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- Language: English
- Creators: Ira A. Fulton Schools of Engineering
Description
Motorcycle fatalities have been increasing at a faster rate than the number of motorcycles being registered in the United States. There is limited analysis on the causes of fatal motorcycle crashes, specifically regarding different demographics, certain driver behavior, and various crash characteristics. It is important to be aware of how these factors relate to each other during a fatal motorcycle crash. This analysis focuses on these factors and explores potential steps to decrease motorcycle fatality rates using research and data from the Fatality Analysis Reporting System (FARS) from the National Highway Traffic Safety Administration (NHTSA), and data from the National Household Travel Survey (NHTS). Based on this data, there are noticeable trends between different genders and age groups. According to the analysis, males have a higher fatality rate than females, and their fatal crashes tend to involve multiple driver infractions such as drinking, speeding, not wearing a helmet, and driving without a license. Similarly, younger drivers have a higher fatality rate than older drivers, and their fatal crashes tend to involve multiple driver infractions. Although older drivers involved in fatal crashes usually drive more cautiously, they tend to be involved in single-vehicle crashes more often than younger drivers. Moving forward, implementing certain training programs directed towards particular demographics has the potential to decrease motorcycle rider fatalities.
ContributorsMoran, Sarah Elizabeth (Co-author) / Santilli, Amy (Co-author) / Pendyala, Ram (Thesis director) / Khoeini, Sara (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval
Description
Background
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
ContributorsYuan, Lei (Author) / Woodard, Alexander (Author) / Ji, Shuiwang (Author) / Jiang, Yuan (Author) / Zhou, Zhi-Hua (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / Ira A. Fulton Schools of Engineering (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2012-05-23
Description
Arizona's transportation infrastructure is in need of an update. The American Society of Civil Engineers (ASCE) State Infrastructure 2017 Report Card scores Arizona's roads at a D+ and Arizona's bridges at a B. These grades are indicative that the serviceability levels of the roads and bridges are less than adequate. These grades may seem tolerable in light of a national bridge C+ grade and a national road D grade, but the real problem lies in Arizona's existing funding gap that is in danger of exponentially increasing in the future. With an influx of vehicles on Arizona's roads and bridges, the cost of building, repairing, and maintaining them will grow and cause a problematic funding shortage. This report explores the current state of Arizona's roads and bridges as well as the policy and funding sources behind them, using statistics from the ASCE infrastructure report card and the Federal Highway Administration. Additionally, it discusses how regular, preventative maintenance for transportation infrastructure is the economically responsible choice for the state because it decreases delays and fuel expenses, prevents possible catastrophes, and increases human safety. To prioritize preventative transportation infrastructure maintenance, the common mentality that allows it to be sidelined for more newsworthy projects needs to be changed. Along with gaining preventative maintenance revenues through increasing vehicular taxes and fees, encouraging transportation policymakers and politicians to make economic decisions in favor of maintenance rather than waiting until failure is a reliable way to encourage regular, preventative maintenance.
ContributorsBurdett, Courtney (Author) / Hjelmstad, Keith (Thesis director) / Pendyala, Ram (Committee member) / Civil, Environmental and Sustainable Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis examines Care Not Cash, a welfare reform measure that replaced traditional cash General Assistance program payments for homeless persons in San Francisco with in-kind social services. Unlike most welfare reform measures, proponents framed Care Not Cash as a progressive policy, aimed at expanding social services and government care for this vulnerable population. Drawing on primary and secondary documents, as well as interviews with homelessness policy experts, this thesis examines the historical and political success of Care Not Cash, and explores the potential need for implementation of a similar program in Phoenix, Arizona.
ContributorsMcCutcheon, Zachary Ryan (Author) / Lucio, Joanna (Thesis director) / Williams, David (Committee member) / Bretts-Jamison, Jake (Committee member) / School of Public Affairs (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Improving the quality of Origin-Destination (OD) demand estimates increases the effectiveness of design, evaluation and implementation of traffic planning and management systems. The associated bilevel Sensor Location Flow-Estimation problem considers two important research questions: (1) how to compute the best estimates of the flows of interest by using anticipated data from given candidate sensors location; and (2) how to decide on the optimum subset of links where sensors should be located. In this dissertation, a decision framework is developed to optimally locate and obtain high quality OD volume estimates in vehicular traffic networks. The framework includes a traffic assignment model to load the OD traffic volumes on routes in a known choice set, a sensor location model to decide on which subset of links to locate counting sensors to observe traffic volumes, and an estimation model to obtain best estimates of OD or route flow volumes. The dissertation first addresses the deterministic route flow estimation problem given apriori knowledge of route flows and their uncertainties. Two procedures are developed to locate "perfect" and "noisy" sensors respectively. Next, it addresses a stochastic route flow estimation problem. A hierarchical linear Bayesian model is developed, where the real route flows are assumed to be generated from a Multivariate Normal distribution with two parameters: "mean" and "variance-covariance matrix". The prior knowledge for the "mean" parameter is described by a probability distribution. When assuming the "variance-covariance matrix" parameter is known, a Bayesian A-optimal design is developed. When the "variance-covariance matrix" parameter is unknown, Markov Chain Monte Carlo approach is used to estimate the aposteriori quantities. In all the sensor location model the objective is the maximization of the reduction in the variances of the distribution of the estimates of the OD volume. Developed models are compared with other available models in the literature. The comparison showed that the models developed performed better than available models.
ContributorsWang, Ning (Author) / Mirchandani, Pitu (Thesis advisor) / Murray, Alan (Committee member) / Pendyala, Ram (Committee member) / Runger, George C. (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2013
Description
Infrastructure systems are facing non-stationary challenges that stem from climate change and the increasingly complex interactions between the social, ecological, and technological systems (SETSs). It is crucial for transportation infrastructures—which enable residents to access opportunities and foster prosperity, quality of life, and social connections—to be resilient under these non-stationary challenges. Vulnerability assessment (VA) examines the potential consequences a system is likely to experience due to exposure to perturbation or stressors and lack of the capacity to adapt. Post-fire debris flow and heat represent particularly challenging problems for infrastructure and users in the arid U.S. West. Post-fire debris flow, which is manifested with heat and drought, produces powerful runoff threatening physical transportation infrastructures. And heat waves have devastating health effects on transportation infrastructure users, including increased mortality rates. VA anticipates the potential consequences of these perturbations and enables infrastructure stakeholders to improve the system's resilience. The current transportation climate VA—which only considers a single direct climate stressor on the infrastructure—falls short of addressing the wildfire and heat challenges. This work proposes advanced transportation climate VA methods to address the complex and multiple climate stressors and the vulnerability of infrastructure users. Two specific regions were chosen to carry out the progressive transportation climate VA: 1) the California transportation networks’ vulnerability to post-fire debris flows, and 2) the transportation infrastructure user’s vulnerability to heat exposure in Phoenix.
ContributorsLi, Rui (Author) / Chester, Mikhail V. (Thesis advisor) / Middel, Ariane (Committee member) / Hondula, David M. (Committee member) / Pendyala, Ram (Committee member) / Arizona State University (Publisher)
Created2022
Description
The emerging multimodal mobility as a service (MaaS) and connected and automated mobility (CAM) are expected to improve individual travel experience and entire transportation system performance in various aspects, such as convenience, safety, and reliability. There have been extensive efforts in the literature devoted to enhancing existing and developing new methodologies and tools to investigate the impacts and potentials of CAM systems. Due to the hierarchical nature of CAM systems and associated intrinsic correlated human factors and physical infrastructures from various resolutions, simply considering components across different levels into a single model may be practically infeasible and computationally prohibitive in operation and decision stages. One of the greatest challenges in existing studies is to construct a theoretically sound and computationally efficient architecture such that CAM system modeling can be performed in an inherently consistent cross-resolution manner. This research aims to contribute to the modeling of CAM systems on layered transportation networks, with a special focus on the following three aspects: (1) layered CAM system architecture with a tight network and modeling consistency, in which different levels of tasks can be efficiently performed at dedicated layers; (2) cross-resolution traffic state estimation in CAM systems using heterogeneous observations; and (3) integrated city logistics operation optimization in CAM for improving system performance.
ContributorsLu, Jiawei (Author) / Zhou, Xuesong (Thesis advisor) / Pendyala, Ram (Committee member) / Xue, Guoliang (Committee member) / Mittelmann, Hans (Committee member) / Arizona State University (Publisher)
Created2022
Description
This dissertation describes a series of four studies on cognitive aging, working memory, and cognitive flexibility in dogs (Canis lupus familiaris) and their wild relatives. In Chapters 2 and 3, I designed assessments for age-related cognitive deficits in pet dogs which can be deployed rapidly using inexpensive and accessible materials. These novel tests can be easily implemented by owners, veterinarians, and clinicians and therefore, may improve care for elderly dogs by aiding in the diagnosis of dementia. In addition, these widely deployable tests may facilitate the use of dementia in pet dogs as a naturally occurring model of Alzheimer’s Disease in humans.In Chapters 4 and 5, I modified one of these tests to demonstrate for the first time that coyotes (Canis latrans) and wolves (Canis lupus lupus) develop age-related deficits in cognitive flexibility. This was an important first step towards differentiating between the genetic and environmental components of dementia in dogs and in turn, humans. Unexpectedly, I also detected cognitive deficits in young, adult dogs and wolves but not coyotes. These finding add to a recent shift in understanding cognitive development in dogs which may improve cognitive aging tests as well as training, care, and use of working and pet dogs. These findings also suggest that the ecology of coyotes may select for flexibility earlier in development.
In Chapter 5, I piloted the use of the same cognitive flexibility test for red and gray foxes so that future studies may test for lifespan changes in the cognition of small-bodied captive canids. More broadly, this paradigm may accommodate physical and behavioral differences between diverse pet and captive animals. In Chapters 4 and 5, I examined which ecological traits drive the evolution of behavioral flexibility and in turn, species resilience. I found that wolves displayed less flexibility than dogs and coyotes suggesting that species which do not rely heavily on unstable resources may be ill-equipped to cope with human habitat modification. Ultimately, this comparative work may help conservation practitioners to identify and protect species that cannot cope with rapid and unnatural environmental change.
ContributorsVan Bourg, Joshua (Author) / Wynne, Clive D (Thesis advisor) / Aktipis, C. Athena (Committee member) / Gilby, Ian C (Committee member) / Young, Julie K (Committee member) / Arizona State University (Publisher)
Created2022
Description
ABSTRACTWith the National Aeronautics and Space Administration (NASA) Psyche Mission, humans will soon have the first opportunity to explore a new kind of planetary body: one composed mostly of metal as opposed to stony minerals or ices. Identifying the composition of asteroids from Earth-based observations has been an ongoing challenge. Although optical reflectance spectra, radar, and orbital dynamics can constrain an asteroid’s mineralogy and bulk density, in many cases there is not a clear or precise match with analogous materials such as meteorites. Additionally, the surfaces of asteroids and other small, airless planetary bodies can be heavily modified over geologic time by exposure to the space environment. To accurately interpret remote sensing observations of metal-rich asteroids, it is therefore necessary to understand how the processes active on asteroid surfaces affect metallic materials. This dissertation represents a first step toward that understanding. In collaboration with many colleagues, I have performed laboratory experiments on iron meteorites to simulate solar wind ion irradiation, surface heating, micrometeoroid bombardment, and high-velocity impacts. Characterizing the meteorite surface’s physical and chemical properties before and after each experiment can constrain the effects of each process on a metal-rich surface in space. While additional work will be needed for a complete understanding, it is nevertheless possible to make some early predictions of what (16) Psyche’s surface regolith might look like when humans observe it up close. Moreover, the results of these experiments will inform future exploration beyond asteroid Psyche as humans attempt to understand how Earth’s celestial neighborhood came to be.
ContributorsChristoph, John Morgan M. (Author) / Elkins-Tanton, Linda (Thesis advisor) / Williams, David (Committee member) / Dukes, Catherine (Committee member) / Sharp, Thomas (Committee member) / Bell III, James (Committee member) / Arizona State University (Publisher)
Created2023
Description
Priced Managed Lanes (MLs) have been increasingly advocated as one of the effective ways to mitigating congestion in recent years. This study explores a new and innovative pricing strategy for MLs called Travel Time Refund (TTR). The proposed TTR provides an additional option to paying drivers that insures their travel time by issuing a refund to the toll cost if they do not reach their destination within specified travel times due to accidents or other unforeseen circumstances. Perceived benefits of TTR include raised public acceptance towards priced MLs, utilization increase of HOV/HOT lanes, overall congestion mitigation, and additional funding for relevant transportation agencies. To gauge travelers’ interests of TTR and to analyse its possible impacts, a stated preference (SP) survey was performed. An exploratory and statistical analysis of the survey responses revealed negative interest towards HOT and TTR option in accordance with common wisdom and previous studies. However, it is found that travelers are less negative about TTR than HOT alone; supporting the idea, that TTR could make HOT facilities more appealing. The impact of travel time reliability and latent variables representing psychological constructs on travelers’ choices in response to this new pricing strategy was also analysed. The results indicate that along with travel time and reliability, the decision maker’s attitudes and the level of comprehension of the concept of HOT and TTR play a significant role in their choice making. While the refund option may be theoretically and analytically feasible, the practical implementation issues cannot be ignored. This study also provides a discussion of the potential implementation considerations that include information provision to connected and non-connected vehicles, distinction between toll-only and refund customers, measurement of actual travel time, refund calculation and processing and safety and human factors issues. As the market availability of Connected and Automated Vehicles (CAVs) is prognosticated by 2020, the potential impact of such technologies on effective demand management, especially on MLs is worth investigating. Simulation analysis was performed to evaluate the system performance of a hypothetical road network at varying market penetration of CAVs. The results indicate that Connected Vehicles (CVs) could potentially encourage and enhance the use of MLs.
ContributorsVadlamani, Sravani (Author) / Lou, Yingyan (Thesis advisor) / Pendyala, Ram (Committee member) / Zhou, Xuesong (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2018