Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item ACCELERATING RESTORATION THROUGH INFORMATION-SHARING: UNDERSTANDING OPERATOR BEHAVIOR FOR IMPROVED MANAGEMENT OF INTERDEPENDENT INFRASTRUCTURE(2024) Yazdisamadi, Mohammadreza; Reilly, Allison C.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation examines the roles that organizations and individuals play in restoring interdependent infrastructure following disasters through three studies. In the first study, we focus on how operator heuristics affect the collective restoration speed of three interdependent infrastructure (electric power, chilled water, and IT networks). We do this by developing a novel framework that embeds an interdependent infrastructure network within an agent-based model that mimics the decisions and patterns observed of actual operators. The study sheds light on how coordination and information exchange by separate infrastructure parties affect decisions and thus restoration outcomes. In the second study, we examine recovery times and total unmet demand for the same three interconnected infrastructure systems assuming a variable fraction of node removals. The work is decomposed by the extent to which operators share information and coordinate strategies, enabling us to identify at what fraction of network failure does coordination and information sharing become beneficial. Our study indicates that prioritizing restoration based on node centrality produces the speediest recovery. We also show that communication among organizations may improve collective performance by as much as 50%. Our final research project uses a serious game, Breakdown, focused on restoration of interdependent infrastructure to assess whether engineering graduate students gain a deeper appreciation for the complexity of interdependent infrastructure and socio-technical systems more broadly. This is the first serious game designed to emphasize the value of cooperation, communication, and strategy in times of crisis in the field of interdependent infrastructure. As a result of playing Breakdown, graduate students demonstrated statistically significant improvements in engineering decision-making under uncertainty and sociotechnical systems concepts. As a result of this dissertation, the interdependent infrastructure community gains insight into (1) how individual operators' behavior influences the speed at which interdependent infrastructure systems recover; (2) how policies and procedures, like sharing information and cooperating, can help improve outcomes; and (3) the ways to teach graduate engineering students about socio-technical systems effectively. Using an agent-based model simulation, it quantifies the effects of human behavior, communication, and cooperation on recovery outcomes. By using a serious game, Breakdown, it proposes an innovative way to teach graduate engineering students about socio-technical systems.Item ONLINE and REAL-TIME TRANSPORTATION SYSTEMS MANAGEMENT and OPERATIONS DECISION SUPPORT WITH INTEGRATED TRAVEL BEHAVIOR and DYNAMIC NETWORK MODELS(2018) Zhu, Zheng; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The acceleration of urbanization is witnessed all around the world. Both population and vehicle ownership are rapidly growing, and the induced traffic congestion becomes an increasingly pervasive problem in people’s daily life. In 2014, transportation congestion caused $160 billion economic loss in 498 U.S. urban areas, which is 5.5 more than that in 1982. Without effective reactions, this number is expect to grow to $192 billion in 2020. In order to mitigate traffic congestion, many transportation demand management (TDM) strategies (e.g. bus rapid lanes, and flextime policy), and active traffic management (ATM) strategies (e.g. real-time user guidance, and adaptive traffic signal control) have been proposed and implemented. Although TDM and ATM have proved their values in theoretical researches or field implementations, it is still hard for transportation engineers to select the optimal strategy when faced with complex traffic conditions. In the science of transportation engineering, mathematical models are usually expected to help estimate traffic conditions under different scenarios. There have been a number of models that help transportation engineers make decisions. However, many of them are developed for offline use and are not suitable for real-time applications due to computational time issues. With the development of computational technologies and traffic monitoring systems, online transportation network modeling is getting closer and closer to reality. The objective of this dissertation is to develop a large-scale mesoscopic transportation model which is integrated with an agent-based travel behavior model. The ultimate goal is to achieve online (real-time) simulation to estimate and predict the traffic performance of the entire Washington D.C. area. The simulation system is expected to support real-time transportation system managements and operations. One of the most challenging issue for this dissertation is the calibration of online simulation models. Model parameters need to be estimated based on real-time traffic data to reflect the reality. Literature review of previous relevant studies indicates a trade-off between computational speed and calibration accuracy. In order to apply the model onto a real-time horizon, experts usually ignore the inherent mechanism of traffic modeling but rely on fast converging technologies to approximate the model parameters. Differently from previous online transportation simulation approaches, the method proposed in this dissertation focuses more on the mechanism of transportation modeling. With the fundamental understanding of the modeling mechanism, one can quickly determine the gradient of model parameters such that the gap between real-time traffic measures and simulation results is minimized. This research is one of the earliest attempts to introduce both agent-based modeling and gradient-based calibration approach to model real-time large-scale networks. The contribution includes: 1) integrate an agent-based travel behavior model into dynamic transportation network models to enhance the behavior realism; 2) propose a fast online calibration procedure that quickly adjusts model parameters based on real-time traffic data. A number of real-world case studies are illustrated to demonstrate the value of this model for both long-term and real-time applications.