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
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item EXPLORING TEMPORAL AND SPATIAL VARYING IMPACTS ON COMMUTE TRIP CHANGE DUE TO COVID-19(2023) Saleh Namadi, Saeed; Niemeier, Deb; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)COVID-19 has deeply affected people’s daily life and travel behaviors. This study uses large-scale mobile device location data at the U.S. county level in the DMV area to reveal the impacts of demographic and socioeconomic variables on commute trip change. The study investigates the contribution of these variables to the temporal and spatial varying impacts on commuter trips. It reflects the short and long-term impact of COVID-19 on travel behavior via linear regression and geographically weighted regression models. The results indicate that commute trips decreased with more white-collar jobs, while blue-collar sectors demonstrated the opposite effect. Unexpectedly, elderly individuals, who were highly vulnerable to COVID-19, negatively correlated with decreased commute trips. Moreover, in the DMV area, counties with a higher proportion of Democratic voters also showed a negative correlation with reduced commute trips. Notably, the pandemic's impact on commuting behaviors was global at the onset of COVID-19. Still, the effects exhibited local correlations as the pandemic evolved, suggesting a geographical impact pattern.Item Empowering Traffic Operations and Safety with Transportation Big Data: Practice Scan, Methodology, and Applications(2022) Yang, Mofeng; Schonfeld, Paul; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the past two decades, along with the technological advancement in mobile sensors and mobile networks, transportation big data, such as probe vehicle data and mobile device location data (MDLD), have been growing dramatically in terms of the spatiotemporal coverage of population and its mobility. These data sources have shown their great potential for large-scale and near real-time transportation applications to support travel behavior analysis, travel demand modeling, traffic operations and safety analyses. The objectives of this dissertation are to (1) comprehensively examine the state-of-the-practice applications and the state-of-the-art models developed based on emerging transportation big data, (2) identify key metrics, and (3) establish a series of big-data driven frameworks to enhance traffic operations and safety. Three main sections are included. The first section of this dissertation presents a literature review on models, tools, and metrics used for various levels of traffic analysis, and analyzes a survey distributed to transportation professionals to quantify the importance of these key metrics for improving traffic operations and safety. Based on the literature review and survey insights, two big-data driven frameworks are proposed accordingly to address both traffic operations and safety issues. In the second section of this dissertation, a big-data driven framework is developed which aims at improving the accuracy and reliability of emergency medical services (EMS) and trauma triage decisions for elderly persons at crash sites. The proposed framework integrates transportation big data sources from both the demand side (such as traffic volumes, and time-dependent vehicle speeds obtained from large-scale probe vehicles) and the supply side (i.e., transportation network features), as well as publicly available statewide crash data with health-related decisions such as EMS and hospital records. Decision tree models are adopted to simulate the decision-making process due to their wide applications, a proven capability in prediction, and interoperability. With records of over 55,000 elderly patients, results demonstrate that the proposed framework contributed to enhanced EMS decision and trauma triage accuracy for the elderly, and saving more lives from severe vehicle crashes. In the third section of this dissertation, a big-data driven framework is proposed for estimating a critical operational metric, namely vehicle volume, on an all-street network, and further estimating the pedestrian and bicyclist crashes at all intersections. This framework employs a series of cloud-based computational algorithms to extract multimodal trajectories and trip rosters from terabytes of MDLD. A scalable map matching and routing algorithm is then applied to snap and route vehicle trajectories to the roadway network. The observed vehicle counts on each roadway segment are weighted and calibrated against ground truth control totals, i.e., Annual Vehicle Miles of Travel (AVMT), and Annual Average Daily Traffic (AADT). The proposed framework is built on Amazon Web Service (AWS) which leverages cloud computing techniques to estimate vehicle volumes for all roadway segments in the state of Maryland using MDLD for the entire year 2019. The estimated vehicle volume is further integrated with statewide crash records to estimate the pedestrian and bicyclist crashes at all intersections with statistical models. Results indicate that the proposed framework can produce reliable vehicle volume estimates and estimated pedestrian and bicyclist crashes, while also demonstrating its transferability and generalization ability. In summary, this dissertation comprehensively examines the literature on transportation big data applications and proposes two big-data driven frameworks demonstrated with two real-world case studies. Results reveal the feasibility and advantages of empowering traffic operations and safety analysis with transportation big data.Item Introducing Frameworks to Analyze Human Mobility Behavior with Advanced Computational Algorithms and Machine Learning Methods Using Mobile Device Location Data(2022) Darzi, Aref; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The emergence of mobile device location data (MDLD) provides new opportunities to analyze human mobility behaviors. The large penetration rate and the possibility of observing human mobility behaviors continuously are among the most important features of the passively collected mobile device location data. However, to utilize MDLD in mobility behavior analysis, comprehensive computational algorithms need to be developed to carefully process the data.This research proposes novel sets of frameworks to extract mobility context from the raw MDLD. First, this study introduces a set of algorithms to construct the travel behavior of mobile device owners along with the non-observable attributes of both trips and travelers by extracting trips, identifying significant activity locations of the travelers such as their home and work locations, and imputing the travel mode. The proposed algorithms in this study were tested against the state-of-practice and state-of-art algorithms developed in the literature. The proposed algorithms were shown to have superior performance compared to other methods. Next, this study further examines the usefulness of the proposed framework in providing near real-time insights on the evolution of human mobility behavior during the Coronavirus disease 2019 (COVID-19) pandemic. As a part of this study, a new metric has also been introduced to measure the social distancing practices from the mobility perspective. Additional investigations are also conducted to understand the linkage between the outbreak of COVID-19 and the mobility behavior of the communities. Lastly, this study seeks to develop a framework to investigate the evacuation behavior of individuals during a natural disaster and construct the evacuation evolution patterns and decisions based on the MDLD. This dissertation evaluates the importance of the historical mobility behavior of the device owners in their decision-making procedure during natural disasters using statistical discrete choice models.Item Multimodal Travel Mode Imputation based on Passively Collected Mobile Device Location Data(2020) Yang, Mofeng; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Passively collected mobile device location (PCMDL) data contains abundant travel behavior information to support travel demand analysis. Compared to traditional travel surveys, PCMDL data have larger spatial, temporal and population coverage while lack of ground truth information. This study proposes a framework to identify trip ends and impute travel modes from the PCMDL data. The proposed framework firstly identify trip ends using the Spatio-temporal Density-based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm. Then three types of features are extracted for each trip to impute travel modes using machine learning methods. A PCMDL dataset with ground truth information is used to calibrate and validate the proposed framework, resulting in 95% accuracy in identifying trip ends and 93% accuracy in imputing five travel modes using the Random Forest (RF) classifier. The proposed framework is then applied to two large-scale PCMDL datasets, covering Maryland and the entire U.S. The mode share results are compared against travel surveys at different geographic levels.Item MULTIMODAL TRAVEL BEHAVIOR ANALYSIS AND MONITORING AT METROPOLITAN LEVEL USING PUBLIC DOMAIN DATA(2019) PENG, BO; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Travel behavior data enable the understanding of why, how, and when people travel, and play a critical role in travel trend monitoring, transportation planning, and policy decision support. Conventional travel behavior data collection methods such as the National Household Travel Survey (NHTS) have been the primary source of travel behavior information for transportation agencies. However, the relatively high cost of traditional travel surveys often prohibits frequent survey cycles (currently once every 5-10 years). With decision makers increasingly requesting recent and up-to-date information on multimodal travel trends, establishing a sustainable and timely travel monitoring program based on available data sources from the public domain is in order. This dissertation developed advanced data processing, expansion, fusion and analysis methods and integrated such methods with existing public domain data into a comprehensive model that allows transportation agencies to track monthly multimodal travel behavior trends, e.g., mode share, number of trips, and trip frequency, at the metropolitan level. Advanced data analytical methods are developed to overcome significant challenges for tracking monthly travel behavior trends of different modes. The proposed methods are tailored to address different challenges for different modes and are flexible enough to accommodate heterogeneous spatial and temporary resolutions and updating schedules of different data sources. For the driving mode, this dissertation developed reliable methods for estimates of local road VMT, various temporal adjustment factors, truck percentage factors, average vehicular occupancy, and average trip length based on additional data from the Travel Monitoring Analysis System and the most recent regional household travel survey to translate HPMS data into monthly number of vehicular and person driving trips for a metropolitan area. For the transit mode, this dissertation collectively exhausted detailed transit network geo-data to complement NTD and developed advanced geo-analysis and statistical methods tailored to the service network of different types of operators to accurately and reliably allocate ridership data to the metropolitan area of interest, and to allocate annual ridership data to each month. The data for non-motorized is even more sparse, although the local government has growing interests and efforts on collecting such data. A two-step statistical model is developed to derive the trend for non-motorized modes and then integrating such trends with base-year number of trips number from most recent household travel survey conducted in the metropolitan areas of interest. Based on the number of trips by modes estimated using the proposed methods, the monthly trend in mode share can be timely estimated and continuously monitored over time for the first time in the literature using public domain data only. The dissertation has demonstrated that it is feasible to develop a comprehensive model for multimodal travel trend monitoring and analysis by integrating a wide range of traffic and travel behavior data sets of multiple travel modes. Based on findings, it can be concluded that the proposed public-domain databases and data processing, expansion, fusion and analysis methods can provide a reliable way to monitor the month-to-month multimodal travel demand at the metropolitan level across the U.S.Item THE INFLUENCE OF URBAN FORM AT DIFFERENT GEOGRAPHICAL SCALES ON TRAVEL BEHAVIOR; EVIDENCE FROM U.S. CITIES(2016) Nasri, Arefeh; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Suburban lifestyle is popular among American families, although it has been criticized for encouraging automobile use through longer commutes, causing heavy traffic congestion, and destroying open spaces (Handy, 2005). It is a serious concern that people living in low-density suburban areas suffer from high automobile dependency and lower rates of daily physical activity, both of which result in social, environmental and health-related costs. In response to such concerns, researchers have investigated the inter-relationships between urban land-use pattern and travel behavior within the last few decades and suggested that land-use planning can play a significant role in changing travel behavior in the long-term. However, debates regarding the magnitude and efficiency of the effects of land-use on travel patterns have been contentious over the years. Changes in built-environment patterns is potentially considered a long-term panacea for automobile dependency and traffic congestion, despite some researchers arguing that the effects of land-use on travel behavior are minor, if any. It is still not clear why the estimated impact is different in urban areas and how effective a proposed land-use change/policy is in changing certain travel behavior. This knowledge gap has made it difficult for decision-makers to evaluate land-use plans and policies. In addition, little is known about the influence of the large-scale built environment. In the present dissertation, advanced spatial-statistical tools have been employed to better understand and analyze these impacts at different scales, along with analyzing transit-oriented development policy at both small and large scales. The objective of this research is to: (1) develop scalable and consistent measures of the overall physical form of metropolitan areas; (2) re-examine the effects of built-environment factors at different hierarchical scales on travel behavior, and, in particular, on vehicle miles traveled (VMT) and car ownership; and (3) investigate the effects of transit-oriented development on travel behavior. The findings show that changes in built-environment at both local and regional levels could be very influential in changing travel behavior. Specifically, the promotion of compact, mixed-use built environment with well-connected street networks reduces VMT and car ownership, resulting in less traffic congestion, air pollution, and energy consumption.Item Exploring Linkages between Travel Behavior and Health with Person-Level Data from Smartphone Applications(2013) Vemulapati, Sapeksha Virinchi; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the past, scholars have explored different variables and linked them with the individual's travel behavior. This study explores the linkages between an individual's health and his/her everyday travel behavior. In order to capture accurate and comprehensive travel behavior information, a smartphone application is developed that can track user location for long periods without the need of user intervention. Focus is placed on designing the application to have minimum respondent burden and long-standing battery life of the smart device. Subjects are recruited through a web survey designed to collect information about the individual's healthy living habits. Data from the application is regressed against the health measure data acquired from the survey. Results show that active modes of travel are positively associated with the person's general health measures. The feasibility of this platform as a data collection method is highlighted while explaining the limitations due to the sample distribution and size.Item Modeling Vehicle Ownership Decisions in Maryland: A Preliminary Stated Preference Survey and Model(2010) Maness, Michael; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the near future, the culmination of new vehicle technologies, greater competition in the energy markets, and government policies to fight pollution and reduce energy consumption will result in changes in the United States' vehicle marketplace. This project proposes to create a stated preference (SP) survey along with discrete choice models to predict future demand for electric, hybrid, alternative fuel, and gasoline vehicles. The survey is divided into three parts: socioeconomics, revealed preference (RP), and SP sections. The socioeconomics portion asks respondents about themselves and their households. The RP portion asks about household's current vehicles. The SP section presents respondents with various hypothetical scenarios over a future five-year period using one of three game designs. The designs correspond to: changing vehicle technology, fuel pricing and availability, and taxation policy. With these changes to the vehicle marketplace, respondents are asked whether they will keep or replace their current vehicles and if he will purchase a new vehicle and its type. To facilitate the design and administering of the survey, a web survey framework, JULIE, was created specifically for creating stated preference surveys. A preliminary trial of the survey was conducted in September and October 2010 with a sample size of 141 respondents. Using the SP results from this preliminary trial, a multinomial logit model is used to estimate future vehicle ownership by vehicle type. The models show that the survey design allows for estimation of important parameters in vehicle choice.