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 Preventing Drowsy Driving in Young Adults Through Messaging Strategies that Influence Perceptions of Control and Risk(2024) Lee, Clark Johnson; Butler III, James; Beck, Kenneth H; Public and Community Health; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Drowsy driving is a serious health and safety problem in the United States: thousands of car crashes on U.S. roadways each year are attributed to this risky driving behavior. Although young drivers under the age of 26 years are especially at risk for being involved in drowsy driving car crashes, few anti-drowsy driving interventions targeting such drivers have been developed. Furthermore, most existing educational materials and interventions against drowsy driving have focused primarily on providing factual information about the dangers of drowsy driving and countermeasures against these dangers rather than on influencing beliefs and motivations underlying drowsy driving behavior, which may explain their apparent ineffectiveness at preventing drowsy driving behavior and resultant car crashes. Recent research indicates that messages targeting perceptions of control may be effective intervention strategies against drowsy driving behavior for young adult drivers by influencing their drowsy driving-related perceptions of risk, intentions, and willingness. This dissertation continues this line of research by pursuing two lines of inquiry. In Study #1, the efficacy of anti-drowsy driving messaging strategies designed to influence perceptions of control and risk related to drowsy driving behavior in reducing drowsy driving intentions, willingness, and behavior in a sample of young adult U.S. drivers between 18 and 25 years of age was evaluated through a randomized controlled trial. Study #1 sought to test the following hypotheses: Hypothesis 1: Participants exposed to interventional messaging strategies primarily aimed at lowering perceptions of control or heightening perceptions of risk related to drowsy driving report significantly less perceived control, greater perceived risk, less intentions, less willingness, and less behavior related to drowsy driving at 30-day post-intervention follow-up compared to participants exposed to messaging strategies providing only factual information about the dangers of drowsy driving; and Hypothesis 2: Participants exposed to interventional messaging strategies aimed at both lowering perceptions of control and heightening perceptions of risk related to drowsy driving report significantly less perceived control, greater perceived risk, less intentions, less willingness, and less behavior related to drowsy driving at 30-day post-intervention follow-up compared to participants exposed to messaging strategies providing only factual information about the dangers of drowsy driving, messaging strategies primarily aimed at lowering perceptions of control related to drowsy driving, or messaging strategies primarily aimed at heightening perceptions of risk related to drowsy driving. In Study #2, the relationships between perceived behavioral control, risk perception, intentions, willingness, and drowsy driving behavior in a sample of young adult U.S. drivers between 18 and 25 years of age were examined. Study #2 sought to test the following hypotheses: Hypothesis 3: The impact of interventional messaging strategies targeting drowsy driving perception of control on drowsy driving intentions, willingness, and behavior is mediated by drowsy driving risk perception such that messages lowering drowsy driving perceptions of control also heighten drowsy driving risk perception, which in turn decreases drowsy driving intentions, willingness, and behavior; Hypothesis 4: Interventional messaging strategies targeting drowsy driving-related perceptions of control or risk have a greater impact on drowsy driving willingness than on drowsy driving intentions; and Hypothesis 5: Drowsy driving willingness is a stronger predictor of drowsy driving behavior than is drowsy driving intentions. Study #1 provided supporting evidence of short-term cognitive effects but not short-term behavioral effects after exposure to messaging interventions designed to influence perceptions of control and risk related to drowsy driving behavior. Perceptions of risk were especially influenced by the messaging strategies examined, including those that provided only factual, knowledge-based information about drowsy driving. Study #2 provided supporting evidence that perceived behavioral control influenced drowsy driving intentions and drowsy driving willingness indirectly through perceptions of risk. Furthermore, willingness to drive drowsy was a stronger predictor of actual drowsy driving behavior than intentions to drive drowsy. The findings from these two studies should inform future research aimed at developing more effective messaging strategies against drowsy driving behavior in young adults.Item A Smart Traffic Incident Management (TIM) System for Estimating Highway Incident Duration and Impacts with and without Surveillance Sensors(2024) Huang, Yen-Lin; Chang, Gang-Len G.L.C; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Highway incidents are major contributors to traffic congestion, causing significant delays for daily roadway users and reducing the reliability and productivity of transportation systems. To mitigate the negative impacts of these incidents and quickly restore highway operations, it is crucial for highway agencies to implement an efficient incident management system. However, providing the public with real-time information about the impacts of incidents at a desired level of precision is challenging due to the complexities involved in obtaining sufficient data and understanding the intricate relationships among the factors that influence these impacts. To address this challenge, this study proposes a Smart Traffic Incident Management (TIM) system that delivers robust and reliable information on estimated clearance durations, resultant queue lengths, time-varying traffic information, and traffic detouring volumes from freeways to adjacent arterials. This initiative aims to improve the effectiveness of incident response, thereby enhancing the resilience and functionality of the transportation network. The proposed system consists of four primary modules. Module 1 aims to robustly predict incident clearance duration through the proposed Knowledge Transferability Analysis (KTA) model, featuring its automated process for assessing, selecting, and transferring existing prediction rules from pre-existing Incident Duration Prediction Models (IDPM). This strategic utilization obviates the necessity for integrating field operators' expertise in formulating prediction rules, thereby alleviating the dependency on an ample volume of incident records for prediction rules calibration. The evaluation results, using I-70 in Maryland for the case study, have demonstrated the effectiveness of the proposed KTA model in not only expediting the development process of an IDPM but also improving the resulting accuracy of the prediction rules. Module 2 endeavors to robustly predict incident queue length by introducing the Real-time Incident Queue Prediction (R-IQP) system. This system's principal model enhances the formulations for queue propagation dynamics by incorporating the influences of incoming drivers' perceptions and responses to progressively constrained traffic conditions. Additionally, two supplementary models are proposed to precisely estimate flow rates, leveraging probe speed information, to accommodate different surveillance environments characterized by varying levels of data availability. The evaluations of the proposed R-IQP system with both the field-collected data and the well-calibrated simulator’s data have proven the capability of the R-IQP system on predicting time-varying queue lengths for incidents with various clearance durations and types of lane blockage statuses. Module 3 introduces a traffic flow model specifically designed for traffic incident management. The proposed Incident-oriented METANET (I-METANET) enhances the widely used METANET model with three key improvements: 1) reflecting the merging behaviors incurred by incidents and their significant yet diminishing effects on speed propagation over upstream segments; 2) incorporating the simultaneous effects of upstream traffic flows and downstream incident-induced queue waves on the speed of a subject segment; and 3) integrating the combined effects of ramp-flow weaving maneuvers and the presence of incident queues on traffic conditions at interchange segments. The proposed I-METANET model, calibrated and validated using field data from I-4 in Florida, has demonstrated its effectiveness in predicting time-varying speeds and flow rates over roadway segments during incident clearance periods. Module 4 focuses on assessing the impact of freeway incidents on nearby local roads. To achieve this, the study developed a Real-time Detour Volume Estimation (R-DVE) system, designed to estimate the volume of traffic diverted from the freeway mainline to its adjacent arterials, even when freeway traffic sensors are unavailable. This system leverages a set of offline speed-flow models developed in Module 2 to estimate traffic flow using probe speed data as input. Additionally, the proposed R-DVE incorporates a Quality Assessment Mechanism (QAM) that integrates a robust customized dynamic speed-flow relations (CDSFR) developed in Module 3 to continually examine the applicability of the estimated flow rates and update the offline speed-flow models. The performance evaluation, based on real-world incident cases on I-95 in Maryland, demonstrated that the R-DVE system can accurately estimate real-time detouring volumes, highlighting its practical applicability. The proposed Smart Traffic Incident Management (TIM) system, delineated through its comprehensive modules, embodies several key features aimed at enhancing incident management efficacy, including 1) providing a systematic decision-making framework for incident clearance duration prediction, particularly valuable for highways lacking sufficient incident records to calibrate prediction rules; 2) incorporating predicted clearance duration to generate timely estimates of incident queue length, with the adaptive capability particularly beneficial for highways under varying levels of traffic sensor availability; 3) predicting time-varying traffic information to faciltiate better incident management and responsive strategies; 4) generating real-time estimates of detour volume originating from each interchange within the impact area, facilitating the execution of appropriate responsive operations contributing to efficient incident management; and 5) exhibiting a dynamic nature by updating estimated information when additional data become available or when there are changes in traffic dynamics or incident clearance operation to ensure the continuous relevance and accuracy of the provided information.Item A DATA-DRIVEN FRAMEWORK FOR THE PREDICTION OF NON-RECURRENT TRAFFIC CONGESTION RECOVERY TIME ON FREEWAYS(2024) Kabiri, Aliakbar; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study introduces a comprehensive approach aimed at improving the management of incident durations. It delves into enhancing traffic incident management by integrating diverse incident datasets, including Maryland State Police incident data and Coordinated Highways Action Response Team (CHART) incident data, to improve the assessment of traffic incident durations. The dissertation employs spatial and temporal thresholds to explore matching different incident datasets and identifies discrepancies between various incident reports. The dissertation also explores methodologies for estimating traffic recovery times of each incident, utilizing historical data and pre-incident conditions as baselines to establish normal traffic conditions. A novel framework is introduced to estimate non-recurrent traffic congestion recovery time, revealing that many incidents recover faster than their reported clearance times. In these cases, traffic flow returns to normal conditions quickly.Further, the study examines predictive modeling for traffic recovery time, highlighting the Random Forest model's effectiveness among various machine learning algorithms. This model's superiority, based on precision, recall, and F1-scores, underlines its potential in accurately predicting traffic incident recovery time categorized as short-duration, medium-duration, and long-duration incidents. In particular, the random forest model results in a precision of 0.7 for short-duration incidents, 0.3 for medium-duration incidents, and 0.5 for long-duration incidents. For instance, the precision of 0.5 for long-duration incidents indicates that half of the cases predicted as long-duration incidents are indeed long-duration incidents. Key predictors such as link-level vehicle volume, clearance time, response time, and number of lanes closed are identified, providing valuable insights for traffic management strategies. This dissertation underscores the importance of data-driven approaches in traffic incident management, aiming to enhance the efficiency of transportation systems through accurate prediction and estimation of incident recovery times.Item RETHINKING MOVEMENT(2024) Gomez, Jose; Tilghman, James; Architecture; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Today, there are numerous transportation methods that are constantly changing our landscape. Despite the diversity of transportation options, our approach toward movement has become outdated. The emergence of autonomous vehicles, electric vehicles, sustainable power sources and advanced infrastructure are currently shaping the way we move throughout the world. The advantages of these technologies are clear; high performance, low to no carbon emissions, automatic systems, and improved safety are clearly the direction of the future. However, their adaptation and implementation is slow and ineffective. Emerging technology presents a viable opportunity to design architecture and mobility as a synergetic system that can facilitate movement, improve accessibility, and reclaim the human experience from outdated infrastructure. It is therefore important to rethink how we move through space in order to design for human wellness. This thesis will explore transportation problems in cities, emerging technologies, sustainable practices, and design guidelines and precedents in search of an efficient moving, self-sufficient, wellness focused future.Item “Quite Young Limbs that Bled”: Accidents, Apathy, and the Failure of American Aviation During the First World War(2024) Getka, Dana; Giovacchini, Saverio; History; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The advent of the First World War saw America’s first concerted attempt at building a world-class air service. Desperate to join the ranks of Britain, Germany, and France, it pushed poorly-built planes out of factories and poorly-trained cadets out of flying schools at an alarming rate. In this thesis, I argue that in blind pursuit of its goals, the United States air service ultimately doomed those whose efforts would bring the organization its prestige: the pilots. Aviators, especially non-combatants in roles such as training, testing, and ferrying, faced unavoidable death or harm every time they stepped into a plane, be it physically, emotionally, or psychologically. Despite their role as non-combatants, these pilots well understood that destruction would characterize their world, provoking emotional responses expected of those engaged in fighting on active fronts. Indeed, flying was a world of combat unto itself, and by war’s end, the Army Air Service had earned the dubious distinction of being the only arm of the United States military in which more men were violently killed in non-combat than in combat roles.Item Examination of US Transportation Public-Private Partnership Experience: Performance and Market(2024) Zhang, Kunqi; Cui, Qingbin; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Worldwide, public-private partnership (P3) project performance and benefits accrued to market participants are understudied. Focusing on the US, this dissertation examines the country’s transportation P3 experience through three empirical studies comparing P3 to design-bid-build (DBB), the traditional delivery method. Throughout, the Information Source for Major Projects database, built by a University of Maryland team in which the author led the data collection effort, served as the data source. In the first study, the researchers examined P3 cost and time performance using piecewise linear growth curve modeling, recognizing that past cross-sectional studies had produced mixed results. With 133 major transportation projects, the longitudinal analysis confirmed P3’s time performance advantage and efficiency diffusion effecting cost savings in DBB, where efficiency diffusion was a new term describing the spillover and internalization of technical and managerial innovations inducing an efficient outcome. The second study used social network analysis to investigate collaboration patterns among different types of players in the P3 market (i.e., public sponsors, special purpose vehicles, investors, lenders, advisors, contractors, and professional service firms). With 135 projects and 1009 organizations, data found that both P3 and DBB networks are small worlds. Exponential random graph modeling revealed that practicing in the DBB market helps firms participate in P3 projects and that large firms (vis-à-vis small/medium-sized firms) are not privileged. The third study, further exploring the P3 market, focused on the Disadvantaged Business Enterprise (DBE) program. Administered by the US Department of Transportation, the program promotes the participation of small, disadvantaged firms in federal-aid projects. Linear regressions on 134 contracts showed that P3 associates with higher DBE goals in terms of percentage of dollars to be awarded to DBEs, whereas the delivery method does not affect the actual attainment. Overall, the findings justify continued policy support towards P3 implementation.Item Econometric Evaluation of Transportation Policies: Decarbonization and Electrification(2024) Burra, Lavan Teja; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The transportation sector, one of the largest contributors to global energy-related emissions, is undergoing a major transition. Governments worldwide are implementing stringent fuel economy and emissions standards, promoting the adoption of electric vehicles--a key technology for decarbonizing the transport sector--through various policy measures. This dissertation contains four chapters, studying the effects of such policies implemented across major vehicle markets and evaluating their effectiveness, with a particular focus on the electrification of light-duty passenger vehicle fleet. The first chapter explores whether multi-car households shift mileage to the most fuel-efficient car in response to increasing driving costs, which carries implications for designing effective fuel economy standards. The second chapter investigates the potential interaction between purchase subsidies given to consumers in buying electric vehicles (EVs) and expanding the public charging network. The third chapter focuses on the effectiveness of purchase subsidies for EV buyers and quantifies the free-rider share, given that this is a commonly employed policy measure worldwide. The final chapter explores the differential effects of level 2 and level 3 chargers, as well as the distributional impacts of public charging network on driving EV uptake across various demographic groups and built environment characteristics. Overall, the chapters in this dissertation employ travel survey data, longitudinal and big data analysis, causal identification, optimal policy design, counterfactual simulations, and a combination of data and economic reasoning to glean insights on the effectiveness and equitable aspects of policies aiming to decarbonize and electrify the transportation sector.Item EQUITY ISSUES IN ELECTRIC VEHICLE ADOPTION AND PLANNING FOR CHARGING INFRASTRUCTURE(2024) Ugwu, Nneoma; Niemeier, Deb; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Electric Vehicles (EVs) offer a sustainable solution to fossil fuel dependency and environmentalpollution from conventional vehicles, crucial for mitigating climate change. However, low market penetration among minority and low-income communities raises equity and environmental justice concerns. This dissertation examines EV adoption and charging station access disparities in Maryland, focusing on sociodemographic factors such as race and income. To address the lack of minority representation in existing EV research surveys, we conducted anonline survey targeting people of color (POC) and low-to-moderate-income households. We received 542 complete responses. Ordinal regression models were used to analyze factors influencing EV interest. We then performed a cumulative accessibility study of EV infrastructure in Maryland. Pearson correlation analysis was used to show the relationship between charging station accessibility and sociodemographics. Population density showed a strong positive correlation (0.87) with charging deployment. We found that Baltimore City, had the highest population density and the highest concentration of EV charging in Maryland. We conducted a case study of Baltimore City’s EV infrastructure investments and policy efforts. Charging stations were categorized based on speed, network, access, and facility type. Spatial analysis andZero-Inflated Poisson (ZIP) regression models at the block group level were employed to investigate the disparities in EV charging infrastructure distribution within the City across minority and non-minority communities. Our findings show substantial disparities in EV perceptions between POC and Whitecommunities. The survey revealed that POC were more than twice more likely than White respondents to indicate that the availability of charging stations affects their interest in EV adoption, while the case studies revealed that POC populations are less likely to have access to EV infrastructure, necessitating targeted investment in charging options and subsidies in these communities. Our study also found the need for policies fostering residential charging station deployment, particularly in minority communities. To ensure equitable EV adoption, strategic investments in economically disadvantaged and rural areas beyond centralized regions are vital. This study informs evidence-based policies prioritizing accessibility, equity, and inclusivity in promoting a cleaner and sustainable transportation landscape.Item TOPOLOGICAL ANALYSIS OF DISTANCE WEIGHTED NORTH AMERICAN RAILROAD NETWORK: EFFICIENCY, ECCENTRICITY, AND RELATED ATTRIBUTES(2023) Elsibaie, Sherief; Ayyub, Bilal M.; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The North American railroad system can be well represented by a network with 302,943 links (track segments) and 250,388 nodes (stations, junctions, and waypoints), and other points of interest based on publicly accessible geographical information obtained from the Bureau of Transportation Statistics (BTS) and the Federal Railroad Administration (FRA). From this large network a slightly more consolidated subnetwork representing the major freight railroads and Amtrak was selected for analysis. Recent improvements in network and graph theory and improvements in all-pairs shortest path algorithms make it more feasible to process certain characteristics on large networks with reduced computation time and resources. The characteristics of networks at issue to support network-level risk and resilience studies include node efficiency, node eccentricity, and other attributes derived from those measures, such as network arithmetic efficiency, network geometric central node, radius, and diameter, and some distribution measures of the node characteristics. Rail distance weighting factors, representing the length of each rail line derived from BTS data, are mapped to corresponding links, and are used as link weights for the purpose of computing all pair shortest paths and subsequent characteristics. This study also compares the characteristics of North American railroad infrastructure subnetworks divided by Class I carriers, which are the largest railroad carriers classified by the Surface Transportation Board (STB) by annual operating revenue, and which together comprise most of the North American railroad network. These network characteristics can be used to inform placement of resources and plan for natural hazard and disaster scenarios. They relate to many practical applications such as network efficiency to distribute traffic and a network’s ability to recover from disruptions. The primary contribution of this thesis is the novel characterization of a detailed network representation of the North American railroad network and Class I carrier subnetworks, with established as well novel network characteristics.Item A Planning Model For Flexible-route Freight Deliveries in Rural Areas Based on Adjusted Tour Length Estimations(2023) Li, Zheyu; Schonfeld, Paul; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Addressing the issue of delivery efficiency and transportation service quality in rural areas, this thesis presents an analysis of total cost of delivery services in regions with low demand density and low road network density. It focuses on designing a cost-effective and efficient freight delivery system, which is crucial for promoting a vibrant rural economy. A flexible-route service model is developed, aiming to improve farm products and other deliveries by optimizing the service zone size and frequency to minimize the average cost per delivered package. The model is tailored for a potential truck operation scenario in the central Appalachian region, serving as a representative case study, with a general formulation of total cost that can be adapted to similar cases elsewhere. Considering the influence of dead-end roads in rural area, this study presents an adjusted formulation of length estimation for Traveling Salesman Problem (TSP) tours based on the literature review and regression on multiple graphs with road network, and develops a mathematical formulation of total cost, integrating operation and user costs, supported by reasonable assumptions and system constraints. The results from the baseline study suggest that one truck can serve a large service area by exceeding the maximum working hours constraint. This observation is made without considering the potential expansion into a multi-zone system, which might be necessary due to the combined factors of road network complexity and the perishability of farm products. The results from our sensitivity analysis show that a system with a single large truck will have the lowest average cost per package when demand is low. Considering an actual road network, this study also explores the possibility of combining the flexible-route delivery service with self-deliveries and the extension of Vehicle Routing Problem (VRP) with maximum working hour constraint. The study concludes with suggested future research directions in this important domain.