Dissertation Defense Announcements

With the widespread use of computers and networks, cybersecurity has emerged as a crucial concern for many businesses as they fight off growing cyber threats by vulnerability exploitation. To identify and mitigate zero-day or unpatched vulnerabilities, intensive defensive measures are required, which calls for a thorough understanding of vulnerability characteristics and threat behavior from several angles. This compels enterprises to spend a considerable amount of money to safeguard their infrastructure from cyberattacks, relying on the costly, ineffective, error-prone, and slow process of experts' input. Therefore, security automation has been a solution for many business owners in the battle against the growing number of cyber threats by vulnerability exploitation.

The modern text analytics architectures have been built in novel ways for a variety of applications, assisting cybersecurity professionals in developing resilient mechanisms against threats. Utilizing such technologies can therefore be a viable approach for processing, understanding, and predicting vulnerabilities that are typically reported through unstructured text.

This dissertation utilizes deep learning, natural language processing, and Information Retrieval to build a series of models that are able to effectively and efficiently parse, assess, analyze, and mitigate the vulnerabilities based on their textual descriptions reported in Common Vulnerabilities and Exposures (CVE) format.
This research offers a cybersecurity language model, as the core component, which is then utilized for characterizing the vulnerabilities as well as retrieving the corresponding course of defense actions. As a result of this work, enterprises and cybersecurity researchers will be able to automatically process domain-specific texts, classify vulnerabilities to cybersecurity standards to obtain high-level knowledge, and retrieve the course of defense actions for the underlying threats.

Autonomous vehicles (AVs) are imminent and they are not in people’s dreams now. Now the burning questions the research community is interested in include how quickly AVs would be implemented for public use, whether people would accept them, and how AVs would change the ecosystem of transportation and the built environment. Stimulated by these questions, this dissertation aims to investigate the factors that influence people’s behavioral intention (BI) to adopt AVs and shared AVs (SAVs). In addition, this study is intended to investigate the potential impacts of AVs on land use patterns and people’s travel behaviors. This dissertation consists of six papers as discussed hereunder.

The first article presents a state-of-the-art literature review to understand people’s perceptions and opinions of AVs and the factors that influence AV adoption. Results show that the socioeconomic profile of individuals and their household, their psychological factors (e.g., usefulness, ease of use, risk), and knowledge and familiarity with AV technologies would affect AV adoption. Additionally, urban form (e.g., density, land use diversity), transportation factors (e.g., travel mode, distance, and time), affinity to new technology, and institutional regulations would influence the AV adoption rate.

The second review study critically analyzes the extant literature and summarizes the short, medium, and long-term effects of AVs based on a SWOT (Strength, Weakness, Opportunity, and Threat) analysis. Results show that AV would influence transportation and human mobility by reducing vehicle ownership, vehicle miles traveled (VMT), congestion, travel costs, energy use, and increasing accessibility, mobility, safety and security, and revenue generation for commercial operators. AVs would encourage dispersed urban development, reduce parking demand, and enhance network capacity. Additionally, AVs would increase the convenience and productivity of passengers by providing amenities for multitasking opportunities.

The third paper investigates the key factors that influence people’s tendency to purchase and use personal AVs after collecting data from the 2019 California Vehicle Survey. Results from the Structural Equation Model (SEM) indicate that working-age adults, children, household income, per capita income, and educational attainment are positively associated with AV purchase intention. Similarly, psychological factors (e.g., perceived enjoyment, usefulness, and safety), prior knowledge of AVs, and experience with emerging technologies significantly influence people’s BI to purchase AVs. This study finds that family structure and psychological factors are the most influential factors in AV purchase intention of households than the built environment, other socioeconomic, and transportation factors.

The fourth paper investigates the key elements of a household’s intentions to use pooled SAVs using the SEM framework. Collecting data from the 2019 California Vehicle survey, this study finds that higher educational attainment, income, labor force participation, Asian population, and urban living are negatively associated with SAVs. In contrast, young and working-age adults are positively associated with SAVs. Study results also show that people who prefer public transportation, car-sharing, ride-hailing, and ride-sharing services are likely to use SAVs. The perceived usefulness, enjoyment, safety associated with AVs, and prior knowledge of AVs significantly influence people to use SAVs. The study concludes that people’s travel behaviors, positive attitudes to shared mobility, and psychological features are the key determinants of SAVs.

The fifth paper studies the potential impacts of AVs on the spatial distribution of household and employment locations using the existing Swindon model of the TRANUS urban simulation platform. Results show that the adoption of AVs encourages people to live outside of the city center by increasing convenience and reducing travel costs. On the other hand, AVs would increase employment opportunities in the city center by inducing more economic activities. This study finds that AVs would allow densification of the existing city center by releasing extra space from parking land areas along with peripheral new development over time.

With the same TRANUS simulation platform, the sixth paper aims to assess the potential impacts of AVs on people’s travel behaviors such as trip generation, travel distance, travel time, and travel costs. Results indicate that AVs would intensify people’s overall travel demand by increasing accessibility. On the other hand, AVs are likely to reduce vehicle ownership, travel distance, travel time, travel costs, and vehicle hours traveled by reducing solo driving and by inducing shared mobility. AVs also have the potential to reduce public and active transportation.

This study makes significant contributions by unraveling critical issues of AVs and their short-, medium-, and long-term impacts. The findings will be helpful for policymakers and professionals to implement appropriate policies to manage travel demand and urban growth, and to urban and transportation scholars in the understanding of the complex mutual relationships between transportation, mobility, and the conditions of urban environments.

Mini-roundabouts are a type of roundabout characterized by a small diameter, and fully traversable central island and splitter islands. They are an alternative intersection design option in areas with constraints requiring additional land acquisition. They may be retrofitted within the existing intersection boundaries. Also, they are better suited for traffic calming and reducing delay, thereby, reducing emissions. They are suited to environments where speeds are relatively low and environmental constraints preclude the use of larger roundabouts with raised central islands. The standard-size roundabouts are safer than traditional minor road stop-controlled or signalized intersections, better suited for traffic calming, and reduce delay as well as emissions. However, the safety benefits associated with mini-roundabouts are not well documented and must be evaluated for planners and engineers to consider more mini-roundabout installations in the United States.

The focus of this research is on evaluating the safety effectiveness of converting a stop-controlled intersection with a speed limit ≥ 35 mph (56.3 kmph) to a mini-roundabout and examining the role of influencing factors on their safety effectiveness in the United States. The methodology includes : 1) identification of mini-roundabout installations in the United States, 2) before and after crash data and traffic volume data collection at selected mini-roundabout locations, 3) before and after analysis for determining safety benefits of mini-roundabouts, 4) safety effectiveness and crash modification factors (CMFs) computation for mini-roundabouts based on before and after crash data, and, 5) examining the effect of traffic characteristics, geometric characteristics, and on-network and off-network characteristics on mini-roundabout safety effectiveness and after period crashes. Crash, traffic volume, and geometry data for 25 mini-roundabouts in eight states was collected to conduct before-after analysis using the naive and Empirical Bayes (EB) method. Additionally, crash and traffic volume data for 723 reference intersections were gathered and used for computing the calibration factors and developing jurisdiction-specific safety performance functions (SPFs).

Results indicated a decrease in total crashes and FI crashes when TWSC/OWSC intersections were converted to mini-roundabouts. However, an increase in PDO crashes was observed. Likewise, an increase in total number of crashes, FI crashes, and PDO when AWSC intersections were converted to mini-roundabouts. Converting a TWSC/OWSC intersection to a mini-roundabout has better safety benefits than converting an AWSC intersection to a mini-roundabout. The number of crashes in the before period, cross-street traffic volume, speed limit at major street and cross-street, and intersection skewness have a statistically significant influence on the safety effectiveness of mini-roundabouts at a 90% confidence level.

These findings are useful to researchers and practitioners for conducting safety benefit analysis and making informed decisions pertaining to converting a stop-controlled intersection to a mini-roundabout.

Career indecision is common among many college students. College students that
identify as LGBTQ+ and students of color may experience greater difficulty in the career
decision-making process. Students belonging to minoritized racial and sexual or gender identities
often deal with the additional stress of managing their multiple marginalized identities. This
additional stress can have an impact on their career development and career choice. A total
sample of seven participants was selected for the study. Participants completed a semi-
structured interview detailing their experiences navigating identity negotiation as racial and
sexual minorities influence the career decision-making process as LGBTQ+ students of color,
within the context of a predominantly white institution in the south. Given the unique personal
and contextual factors, Social Cognitive Career Theory was used to better understand the
experiences of these selected participants. The findings should support existing themes that have
emerged when looking at the experiences of other marginalized groups and should provide
additional insight to inform more multiculturally competent career counseling.