UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
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 given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item A BIG-DATA-DRIVEN FRAMEWORK FOR SPATIOTEMPORAL TRAVEL DEMAND ESTIMATION AND PREDICTION(2023) Hu, Songhua; Schonfeld, Paul; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Traditional travel demand models heavily rely on travel surveys, simplify future demand forecasting, and show low sensitivity in response to spatiotemporal dynamics. This study proposes a deep-learning-driven framework based on mobile device location data (MDLD) for estimating and predicting large-scale travel demand at both individual and aggregated levels. This study first introduces how raw MDLD should be parsed to distill trip rosters and estimate population flow. Based on derived information, this study reexamines relations between average population flow and its determinants such as built environment, socioeconomics, and demographics, via a set of explainable machine learning (EML) models. Different interpretation approaches are employed and compared to understand nonlinear and interactive relations learned by EML models. Next, this study proposes a Multi-graph Multi-head Adaptive Temporal Graph Convolutional Network (Multi-ATGCN), a general deep learning framework that fuses multi-view spatial structures, multi-head temporal patterns, and various external effects, for multi-step citywide population flow forecasting. Multi-ATGCN is designed to comprehensively address challenges such as complex spatial dependency, diverse temporal patterns, and heterogeneous external effects in spatiotemporal population flow forecasting. Last, at an individual level, this study proposes a Hierarchical Activity-based Framework (HABF) for simultaneously predicting the activity, departure time, and location of the origin and destination of the next trip, incorporating both internal (individual characteristics) and external (calendar, point-of-interests (POIs)) information. For each individual, HABF first predicts activities via an Interpretable Hierarchical Transformer (IHTF). IHTF can efficiently handle big data benefiting from its transformer-based design to avoid recursion. Meanwhile, loss functions used in semantic segmentation are introduced into IHTF to address imbalanced distributions of activity types. Then, a local plus global probabilistic generator is designed to generate locations based on predicted activities and historical places, allowing individuals to visit new or historically-sparse places. Analyses are performed on several real-world datasets to demonstrate the model's capability in forecasting large-scale high-resolution human mobility in a timely and credible manner. Altogether, this study provides sound evidence, practically and theoretically, of the feasibility and reliability of realizing data-driven travel demand estimation and prediction at different spatiotemporal resolutions and scales.Item UNDERSTANDING GEOSPATIAL DYNAMICS OF PARASITE MIGRATION AND HUMAN MOBILITY AS FACTORS CONTRIBUTING TO MALARIA TRANSMISSION IN THE GREATER MEKONG SUBREGION(2021) Li, Yao; Stewart, Kathleen; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Much effort has been made to control malaria over the past decades in South-East Asia Confirmed cases of Plasmodium falciparum (P.f.) and Plasmodium vivax (P.v.) malaria were reduced by 46%, and mortality by 60%. However, malaria remains a major problem in the Greater Mekong Subregion (GMS) with the emerging resistance to the artemisinins and their partner drugs. This raises concerns that the usefulness of first-line malaria treatments may be diminishing in the GMS, and that drug resistance could spread worldwide. Estimating malaria parasite migration patterns is crucial for malaria elimination as well as understanding the role that human mobility plays in malaria transmission. This dissertation will focus on the GMS, especially Cambodia and Myanmar which have been widely regarded as the epicenter of emerging resistance to artemisinin-based combination therapies. This dissertation is structured as three separate studies that look first at the movement of malaria parasites across a region, and then two studies that focus on human movement and how these movements can lead to increased exposure as well as transmission of malaria. In the first study, a semi-automatic workflow was developed to select the optimal number of demes that will maximize model accuracy and minimize computing time when computing estimated effective migration surfaces. A validation analysis showed that the optimized grids displayed both high model accuracy and reduced processing time compared to grid densities selected in an unguided manner. In the second study, an agent-based simulation model was built to estimate and simulate the daily movements of local populations in Singu and Ann Townships in Myanmar in order to identify how two townships in different parts of Myanmar compared with respect to mobility and P.v. and P.f. positivity. The third study examined mobility patterns of local village populations in Singu Township, Myanmar when they traveled longer distances outside of Singu, and discuss these patterns of regional travel in the context of daily mobility within the township.Item FAIR URBAN CRIME PREDICTION WITH HUMAN MOBILITY BIG DATA(2021) Wu, Jiahui; Frias-Martinez, Vanessa; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Crime imposes significant costs on society. Reported crime data is important in quantifying the severity of crimes, based on which decision-makers would allocate resources for crime interventions. Human mobility big data has triggered the interest in various fields to study the relationship between urban crimes and mobility at a large scale, especially the predictive power of mobility for urban crimes. This research direction can enrich our understanding of crimes and better inform crime-related decision-making. One concern about reported crime data is the bias issue. The bias could be produced by different levels of residents’ willingness to report potential crime incidents and police activity in neighborhoods. While lots of studies about crime prediction are aware of biases in reported crimes, few of them propose solutions to address or mitigate this issue or to evaluate how this issue would affect prediction models in terms of accuracy or fairness. My dissertation research aims to explore the potential of human mobility big data for crime prediction. Specifically, my dissertation will advance the state-of-the-art by addressing three challenges in mobility-based crime prediction: 1) Constructing mobility features might be sensitive to different methodological choices. Without careful examination of these choices, there might be conflicting findings. One critical area of mobility analysis to predict crime is the identification of urban hotspots. Therefore, my work performs a systematic spatial sensitivity analysis on the impact of these choices and provides guidelines to identify the most stable ones. 2) Under-reporting generates biases in reported crime data. To address such bias, I develop a Bayesian model for long-term crime prediction that infers the unobserved true number of crime incidents. Comprehensive experiments show how the accuracy and fairness of long-term crime prediction would be affected by modeling the under-reporting of crimes. 3) Although empirical studies show promising results about the relationship between human mobility and long-term crime prediction, the effects of mobility features on short-term crime prediction have yet to be explored. Therefore, my work conducts a series of experiments to explore how incorporating mobility features into short-term crime prediction models affects their performance in terms of accuracy and fairness.