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Densifying cities continuously call for new construction, renovation and demolition projects, each generating vast amounts of heavy goods vehicle (HGV) transports. However, how construction transportation affects the urban traffic network remains largely unexplored. This study addresses spatiotemporal network impacts from construction transport, by leveraging traditional traffic and transport simulation. To this end, a framework is presented including (i) a simulation model to compute traffic effects caused by varying off-site construction site transport demands, and (ii) conceptual applications of the simulation model showing construction logistic planning strategies to mitigate congestion disturbances. Simulations are conducted in MATSim using detailed secondary datasets describing site-specific transport arrivals from a case of six projects in Norrköping, Sweden. Subsequently, increasing transport demands were assessed on various time-window arrival scenarios against the baseline schedule, which can be used as decision support in urban planning. Results highlight how rigorous construction transport planning avoiding peak-traffic hours can significantly alleviate traffic congestion. This study also emphasizes the need to combine all simultaneous construction projects’ demands when evaluating disturbances on city-level, alongside the impact on individual links and microenvironments. This study adds knowledge by visualizing the traffic impact during urban transformation.

The shift toward electric vehicles requires the development of an extensive public electric charging infrastructure. With the aim of simulating hundreds of configurations for charging stations, street direc tions, crossing, etc., we need to find the best solution in short periods of time to predict and prevent traffic congestion. Thus, we study dif ferent models to discretize and manage vehicle movements using a syn chronous cellular automata, with an emphasis in reducing the amount of (frequently accessed) memory and execution time, and improving the thread parallelism. This is guided by the classical lemma of computer architecture “make the common case fast”, thus optimizing those code sections where most of the execution time is spent. Experiments carried out for microscopic traffic simulations indicate that compiled languages increase run-time efficiency by more than 70×. Then several strategies are studied, such as storing future velocities of each vehicle so that neigh bor vehicles can benefit from this information. Using a single 12-core PC, we get to a total run-time for a unidimensional simulation that is very close to that reached by supercomputers composed of thousands of cores that use interpreted languages. This may also greatly reduce the energy consumed. Although some performance degradation may occur when complex situations are introduced (crossroads, traffic lights, etc.), this degradation would not be significant if the length of the streets were large enough

Densifying cities continuously call for new construction, renovation and demolition projects, each generating vast amounts of heavy goods vehicle (HGV) transports. However, how construction transportation affects the urban traffic network remains largely unexplored. This study addresses spatiotemporal network impacts from construction transport, by leveraging traditional traffic and transport simulation. To this end, a framework is presented including (i) a simulation model to compute traffic effects caused by varying off-site construction site transport demands, and (ii) conceptual applications of the simulation model showing construction logistic planning strategies to mitigate congestion disturbances. Simulations are conducted in MATSim using detailed secondary datasets describing site-specific transport arrivals from a case of six projects in Norrköping, Sweden. Subsequently, increasing transport demands were assessed on various time-window arrival scenarios against the baseline schedule, which can be used as decision support in urban planning. Results highlight how rigorous construction transport planning avoiding peak-traffic hours can significantly alleviate traffic congestion. This study also emphasizes the need to combine all simultaneous construction projects’ demands when evaluating disturbances on city-level, alongside the impact on individual links and microenvironments. This study adds knowledge by visualizing the traffic impact during urban transformation.

Densifying cities continuously call for new construction, renovation and demolition projects, each generating vast amounts of heavy goods vehicle (HGV) transports. However, how construction transportation affects the urban traffic network remains largely unexplored. This study addresses spatiotemporal network impacts from construction transport, by leveraging traditional traffic and transport simulation. To this end, a framework is presented including (i) a simulation model to compute traffic effects caused by varying off-site construction site transport demands, and (ii) conceptual applications of the simulation model showing construction logistic planning strategies to mitigate congestion disturbances. Simulations are conducted in MATSim using detailed secondary datasets describing site-specific transport arrivals from a case of six projects in Norrköping, Sweden. Subsequently, increasing transport demands were assessed on various time-window arrival scenarios against the baseline schedule, which can be used as decision support in urban planning. Results highlight how rigorous construction transport planning avoiding peak-traffic hours can significantly alleviate traffic congestion. This study also emphasizes the need to combine all simultaneous construction projects’ demands when evaluating disturbances on city-level, alongside the impact on individual links and microenvironments. This study adds knowledge by visualizing the traffic impact during urban transformation.

Densifying cities continuously call for new construction, renovation and demolition projects, each generating vast amounts of heavy goods vehicle (HGV) transports. However, how construction transportation affects the urban traffic network remains largely unexplored. This study addresses spatiotemporal network impacts from construction transport, by leveraging traditional traffic and transport simulation. To this end, a framework is presented including (i) a simulation model to compute traffic effects caused by varying off-site construction site transport demands, and (ii) conceptual applications of the simulation model showing construction logistic planning strategies to mitigate congestion disturbances. Simulations are conducted in MATSim using detailed secondary datasets describing site-specific transport arrivals from a case of six projects in Norrköping, Sweden. Subsequently, increasing transport demands were assessed on various time-window arrival scenarios against the baseline schedule, which can be used as decision support in urban planning. Results highlight how rigorous construction transport planning avoiding peak-traffic hours can significantly alleviate traffic congestion. This study also emphasizes the need to combine all simultaneous construction projects’ demands when evaluating disturbances on city-level, alongside the impact on individual links and microenvironments. This study adds knowledge by visualizing the traffic impact during urban transformation.

There is an increasing recognition of the need for additional guidance for utilizing simulation tools, considering the increasing complexity of the transportation system, the interactions of several factors in impacting system performance, and the increasing sophistication of strategies, technologies, and applications considered to increase system performance. This project provides guidance and directions to improve the Florida Department of Transportation (FDOT) microsimulation modeling practice, including: the identification of the current and potential FDOT applications of transportation system simulation and associated needs; assessment of existing simulation manuals and guidance in Florida and providing recommendations for additions to the manual; assessment of the ability of available microsimulation platforms and applications to meet the simulation needs in Florida; provision of direction for the application of multiresolution simulation as part of the modeling practice in Florida; exploration of the development of simulation modeling clearinghouse practices; and identification of FDOT training needs in relation to the use of transportation system simulation.

In this thesis, a convolutional neural network is used to model the behaviour of individual vehicles on a stretch of the U.S. 101 highway during rush hour. This model is then extended to model the collective behaviour of all vehicles on the stretch of road and a 15 minute simulation is carried out. Using an initial vehicle layout, images displaying the position of nearby vehicles and information of vehicles entering the road, the simulation performs well for the first five minutes, while the performance deteriorates for the subsequent 10 minutes., Det finns olika typer av så kallade artificiella neurala nätverk som används till olika saker. En av dessa kallas för konvolutionella neurala nätverk och används till att bearbeta bilder. Denna typ av artificiella neurala nätverk används bland annat för klassificering av bilder och för att styra självkörande bilar. I den här uppsatsen används ett konvolutionellt neuralt nätverk för att försöka förutspå hur en bil ska bete sig, baserat på en bild uppifrån som visar var andra bilar befinner sig i närheten av den aktuella bilen. Sedan försöks det förutspås hur alla bilar på en del av en motorväg i USA beter sig i rusningstrafik. Även om det går att delvis förutspå beteendet hos bilarna, upptäcks det att det inte räcker med enbart en bild uppifrån utan att det krävs ytterligare information.

The industrial network infrastructures are transforming to a horizontal architecture to enable data availability for advanced applications and enhance flexibility for integrating new tech-nologies. The uninterrupted operation of the legacy systems needs to be ensured by safeguarding their requirements in network configuration and resource management. Network traffic modeling is essential in understanding the ongoing communication for resource estimation and configuration management. The presented work proposes a two-step approach for modeling aggregated traffic classes of brownfield installation. It first detects the repeated work-cycles and then aims to identify the operational states to profile their characteristics. The performance and influence of the approach are evaluated and validated in two experimental setups with data collected from an industrial plant in operation. The comparative results show that the proposed method successfully captures the temporal and spatial dynamics of the network traffic for characterization of various communication states in the operational work-cycles.

To know the entry time of bus to a bus stop is the fundamental data each travel clients need to know. Sitting tight for quite a while at a bus stop debilitates the individuals to depend upon open transports. In this paper, we display a framework which can foresee the landing time of bus to a specific bus stop considering the ongoing parameters that influences the travel time of transport. With transport module, the ongoing parameters that influence travel time are persistently gathered and used to foresee the bus arrival time at different transport stops. A portable application is created to help the questioning clients in getting the arrival time of bus to a specific stop. As there will be postpone in travel time because of the vehicles on street, street movement is displayed utilizing M/G/1 lining hypothesis to ascertain the delay created by other vehicles going in the same street. Server predicts the arrival time on the real time basis by bus module and the data shown in the database of server.

To know the entry time of bus to a bus stop is the fundamental data each travel clients need to know. Sitting tight for quite a while at a bus stop debilitates the individuals to depend upon open transports. In this paper, we display a framework which can foresee the landing time of bus to a specific bus stop considering the ongoing parameters that influences the travel time of transport. With transport module, the ongoing parameters that influence travel time are persistently gathered and used to foresee the bus arrival time at different transport stops. A portable application is created to help the questioning clients in getting the arrival time of bus to a specific stop. As there will be postpone in travel time because of the vehicles on street, street movement is displayed utilizing M/G/1 lining hypothesis to ascertain the delay created by other vehicles going in the same street. Server predicts the arrival time on the real time basis by bus module and the data shown in the database of server.

To know the entry time of bus to a bus stop is the fundamental data each travel clients need to know. Sitting tight for quite a while at a bus stop debilitates the individuals to depend upon open transports. In this paper, we display a framework which can foresee the landing time of bus to a specific bus stop considering the ongoing parameters that influences the travel time of transport. With transport module, the ongoing parameters that influence travel time are persistently gathered and used to foresee the bus arrival time at different transport stops. A portable application is created to help the questioning clients in getting the arrival time of bus to a specific stop. As there will be postpone in travel time because of the vehicles on street, street movement is displayed utilizing M/G/1 lining hypothesis to ascertain the delay created by other vehicles going in the same street. Server predicts the arrival time on the real time basis by bus module and the data shown in the database of server.

To know the entry time of bus to a bus stop is the fundamental data each travel clients need to know. Sitting tight for quite a while at a bus stop debilitates the individuals to depend upon open transports. In this paper, we display a framework which can foresee the landing time of bus to a specific bus stop considering the ongoing parameters that influences the travel time of transport. With transport module, the ongoing parameters that influence travel time are persistently gathered and used to foresee the bus arrival time at different transport stops. A portable application is created to help the questioning clients in getting the arrival time of bus to a specific stop. As there will be postpone in travel time because of the vehicles on street, street movement is displayed utilizing M/G/1 lining hypothesis to ascertain the delay created by other vehicles going in the same street. Server predicts the arrival time on the real time basis by bus module and the data shown in the database of server.

To know the entry time of bus to a bus stop is the fundamental data each travel clients need to know. Sitting tight for quite a while at a bus stop debilitates the individuals to depend upon open transports. In this paper, we display a framework which can foresee the landing time of bus to a specific bus stop considering the ongoing parameters that influences the travel time of transport. With transport module, the ongoing parameters that influence travel time are persistently gathered and used to foresee the bus arrival time at different transport stops. A portable application is created to help the questioning clients in getting the arrival time of bus to a specific stop. As there will be postpone in travel time because of the vehicles on street, street movement is displayed utilizing M/G/1 lining hypothesis to ascertain the delay created by other vehicles going in the same street. Server predicts the arrival time on the real time basis by bus module and the data shown in the database of server.

To know the entry time of bus to a bus stop is the fundamental data each travel clients need to know. Sitting tight for quite a while at a bus stop debilitates the individuals to depend upon open transports. In this paper, we display a framework which can foresee the landing time of bus to a specific bus stop considering the ongoing parameters that influences the travel time of transport. With transport module, the ongoing parameters that influence travel time are persistently gathered and used to foresee the bus arrival time at different transport stops. A portable application is created to help the questioning clients in getting the arrival time of bus to a specific stop. As there will be postpone in travel time because of the vehicles on street, street movement is displayed utilizing M/G/1 lining hypothesis to ascertain the delay created by other vehicles going in the same street. Server predicts the arrival time on the real time basis by bus module and the data shown in the database of server.

Road transport is a major source of environmental pollution. Cars and trucks, which are the most common types of vehicles, exhaust a variety of pollutants (e.g. NOx and PM) that are detrimental to human health. Research on the ecological impacts of road vehicles has highlighted the importance of reducing pollutant emissions. This chapter aims to investigate the impacts of road maintenance sites on pollution in the surrounding environment, which is a slightly different and interdisciplinary aspect of the problem. Road pavement and infrastructure (bridges, viaducts, and tunnels) must be maintained for the road network to function properly. Maintenance sites interrupt the normal flow of traffic, which leads to traffic jams, higher travel times, and pollutant emissions. This chapter explores a variety of approaches to traffic emissions modeling to identify a numerical model capable of determining the ecological impacts of maintenance sites on the surrounding environment. A simple simulation will be conducted to demonstrate the importance of the subject. Research gaps are presented at the end of the chapter to guide future studies.

To know the entry time of bus to a bus stop is the fundamental data each travel clients need to know. Sitting tight for quite a while at a bus stop debilitates the individuals to depend upon open transports. In this paper, we display a framework which can foresee the landing time of bus to a specific bus stop considering the ongoing parameters that influences the travel time of transport. With transport module, the ongoing parameters that influence travel time are persistently gathered and used to foresee the bus arrival time at different transport stops. A portable application is created to help the questioning clients in getting the arrival time of bus to a specific stop. As there will be postpone in travel time because of the vehicles on street, street movement is displayed utilizing M/G/1 lining hypothesis to ascertain the delay created by other vehicles going in the same street. Server predicts the arrival time on the real time basis by bus module and the data shown in the database of server.

This dissertation presents a series of traffic management studies including freeway and intersection traffic modeling, estimation, control methodologies and field implementation tests. First, a traffic flow prediction method that combines the most recent traffic data with historical traffic data is studied. An autoregressive moving average with exogenous input (ARMAX) model is estimated on-line based on the most recent vehicle detector station (VDS) data. Results obtained using empirical freeway mainline and on-ramp data show that this method outperforms methods that rely only on the historical average of the data to perform a prediction, especially during days with unusual traffic flow demands. Second, two freeway control strategies: coordinated ramp metering (CRM) and variable speed advisory (VSA) are investigated and implemented in field tests. In the control of CRM study, the freeway is modeled by the cell transmission model (CTM) and the control problem is solved by the model predictive control (MPC) scheme. The proposed CRM is deployed in a segment of California State Route 99 Northbound (SR-99N) for a five-week field test. The test results shows that the freeway efficiency can be improved by 7.25% for morning peak hours. In the VSA control study, an advisory speed limit control is designed by using traffic flow stabilization of the Lighthill-Whitham-Richards (LWR) model. The proposed VSA is deployed in a segment of California State Route 78 Eastbound (SR-78E) for a five-week field test. The test results shows that the freeway efficiency can be improved by 8.71% for morning peak hours. Both control strategies indicate freeway efficiency improvement in congested traffic. Third, the large-scale signalized intersection traffic network control by offset optimization is also studied. The traffic network is described by a directed graph and the traffic dynamic is represented by continuous-time fluid queue model with sinusoidal arrival and departure rate assumption.

This dissertation presents a series of traffic management studies including freeway and intersection traffic modeling, estimation, control methodologies and field implementation tests. First, a traffic flow prediction method that combines the most recent traffic data with historical traffic data is studied. An autoregressive moving average with exogenous input (ARMAX) model is estimated on-line based on the most recent vehicle detector station (VDS) data. Results obtained using empirical freeway mainline and on-ramp data show that this method outperforms methods that rely only on the historical average of the data to perform a prediction, especially during days with unusual traffic flow demands. Second, two freeway control strategies: coordinated ramp metering (CRM) and variable speed advisory (VSA) are investigated and implemented in field tests. In the control of CRM study, the freeway is modeled by the cell transmission model (CTM) and the control problem is solved by the model predictive control (MPC) scheme. The proposed CRM is deployed in a segment of California State Route 99 Northbound (SR-99N) for a five-week field test. The test results shows that the freeway efficiency can be improved by 7.25% for morning peak hours. In the VSA control study, an advisory speed limit control is designed by using traffic flow stabilization of the Lighthill-Whitham-Richards (LWR) model. The proposed VSA is deployed in a segment of California State Route 78 Eastbound (SR-78E) for a five-week field test. The test results shows that the freeway efficiency can be improved by 8.71% for morning peak hours. Both control strategies indicate freeway efficiency improvement in congested traffic. Third, the large-scale signalized intersection traffic network control by offset optimization is also studied. The traffic network is described by a directed graph and the traffic dynamic is represented by continuous-time fluid queue model with sinusoidal arrival and departure rate assumption.

In this paper we consider the loading processes at seaside link of Seaport automobile Automobile Terminal (SAT) as a bulk queuing model of the form M X g /D/1// s . We deduce the recursive equation for the steadystate probabilities concerning this queue model. Using this recursive equation, we obtain the explicit expressions for the steady-state probabilities with indices up to four. All these steady-state probabilities are expressed as a function on the utlization factor (  ) and the size of every arriving group at the ramp ( g ). Related numerical and graphical results are also presented. As applications, it is considered and discussed the modeling process at the SAT in the Port of Bar.

Models of street networks underlie research in urban travel behavior, accessibility, design patterns, and morphology. These models are commonly defined as planar, meaning they can be represented in two dimensions without any underpasses or overpasses. However, real-world urban street networks exist in three-dimensional space and frequently feature grade separation such as bridges and tunnels: planar simplifications can be useful but they also impact the results of real-world street network analysis. This study measures the nonplanarity of drivable and walkable street networks in the centers of 50 cities worldwide, then examines the variation of nonplanarity across a single city. It develops two new indicators - the Spatial Planarity Ratio and the Edge Length Ratio - to measure planarity and describe infrastructure and urbanization. While some street networks are approximately planar, we empirically quantify how planar models can inconsistently but drastically misrepresent intersection density, street lengths, routing, and connectivity.

Models of street networks underlie research in urban travel behavior, accessibility, design patterns, and morphology. These models are commonly defined as planar, meaning they can be represented in two dimensions without any underpasses or overpasses. However, real-world urban street networks exist in three-dimensional space and frequently feature grade separation such as bridges and tunnels: planar simplifications can be useful but they also impact the results of real-world street network analysis. This study measures the nonplanarity of drivable and walkable street networks in the centers of 50 cities worldwide, then examines the variation of nonplanarity across a single city. It develops two new indicators - the Spatial Planarity Ratio and the Edge Length Ratio - to measure planarity and describe infrastructure and urbanization. While some street networks are approximately planar, we empirically quantify how planar models can inconsistently but drastically misrepresent intersection density, street lengths, routing, and connectivity.

Purpose. The work is aimed to design a system for automatic selection of the optimal traffic modes and automatic monitoring of the electric energy consumption by electric transport. This automatic system should provide for the minimum energy expenses. Methodology. Current methodologies: 1) mathematical modeling of traffic modes of ground electric vehicles; 2) comparison of modelling results with the statistical monitoring; 3) system development for automatic choice of traffic modes of electric transport with minimal electrical energy consumptions taking into account the given route schedules and the limitations imposed by the general traffic rules. Findings. The authors obtained a mathematical dependency of the energy consumption by electric transport enterprises on the monthly averaged environment temperature was obtained. A system which allows for an automatic selection of the speed limit and provides automatic monitoring of the electrical energy consumption by electric vehicles was proposed in the form of local network, which works together with existing GPS system. Originality. A mathematical model for calculating the motion curves and energy consumption of electric vehicles has been developed. This model takes into account the characteristic values of the motor engine and the steering system, the change of the mass when loading or unloading passengers, the slopes and radii of the roads, the limitations given by the general traffic rules, and other factors. The dependency of the energy consumption on the averaged monthly environment temperature for public electric transport companies has been calculated. Practical value. The developed mathematical model simplifies the calculations of the traffic dynamics and energy consumption. It can be used for calculating the routing maps, for design and upgrade of the power networks, for development of the electricity saving measures. The system simplifies the work of the vehicle driver and allows reducing the energy consump, Цель. Исследование предполагает разработку системы автоматизированного выбора оптимальных режимов движения и автоматического контроля расхода электроэнергии средствами электротранспорта с целью обеспечения минимально возможных затрат энергии. Методика. В качестве методики используются: 1) математическое моделирование режимов движения средств наземного электрического транспорта; 2) сравнение результатов моделирования с данными статистических наблюдений; 3) разработка системы автоматизированного выбора режимов движения электротранспорта с минимально возможными затратами электроэнергии в условиях выполнения графика и соблюдения ограничений, регламентированных правилами движения. Результаты. Авторами получена математическая зависимость расхода электроэнергии предприятиями электротранспорта от режимов движения и среднемесячной температуры окружающей среды. Разработан проект системы автоматизированного выбора скоростного режима движения и автоматизированного контроля расхода электроэнергии транспортными средствами в виде локальной компьютерной сети, работающей совместно с действующей системой спутниковой навигации. Научная новизна. Разработана математическая модель расчета кривых движения и расхода электроэнергии транспортным средством, учитывающая характеристики тягового двигателя и системы управления, изменение массы при посадке и высадке пассажиров, уклоны и радиусы кривых пути движения, ограничения, накладываемые дорожными знаками, и другие факторы. Установлена зависимость расхода электроэнергии предприятиями электротранспорта от среднемесячной температуры окружающей среды. Практическая значимость. Созданная математическая модель упрощает расчеты динамики движения и расхода электроэнергии транспортными средствами при их эксплуатации. Она может найти применение при разработке технологических карт маршрутов, проектировании и модернизации тяговых сетей, разработке средств энергосбережения. Автоматизированная система выбора скоростного режима движения облегчает работу вод, Мета. Дослідження передбачає розробку системи автоматизованого вибору оптимальних режимів руху та автоматичного контролю витрат електроенергії засобами електротранспорту з метою забезпечення мінімально можливих витрат енергії. Методика. У якості методики застосовуються: 1) математичне моделювання режимів руху засобів наземного електричного транспорту; 2) порівняння результатів моделювання з даними статистичних спостережень; 3) а також розробка системи автоматизованого вибору режимів руху електротранспорту з мінімально можливими затратами електроенергії в умовах виконання графіка та дотримання обмежень, регламентованих правилами руху. Результати. Авторами одержана математична залежність витрат електроенергії підприємствами електротранспорту від режимів руху та середньомісячної температури навколишнього середовища. Розроблено проект системи автоматизованого вибору швидкісного режиму руху та автоматизованого контролю витрат електроенергії транспортними засобами у вигляді локальної комп’ютерної мережі, працюючої сумісно з діючою системою супутникової навігації. Наукова новизна. Розроблена математична модель розрахунку кривих руху та витрат електроенергії транспортним засобом, яка враховує характеристики тягового двигуна та системи керування, зміну маси при посадці та висадці пасажирів, уклони та радіуси кривих шляху руху, обмеження, що накладаються дорожніми знаками, та інші чинники. Встановлена залежність витрат електроенергії підприємствами електротранспорту від середньомісячної температури навколишнього середовища. Практична значимість. Створена математична модель спрощує розрахунки динаміки руху та витрат електроенергії транспортними засобами при їх експлуатації. Вона може знайти використання під час розробки технологічних карт маршрутів, проектуванні та модернізації тягових мереж, розробці засобів енергозбереження. Автоматизована система вибору швидкісного режиму руху полегшує роботу водія транспортного засобу, дозволяє зменшити затрати електроенергії, а також забе

This thesis proposes a modeling framework to characterize equilibrium traffic flow relations for heterogeneous traffic composed of both standard and Cooperative Adaptive Cruise Control (CACC) vehicles, capturing the impact of CACC market penetration and vehicle arrangement within a lane. The resulting parameterized fundamental diagram is then integrated with the first-order macroscopic traffic model, creating the ability to characterize operational performance on a network for heterogeneous traffic with varying CACC market penetration. This first-order approach is demonstrated through an illustrative case study which considers a small network with time-varying demand, temporary capacity reductions, and CACC market penetration ranging from 0.0 to 1.0. The results indicate that maximum throughput initially decreases as CACC traffic is introduced, but eventually improves significantly for CACC market penetration rates above 0.4. Additionally, they show that when an incident abruptly reduces road capacity, introducing even a small fraction of CACC vehicles reduces the speed at which the congestion wavefront propagates upstream.

Maritime traffic modeling serves the purpose of extracting human-readable information and discovering knowledge in the otherwise illegible mass of traffic data. The goal of this study is to examine the presence and character of fluctuations in maritime traffic patterns. The main objective is to identify such fluctuations and capture them in terms of a concept drift, i.e., unforeseen shifts in statistical properties of the modeled target occurring over time. The empirical study is based on a collection of AIS vessel tracking data, spanning over a year. The scope of the study limits the AIS data area to the Baltic region (9-31°E, 53-66°N), which experiences some of the most dense maritime traffic in the world. The investigations employ a novel maritime traffic modeling method based on the potential fields concept, adapted for this study to facilitate the examination of concept drift. The concept drift is made apparent in course of the statistical and visual analysis of the experimental results. This study shows a number of particular cases, in which the maritime traffic is affected by concept drifts of varying extent and character. The visual representations of the traffic models make shifts in the traffic patterns apparent and comprehensible to human eye. Based on the experimental outcomes, the robustness of the modeling method against concept drift in traffic is discussed and improvements are proposed. The outcomes provide insights into regularly reoccurring drifts and irregularities within the traffic data itself that may serve to further optimize the modeling method, and - in turn - the performance of detection based on it. © 2017 The Authors. Published by Elsevier B. V.

In recent years, connected vehicles (CVs) and automated vehicles (AVs) have emerged as a realistic and viable transportation option. Research centers and companies have dedicated substantial efforts to the technology, motivated largely by the potential safety benefits that can be realized through the elimination of human error, the enhancement of mobility via reduction of congestion and optimization of trips, and the associated positive environmental impacts. Both sensors and control mechanisms are needed for this technology to succeed. The goal of this study is to make use of vehicle connectivity via vehicle-to-vehicle (V2V) (i.e., exchanging information between vehicles) and vehicle-to-infrastructure (V2I) (i.e., exchanging information with the infrastructure, including intersection controllers) features, leveraging both connected and automated capabilities, to develop control algorithms/systems that deliver solutions/recommendations for connected automated vehicles (CAVs) [1] as they proceed through intersections. The algorithms developed in this report deliver optimal and/or near-optimal solutions, which required extensive simulations and field experiments for validation. In the work described in this report, the research group combined mathematical modeling, optimal control theory, and optimization into a simulation framework that allows vehicles to cross an intersection safely, while incurring the least amount of delay. These models feature kinematic, dynamic and static constraints. Different versions of the model were developed, ranging from exact solutions that cannot be implemented in real-time to heuristic solutions that are computationally efficient. The results of the final proposed model were compared to other control techniques already implemented in the field, and demonstrated that a reduction of at least 50% in delay was achievable. An interesting byproduct of this model was the reduction in fuel consumption, and thus emissions, by more than 10%.

In recent years, connected vehicles (CVs) and automated vehicles (AVs) have emerged as a realistic and viable transportation option. Research centers and companies have dedicated substantial efforts to the technology, motivated largely by the potential safety benefits that can be realized through the elimination of human error, the enhancement of mobility via reduction of congestion and optimization of trips, and the associated positive environmental impacts. Both sensors and control mechanisms are needed for this technology to succeed. The goal of this study is to make use of vehicle connectivity via vehicle-to-vehicle (V2V) (i.e., exchanging information between vehicles) and vehicle-to-infrastructure (V2I) (i.e., exchanging information with the infrastructure, including intersection controllers) features, leveraging both connected and automated capabilities, to develop control algorithms/systems that deliver solutions/recommendations for connected automated vehicles (CAVs) [1] as they proceed through intersections. The algorithms developed in this report deliver optimal and/or near-optimal solutions, which required extensive simulations and field experiments for validation. In the work described in this report, the research group combined mathematical modeling, optimal control theory, and optimization into a simulation framework that allows vehicles to cross an intersection safely, while incurring the least amount of delay. These models feature kinematic, dynamic and static constraints. Different versions of the model were developed, ranging from exact solutions that cannot be implemented in real-time to heuristic solutions that are computationally efficient. The results of the final proposed model were compared to other control techniques already implemented in the field, and demonstrated that a reduction of at least 50% in delay was achievable. An interesting byproduct of this model was the reduction in fuel consumption, and thus emissions, by more than 10%.

In recent years, connected vehicles (CVs) and automated vehicles (AVs) have emerged as a realistic and viable transportation option. Research centers and companies have dedicated substantial efforts to the technology, motivated largely by the potential safety benefits that can be realized through the elimination of human error, the enhancement of mobility via reduction of congestion and optimization of trips, and the associated positive environmental impacts. Both sensors and control mechanisms are needed for this technology to succeed. The goal of this study is to make use of vehicle connectivity via vehicle-to-vehicle (V2V) (i.e., exchanging information between vehicles) and vehicle-to-infrastructure (V2I) (i.e., exchanging information with the infrastructure, including intersection controllers) features, leveraging both connected and automated capabilities, to develop control algorithms/systems that deliver solutions/recommendations for connected automated vehicles (CAVs) [1] as they proceed through intersections. The algorithms developed in this report deliver optimal and/or near-optimal solutions, which required extensive simulations and field experiments for validation. In the work described in this report, the research group combined mathematical modeling, optimal control theory, and optimization into a simulation framework that allows vehicles to cross an intersection safely, while incurring the least amount of delay. These models feature kinematic, dynamic and static constraints. Different versions of the model were developed, ranging from exact solutions that cannot be implemented in real-time to heuristic solutions that are computationally efficient. The results of the final proposed model were compared to other control techniques already implemented in the field, and demonstrated that a reduction of at least 50% in delay was achievable. An interesting byproduct of this model was the reduction in fuel consumption, and thus emissions, by more than 10%.

Engineering and Policy Analysis, Policy Analysis, Technology, Policy and Management

Engineering and Policy Analysis, Policy Analysis, Technology, Policy and Management

The Automatic Identification System (AIS) is used to identify and locate active maritime vessels. Datasets of AIS messages recorded over time make it possible to model ship movements and analyze traffic events. Here, the maritime traffic is modeled using a potential fields method, enabling the extraction of traffic patterns and anomaly detection. A software tool named STRAND, implementing the modeling method, displays real-world ship behavior patterns, and is shown to generate traffic rules spontaneously. STRAND aids maritime situational awareness by displaying patterns of common behaviors and highlighting suspicious events, i.e., abstracting informative content from the raw AIS data and presenting it to the user. In this it can support decisions regarding, e.g., itinerary planning, routing, rescue operations, or even legislative traffic regulation. This study in particular focuses on identification and analysis of traffic rules discovered based on the computed traffic models. The case study demonstrates and compares results from three different areas, and corresponding traffic rules identified in course of the result analysis. The ability to capture distinctive, repetitive traffic behaviors in a quantitative, automatized manner may enhance detection and provide additional information about sailing practices.

Multi-hop wireless networks lack widespread deployment due to their performance issues. There are very few models which can aid detailed analysis of performance bottlenecks and design improvements. Our objective is to develop hybrid (analytical and numerical) models which can efficiently approximate the performance of wireless multi-hop networks. We focus on modeling the MAC and routing protocol performance, and their impact on network performance. For MAC layer modeling, we improve a fixed-point loss network model to evaluate throughput and packet loss for 802.11 MAC protocol. For routing, we model the OLSR protocol, one of the popular routing protocols. Unlike any work before, we give a combined model of a MAC and Routing Protocol which captures the cross-layer interaction. Most of the performance models assume smooth or exponential input traffic models in the usual Markovian framework. However, it is known, at least for wired networks, that traffic sources can have heavy-tails and the traffic arrival pattern bursty even at large timescales, which is characterized by self-similarity and long-range dependence (LRD). This can significantly impact network performance through higher queue-occupancy, service times, packet losses, etc. Both the presence of these phenomenon and their impact on network performance of wireless multi-hop networks has not be studied before. Hence, we study traffic LRD in three stages, which form the main contributions of this work. Heavy-tails in traffic sources, for example file sizes on the web, have been attributed as the main causes of traffic LRD. Previous protocol studies, for both wired and wireless networks, have argued that the network protocols dynamics affect only the small-time scale properties, and that they cannot create traffic LRD by themselves. However, recent work show that ALOHA protocol can lead to heavy-tailed delays in certain situations. With that as a motivation, we investigate the role of more complex 802.11 MAC prot

In this research we describe the development of a common evaluation and benchmarking platform that has been developed within the framework of the European Union COST Action MULTITUDE. The main goal of this platform is to provide a testbed in which a number of algorithms can be implemented and tested under the same conditions. The objective is not to conclude that one approach is “best”, but to provide a support comparison in a variety of settings and conditions in order to help determine the particular situations and conditions under which one approach might behave more favorably than another. The design of the platform is presented, along with a detailed experimental design for the application of different OD estimation algorithms. The considered algorithms are then presented, along with a demonstration of the extensibility of the presented framework to accommodate additional data sources. The presented results demonstrate that the developed framework is capable of supporting the development, application and testing of a wide range of algorithms. First, both off-line/planning level algorithms (like the Bilevel-DUE) and on-line algorithms (like that SPSA AD-PI and the KFX2) are presented., Transport and Planning, Civil Engineering and Geosciences

In this paper we investigate an event-driven Inter-Vehicle Communication protocol to mitigate shock waves in dense traffic situation. From previous work we know that flooding or frequent beaconing require excessive network resources. To overcome this limitation we focus on an efficient message exchange mechanism requiring as few network resources as possible, while still providing timely and accurate traffic information. We designed Density Redistribution through Intelligent Velocity Estimation (DRIVE), an event-driven Inter-Vehicle Communication protocol that learns about traffic conditions ahead and recommends optimal velocities in order to prevent the formation of vehicular shock waves. We demonstrate that our approach of reacting in case of traffic fluctuations leads to significant improvements in overall traffic flow. Furthermore we show that even a low number of equipped vehicles is sufficient to achieve this target.

We address the problem of shock wave formation in uncoordinated highway traffic. First, we identify the combination of heavy traffic and small traffic perturbations or unexpected driver actions as the main causes of highway traffic jams. Then we introduce a novel distributed communication protocol that enables us to eliminate upstream shock wave formation even with low system penetration rates. Based on traffic information ahead, we propose a Cooperative Advanced Driver Assistance System (CADAS) that recommends non-intuitive velocity reductions in order to redistribute traffic more uniformly thereby eliminating traffic peaks. Simulation results show that CADAS significantly increases the average velocity and therewith reduces the overall travel time and avoids unnecessary slowdowns.

In this paper we investigate an event-driven Inter-Vehicle Communication protocol to mitigate shock waves in dense traffic situation. From previous work we know that flooding or frequent beaconing require excessive network resources. To overcome this limitation we focus on an efficient message exchange mechanism requiring as few network resources as possible, while still providing timely and accurate traffic information. We designed Density Redistribution through Intelligent Velocity Estimation (DRIVE), an event-driven Inter-Vehicle Communication protocol that learns about traffic conditions ahead and recommends optimal velocities in order to prevent the formation of vehicular shock waves. We demonstrate that our approach of reacting in case of traffic fluctuations leads to significant improvements in overall traffic flow. Furthermore we show that even a low number of equipped vehicles is sufficient to achieve this target.

We address the problem of shock wave formation in uncoordinated highway traffic. First, we identify the combination of heavy traffic and small traffic perturbations or unexpected driver actions as the main causes of highway traffic jams. Then we introduce a novel distributed communication protocol that enables us to eliminate upstream shock wave formation even with low system penetration rates. Based on traffic information ahead, we propose a Cooperative Advanced Driver Assistance System (CADAS) that recommends non-intuitive velocity reductions in order to redistribute traffic more uniformly thereby eliminating traffic peaks. Simulation results show that CADAS significantly increases the average velocity and therewith reduces the overall travel time and avoids unnecessary slowdowns.

In this research we describe the development of a common evaluation and benchmarking platform that has been developed within the framework of the European Union COST Action MULTITUDE. The main goal of this platform is to provide a testbed in which a number of algorithms can be implemented and tested under the same conditions. The objective is not to conclude that one approach is “best”, but to provide a support comparison in a variety of settings and conditions in order to help determine the particular situations and conditions under which one approach might behave more favorably than another. The design of the platform is presented, along with a detailed experimental design for the application of different OD estimation algorithms. The considered algorithms are then presented, along with a demonstration of the extensibility of the presented framework to accommodate additional data sources. The presented results demonstrate that the developed framework is capable of supporting the development, application and testing of a wide range of algorithms. First, both off-line/planning level algorithms (like the Bilevel-DUE) and on-line algorithms (like that SPSA AD-PI and the KFX2) are presented., Transport and Planning, Civil Engineering and Geosciences

This report presents two research efforts that have been published as conference papers through the Transportation Research Board Annual Meeting, the first of which is under review for journal publication. The first research effort investigates the general application of naturalistic driving data to the modeling of car following behavior. The driver-specific data available from naturalistic driving studies provides a unique perspective from which to test and calibrate car-following models. As equipment and data storage costs continue to decline, the collection of data through in situ probe-type vehicles is likely to become more popular, and thus there is a need to assess the feasibility of these data for the modeling of driver car-following behavior. The first research effort seeks to focus on the costs and benefits of naturalistic data for use in mobility applications. Any project seeking to utilize naturalistic data should plan for a complex and potentially costly data reduction process to extract mobility data. A case study is provided using the database from the 100-Car Study, conducted by the Virginia Tech Transportation Institute. One thousand minutes worth of data comprised of over 2,000 car-following events recorded across eight drivers is compiled herein, from a section of multilane highway located near Washington, D.C. The collected event data is used to calibrate four different car following models, and a comparative analysis of model performance is conducted. The results of model calibration are given in tabular format, displayed on the fundamental diagram, and shown with sample event charts of speed-vs.-time and headway-vs.-time. The authors demonstrate that the Rakha-Pasumarthy-Adjerid model performs best both in matching individual drivers and in matching aggregate results, when compared with the Gipps, Intelligent Driver, and Gaxis-Herman-Rothery models. The second effort examines how insights gained from naturalistic data may serve to improve existi

This report presents two research efforts that have been published as conference papers through the Transportation Research Board Annual Meeting, the first of which is under review for journal publication. The first research effort investigates the general application of naturalistic driving data to the modeling of car following behavior. The driver-specific data available from naturalistic driving studies provides a unique perspective from which to test and calibrate car-following models. As equipment and data storage costs continue to decline, the collection of data through in situ probe-type vehicles is likely to become more popular, and thus there is a need to assess the feasibility of these data for the modeling of driver car-following behavior. The first research effort seeks to focus on the costs and benefits of naturalistic data for use in mobility applications. Any project seeking to utilize naturalistic data should plan for a complex and potentially costly data reduction process to extract mobility data. A case study is provided using the database from the 100-Car Study, conducted by the Virginia Tech Transportation Institute. One thousand minutes worth of data comprised of over 2,000 car-following events recorded across eight drivers is compiled herein, from a section of multilane highway located near Washington, D.C. The collected event data is used to calibrate four different car following models, and a comparative analysis of model performance is conducted. The results of model calibration are given in tabular format, displayed on the fundamental diagram, and shown with sample event charts of speed-vs.-time and headway-vs.-time. The authors demonstrate that the Rakha-Pasumarthy-Adjerid model performs best both in matching individual drivers and in matching aggregate results, when compared with the Gipps, Intelligent Driver, and Gaxis-Herman-Rothery models. The second effort examines how insights gained from naturalistic data may serve to improve existi

In this paper we present a study of highway traffic flow optimization using Partial Velocity Synchronization (PVS). PVS is a Cellular Automaton (CA) model that is extended by a communication layer providing the ability to exchange relevant information between vehicles. We show that it is possible to enhance traffic flow on highways significantly with a small number of velocity recommendations computed from the traffic conditions ahead. Furthermore we show that only a limited number of hops in an information chain is necessary to reschedule the vehicles on a given highway segment to avoid the formation of shockwaves. Our results show that traffic flow will be increased while travel time and emissions will be reduced dramatically.

In this paper we present a study of highway traffic flow optimization using Partial Velocity Synchronization (PVS). PVS is a Cellular Automaton (CA) model that is extended by a communication layer providing the ability to exchange relevant information between vehicles. We show that it is possible to enhance traffic flow on highways significantly with a small number of velocity recommendations computed from the traffic conditions ahead. Furthermore we show that only a limited number of hops in an information chain is necessary to reschedule the vehicles on a given highway segment to avoid the formation of shockwaves. Our results show that traffic flow will be increased while travel time and emissions will be reduced dramatically.

This report presents two research efforts that have been published as conference papers through the Transportation Research Board Annual Meeting, the first of which is under review for journal publication. The first research effort investigates the general application of naturalistic driving data to the modeling of car following behavior. The driver-specific data available from naturalistic driving studies provides a unique perspective from which to test and calibrate car-following models. As equipment and data storage costs continue to decline, the collection of data through in situ probe-type vehicles is likely to become more popular, and thus there is a need to assess the feasibility of these data for the modeling of driver car-following behavior. The first research effort seeks to focus on the costs and benefits of naturalistic data for use in mobility applications. Any project seeking to utilize naturalistic data should plan for a complex and potentially costly data reduction process to extract mobility data. A case study is provided using the database from the 100-Car Study, conducted by the Virginia Tech Transportation Institute. One thousand minutes worth of data comprised of over 2,000 car-following events recorded across eight drivers is compiled herein, from a section of multilane highway located near Washington, D.C. The collected event data is used to calibrate four different car following models, and a comparative analysis of model performance is conducted. The results of model calibration are given in tabular format, displayed on the fundamental diagram, and shown with sample event charts of speed-vs.-time and headway-vs.-time. The authors demonstrate that the Rakha-Pasumarthy-Adjerid model performs best both in matching individual drivers and in matching aggregate results, when compared with the Gipps, Intelligent Driver, and Gaxis-Herman-Rothery models. The second effort examines how insights gained from naturalistic data may serve to improve existi

Researchers who aim to evaluate proposed modifications to the Internet’s architecture face a unique set of challenges. Internet-based measurements provide limited value to such evaluations, as the quantities being measured are easily lost to ambiguity and idiosyncrasy. While simulations offer more control, Internet-like environments are difficult to construct due to the lack of ground truth in critical areas, such as topological structure and traffic patterns. This thesis develops a network topology and traffic models for a simulation-based evaluation of the PURSUIT rendezvous system, the name-based interdomain routing mechanism of an information-centric future Internet architecture. Although the empirical data used to construct the employed models is imperfect, it is nonetheless useful for identifying invariants which can shed light upon significant architectural characteristics. The contribution of this work is twofold. In addition to being directly applicable to the evaluation of PURSUIT’s rendezvous system, the methods used in this thesis may be applied more generally to any studies which aim to simulate Internet-like systems., Forskare som syftar till att utvärdera föreslagna ändringar av Internet arkitektur står inför en unik uppsättning utmaningar. Internet-baserade mätningar ger begränsat värde för sådana utvärderingar, eftersom de kvantiteter som mäts är lätt förlorade mot tvetydighet och egenhet. Även om simuleringar ger mer kontroll är Internet-liknande miljöer svåra att konstruera på grund av bristen på kända principer i kritiska områden, såsom topologiska struktur och trafikmönster. Denna avhandling utvecklar en nättopologi och trafikmodeller för en simulering baserad utvärdering av PURSUIT mötesplatsen systemet, den namn-baserade interdomän routing mekanismen för en informations-centrerad arkitektur av framtidens Internet. Även om de empiriska data som används för att konstruera modeller är bristfällig, är det ändå användbart för att identifiera invarianter som kan belysa viktiga arkitektoniska egenskaper. Bidraget från detta arbete har två syften. Förutom att vara direkt tillämplig för utvärderingen av PURSUITs rendezvous system, kan de metoder som används i denna avhandling användas mer allmänt för studier som syftar till att simulera Internet-liknande system.

Within Ericsson there is a continuous activity of traffic modeling. Traffic modeling is a practice to analyze traffic patterns and determine necessary resources to handle it optimally. This activity focuses on gathering and analyzing live network measurements, implementing and presenting traffic models. One example of concept in packet traffic modeling is transmission object log which is an aggregation of packet data traces from a measured network over a transmission period. These trace logs that are simple list of all transmission objects contain a vast number of data. When the amount of data increases in these logs several problems can occur such as expensive analysis time, costly data storage and even statistical analysis and data processing in software environments run out of memory. On the other hand, sophisticated and costly computing systems are required for analysis and storage of the data. Therefore, monitoring and analyzing these large traces motivate data reduction. The goal of this thesis is to reduce the number of traffic objects in large object trace logs while preserving the statistical characteristics of the original transmission objects. Sampling techniques are wildly used to cope with the issues of large amount of data in network monitoring. First, this thesis aims to assess the impact of two sampling techniques as a reduction method. Second, to analyze traffic characteristics and showing the effects of sampling, some statistical properties of both original and sampled datasets as well as their distribution plots will be discussed. The distortion introduced by sampling as the distance between the distribution of properties for sampled and unsampled traffic is also presented by a statistical metric. One of the issues in sampling technique is the sampling size. In order to estimate the sampling size and reduce the logs to a certain level, the concept of offline marginal utility as a complementary method to sampling is proposed in this report.

In this paper we address the problem of uncoor- dinated highway traffic. We first identify the main causes of the capacity drop, namely high traffic demand and inadequate driver reaction. In the past, traffic and user behavior have been accurately described by cellular automata (CA) models. In this paper we extend the CA model to deal with highway traffic fluctuations and jams. Specifically, the model incorporates the communication layer between vehicles. The model thus enables us to study the impact of inter-vehicular communications and in particular the delivery of critical and timely upstream traffic information on driver reaction. Based on the newly-available traffic metrics, we propose an Advanced Driver Assistance System (ADAS) that suggests non-intuitive speed reduction in order to avoid the formation of so-called phantom jams. The results show that using such a system considerably increases the overall traffic flow, reduces travel time and avoids unnecessary slow-downs.

In this paper we address the problem of uncoor- dinated highway traffic. We first identify the main causes of the capacity drop, namely high traffic demand and inadequate driver reaction. In the past, traffic and user behavior have been accurately described by cellular automata (CA) models. In this paper we extend the CA model to deal with highway traffic fluctuations and jams. Specifically, the model incorporates the communication layer between vehicles. The model thus enables us to study the impact of inter-vehicular communications and in particular the delivery of critical and timely upstream traffic information on driver reaction. Based on the newly-available traffic metrics, we propose an Advanced Driver Assistance System (ADAS) that suggests non-intuitive speed reduction in order to avoid the formation of so-called phantom jams. The results show that using such a system considerably increases the overall traffic flow, reduces travel time and avoids unnecessary slow-downs.

Within Ericsson there is a continuous activity of traffic modeling. Traffic modeling is a practice to analyze traffic patterns and determine necessary resources to handle it optimally. This activity focuses on gathering and analyzing live network measurements, implementing and presenting traffic models. One example of concept in packet traffic modeling is transmission object log which is an aggregation of packet data traces from a measured network over a transmission period. These trace logs that are simple list of all transmission objects contain a vast number of data. When the amount of data increases in these logs several problems can occur such as expensive analysis time, costly data storage and even statistical analysis and data processing in software environments run out of memory. On the other hand, sophisticated and costly computing systems are required for analysis and storage of the data. Therefore, monitoring and analyzing these large traces motivate data reduction. The goal of this thesis is to reduce the number of traffic objects in large object trace logs while preserving the statistical characteristics of the original transmission objects. Sampling techniques are wildly used to cope with the issues of large amount of data in network monitoring. First, this thesis aims to assess the impact of two sampling techniques as a reduction method. Second, to analyze traffic characteristics and showing the effects of sampling, some statistical properties of both original and sampled datasets as well as their distribution plots will be discussed. The distortion introduced by sampling as the distance between the distribution of properties for sampled and unsampled traffic is also presented by a statistical metric. One of the issues in sampling technique is the sampling size. In order to estimate the sampling size and reduce the logs to a certain level, the concept of offline marginal utility as a complementary method to sampling is proposed in this report.

Researchers who aim to evaluate proposed modifications to the Internet’s architecture face a unique set of challenges. Internet-based measurements provide limited value to such evaluations, as the quantities being measured are easily lost to ambiguity and idiosyncrasy. While simulations offer more control, Internet-like environments are difficult to construct due to the lack of ground truth in critical areas, such as topological structure and traffic patterns. This thesis develops a network topology and traffic models for a simulation-based evaluation of the PURSUIT rendezvous system, the name-based interdomain routing mechanism of an information-centric future Internet architecture. Although the empirical data used to construct the employed models is imperfect, it is nonetheless useful for identifying invariants which can shed light upon significant architectural characteristics. The contribution of this work is twofold. In addition to being directly applicable to the evaluation of PURSUIT’s rendezvous system, the methods used in this thesis may be applied more generally to any studies which aim to simulate Internet-like systems., Forskare som syftar till att utvärdera föreslagna ändringar av Internet arkitektur står inför en unik uppsättning utmaningar. Internet-baserade mätningar ger begränsat värde för sådana utvärderingar, eftersom de kvantiteter som mäts är lätt förlorade mot tvetydighet och egenhet. Även om simuleringar ger mer kontroll är Internet-liknande miljöer svåra att konstruera på grund av bristen på kända principer i kritiska områden, såsom topologiska struktur och trafikmönster. Denna avhandling utvecklar en nättopologi och trafikmodeller för en simulering baserad utvärdering av PURSUIT mötesplatsen systemet, den namn-baserade interdomän routing mekanismen för en informations-centrerad arkitektur av framtidens Internet. Även om de empiriska data som används för att konstruera modeller är bristfällig, är det ändå användbart för att identifiera invarianter som kan belysa viktiga arkitektoniska egenskaper. Bidraget från detta arbete har två syften. Förutom att vara direkt tillämplig för utvärderingen av PURSUITs rendezvous system, kan de metoder som används i denna avhandling användas mer allmänt för studier som syftar till att simulera Internet-liknande system.