Civil & Environmental Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2753
Browse
7 results
Search Results
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 DEVELOPING A TOUR-BASED TRIP IDENTIFICATION ALGORITHM USING MOBILE DEVICE LOCATION DATA(2022) Kabiri, Aliakbar; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis presents a novel trip identification algorithm that supports travel behavior analysis based on mobile device location data. The proposed trip identification algorithm is applied to a large-scale Location-based Service (LBS) dataset consisting of the location points of a large representative sample of United States residents with over 40 million users in January 2020. Firstly, the proposed framework divides sightings into long-distance and short-distance home-based tours and then identifies the trips on each type of tour using different methods. Furthermore, the Maryland Statewide Household Travel Survey 2018/2019 and the National Household Travel Survey (NHTS) 2017 validate the derived trips. The results showed that several metrics of the trips from mobile device location data and travel surveys follow similar trends. In addition, the impact of coronavirus disease 2019 (COVID-19) on the travel behavior of the population is studied as a real-world application of the proposed algorithm.Item NATIONWIDE ANNUAL AVERAGE DAILY TRAFFIC (AADT) ESTIMATION ON NON-FEDERAL AID SYSTEM (NFAS) ROADS BY MACHINE LEARNING WITH DATA MINING OF BUILT-IN ENVIRONMENT(2020) Sun, Qianqian; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study aims to address the nationwide gap in AADT data on NFAS roads in U.S. With a Spatial Autoregressive Model as a benchmark, two machine-learning approaches, i.e. Artificial Neural Network and Random Forest, show notable improvement in the accuracy of estimating AADT according to five measures, i.e. MSE, RSQ, RMSE, MAE, and MAPE. A data-mining of the built-in environment from three perspectives, i.e. on-road and off-road features, network centralities, and neighboring influences, paves the way for AADT estimation, which covers 87 variables in centrality, neighboring traffic, demographics, employment, land-use diversity, road network density, urban design, destination accessibility, etc. Data integration using different buffering sizes and statistical analysis of linearity and monotonicity promote the variable selection for estimation. When implementing the machine-learning approaches, not only the estimation performance is analyzed, but also the relationship between each variable and AADT, the interplays among variables, variable importance measures are thoroughly discussed.Item UNDERSTANDING AND MODELLING TIME USE, WELL BEING AND DYNAMICS IN ACTIVITY-TRAVEL BEHAVIOR: A CHOICE BASED APPROACH(2018) Dong, Han; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Understanding the determinants of activity and travel related choices is critical for policy-makers, planners and engineers who are in charge of the management and design of large scale transportation systems. These systems, and their externalities, are interwoven with human actions and communities’ evolution. Traditionally, individual decision-making and travel behaviour studies are based on random utility models (RUM) and discrete choice analysis. To extend the ability of modellers to represent and forecast complex travel behaviour, this dissertation expands existing models to accommodate the influence of variables other than the traditional socio-demographics or level of service variables. In this thesis, technology innovations, psychological factors, and perceptions of future uncertainty are integrated into the classical RUMs and their effects on activity-travel decision making are investigated. Technology innovations, such as telecommunication, online communities and entertainment, release individual’s time and space constraints. They also modify people’s activity and travel choices. An integrated discrete-continuous RUM is proposed to study individuals’ participation in leisure activities, which is an important component of activity scheduling analysis and tour/trip formation. Leisure alternatives considered include: computer/internet related activity, in-home activity, and out-of-home activity. Compared to previous discrete-continuous models, interdependence among activities and the related time usage is explored using a modelling structure that accommodate full correlation among decision variables of different types. Standard random utility models are extended by including attitudes and perceptions as latent variables; these constructs are expected to enhance the behavioural representation of the choice process. A simultaneous structural model is proposed to represent the mutual effects existing between psychological factors and activity choices. Biases due to endogeneity in psychological factors and activity choices are taken into consideration in the model. To further extend the behavioural realism of our model, this thesis proposes a new simultaneous equation model formulation that links psychological indicators to activity participation and time use decisions. Unlike previous studies, the proposed method allows the psychological factors to be correlated with time use decisions and serve as an attribute in time use choice model. A new iterative simulated maximization estimation method is also proposed to accommodate possible endogeneity bias in the model system. A simulation experiment shows that the estimation method produces consistent and unbiased estimation results. Moreover, a real case study is also implemented in the context of participation in leisure activities, linking emotions, activity involvement and time use. After exploring individual’s decisions on activity and time use choices, a dynamic discrete choice model framework is proposed to accommodate stochasticity in individual behaviour over time. Following previous studies, activity patterns are decomposed into tour and stop sequences. Accordingly, a tour choice model and a stop choice model are jointly formulated under a unified framework with a hierarchical structure where stop choices are assumed to be conditional on tour choices. The results indicate that individuals are sensitive to current and future changes in travel and activity characteristics and that a dynamic formulation better represents multi-day travel behaviour.Item Distributed System Behavior Modeling of Urban Systems with Ontologies, Rules and Message Passing Mechanisms(2017) Montezzo Coelho, Maria Eduarda; Austin, Mark A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Modern infrastructures are defined by spatially distributed network structures, concurrent subsystem-level behaviors, distributed control and decision making, and interdependencies among subsystems that are not always well understood. This work presents a model of system-level interactions that simulates distributed system behaviors through the use of ontologies, rules checking, and message passing mechanisms. We take initial steps toward the behavior modeling of large-scale urban networks as collections of networks that interact via many-to-many association relationships. We conclude with ideas for scaling up the simulations with mediators assembled from Apache Camel technology.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 A MODEL SYSTEM TO EVALUATE THE IMPACTS OF VEHICLE-RELATED TAXATION POLICIES ON HOUSEHOLD GREENHOUSE GAS EMISSIONS(2014) LIU, YAN; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis proposes a model system to forecast household-level greenhouse gas emissions (GHGEs) from private transportation and to evaluate effects of car-related taxation schemes on vehicle emissions. The system contains four sub-models which specifically capture households' vehicle type and vintage, quantity, usage, and greenhouse gas emissions rates for different vehicle types. An integrated discrete-continuous vehicle ownership model is successfully implemented, while MOVES2014 (Motor Vehicle Emission Simulator 2014) is utilized. The model system has been applied to the Washington D.C. Metropolitan Area. The 2009 National Household Travel Survey (NHTS) with supplementary data from the Consumer Reports, the American Fact Finder and the 2009 State Motor Vehicle Registrations (SMVR) are used for estimations and predictions. Three tax schemes, vehicle ownership tax, purchase tax and fuel tax, have been proposed and their impacts on vehicle GHGEs reduction are predicted. The proposed model system can be extended to other regions, counties, states and nations.