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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.


Date Range
2012 2019


This study explores an innovative framework for a self-sustained traffic operations system using vehicle-to-vehicle (V2V) communications alone. The proposed framework is envisioned as the foundation to an alternative or supplemental traffic operation and management system, which could be particularly helpful under abnormal traffic conditions caused by unforeseen disasters and special events. Its two major components, a distributed traffic monitoring and platoon information aggregation system and a platoon-based automated intersection control system, are investigated in this study. The distributed traffic monitoring and platoon information aggregation system serves as the foundation. Specifically, each equipped vehicle, through the distributed protocols developed, keeps track …

Contributors
Li, Peiheng, Lou, Yingyan, Zhou, Xuesong, et al.
Created Date
2017

Modern intelligent transportation systems (ITS) make driving more efficient, easier, and safer. Knowledge of real-time traffic conditions is a critical input for operating ITS. Real-time freeway traffic state estimation approaches have been used to quantify traffic conditions given limited amount of data collected by traffic sensors. Currently, almost all real-time estimation methods have been developed for estimating laterally aggregated traffic conditions in a roadway segment using link-based models which assume homogeneous conditions across multiple lanes. However, with new advances and applications of ITS, knowledge of lane-based traffic conditions is becoming important, where the traffic condition differences among lanes are recognized. …

Contributors
Zhou, Zhuoyang, Mirchandani, Pitu, Askin, Ronald, et al.
Created Date
2015

The main objective of this research is to develop an integrated method to study emergent behavior and consequences of evolution and adaptation in engineered complex adaptive systems (ECASs). A multi-layer conceptual framework and modeling approach including behavioral and structural aspects is provided to describe the structure of a class of engineered complex systems and predict their future adaptive patterns. The approach allows the examination of complexity in the structure and the behavior of components as a result of their connections and in relation to their environment. This research describes and uses the major differences of natural complex adaptive systems (CASs) …

Contributors
Haghnevis, Moeed, Askin, Ronald G, Armbruster, Dieter, et al.
Created Date
2013

I study the problem of locating Relay nodes (RN) to improve the connectivity of a set of already deployed sensor nodes (SN) in a Wireless Sensor Network (WSN). This is known as the Relay Node Placement Problem (RNPP). In this problem, one or more nodes called Base Stations (BS) serve as the collection point of all the information captured by SNs. SNs have limited transmission range and hence signals are transmitted from the SNs to the BS through multi-hop routing. As a result, the WSN is said to be connected if there exists a path for from each SN to …

Contributors
Surendran, Vishal Sairam Jaitra, Sefair, Jorge, Mirchandani, Pitu, et al.
Created Date
2019

To guide the timetabling and vehicle assignment of urban bus systems, a group of optimization models were developed for scenarios from simple to complex. The model took the interaction of prospective passengers and bus companies into consideration to achieve the maximum financial benefit as well as social satisfaction. The model was verified by a series of case studies and simulation from which some interesting conclusions were drawn. Dissertation/Thesis Simulation File, including CSV data file

Contributors
Huang, Shiyang, Askin, Ronald G, Mirchandani, Pitu, et al.
Created Date
2014

Healthcare operations have enjoyed reduced costs, improved patient safety, and innovation in healthcare policy over a huge variety of applications by tackling prob- lems via the creation and optimization of descriptive mathematical models to guide decision-making. Despite these accomplishments, models are stylized representations of real-world applications, reliant on accurate estimations from historical data to jus- tify their underlying assumptions. To protect against unreliable estimations which can adversely affect the decisions generated from applications dependent on fully- realized models, techniques that are robust against misspecications are utilized while still making use of incoming data for learning. Hence, new robust techniques are …

Contributors
Bren, Austin, Saghafian, Soroush, Mirchandani, Pitu, et al.
Created Date
2018

This research develops heuristics for scheduling electric power production amid uncertainty. Reliability is becoming more difficult to manage due to growing uncertainty from renewable resources. This challenge is compounded by the risk of resource outages, which can occur any time and without warning. Stochastic optimization is a promising tool but remains computationally intractable for large systems. The models used in industry instead schedule for the forecast and withhold generation reserve for scenario response, but they are blind to how this reserve may be constrained by network congestion. This dissertation investigates more effective heuristics to improve economics and reliability in power …

Contributors
Lyon, Joshua, Zhang, Muhong, Hedman, Kory W, et al.
Created Date
2015

Feature learning and the discovery of nonlinear variation patterns in high-dimensional data is an important task in many problem domains, such as imaging, streaming data from sensors, and manufacturing. This dissertation presents several methods for learning and visualizing nonlinear variation in high-dimensional data. First, an automated method for discovering nonlinear variation patterns using deep learning autoencoders is proposed. The approach provides a functional mapping from a low-dimensional representation to the original spatially-dense data that is both interpretable and efficient with respect to preserving information. Experimental results indicate that deep learning autoencoders outperform manifold learning and principal component analysis in reproducing …

Contributors
Howard, Phillip Ryan, Runger, George, Montgomery, Douglas, et al.
Created Date
2016

In this dissertation, an innovative framework for designing a multi-product integrated supply chain network is proposed. Multiple products are shipped from production facilities to retailers through a network of Distribution Centers (DCs). Each retailer has an independent, random demand for multiple products. The particular problem considered in this study also involves mixed-product transshipments between DCs with multiple truck size selection and routing delivery to retailers. Optimally solving such an integrated problem is in general not easy due to its combinatorial nature, especially when transshipments and routing are involved. In order to find out a good solution effectively, a two-phase solution …

Contributors
Xia, Mingjun, Askin, Ronald, Mirchandani, Pitu, et al.
Created Date
2013

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 …

Contributors
Wang, Ning, Mirchandani, Pitu, Murray, Alan, et al.
Created Date
2013