ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Transportation
Description
Nowadays ports play a critic role in the supply chains of contemporary companies and global commerce. Since the ports' operational effectiveness is critical on the development of competitive supply chains, their contribution to regional economies is essential. With the globalization of markets, the traffic of containers flowing through the different ports has increased significantly in the last decades. In order to attract additional container traffic and improve their comparative advantages over the competition, ports serving same hinterlands explore ways to improve their operations to become more attractive to shippers. This research explores the hypothesis that lowering the variability of the service time observed in the handling of containers, a port reduces the total logistics costs of their customers, increase its competiveness and that of their customers. This thesis proposes a methodology that allows the quantification of the variability existing in the services of a port derived from factors like inefficient internal operations, vessel congestion or external disruptions scenarios. It focuses on assessing the impact of this variability on the user's logistic costs. The methodology also allows a port to define competitive strategies that take into account its variability and that of competing ports. These competitive strategies are also translated into specific parameters that can be used to design and adjust internal operations. The methodology includes (1) a definition of a proper economic model to measure the logistic impact of port's variability, (2) a network analysis approach to the defined problem and (3) a systematic procedure to determine competitive service time parameters for a port. After the methodology is developed, a case study is presented where it is applied to the Port of Guaymas. This is done by finding service time parameters for this port that yield lower logistic costs than the observed in other competing ports.
ContributorsMeneses Preciado, Cesar (Author) / Villalobos, Jesus R (Thesis advisor) / Gel, Esma S (Committee member) / Maltz, Arnold B (Committee member) / Arizona State University (Publisher)
Created2011
Description
Concern regarding the quality of traffic data exists among engineers and planners tasked with obtaining and using the data for various transportation applications. While data quality issues are often understood by analysts doing the hands on work, rarely are the quality characteristics of the data effectively communicated beyond the analyst. This research is an exercise in measuring and reporting data quality. The assessment was conducted to support the performance measurement program at the Maricopa Association of Governments in Phoenix, Arizona, and investigates the traffic data from 228 continuous monitoring freeway sensors in the metropolitan region. Results of the assessment provide an example of describing the quality of the traffic data with each of six data quality measures suggested in the literature, which are accuracy, completeness, validity, timeliness, coverage and accessibility. An important contribution is made in the use of data quality visualization tools. These visualization tools are used in evaluating the validity of the traffic data beyond pass/fail criteria commonly used. More significantly, they serve to educate an intuitive sense or understanding of the underlying characteristics of the data considered valid. Recommendations from the experience gained in this assessment include that data quality visualization tools be developed and used in the processing and quality control of traffic data, and that these visualization tools, along with other information on the quality control effort, be stored as metadata with the processed data.
ContributorsSamuelson, Jothan P (Author) / Pendyala, Ram M. (Thesis advisor) / Ahn, Soyoung (Committee member) / Arizona State University (Publisher)
Created2011
Description
Thousands of children are being injured every day in bicycling accidents. Interventions, like Safe Routes to School, are currently in place to combat injury rates by providing programs to teach children safe biking behaviors. In order to develop effective behavioral change programs, behavior and the components of which it is composed must be understood. Attitudes, subjective norms, and self-efficacy are important predictors of intention to perform a behavior. The purpose of this study was to ascertain the extent to which attitude, subjective norms, self-efficacy, and bike rodeo participation explain third through eighth graders' intentions to bike safely. These constructs were tested using a survey research design in a purposive sample of fifty-seven third through eighth grade students in Safe Routes to School schools in the Southwest. Students took an online survey in the computer lab at their respective schools supervised by a teacher. The study found attitudes to be comprised of three factors: happy/safe, not afraid, and calm. Overall, the model explained approximately 71% of the variance in children's intentions to bike safely, R2=.749, Adjusted R2=.713, F(7, 49)=20.854, p<.01. The significant predictors were happy/safe attitudes, subjective norms, self-efficacy, and a quadratic self-efficacy term explaining 10% (p=.019), 28% (p<.01), 18% (p<.01), and 15% (p<.01) respectively. The results of the study can be used to create future and improve current bike safety interventions for children.
ContributorsPayton, Kayla G (Author) / Rodriguez, Ariel (Thesis advisor) / Tyrrell, Timothy (Committee member) / Maruyama, Kenichi (Committee member) / Arizona State University (Publisher)
Created2011
Description
In the U.S., high-speed passenger rail has recently become an active political topic, with multiple corridors currently being considered through federal and state level initiatives. One frequently cited benefit of high-speed rail proposals is that they offer a transition to a more sustainable transportation system with reduced greenhouse gas emissions and fossil energy consumption. This study investigates the feasibility of high-speed rail development as a long-term greenhouse gas emission mitigation strategy while considering major uncertainties in the technological and operational characteristics of intercity travel. First, I develop a general model for evaluating the emissions impact of intercity travel modes. This model incorporates aspects of life-cycle assessment and technological forecasting. The model is then used to compare future scenarios of energy and greenhouse gas emissions associated with the development of high-speed rail and other intercity travel technologies. Three specific rail corridors are evaluated and policy guidelines are developed regarding the emissions impacts of these investments. The results suggest prioritizing high-speed rail investments on short, dense corridors with fewer stops. Likewise, less emphasis should be placed on larger investments that require long construction times due to risks associated with payback of embedded emissions as competing technology improves.
ContributorsBurgess, Edward (Author) / Williams, Eric (Thesis advisor) / Fink, Jonathan (Thesis advisor) / Yaro, Robert (Committee member) / Arizona State University (Publisher)
Created2011
Description
Modern day driving continues to burgeon with attention detractors found inside and outside drivers' vehicles (e.g. cell phones, other road users, etc.). This study explores a regularly disregarded attention detractor experienced by drivers: self-regulation. Results suggest self-regulation and WMC has the potential to affect attentional control, producing maladaptive changes in driving performance in maximum speed, acceleration, and time headway.
ContributorsSinocruz, Jerome Q (Author) / Sanchez, Christopher A (Thesis advisor) / Branaghan, Russel J (Committee member) / Becker, David V (Committee member) / Arizona State University (Publisher)
Created2012
Description
Rapid developments are occurring in the arena of activity-based microsimulation models. Advances in computational power, econometric methodologies and data collection have all contributed to the development of microsimulation tools for planning applications. There has also been interest in modeling child daily activity-travel patterns and their influence on those of adults in the household using activity-based microsimulation tools. It is conceivable that most of the children are largely dependent on adults for their activity engagement and travel needs and hence would have considerable influence on the activity-travel schedules of adult members in the household. In this context, a detailed comparison of various activity-travel characteristics of adults in households with and without children is made using the National Household Travel Survey (NHTS) data. The analysis is used to quantify and decipher the nature of the impact of activities of children on the daily activity-travel patterns of adults. It is found that adults in households with children make a significantly higher proportion of high occupancy vehicle (HOV) trips and lower proportion of single occupancy vehicle (SOV) trips when compared to those in households without children. They also engage in more serve passenger activities and fewer personal business, shopping and social activities. A framework for modeling activities and travel of dependent children is proposed. The framework consists of six sub-models to simulate the choice of going to school/pre-school on a travel day, the dependency status of the child, the activity type, the destination, the activity duration, and the joint activity engagement with an accompanying adult. Econometric formulations such as binary probit and multinomial logit are used to obtain behaviorally intuitive models that predict children's activity skeletons. The model framework is tested using a 5% sample of a synthetic population of children for Maricopa County, Arizona and the resulting patterns are validated against those found in NHTS data. Microsimulation of these dependencies of children can be used to constrain the adult daily activity schedules. The deployment of this framework prior to the simulation of adult non-mandatory activities is expected to significantly enhance the representation of the interactions between children and adults in activity-based microsimulation models.
ContributorsSana, Bhargava (Author) / Pendyala, Ram M. (Thesis advisor) / Ahn, Soyoung (Committee member) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2010
Description
This research covers the possibility of airports serving as virus scanning hubs for future intercontinental travels. This aims at providing an idea for better control of tackling potential harmful viruses unknowingly carried by travelers. The benefit of this research is to help prevent less blow to the local economy and businesses, help keep travel industries, especially airlines, operating, slow down the rate of infection, and decrease cases and death rates, by providing a more secure health check for incoming and outgoing air travelers.
ContributorsLiu, Shelby (Author) / Feil, Magnus (Thesis advisor) / Mejía, Mauricio (Committee member) / Xian, Xiaojun (Committee member) / Arizona State University (Publisher)
Created2021
Description
With the advent of new mobility services and technologies, the complexity of understanding the mobility patterns has been gradually intensified. The availability of large datasets, in conjunction with the transportation revolution, has been increased and incurs high computing costs. These two critical challenges require us to methodologically handle complex transportation problems with numerical performance: fast, high-precision solutions, and reliable structure under different impact factors. That is, it is imperative to introduce a new type of modeling strategy, advancing the conventional transportation planning models. In order to do this, we leverage the backbone of the underlying algorithm behind machine learning (ML): computational graph (CG) and automatic differentiation (AD). CG is a directed acyclic graph (DAG) where each vertex represents a mathematical operation, and each edge represents data transfer. AD is an efficient algorithm to analytically compute gradients of necessary functionality. Embedding the two key algorithms into the planning models, specifically parametric-based econometric models and network optimization models, we theoretically and practically develop different types of modeling structures and reformulate mathematical formulations on basis of the graph-oriented representation.
Three closely related analytical and computational frameworks are presented in this dissertation, based on a common modeling methodology of CG abstraction. First, a two-stage interpretable machine learning framework developed by a linear regression model, coupled with a neural network layered by long short-term memory (LSTM) shows the capability of capturing statistical characteristics with enhanced predictability in the context of day-to-day streaming datasets. Second, AD-based computation in estimating for discrete choice models proves more efficiency of handling complex modeling structure than the standard optimization solver relying on numerical gradients, outperforming the standard methods, Biogeme and Apollo. Lastly, CG allows modelers to take advantage of a special problem structure for the feedback loops, a new class of problem reformulation developed through Lagrangian relaxation (LR), which makes CG based model well suited for reaching a high degree of the integrated demand-supply consistency.
Overall, the deep integration of the practically important planning models with the underlying computationally efficient ML algorithms can enhance behavioral understanding of interactions in real-world urban systems, and the proposed differentiable mathematical structures will enable transportation decision-makers to accurately evaluate different demand-side and supply-side scenarios with a higher degree of convergency and optimality in more complex transportation systems.
ContributorsKim, Taehooie (Author) / Pendyala, Ram RP (Thesis advisor) / Zhou, Xuesong XZ (Thesis advisor) / Pan, Rong RP (Committee member) / Lou, Yingyan YL (Committee member) / Arizona State University (Publisher)
Created2021
Description
With the acceleration of urbanization in many parts of the world, transportation challenges such as traffic congestion, increasing carbon emissions, and the “first/last-mile” connectivity problems for commuter travel have arisen. Transport experts and policymakers have proposed shared transportation, such as dockless e-scooters and bike-sharing programs, to solve some of these urban transportation issues. In cities with high population densities, multimodal mobility hubs designed to integrate shared and public transportation can be implemented to achieve faster public connections and thus increase access to public transport on both access and egress sides. However, haphazard drop-offs of these dockless vehicles have led to complaints from community members and motivated the need for neighborhood-level parking areas (NLPAs). Simultaneously, concerns about the equitable distribution of transportation infrastructure have been growing and have led to the Biden Administration announcing the Justice40 Initiative which requires 40% of certain federal investments to benefit disadvantaged communities. To plan a system of NLPAs to address not only the transportation shortcomings while elevating these recent equity goals, this thesis develops a multi-objective optimal facility location model that maximizes coverage of both residential areas and transit stations while including a novel constraint to satisfy the requirements of Justice40. The model is applied to the City of Tempe, Arizona, and uses GIS data and spatial analyses of the existing public transportation stops, estimates of transit station boardings, population by census block, and locations of disadvantaged communities to optimize NLPA location. The model generates Pareto optimal tradeoff curves for different numbers of NLPAs to find the non-dominated solutions for the coverage of population nodes and boardings. The analysis solves the multi-objective model with and without the equity constraint, showing the effect of considering equity in developing a multimodal hub system, especially for disadvantaged communities. The proposed model can provide a decision support tool for transport and public authorities to plan future investments and facilitate multimodal transport.
ContributorsQuan, Hejun (Author) / Kuby, Michael (Thesis advisor) / Frazier, Amy (Thesis advisor) / Tong, Daoqin (Committee member) / Arizona State University (Publisher)
Created2022
Description
The aim of this dissertation is to develop an understanding of the relationships between the daily commute, commuting stress, and Health-Related Quality of Life (HRQOL) based on a case study in Georgetown Guyana. Three separate but connected pieces of work were attempted to accomplish this aim. First, a scoping review was conducted using the Joanna Briggs Guidelines to elucidate the factors that contribute to commuting stress that have already been explored in the literature. This scoping review unearthed 11 factors across three broad categories. The commute-specific factors which contribute to commuting stress include, the length of the commute and the mode of the commute (whether active or non/active). The built environment factors include the levels of traffic congestion, the type of infrastructure that is in place, the landscape that lines the commuting route, and the experience of non-compliant fellow commuters. Personal factors include gender, age, hours of work, and quality of sleep. These factors along with a few others were then tested within a binomial regression framework that utilized data from 427 working adults. The results mirrored what was found in the literature. In addition, there was clarification of the roles of two factors for which the literature appeared to have not comprehensively addressed. These are modes of commute, that is persons who commute by private means are less likely to experience commuter stress than persons who commute via public means. In the third task, the relationship between these novel commute-specific factors and HRQOL. The result of this study demonstrated that persons who used private commuting and who were more satisfied with the commute infrastructure were more likely to have higher HRQOL scores than those who were not satisfied with the commute infrastructure in place and those who used public transportation. The results further demonstrated that commuting stress mediated the relationship between satisfaction with commute infrastructure and HRQOL, but it did not mediate the relationship between commuting mode and HRQOL. To address these issues, it is recommended that action be taken at the micro, meso, and macroeconomic levels. Keywords: urbanization, daily commute, stress, health-related quality of life, Guyana
ContributorsVan-Veen, Davon (Author) / Chhetri, Netra NC (Thesis advisor) / Jamme, Hue-Tam HJ (Committee member) / Ross, Heather HR (Committee member) / Arizona State University (Publisher)
Created2023