Dynamic traffic assignment-based modeling paradigms for sustainable transportation planning and urban development

textTransportation planning and urban development in the United States have synchronously emerged over the past few decades to encompass goals associated with sustainability, improved connectivity, complete streets and mitigation of environmental impacts. These goals have evolved in tandem with some of the relatively more traditional objectives of supply-side improvements such as infrastructure and capacity expansion. Apart from the numerous federal regulations in the US transportation sector that reassert sustainability motivations, metropolitan planning organizations and civic societies face similar concerns in their decision-making and policy implementation. However, overall transportation planning to incorporate these wide-ranging objectives requires characterization of large-scale transportation systems and traffic flow through them, which is dynamic in nature, computationally intense and a non-trivial problem. Thus, these contemporary questions lie at the interface of transportation planning, urban development and sustainability planning. They have the potential of being effectively addressed through state-of-the-art transportation modeling tools, which is the main motivation and philosophy of this thesis. From the research standpoint, some of these issues have been addressed in the past typically from the urban design, built-environment, public health and vehicle technology and mostly qualitative perspectives, but not as much from the traffic engineering and transportation systems perspective---a gap in literature which the thesis aims to fill. Specifically, it makes use of simulation-based dynamic traffic assignment (DTA) to develop modeling paradigms and integrated frameworks to seamlessly incorporate these in the transportation planning process. In addition to just incorporating them in the planning process, DTA-based paradigms are able to accommodate numerous spatial and temporal dynamics associated with system traffic, which more traditional static models are not able to. Besides, these features are critical in the context of the planning questions of this study. Specifically, systemic impacts of suburban and urban street pattern developments typically found in US cities in past decades of the 20th century have been investigated. While street connectivity and design evolution is mostly regulated through local codes and subdivision ordinances, its impacts on traffic and system congestion requires modeling and quantitative evidence which are explored in this thesis. On the environmental impact mitigation side, regional emission inventories from the traffic sector have also been quantified. Novel modeling approaches for the street connectivity-accessibility problem are proposed. An integrated framework using the Environmental Protection Agency's regulatory MOVES model has been developed, combining it with mesoscopic-level DTA simulation. Model demonstrations and applications on real and large-sized study areas reveal that different levels of connectivity and accessibility have substantial impacts on system-wide traffic---as connectivity levels reduce, traffic and congestion metrics show a gradually increasing trend. As regards emissions, incorporation of dynamic features leads to more realistic emissions inventory generation compared to default databases and modules, owing to consideration of the added dynamic features of system traffic and region-specific conditions. Inter-dependencies among these sustainability planning questions through the common linkage of traffic dynamics are also highlighted. In summary, the modeling frameworks, analyses and findings in the thesis contribute to some ongoing debates in planning studies and practice regarding ideal urban designs, provisions of sustainability and complete streets. Furthermore, the integrated emissions modeling framework, in addition to sustainability-related contributions, provides important tools to aid MPOs and state agencies in preparation of state implementation plans for demonstrating conformity to national ambient air-quality standards in their regions and counties. This is a critical condition for them to receive federal transportation funding.Civil, Architectural, and Environmental Engineerin

Economical Approach To Design Of Passive Distributed Antenna System

With increase in the indoor usage of communication, there has been increase in the need for optimal design of mobile coverage for buildings with a lot of users. Cellular service companies had been pushing the limits with their macro-cell approach however, with the advent of 4G LTE and their higher frequency use, the penetration inside the buildings adds to their troubles. A Distributed Antenna System(DAS) extends the mobile coverage from the base station to distributed antennas through a network topology of coaxial cables and power splitters. Though the solution of DAS would solve the problem of mobile coverage but the total cost of ownership is a major obstacle. To reduce the total cost of ownership for enterprises, the need to optimize the design arises. This work researches the use of a popular computational method to optimize the design of in-building passive distributed antenna system with iterative improvements. The application of Particle Swarm Optimization(PSO) to the design problem reduces the cost of the deployment and also provides a quicker solution than brute force search. The model converges on an optimal design solution and stops execution at the stop criteria which has been empirically proven as appropriate. To make the design topology compatible with the particle swarm optimization, tree topology of passive DAS is converted to Prufer code. This allows the PSO algorithm to traverse through different solutions in the Euclidean space. The current optimization methods have only been applied to either optimizing the length of the cable or the equipment selection. This approach provides optimization for the complete deployment of passive DAS. Test results of the model show that we achieve the design way more quickly due to reduction in the complexity and the cost is reduced for the deployment due to optimal design

Experiments on the DCASE Challenge 2016: Acoustic Scene Classification and Sound Event Detection in Real Life Recording

In this paper we present our work on Task 1 Acoustic Scene Classi- fication and Task 3 Sound Event Detection in Real Life Recordings. Among our experiments we have low-level and high-level features, classifier optimization and other heuristics specific to each task. Our performance for both tasks improved the baseline from DCASE: for Task 1 we achieved an overall accuracy of 78.9% compared to the baseline of 72.6% and for Task 3 we achieved a Segment-Based Error Rate of 0.76 compared to the baseline of 0.91

Recognition of Aminated Guests by Acyclic Cucurbiturils in Biological Conditions

The acyclic cucurbituril Motor2 has already been well documented in its binding to several types of molecular guests in phosphate buffer. However, while these tests provide a rough idea of motor2 affinity to different types of guests, they are incomplete in that they do not reflect how motor2 actually binds in body conditions. The human body contains many proteins and macromolecules that can affect the host-guest interactions of motor2, so it is important for new binding constants to be measured for motor2 in body conditions. In order to do this, Isothermal Titration Calorimetry (ITC) was used to measure motor2 binding constants to several different guest types in several different solutions, including albumin and fetal bovine serum. It was found that when tested with cyclic, monoaminated guests, motor2 binding affinity did not decrease significantly from phosphate to protein serum solvents. This retained affinity held across several different ring sizes and shapes. Motor2 binding affinity did suffer greatly in protein serum for guests that were linear, regardless of how many amines they had. The results also indicated that more hydrophobic guests do not bind as well to motor2 once albumin and other proteins ae introduced to solution, while hydrophilic, polar guests have better affinity retention. The ITC testing results indicated that motor2 binding in body conditions is heavily dependent on the shape of the guests it is binding to, and that motor2 would be most effective at its purpose in the human body if it was used to target cyclic amines and similar types.LSAMP NS

Ringo: Interactive Graph Analytics on Big-Memory Machines

We present Ringo, a system for analysis of large graphs. Graphs provide a way to represent and analyze systems of interacting objects (people, proteins, webpages) with edges between the objects denoting interactions (friendships, physical interactions, links). Mining graphs provides valuable insights about individual objects as well as the relationships among them. In building Ringo, we take advantage of the fact that machines with large memory and many cores are widely available and also relatively affordable. This allows us to build an easy-to-use interactive high-performance graph analytics system. Graphs also need to be built from input data, which often resides in the form of relational tables. Thus, Ringo provides rich functionality for manipulating raw input data tables into various kinds of graphs. Furthermore, Ringo also provides over 200 graph analytics functions that can then be applied to constructed graphs. We show that a single big-memory machine provides a very attractive platform for performing analytics on all but the largest graphs as it offers excellent performance and ease of use as compared to alternative approaches. With Ringo, we also demonstrate how to integrate graph analytics with an iterative process of trial-and-error data exploration and rapid experimentation, common in data mining workloads.Comment: 6 pages, 2 figure

A Review of Question Answering Systems: Approaches, Challenges, and Applications

Question answering (QA) systems are a type of natural language processing (NLP) technology that provide precise and concise answers to questions posed in natural language. These systems have the potential to revolutionize the way we access information and can be applied in a wide range of fields including education, customer service, and health care.There are several approaches to building QA systems, including rule-based, information retrieval, and machine learning-based approaches. Rule-based systems rely on predefined rules and patterns to extract answers from a given text, while information retrieval systems use search algorithms to retrieve relevant information from a large database. Machine learning-based systems, on the other hand, use training data to learn to extract answers from text.One of the main challenges faced by QA systems is the need to understand the context and intent behind a question. This requires the system to have a deep understanding of the language and the ability to make inferences based on the given information. Another challenge is the need to extract relevant information from a large and potentially unstructured dataset.Despite these challenges, QA systems have a wide range of applications, including education, customer service, and health care. In education, QA systems can be used to provide personalized learning experiences and help students learn more efficiently. In customer service, QA systems can be used to handle a high volume of queries and provide quick and accurate responses to customers. In health care, QA systems can be used to assist doctors and patients by providing timely and accurate information about medical conditions and treatments.Overall, this review aims to provide a comprehensive overview of QA systems, their approaches, challenges, and applications. By understanding the current state of development and the potential impact of QA systems, we can better utilize these technologies to improve various industries and enhance the way we access information