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Description
In an effort to address the lack of literature in on-campus active travel, this study aims to investigate the following primary questions:<br/>• What are the modes that students use to travel on campus?<br/>• What are the motivations that underlie the mode choice of students on campus?<br/>My first stage of research involved a series of qualitative investigations. I held one-on-one virtual interviews with students in which I asked them questions about the mode they use and why they feel that their chosen mode works best for them. These interviews served two functions. First, they provided me with insight into the various motivations underlying student mode choice. Second, they provided me with an indication of what explanatory variables should be included in a model of mode choice on campus.<br/>The first half of the research project informed a quantitative survey that was released via the Honors Digest to attract student respondents. Data was gathered on travel behavior as well as relevant explanatory variables.<br/>My analysis involved developing a logit model to predict student mode choice on campus and presenting the model estimation in conjunction with a discussion of student travel motivations based on the qualitative interviews. I use this information to make a recommendation on how campus infrastructure could be modified to better support the needs of the student population.
ContributorsMirtich, Laura Christine (Author) / Salon, Deborah (Thesis director) / Fang, Kevin (Committee member) / School of Public Affairs (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Defines the concept of the arcology as conceived by architect Paolo Soleri. Arcology combines "architecture" and "ecology" and explores a visionary notion of a self-contained urban community that has agricultural, commercial, and residential facilities under one roof. Two real-world examples of these projects are explored: Arcosanti, AZ and Masdar City, Abu Dhabi, UAE. Key aspects of the arcology that could be applied to an existing urban fabric are identified, such as urban design fostering social interaction, reduction of automobile dependency, and a development pattern that combats sprawl. Through interviews with local representatives, a holistic approach to applying arcology concepts to the Phoenix Metro Area is devised.
ContributorsSpencer, Sarah Anne (Author) / Manuel-Navarrete, David (Thesis director) / Salon, Deborah (Committee member) / Barrett, The Honors College (Contributor) / School of Geographical Sciences and Urban Planning (Contributor) / School of Sustainability (Contributor)
Created2015-05
Residential Choice’s Impact on Sustainable Transportation Options: A Study in the Phoenix Metro Area
Description
This study adds to the literature about residential choice and sustainable transportation. Through the interviews and the personal stories gathered, there was diversity shown in the residential location choice process. We also noticed that “commute” means different things to different households, and that many people did not consider their commute to work to be a primary factor determining their final home location. Moreover, many people were willing to increase their commute time, or trade access to desirable amenities for a longer commute. Commuting time to work was one example of the tradeoffs that homeowners make when choosing a home, but there were also others such as architectural type and access to neighborhood amenities. Lastly, time constraints proved to be a very significant factor in the home buying process. Several of our households had such strict time constraints that limited their search to a point of excluding whole areas. Overall, our study sheds light on transportation’s role in residential choice and underscores the complexity of the location choice process.
ContributorsKats, Elyse Nicole (Author) / Salon, Deborah (Thesis director) / Kuminoff, Nicolai (Committee member) / School of Sustainability (Contributor) / School of Geographical Sciences and Urban Planning (Contributor) / School of Community Resources and Development (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Since 1979, Phoenix has been organized into 15 theoretically self-contained urban villages in order to manage rapid growth. The major objective of the village plan was to decrease demand for personal vehicle use by internalizing travel to the closest village core, or an adjacent village core, instead of expanding travel to one metropolitan core. Phoenix’s transition from a monocentric urban structure to a more polycentric structure has yet to be studied for its efficacy on this goal of turning personal vehicle travel inward. This paper pairs more conventional measures of automobile dependence, such as, use of alternative modes of transportation in place of private vehicle use and commute times, with more nuanced measures of internal travel between work and home, job housing ratio, and job industry breakdowns to describe Phoenix’s reliance on automobiles. Phoenix’s internal travel ratios were higher when compared to adjacent cities and either on-par or lower when compared to non-adjacent cities that were comparable to Phoenix in population density and size.
ContributorsCuiffo, Kathryn Victoria (Author) / King, David (Thesis director) / Salon, Deborah (Committee member) / Dean, W.P. Carey School of Business (Contributor) / Department of Psychology (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Transit ridership is declining in most cities throughout America. Public transportation needs to be improved in order for cities to handle urban growth, reduce carbon footprint, and increase mobility across income groups. In order to determine what causes changes in transit ridership, I performed a descriptive analysis of five metro areas in the United States. I studied changes in transit ridership in Dallas, Denver, Minneapolis, Phoenix, and Seattle from 2013 through 2017 to determine where public transportation works and where it does not work. I used employment, commute, and demographic data to determine what affects transit ridership. Each metro area was studied as a separate case because the selected cities are difficult to compare directly. The Seattle metro area was the only metro to increase transit ridership throughout the period of the study. The Minneapolis metro area experienced a slight decline in transit ridership, while Phoenix and Denver declined significantly. The Dallas metro area declined most of the five cities studied. The denser metro areas fared much better than the less dense areas. In order to increase transit ridership cities should increase the density of their city and avoid sprawl. Certain factors led to declines in ridership in certain metro areas but not all. For example, gentrification contributed to ridership decline in Denver and Minneapolis, but Seattle gentrified and increased ridership. Dallas and Phoenix experienced low-levels of gentrification but experienced declining ridership. Therefore, organizations such as the American Public Transportation Association (APTA) who attempt to find the single factor causing the decline in transit ridership, or the one factor that will increase ridership are misguided. Above all, this thesis shows that there is no single factor causing the ridership decline in each metro area, and it is wise to study each metro area as a separate case.
ContributorsBarro, Joshua Andrew (Co-author) / Barro, Joshua (Co-author) / King, David (Thesis director) / Salon, Deborah (Committee member) / School of Politics and Global Studies (Contributor) / Walter Cronkite School of Journalism & Mass Comm (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Many researchers aspire to create robotics systems that assist humans in common office tasks, especially by taking over delivery and messaging tasks. For meaningful interactions to take place, a mobile robot must be able to identify the humans it interacts with and communicate successfully with them. It must also be able to successfully navigate the office environment. While mobile robots are well suited for navigating and interacting with elements inside a deterministic office environment, attempting to interact with human beings in an office environment remains a challenge due to the limits on the amount of cost-efficient compute power onboard the robot. In this work, I propose the use of remote cloud services to offload intensive interaction tasks. I detail the interactions required in an office environment and discuss the challenges faced when implementing a human-robot interaction platform in a stochastic office environment. I also experiment with cloud services for facial recognition, speech recognition, and environment navigation and discuss my results. As part of my thesis, I have implemented a human-robot interaction system utilizing cloud APIs into a mobile robot, enabling it to navigate the office environment, identify humans within the environment, and communicate with these humans.
ContributorsDSouza, Daniel Anand (Author) / Kambhampati, Subbarao (Thesis director) / Zhang, Yu (Committee member) / Computer Science and Engineering Program (Contributor) / School of Computing, Informatics, and Decision Systems Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Traditional Reinforcement Learning (RL) assumes to learn policies with respect to reward available from the environment but sometimes learning in a complex domain requires wisdom which comes from a wide range of experience. In behavior based robotics, it is observed that a complex behavior can be described by a combination of simpler behaviors. It is tempting to apply similar idea such that simpler behaviors can be combined in a meaningful way to tailor the complex combination. Such an approach would enable faster learning and modular design of behaviors. Complex behaviors can be combined with other behaviors to create even more advanced behaviors resulting in a rich set of possibilities. Similar to RL, combined behavior can keep evolving by interacting with the environment. The requirement of this method is to specify a reasonable set of simple behaviors. In this research, I present an algorithm that aims at combining behavior such that the resulting behavior has characteristics of each individual behavior. This approach has been inspired by behavior based robotics, such as the subsumption architecture and motor schema-based design. The combination algorithm outputs n weights to combine behaviors linearly. The weights are state dependent and change dynamically at every step in an episode. This idea is tested on discrete and continuous environments like OpenAI’s “Lunar Lander” and “Biped Walker”. Results are compared with related domains like Multi-objective RL, Hierarchical RL, Transfer learning, and basic RL. It is observed that the combination of behaviors is a novel way of learning which helps the agent achieve required characteristics. A combination is learned for a given state and so the agent is able to learn faster in an efficient manner compared to other similar approaches. Agent beautifully demonstrates characteristics of multiple behaviors which helps the agent to learn and adapt to the environment. Future directions are also suggested as possible extensions to this research.
ContributorsVora, Kevin Jatin (Author) / Zhang, Yu (Thesis advisor) / Yang, Yezhou (Committee member) / Praharaj, Sarbeswar (Committee member) / Arizona State University (Publisher)
Created2021
Description
Retinotopic map, the map between visual inputs on the retina and neuronal activation in brain visual areas, is one of the central topics in visual neuroscience. For human observers, the map is typically obtained by analyzing functional magnetic resonance imaging (fMRI) signals of cortical responses to slowly moving visual stimuli on the retina. Biological evidences show the retinotopic mapping is topology-preserving/topological (i.e. keep the neighboring relationship after human brain process) within each visual region.
Unfortunately, due to limited spatial resolution and the signal-noise ratio of fMRI, state of art retinotopic map is not topological.
The topic was to model the topology-preserving condition mathematically, fix non-topological retinotopic map with numerical methods, and improve the quality of retinotopic maps.
The impose of topological condition, benefits several applications.
With the topological retinotopic maps, one may have a better insight on human retinotopic maps, including better cortical magnification factor quantification, more precise description of retinotopic maps, and potentially better exam ways of in Ophthalmology clinic.
ContributorsTu, Yanshuai (Author) / Wang, Yalin (Thesis advisor) / Lu, Zhong-Lin (Committee member) / Crook, Sharon (Committee member) / Yang, Yezhou (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2022
Description
This dissertation consists of three chapters that investigate the rapid adoption and complex implementation of city commitments to transition to 100% renewable energy (100RE). The first paper uses a two-stage, mixed methods approach to examine 100RE commitments across the US, combining a multivariate regression of demographic, institutional, and policy factors in adoption and six interview-based state case studies to discuss implementation. Adoption of this non-binding commitment progressed rapidly for city councils around the US. Results show that many cities passed 100RE commitments with no implementation plan and minimal understanding of implementation challenges. This analysis highlights that many cities will need new institutions and administrative capacities for successful implementation of these ambitious new policies. While many cities abandoned the commitment soon after adoption, collaboration allowed cities in a few states to break through and pursue implementation, examined further in the next two studies. The second paper is a qualitative case study examining policymaking for the Utah Community Renewable Energy Act. Process tracing methods are used to identify causal factors in enacting this legislation at the state level and complementary resolutions at the local level. This Act was passed through the leadership and financial backing of major cities and committed the investor-owned utility to fulfill any city 100RE resolutions passed through 2019. Finally, the third paper is a mixed-methods, descriptive case study of the benefits of Community Choice Aggregation (CCA) in California, which many cities are using to fulfill their 100RE commitments. Cities have adopted CCAs to increase their local voice in the energy process, while fulfilling climate and energy goals. Overall, this research shows that change in the investor-owned utility electricity system is in fact possible from the city scale, though many cities will need institutional innovation to implement these policies and achieve the change they desire. While cities with greater resources are better positioned to make an impact, smaller cities can collaborate to similarly influence the energy system. Communities are interested in lowering energy costs for customers where possible, but the central motivations in these cases were the pursuit of sustainability and increasing local voice in energy decision-making.
ContributorsKunkel, Leah Christine (Author) / Breetz, Hanna L (Thesis advisor) / Parker, Nathan (Committee member) / Salon, Deborah (Committee member) / Arizona State University (Publisher)
Created2022
Description
Multiple robotic arms collaboration is to control multiple robotic arms to collaborate with each other to work on the same task. During the collaboration, theagent is required to avoid all possible collisions between each part of the robotic arms.
Thus, incentivizing collaboration and preventing collisions are the two principles which
are followed by the agent during the training process. Nowadays, more and more
applications, both in industry and daily lives, require at least two arms, instead of
requiring only a single arm. A dual-arm robot satisfies much more needs of different
types of tasks, such as folding clothes at home, making a hamburger in a grill or
picking and placing a product in a warehouse.
The applications done in this paper are all about object pushing. This thesis
focuses on how to train the agent to learn pushing an object away as far as possible.
Reinforcement Learning (RL), which is a type of Machine Learning (ML), is then
utilized in this paper to train the agent to generate optimal actions. Deep Deterministic
Policy Gradient (DDPG) and Hindsight Experience Replay (HER) are the two RL
methods used in this thesis.
ContributorsLin, Steve (Author) / Ben Amor, Hani (Thesis advisor) / Redkar, Sangram (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2023