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To evaluate the traffic state over time and space, several models can be used. A typical model for estimating the state of the traffic for a stretch of road or a road network is the cell transmission model, which is a form of state space model. This kind of model typically needs to be calibrated since the different roads have different properties. This thesis will present a calibration framework for the velocity based cell transmission model, the CTM-v. The cell transmission model for velocity is a discrete time dynamical system that can model the evolution of the velocity field on highways. Such a model can be fused with an ensemble Kalman filter update algorithm for the purpose of velocity data assimilation. Indeed, enabling velocity data assimilation was the purpose for ever developing the model in the first place and it is an essential part of the Mobile Millennium research project. Therefore a systematic methodology for calibrating the cell transmission is needed. This thesis presents a framework for calibration of the velocity based cell transmission model that is combined with the ensemble Kalman filter. The framework consists of two separate methods, one is a statistical approach to calibration of the fundamental diagram. The other is a black box optimization method, a simplification of the complex method that can solve inequality constrained optimization problems with non-differentiable objective functions. Both of these methods are integrated with the existing system, yielding a calibration framework, in particular highways were stationary detectors are part of the infrastructure. The output produced by the above mentioned system is highly dependent on the values of its characterising parameters. Such parameters need to be calibrated so as to make the model a valid representation of reality. Model calibration and validation is a process of its own, most often tailored for the researchers models and purposes. The combination of the two methods are tested in a suit of experiments for two separate highway models of Interstates 880 and 15, CA which are evaluated against travel time and space mean speed estimates given by Bluetooth detectors with an error between 7.4 and 13.4 % for the validation time periods depending on the parameter set and model.

Macrophages are target cells for human immunodeficiency virus (HIV) and simian immunodeficiency virus (SIV) infection that serve as viral reservoirs in brain, lung, gut, and other tissues, and play important roles in disease pathogenesis, particularly HIV/SIV-associated neurological disease. Macrophages express low levels of the HIV/SIV receptor CD4, but mechanisms by which macrophage-tropic viruses use low CD4 to mediate spreading infections are poorly understood. One mechanism involves enhanced envelope glycoprotein (Env) interaction with CD4 or CCR5, but this phenotype is frequently associated with increased neutralization sensitivity to antibodies targeting CD4/CCR5 binding sites. Moreover, this mechanism does not explain how these neutralization-sensitive viruses evade immune responses while establishing spreading infections. In this dissertation, we sought to identify SIV Env determinants for macrophage tropism and characterize mechanisms by which they enhance virus replication in macrophages. To identify viral variants capable of inducing macrophage-associated pathogenesis, we cloned Env sequences from SIV-infected macaques at early and late stage infection, and identified an early variant in blood that shares >98% sequence identity with the consensus sequence of late variants in brain from macaques with neurological disease. SIV clones encoding this Env variant mediated high levels of fusion, replicated efficiently in rhesus PBMC and macrophages, and induced multinucleated giant cell formation upon infection of macrophage cultures. We identified an N-linked glycosylation site, N173 in the V2 region, as a determinant of macrophage tropism. Loss of N173 enhanced SIVmac239 macrophage tropism, while restoration of N173 in SIVmac251 reduced macrophage tropism, but enhanced neutralization resistance to CD4/CCR5 binding site antibodies. SIVmac239 N173Q, which lacks the N173 glycosylation site, mediated CD4-independent fusion and cell-to-cell transmission with CCR5-expressing cells, but could not infect CD4-negative cells in single-round infections. Thus, CD4-independent phenotypes were detected only in the context of cell-cell contact. The N173Q mutation had no effect on SIVmac239 gp120 binding to CD4 in BIACORE and co-immunoprecipitation assays. These findings suggest that loss of the N173 glycosylation site increases SIVmac239 replication in macrophages by enhancing CD4-independent cell-to-cell transmission through CCR5-mediated fusion. This mechanism may facilitate escape of macrophage-tropic viruses from neutralizing antibodies, while promoting spreading infections by these viruses in vivo.

The cell transmission model (CTM) based single destination system optimal dynamic traffic assignment (SD-SO-DTA) model has been widely applied to situations such as mass evacuations on a transportation network. Although formulated as a linear programming (LP) model, embedded multi-period cell network representation yields an extremely large model for real-size networks. As a result, most of these models are not solvable using existing LP solvers. Solutions obtained by LP also involve holding vehicles at certain locations, violating CTM flow dynamics. This doctoral research is aimed at developing innovative algorithms that overcome both computational efficiency and solution realism issues. We first prove that the LP formulation of the SD-SO-DTA problem is equivalent to the earliest arrival flow (EAF), and then develop efficient algorithms to solve EAF. Two variants of the algorithm are developed under different model assumptions and network operating conditions. For the case of time-varying network parameters, we develop a network flow algorithm on a time-expanded network. The main challenge in this approach is to address the issue of having backward wave speed lower than forward wave speed. This situation leads to non-typical constraints involving coefficients with value of less than 1. In this dissertation we develop a new network algorithm to solve this problem in optimal, even with coefficients of value less than 1. Additionally, the developed approach solves for optimal flows that exhibit non-vehicle-holding properties, which is a major breakthrough compared to all existing solution techniques for SD-SODTA. For the case of time-invariant network parameters, we reduce the SD-SO-DTA to a standard EAF problem on a dynamic network, which is constructed on the original roadway network without dividing it into cells. We prove that the EAF under free flow status is one of the optimal solutions of SD-SO-DTA, if cell properties follow a trapezoidal/triangular fundamental diagram. We use chain flows obtained on a static network to induce dynamic flows, an approach applicable to large-scale networks. Another contribution of this research is to provide a simple and practical algorithm solving the EAF with multiple sources, which has been an active research area for many years. Most existing studies involve submodular function optimization as subroutines, and thus are not practical for real-life implementation. This study’s contribution in this regard is the development of a practical algorithm that avoids submodular function optimization. The main body of the given method is comprised of |S⁺| iterations of earliest arrival s - t flow computations, where |S⁺| is the number of sources. Numerical results show that our multi-source EAF algorithm solves the SD-SO-DTA problem with time-invariant parameters to optimum.

Road traffic increases constantly and the negative consequences in the form of traffic jams can be realized especially in urban areas. In order to provide real time traffic information to road users and traffic managers, accurate computer models gain relevance. A software called Mobile Millennium Stockholm (MMS) was developed to estimate and predict travel times and has been implemented on a 7km test stretch in the north of Stockholm. The core of the software is the cell transmission model (CTM) which is a macroscopic traffic flow model based on aggregated speed observations. This thesis focuses on different calibration techniques of the so called fundamental diagram as an important input factor to the CTM. The diagrams illustrate the mathematical function which defines the relation between traffic flow, density and speed. The calibration is performed in different scenarios based on the least square (LS) and total least square (TLS) error minimization. Furthermore, sources, representing the traffic demand, and sinks, representing the surrounding of the modeled network, are implemented as dynamic parameters to model the change in traffic behavior throughout the day. Split ratios, as a representation of the drivers‘ route choice in the CTM are estimated and implemented as well. For the framework of this work, the MMS software is run in a pure prediction mode. The CTM is based on the source, sink, split and fundamental diagram parameters only and run forward in time. For each fundamental diagram calibration scenario an independent model run is performed. The evaluation of the scenarios is based on the output of the model. The results are compared to existing Bluetooth travel time measurements for the test stretch, which are used as ground truth observations, and a mean average percentage error (MAPE) is calculated. This leads to a most reasonable technique for the fundamental diagram calibration – the total least square error minimization.

Traffic problems caused by congestion are increasing in cities all over the world. As a traffic management tool traffic predictions can be used in order to make prevention actions against traffic congestion. There is one software for traffic state estimations called Mobile Millennium Stockholm (MMS) that are a part of a project for estimate real-time traffic information.In this thesis a framework for running traffic predictions in the MMS software have been implemented and tested on a stretch north of Stockholm. The thesis is focusing on the implementation and evaluation of traffic prediction by running a cell transmission model (CTM) forward in time.This method gives reliable predictions for a prediction horizon of up to 5 minutes. In order to improve the results for traffic predictions, a framework for dynamic inputs of demand and sink capacity has been implemented in the MMS system. The third part of the master thesis presents a model which adjusts the split ratios in a macroscopic traffic model based on driver behavior during congestion.

Les macrophages sont une cible cellulaire importante du VIH-1 et sont impliqués dans la propagation virale et la constitution du réservoir. Les patients infectés par le VIH-2 présentent un contrôle naturel de l'infection qui est généralement absent chez les patients infectés par le VIH-1. Nous avons étudié ici la relation entre les macrophages et le VIH-2 afin d'évaluer leur contribution à la physiopathologie de l'infection. L'assemblage de particules virales dans des macrophages dérivés de monocytes (MDM) infectés par le VIH-2 se fait au niveau de la membrane de compartiments internes semblables aux VCC documentés dans les MDM infectés par le VIH-1. Les VCC des MDM infectés par le VIH-1 et le VIH-2 partagent la même composition protéique, et la même morphologie. Contrairement à Gag du VIH-1, la protéine Gag du VIH-2 est absente du cytosol et presque exclusivement localisée dans les VCC, ce qui suggère que Gag du VIH-2 est rapidement transportée vers le VCC une fois synthétisée dans le cytosol. Les particules de VIH-2 produites de novo par les MDM peuvent mûrir, mais sont faiblement infectieuses et se transmettent inefficacement aux cellules T activés. Cette faible infectiosité n'est pas associée avec l'expression du facteur de restriction BST-2 et n'est pas non plus améliorée par une baisse des niveaux d'expression de BST-2 induite par Vpu. Nos données suggèrent que les macrophages infectés par le VIH-2 ne contribuent probablement pas à la production et à la dissémination du virus in vivo. Cependant, les macrophages infectés par le VIH-2 peuvent représenter une source potentielle d'antigènes viraux qui pourraient stimuler les réponses des cellules T spécifiques du virus. / Macrophages are an important cellular target of HIV-1 and are potentially involved in viral spreading and constitution of the viral reservoir. HIV-2-infected patients exhibit a natural virological control of the infection that is generally absent from HIV-1-infected patients. Here, we studied the relationship between macrophages and HIV-2 to approach their potential contribution to the physiopathology of HIV-2 infection. Viral particles assembly in HIV-2-infected monocyte-derived macrophages (MDMs) occurred at the limiting membrane of internal compartments similar to virus-containing compartments (VCCs) documented in HIV-1-infected MDMs. Indeed, VCCs from HIV-1 and HIV-2-infected MDMs shared protein composition, as seen by confocal microcopy, and morphology, as seen by electron microscopy. Strikingly, HIV-2 Gag was mostly absent from the cytosol and almost exclusively localized to the VCCs, whereas HIV-1 Gag was distributed in both locations, suggesting that HIV-2 Gag is rapidly transported to the VCC membranes once synthesized in the cytosol. HIV-2 particles produced de novo by MDMs can mature, but are poorly infectious and inefficiently transmitted to activated T cells. This low infectivity neither correlate with expression of the restriction factor BST-2, nor was improved by Vpu-induced down-modulation of BST-2 levels. Our data suggest that, HIV-2-infected macrophages are unlikely to contribute to viral production and dissemination in vivo. However, HIV-2-infected macrophages accumulate large amounts of intracellular virus that may represent a potential source of viral antigens that could stimulate virus specific T cell responses.

Urban transportation networks are vulnerable to various incidents. In order to combat the negative effects due to incident-related congestion, various mitigation strategies have been proposed and implemented. The effectiveness of these congestion mitigation strategies for incident conditions largely depends on the accuracy of information regarding network conditions. Therefore, an efficient and accurate procedure to determine the link states, reflected by flows and density over time, is essential to incident management. This thesis presents a user equilibrium Dynamic Traffic Assignment (DTA) model that incorporates the Cell Transmission Model (CTM) to evaluate the temporal variation of flow and density over links, which reflect the link states of a transportation network. Encapsulation of the CTM equips the model with the capability of accepting inputs of incidents like duration and capacity reduction. Moreover, the proposed model is capable of handling multiple origin-destination (OD) pairs. By using this model, the temporal variation of flows over links can be readily evaluated. The visualized prediction of link density variations is used to investigate the link state relationships. By isolating the effects of an incident, the parallel routes of a specific OD pair display the relationship of substituting for each other, which is consistent with the general expectation regarding such parallel routes. A closer examination of the density variations confirms the existence of a substitution relationship between the unshared links of the two parallel routes. This information regarding link state relationship can be used as general guidance for incident management purposes. / Master of Science

This dissertation addresses the development and analysis of optimization models for evacuation planning. Specifically we consider the cases of large-scale regional evacuation using household vehicles and hospital evacuation. Since it is difficult to estimate the exact number of people evacuating, we first consider the case where the population size is uncertain. We review the methods studied in the literature, mainly the strategy of using a deterministic counterpart, i.e., a single deterministic parameter to represent the uncertain population, and we show that these methods are not very effective in generating a good traffic management strategy. We provide alternatives, where we describe some networks where an optimal policy exist independent of the demand realization and we propose some simple heuristics for more complex ones. Next we consider the traffic management tools that can be generated from an evacuation plan. We start by introducing the cell transmission model with flow reduction. This model captures the flow reduction after the onset of congestion. We then discuss the management tools that can be extracted from this model. We also propose some simplification to the model formulation to enhance its tractability. A heuristic for generating a solution is also proposed, and its solution quality is analyzed. Finally, we discuss the hospital evacuation problem where we develop an integer programming model that integrates the building evacuation with the transportation of patients. The impact of building evacuation capabilities on the transportation plan is investigated through the case of a large regional hospital case study. We also propose a decomposition scheme to improve the tractability of the integer program. / Ph. D.

In the past, the study of Emergency Vehicle (EV) signal preemption has been mostly done using field studies. None of the simulation models that are currently commercially available have the capability to model the presence of EVs and simulate the traffic dynamics of the vehicles surrounding them. This study presents a macroscopic traffic model for examining the effect of signal preemption for EVs on traffic control measures, roadway capacity, and delays incurred to the vehicles on the side streets. The model is based on the cell transmission model, which is consistent with the hydrodynamic theory of traffic flow. A special component, in the form of a moving bottleneck that handles the traffic dynamics associated with the presence of EVs, was developed in the model. Several test scenarios were constructed to demonstrate the capabilities of the model for studying the impact of signal preemption on an arterial with multiple intersections under various traffic demand levels and varying frequencies of the arrival of EVs. Performance measures, such as average vehicle delay, maximum delay, and standard deviation of delay to traffic on all approaches, were obtained. An additional advantage of the model, apart from the capability to model EVs, is that the state-space equations used in the model can be easily incorporated into a mathematical programming problem. By coupling with a desired objective function, the model can be solved analytically. Optimal solutions can be generated to obtain insights into the development of traffic control strategies in the presence of EVs. / Master of Science