The purpose of this study was to include consideration for intersections into the previously created GIS traffic control planning tool. Available data for making intersection control calculations were collected and integrated into the design of the tool. The limitations created by required assumptions were addressed, as well as more advanced techniques for overcoming these problems. The tool can be use to estimate capacity calculations at any signalized intersection within the NCTCOG modeling region. These calculations can be used to inform users about the effects of a construction plan. Inputs for using dynamic traffic assignment to further understand these effects is then addressed, focusing on the development of a subnetwork to reduce computation time for multiple temporary traffic control plans. / text

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No description available.

Road safety and traffic congestion are two of the critical issues facing the transportation profession today. As a means to promote safety and efficiency at United States intersections modern roundabouts are becoming more and more common. Over the last ten years, roundabouts implementation methodologies have been developed using data collected at U.S. roundabouts. These methodologies were first published in National Cooperative Highway Report 572: Roundabouts in the United States and more recently in the second edition of the national roundabout guidelines. This work attempts to validate the use of these methodologies for roundabouts in the state of Texas and also enhance guidelines for evaluating roundabout operations by exploring the effects of exiting flow, origin-destination patterns, and mean speed on roundabout entry-lane capacity. Capacity results from VISSIM are compared to the Highway Capacity Manual entry-lane capacity curve and results from SIDRA. / text

With the integration of renewable and distributed energy resources (DER) and advances in metering infrastructure, power systems are undergoing rapid modernization that brings forward new challenges and possibilities, which call for more advanced learning, analysis, and planning tools. While there are numerous problems present in the modern power grid, in this work, this work has addressed four of the most prominent challenges and has shown that how the new advances in generation and metering can be leveraged to address the challenges that arose by them. With regards to learning in power systems, we first have tackled power distribution system topology identification, since knowing the topology of the power grid is a crucial piece in any meaningful optimization and control task. The topology identification presented in this work is based on the idea of emph{prob-to-learn}, which is perturbing the power grid with small power injections and using the metered response to learn the topology. By using maximum-likelihood estimation, we were able to formulate the topology identification problem as a mixed-integer linear program. We next have tackled the prominent challenge of finding optimal flexibility of aggregators in distribution systems, which is a crucial step in utilizing the capacity of distributed energy resources as well as flexible loads of the distribution systems and to aid transmission systems to be more efficient and reliable. We have shown that the aggregate flexibility of a group of devices with uncertainties and non-convex models can be captured with a quadratic classifier and using that classifier we can design a virtual battery model that best describes the aggregate flexibility. For power system analysis and planning, we have addressed fast probabilistic hosting capacity analysis (PHCA), which is studying how DERs and the intermittency that they bring to the power system can impact the power grid operation in the long term. We have shown that interconnection studies can be sped up by a factor of 20 without losing any accuracy. By formulating a penalized optimal power flow (OPF), we were able to pose PHCA as an instance of multiparametric programming (MPP), and then leveraged the nice properties of MPP to efficiently solve a large number of OPFs. Regarding planning in power systems, we have tackled the problem of strategic investment in energy markets, in which we have utilized the powerful toolbox of multiparametric programming to develop two algorithms for strategic investment. Our MPP-aided grid search algorithm is useful when the investor is only considering a few locations and our MPP-aided gradient descent algorithm is useful for investing in a large number of locations. We next have presented a data-driven approach in finding the flexibility of aggregators in power systems. Finding aggregate flexibility is an important step in utilizing the full potential of smart and controllable loads in the power grid and it's challenging since an aggregator controls a large group of time-coupled devices that operate with non-convex models and are subject to random externalities. We have shown that the aggregate flexibility can be accurately captured with an ellipsoid and then used Farkas' lemma to fit a maximal volume polytope inside the aforementioned ellipsoid. The numerical test showcases that we can capture 10 times the volume that conventional virtual generator models can capture. / Doctor of Philosophy / With the integration of renewable and distributed energy resources (DER) and advances in metering infrastructure, power systems are undergoing rapid modernization that brings forward new challenges and possibilities, which call for more advanced learning, analysis, and planning tools. While there are numerous problems present in the modern power grid, in this work, this work has addressed four of the most prominent challenges and has shown that how the new advances in generation and metering can be leveraged to address the challenges that arose by them. With regards to learning in power systems, we first have tackled power distribution system topology identification, since knowing the topology of the power grid is a crucial piece in any meaningful optimization and control task. We next have tackled the prominent challenge of finding optimal flexibility of aggregators in distribution systems, which is a crucial step in utilizing the capacity of distributed energy resources as well as flexible loads of the distribution systems and to aid transmission systems to be more efficient and reliable. For power system analysis and planning, we have addressed fast probabilistic hosting capacity analysis (PHCA), which is studying how DERs and the intermittency that they bring to the power system can impact the power grid operation in the long term. We have shown that interconnection studies can be sped up by a factor of 20 without losing any accuracy. Regarding planning in power systems, we have tackled the problem of strategic investment in energy markets, in which we have utilized the powerful toolbox of multiparametric programming to develop two algorithms for strategic investment. We next have presented a data-driven approach in finding the flexibility of aggregators in power systems. Finding aggregate flexibility is an important step in utilizing the full potential of smart and controllable loads in the power grid and it's challenging since an aggregator controls a large group of time-coupled devices that operate with non-convex models and are subject to random externalities.

Multihop Cellular Networks (MCNs) form combined wireless paradigm that carries the advantages of both traditional cellular networks and wireless multihop relay. Cellular networks depend on a fixed infrastructure to provide wide area coverage for users with high mobility profile. Multihop relay networks depend on wireless devices inside the network to relay signals through multiple hops from source to destination. MCNs were proposed to overcome inherent drawbacks in cellular networks like congestion and dead spots. These gains build on the characteristics of multihop relay that result in increased capacity, decrease energy depletion and virtually extended coverage. But while these gains have been widely accepted and advocated, they have not been verified in rigor. A realistic need therefore exists to quantify these gains in order to realize more capable network management functionalities for this new paradigm. In this thesis, we present an analytical framework for MCNs. We quantify the capacity and energy consumption in MCNs, while considering various call distributions, network loads and transmission power. We apply our framework to Code Division Multiple Access (CDMA) cellular networks, which are very dependent on interference levels in their performance. Our results show that capacity can be increased in CDMA cellular networks using multihop relay by increasing either the number of simultaneous calls or data rates. We also demonstrate that consumed energy is decreased in MCNs, especially in environments with high path loss. We validate that multihop relay is most rewarding when calls tend to originate near cell borders. Beyond verifying basic claims, we explore other potential gains of MCNs. We investigate the viability of congestion relief and load balancing and substantiate the benefits for congested cells neighbored by lightly loaded cells. Load balancing has also been shown to increase data rates and fairness in user allocations. Lastly, we explore enabling multimedia applications in MCNs and study the application of data rate adaptations given multiple classes of service. A key advantage of our work is that, while applied to CDMA in this thesis, the presented analytical framework can be extended to other technologies. The framework also accommodates both mobile and fixed network relay elements, expanding its applicability to next generation cellular networks. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2009-01-30 09:34:39.735

The continuing growth in aviation has meant that the 35 largest airports in Europe reached saturation in 2005. The consequences have been increasing air traffic congestion, delays and associated costs. There is therefore a clear need to create more capacity. However, airports in particular and the air transport system in general are also subject to sudden fluctuations in demand and capacity. This research synthesizes the mechanisms of airport capacity fluctuations through the analytical formulation of concepts of capacity dynamics, capacity elasticities and capacity stability. It demonstrates the usability of these concepts through, firstly, a case study application to Brussels National Airport and, secondly, the development of a 'proof of concept' decision-support tool for strategic and tactical airport planning. Capacity dynamics and elasticities provide a performance indication as to how quickly capacity is able to change in response to fluctuations brought about by one or more capacity disrupters, whilst capacity stability provides airport planners with a measure of capacity robustness. These three concepts - capacity dynamics, elasticities and stability - contribute to a better a priori understanding of the airport system to be modelled. They demonstrate a better quantification of the impact and sensitivity of all the factors that affect runway capacity. It is also shown how the three concepts can assist in a better quantification of the risk of potential capacity fluctuation within the scope of airport planning. Based on this analytical formulation and quantification, mitigation should be an integral part of any effective airport plan in order to predict better the response to any given potential capacity degradation. It has been found that, from a capacity perspective, an airport becomes less stable the higher its level of performance. This capacity/stability paradox enables the ultimate goal of investment in capacity enhancement to be challenged, and it is legitimately questioned whether a similar investment would not be more worthwhile at secondary airports rather than at major airports.

The subject of Master’s thesis is the variant redevelopment of cover crossroad, which is part of over-level junction of roads I/52 and II/152 near the village Želešice. The existing junctions are unsatisfactory in terms of capacity and long queues arise. The thesis contains a proposal of 3 variants, which are looking for different solutions to increase the capacityy of level crossings. Part of the thesis is a traffic survey, determination of traffic intensity, capacity analysis of current state and designed variants.

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The dissertation develops analytical models that estimate the capacity of freeway weaving sections. The analytical models are developed using simulated data that were compiled using the INTEGRATION software. Consequently, the first step of the research effort is to validate the INTEGRATION lane-changing modeling procedures and the capacity estimates that are derived from the model against field observations. The INTEGRATION software is validated against field data gathered by the University of California at Berkeley by comparing the lateral and longitudinal distribution of simulated and field observed traffic volumes categorized by O-D pair on nine weaving sections in the Los Angeles area. The results demonstrate a high degree of consistency between simulated and field observed traffic volumes within the various weaving sections. Subsequently, the second validation effort compares the capacity estimates of the INTEGRATION software to field observations from four weaving sections operating at capacity on the Queen Elizabeth Way (QEW) in Toronto, Canada. Again, the results demonstrate that the capacity estimates of the INTEGRATION software are consistent with the field observations both in terms of absolute values and temporal variability across different days. The error was found to be in the range of 10% between simulated and field observed capacities. Prior to developing the analytical models, the dissertation presents a systematic analysis of the factors that impact the capacity of freeway weaving sections, which were found to include the length of the weaving section, the weaving ratio (a new parameter that is developed as part of this research effort), the percentage of heavy vehicles, and the speed limit differential between freeway and on- and off-ramps. The study demonstrates that the weaving ratio, which is currently defined as the ratio of the lowest weaving volume to the total weaving volume in the 2000 Highway Capacity Manual, has a significant impact on the capacity of weaving sections. The study also demonstrates that the weaving ratio is an asymmetric function and thus should reflect the source of the weaving volume. Consequently, a new definition for the weaving ratio is introduced that explicitly identifies the source of the weaving volume. In addition, the study demonstrates that the length of the weaving section has a larger impact on the capacity of weaving sections for short lengths and high traffic demands. Furthermore, the study demonstrates that there does not exist enough evidence to conclude that the speed limit differential between mainline freeway and on- and off-ramps has a significant impact on weaving section capacities. Finally, the study demonstrates that the HCM procedures model the heavy duty vehicle impacts reasonably well. This dissertation presents the development of new capacity models for freeway weaving sections. In these models, a new definition of the weaving ratio that explicitly accounts for the source of weaving volume is introduced. The proposed analytical models estimate the capacity of weaving sections to within 12% of the simulated data, while the HCM procedures exhibit errors in the range of 114%. Among the newly developed models, the Artificial Neural Network (ANN) models performs slightly better that the statistical models in terms of model prediction errors. However, the sensitivity analysis results demonstrate unrealistic behavior of the ANN models under certain conditions. Consequently, the use of a statistical model is recommended because it provides a high level of accuracy while providing accurate model responses to changes in model input parameters (good response to the gradient of the input parameters). / Ph. D.