The primary objective of this dissertation is to advance the existing empirical literature on the relationship between transportation and quality of life, with a specific focus on wellbeing indicators and their applicability in the transportation sector. To achieve this, the dissertation is structured around four primary areas of inquiry. Firstly, it introduces a subjective wellbeing scoring method that generates episode-level wellbeing scores, which can be aggregated to produce daily person-level wellbeing scores. This method can be utilized as a post-processor of activity-based travel demand model outputs to assess equity implications in various planning scenarios. Secondly, the dissertation examines the intricate relationships between mobility poverty, time poverty, and subjective wellbeing. It compares the rates of time poverty and zero-trip making among different socio-demographic groups and evaluates their alignment with subjective wellbeing. Thirdly, this research investigates the association between automobile use and satisfaction with daily travel routines (thus, wellbeing). This analysis aims to provide an understanding of why automobile use remains the primary mode of transportation, despite attempts to shift towards alternative modes of transportation. The fourth area of investigation focuses on the wellbeing impacts of the COVID-19 pandemic. Specifically, the chapter examines the resurgence in travel and discretionary out-of-home activities, as well as the slow return of workers to workplaces by using the subjective wellbeing indicator and time poverty. Additionally, the chapter identifies groups that were disproportionately impacted and provides strategies to mitigate adverse consequences for vulnerable socio-economic and demographic groups in future disruptions. Overall, this dissertation contributes to the literature on transportation and quality of life by introducing a reliable subjective wellbeing scoring method that can be used to evaluate the quality of life implications of transportation systems. It also offers practical applications of wellbeing indicators in identifying differences in wellbeing across the population and provides opportunities for targeted interventions and the development of transportation policies to address equity and sustainability issues. Furthermore, to demonstrate the practicality of the generated knowledge in this dissertation, a web-based wellbeing platform is developed to track changes in the wellbeing of individuals that arise from their daily activity and travel patterns.

The COVID-19 pandemic has revealed the fault lines in society. Whether it be remote work, remote learning, online shopping, grocery and meal deliveries, or medical care, disparities and inequities among socio-economic and demographic groups leave some segments of society more vulnerable and less adaptable. This thesis aims to identify vulnerable and less adaptable groups in the context of access to food. Using a comprehensive behavioral survey data set collected during the height of the pandemic in 2020, this thesis aims to provide insights on the groups that may have experienced food access vulnerability during the disruption when businesses and establishments were restricted, the risk of contagion was high, and accessing online platforms required technology-savviness and the ability to afford delivery charges. This thesis presents estimation results for a simultaneous equations model of six endogenous choice variables defined by a combination of two food types (groceries and meals) and three access modalities (in-person, online with in-person pickup, and online with delivery). The model estimation results show that attitudes and perceptions play a significant role in shaping pandemic-era access modalities. The model revealed that even after controlling for a host of attitudinal indicators, minorities, those having low household incomes, those living in low-density or rural locations, females, and those with lower educational attainment are particularly vulnerable to being left behind and experiencing challenges in accessing food during a severe and prolonged disruption. Social programs should aim to provide these vulnerable groups with tools and financial resources to leverage online activity engagement and access modalities. Policy recommendations to increase food access for the mostvulnerable in future disruption scenarios are explored.

Infrastructure systems are facing non-stationary challenges that stem from climate change and the increasingly complex interactions between the social, ecological, and technological systems (SETSs). It is crucial for transportation infrastructures—which enable residents to access opportunities and foster prosperity, quality of life, and social connections—to be resilient under these non-stationary challenges. Vulnerability assessment (VA) examines the potential consequences a system is likely to experience due to exposure to perturbation or stressors and lack of the capacity to adapt. Post-fire debris flow and heat represent particularly challenging problems for infrastructure and users in the arid U.S. West. Post-fire debris flow, which is manifested with heat and drought, produces powerful runoff threatening physical transportation infrastructures. And heat waves have devastating health effects on transportation infrastructure users, including increased mortality rates. VA anticipates the potential consequences of these perturbations and enables infrastructure stakeholders to improve the system's resilience. The current transportation climate VA—which only considers a single direct climate stressor on the infrastructure—falls short of addressing the wildfire and heat challenges. This work proposes advanced transportation climate VA methods to address the complex and multiple climate stressors and the vulnerability of infrastructure users. Two specific regions were chosen to carry out the progressive transportation climate VA: 1) the California transportation networks’ vulnerability to post-fire debris flows, and 2) the transportation infrastructure user’s vulnerability to heat exposure in Phoenix.

To reduce the environmental burden of transport, previous studies have resorted on solutions that accentuate towards techno-economical pathways. However, there is growing evidence that transport behaviors, lifestyle choices, and the role of individuals' attitudes/perceptions are considered influential factors in shaping households’ engagement with sustainable technologies in the face of environmental crises. The objective of this dissertation is to develop multidimensional econometric model systems to explore complex relationships that can help us understand travel behaviors' implications for transport and household energy use. To this end, the second chapter of this dissertation utilizes the latent segmentation approach to quantify and unravel the relationship between attitudes and behaviors while recognizing the presence of unobserved heterogeneity in the population. It was found that two-thirds of the population fall in the causal structure where behavioral experiences are shaping attitudes, while for one-third attitudes are shaping behaviors. The findings have implications on the energy-behavior modeling paradigm and forecasting household energy use. Building on chapter two, the third chapter develops an integrated modeling framework to explore the factors that influence the adoption of on-demand mobility services and electric vehicle ownership while placing special emphasis on attitudes/perceptions. Results indicated that attitudes and values significantly affect the use of on-demand transportation services and electric vehicle ownership, suggesting that information campaigns and free trials/demonstrations would help advance towards the sustainable transportation future and decarbonize the transport sector. The integrated modeling framework is enhanced, in chapter four, to explore the interrelationship between transport and residential energy consumption. The findings indicated the existence of small but significant net complimentary relationships between transport and residential energy consumption. Additionally, the modeling framework enabled the comparison of energy consumption patterns across market segments. The resulting integrated transport and residential energy consumption model system is utilized, in chapter fifth, to shed light on the overall household energy footprint implications of shifting vehicle/fuel type choices. Results indicated that electric vehicles are driven as much as gasoline vehicles are. Interestingly, while an increase in residential energy consumption was observed with the wide-scale adoption of electric vehicles, the total household energy use decreased, indicating benefits associated with transportation electrification.