffic scenarios is corroborated using NS-2 based simulations. Possible applications based on the estimation technique are discussed. The implementation of the estimation technique in the Linux kernel is presented in order to validate our estimation technique in a realistic network environment.

Network simulation is an indispensable tool for studying Internet-scale networks due to the heterogeneous structure, immense size and changing properties. It is crucial for network simulators to generate representative traffic, which is necessary for effectively evaluating next-generation network protocols and applications. With network simulation, we can make a distinction between foreground traffic, which is generated by the target applications the researchers intend to study and therefore must be simulated with high fidelity, and background traffic, which represents the network traffic that is generated by other applications and does not require significant accuracy. The background traffic has a significant impact on the foreground traffic, since it competes with the foreground traffic for network resources and therefore can drastically affect the behavior of the applications that produce the foreground traffic. This dissertation aims to provide a solution to meaningfully generate background traffic in three aspects. First is realism. Realistic traffic characterization plays an important role in determining the correct outcome of the simulation studies. This work starts from enhancing an existing fluid background traffic model by removing its two unrealistic assumptions. The improved model can correctly reflect the network conditions in the reverse direction of the data traffic and can reproduce the traffic burstiness observed from measurements. Second is scalability. The trade-off between accuracy and scalability is a constant theme in background traffic modeling. This work presents a fast rate-based TCP (RTCP) traffic model, which originally used analytical models to represent TCP congestion control behavior. This model outperforms other existing traffic models in that it can correctly capture the overall TCP behavior and achieve a speedup of more than two orders of magnitude over the corresponding packet-oriented simulation. Third is network-wide traffic generation. Regardless of how detailed or scalable the models are, they mainly focus on how to generate traffic on one single link, which cannot be extended easily to studies of more complicated network scenarios. This work presents a cluster-based spatio-temporal background traffic generation model that considers spatial and temporal traffic characteristics as well as their correlations. The resulting model can be used effectively for the evaluation work in network studies.

Advances in oil extraction technology such as hydraulic fracturing have improved capabilities to extract and produce oil in the Bakken and Three Forks shale formations located in North Dakota, Montana, Manitoba, and Saskatchewan. From 2004 to the present, there has been a significant increase in oil rigs and new oil wells in these areas, resulting in increased impacts to the local, county, state, and federal roadway network. Traditional methods of rural traffic forecasting using an established growth rate are not sufficient under the changing traffic levels. The goal of this research is to develop a traffic model that will improve segment specific traffic forecasts for use in highway design and planning. The traffic model will consist of five main components: 1) a Geographic Information Systems (GIS) network model of local, county, state and federal roads, 2) a truck costing model for use in estimating segment specific user costs, 3) a spatial oil location model to estimate future oil development areas, 4) a series of mathematical programming models to optimize a multi-region oil development area for nine individual input/output movements, and 5) an aggregation of multiple routings to segment specific traffic levels in a GIS network model.

The last ten years, online multiplayer games have become very popular. During the same time period mobile terminals and cellular networks have undergone a tremendous technical evolution. Therefore it is natural to wonder why we have not seen an online mobile multiplayer gaming revolution yet. The answer to this question is of great value for companies selling mobile systems. This answer is important in order to understand how to fill up today’s empty networks with traffic and what kind of traffic these games will generate. This thesis is a continuation of Mattias Åkervik’s thesis. It gives the reader an understanding of what kind of wireless technologies are on the market today and how they perform. Given this performance background, some suitable games were chosen to examine how they perform over a particular cellular network and to determine the perceived gaming quality that a user experience. The thesis also examines the particular packet traffic characteristics generated by these games to gain a better understanding of how to better adapt cellular networks towards gaming. Finally the market will be analyzed. Not only how large this potential market is, but to examine if there are some market issues preventing the revolution in network cellular on-line multiplayer games. / De senaste tio åren har multiplayer gaming blivit väldigt populärt. Under samma tidsperiod har cellulära terminaler och de mobile nätverken genomgått en stor teknisk evolution. Därför kan man undra varför inte mobile multiplayer gaming har slagit igenom än. Svaret på denna fåga är värdefullt för föreatag som säljer mobila system, men även mobiloperatörerna, dådet kan ge en hint hur man bättre kan fylla ut dagens tomma 3G system med trafik. Detta examensarbete är en fortsättning på Mattias Åkerviks arbete. Det ger läsaren en förståelse i dagens trådlösa teknologier och vad de kan prestera. Med detta i bagaget kommer ett antal spel undersökas och hur deras spelupplevelse influeras av begräsningarna som de mobila nätverken har. Arbetet kommer också att behandla vilken trafik dessa spel genererar, då detta kan ge en bättre förståelse i hur man kan anpassa nätverken i ett gamingsyfte. Slutligen kommer marknaden att analyseras. Inte bara det potentiella värdet av gamingmarknaden, men även om det finns några marknadsstrukturella orsaker som bromsar utvecklingen av mobila multiplayer spel.

This research effort first characterizes the trucks traveling along US highways by analyzing data from Interstate 81. It is hypothesized that I-81 is typical of US highways and thus can provide some insight into typical truck characteristics. These truck characteristics are important for the development of an exhaustive vehicle performance procedure. Analysis was done based on data collected at the Troutville weigh station. The characterization involves an analysis of vehicle class distribution, GVW (Gross Vehicle Weight) distribution, vehicle volume distribution, Average Weight on Tractive Axle (AWTA), and typical weight-to-power ratios. The thesis then assembles a database of systematic field data that can be utilized for the validation of vehicle performance models. This database is unique because it was conducted in a controlled field environment where the vehicle is only constrained by its dynamics. Using the assembled field database, a simple constant power vehicle dynamics model for estimating maximum vehicle acceleration levels based on a vehicle's tractive effort and aerodynamic, rolling, and grade resistance forces was tested and validated. In addition, typical model input parameters for different vehicle, pavement, and tire characteristics are included in the thesis. The model was found to predict vehicle speeds at the conclusion of the travel along the section to within 5 km/h (3.1 mi/h) of field measurements, thus demonstrating the validity and applicability of the model. Finally, the research effort introduces the concept of variable power in order to enhance current state-of-the-art vehicle dynamics models and capture the build-up of power as a vehicle engages in gearshifts at low travel speeds. The proposed enhancement to the current state-of-practice vehicle dynamics model allows the model to reflect typical vehicle acceleration behavior more accurately. Subsequently, the model parameters are calibrated using field measurements along a test roadway facility. / Master of Science

The St. Charles streetcar is an important transit line in the city of New Orleans, with about 65,000 people living within a ½ mile walking distance from it. However, the line experiences a very high streetcar/automobile crash rate due in large part to the large number of grade vehicle crossings over the tracks that lack signalization. Through traffic modeling, the closure of many of these vehicle crossings and the diversion of automotive traffic to the remaining, signalized crossings is analyzed to determine traffic impacts on street network. The result is a modest increase in traffic, about 7-8%, at the remaining signalized intersections.

Existing macroscopic models for the flow of traffic treat traffic as a continuum or employ techniques similar to those used in the kinetic theory of gases. Spurious two- way propagation of disturbances that are physically unacceptable are predicted by continuum models for the flow of traffic. The number of vehicles in a typical section of a freeway does not justify traffic being treated as a continuum. It is also important to recognize that the basic premises of kinetic theory are not appropriate for the flow of traffic. A model for the flow of traffic that does not treat traffic as a continuum or use notions from kinetic theory is developed in this dissertation and corroborated with traffic data collected from the sensors deployed on US 183 freeway in Austin, Texas, USA. The flow of traffic exhibits distinct characteristics under different conditions and reflects the congestion during peak hours and relatively free motion during off-peak hours. This requires one to use different governing equations to describe the diverse traffic characteristics, namely the different traffic flow regimes of response. Such an approach has been followed in this dissertation. An observer based on extended Kalman filtering technique has been utilized for the purpose of estimating the traffic state. Historical traffic data has been used for model calibration. The estimated model parameters have consistent values for different traffic conditions. These esti- mated model parameters are then subsequently used for estimation of the state of traffic in real-time. A short-term traffic state forecasting approach, based on the non-continuum traffic model, which incorporates weighted historical and real-time traffic information has been developed. A methodology for predicting trip travel time based on this approach has also been developed. Ten and fifteen minute predictions for traffic state and trip travel time seem to agree well with the traffic data collected on US 183.

nÃo hà / A modelagem do trÃfego veicular em grandes Ãreas urbanas à uma ferramenta fundamental na anÃlise do desempenho de novas estratÃgias e polÃticas de gerÃncia e controle de trÃfego, idealizadas para potencializar a eficiÃncia do sistema de transportes. Com o uso de simuladores de trÃfego à possÃvel realizar estudos sobre os impactos de diferentes alternativas de intervenÃÃo, tais como a implementaÃÃo ou duplicaÃÃo de vias, alteraÃÃes na circulaÃÃo viÃria, entre outras aplicaÃÃes. Entretanto, os simuladores mais utilizados foram desenvolvidos em outros paÃses e geralmente nÃo representam o comportamento do trÃfego das redes viÃrias urbanas brasileiras, sendo, portanto necessÃria a calibraÃÃo dos seus parÃmetros. Com a evoluÃÃo tecnolÃgica dos modelos de microssimulaÃÃo de trÃfego, o processo de calibraÃÃo tornou-se ainda mais difÃcil devido ao maior nÃvel de complexidade dos modelos, o que implica no aumento do nÃmero de parÃmetros a serem calibrados. Diante do exposto e a fim de permitir a utilizaÃÃo de microssimuladores de trÃfego na modelagem de malhas viÃrias urbanas brasileiras, torna-se indispensÃvel o uso de mÃtodos computacionais de otimizaÃÃo que garantam, de forma eficaz, a calibraÃÃo simultÃnea de uma grande quantidade de parÃmetros. Essa dissertaÃÃo de mestrado busca verificar a aplicabilidade de algoritmos genÃticos para calibraÃÃo de modelos de microssimulaÃÃo do trÃfego veicular em malhas viÃrias urbanas como as encontradas no cenÃrio nacional. Ao final dessa pesquisa, concluiu-se que o mÃtodo de calibraÃÃo utilizado revelou-se eficiente considerando os resultados atingidos no estudo de caso realizado. AlÃm disto, acredita-se que a metodologia aqui proposta juntamente com os valores encontrados para os parÃmetros do microssimulador e do algoritmo genÃtico possam ser utilizados em outras malhas viÃrias urbanas, desde que estas apresentem caracterÃsticas semelhantes as da malha viÃria aqui simulada. / Traffic simulation modeling of mid-sized and big cities is a fundamental tool to analyze the performance of strategies idealized to maximize efficiency of urban transport systems. Traffic microsimulation models allow for foreseeing the impacts of different alternatives of solution, such as creation or duplication of lanes, reversion of flow direction, and implementation of priority lanes for public transport. However, the main microsimulation software packages available were developed in other countries, what means that they usually do not suit traffic behavior found in Brazilian urban centers, so parameter calibration is necessary to better adjust the models to local reality. Advances in computational capabilities have allowed traffic microsimulation models incorporate greater amount of parameters to be calibrated, so the process of calibration has become relatively more complex. Therefore, the use of computational methods to optimize the parametersâ values simultaneously is indispensable. This Masterâs thesis aims to verify the applicability of genetic algorithms to calibrate microsimulation traffic models in urban Brazilian networks. At the end of this study, it was concluded that the calibration method used proved to be efficient. It is believed that the proposed methodology to calibrate the microsimulation software and the genetic algorithm, as well as the final configuration of the genetic algorithm selected, may be applied to other urban networks, provided that they present similar characteristics to the studied network.