Parking policies and regulations are important tools in planning for the governance of urban mobility. The proper design and location of parking facilities, in fact, contributes to an efficient use of the transportation system (or it may reduce its efficiency, when these infrastructures are not properly planned). This paper discusses the role of parking as part of the policy packages for strategic planning aimed at increasing the sustainability of urban and metropolitan areas. In particular, the integration of parking strategies in a comprehensive vision for the future of a city may significantly improve the allocation of resources and the reduction of the overall environmental externalities.

The role of parking in the strategic planning of cities is discussed through the analysis of several recent projects in the city of Bari (Italy). The paper discusses the way these projects are linked (or eventually not linked) to broader strategies for urban mobility, and how they might be coordinated into policy packages that promote more sustainable transportation. The use of an integrated land use transportation modeling approach to simulate the long-term evolution of the urban area may significantly contribute to estimate the long-term effects of the proposed policies. This approach may successfully support the process of policy evaluation and the selection of the optimal strategies to implement.

Automobile manufacturers are pushing the rapid development of automated vehicles (AVs) despite a limited understanding of consumer demand and potential impacts on travel behavior. An effective policy response depends on an improved understanding of who will be interested in (early) adoption of AVs, what the users’ preferred business models (private vs. shared) will be, and how the eventual adoption of shared automated vehicle (SAV) services will likely impact personal/household vehicle ownership levels. This study addresses these topics through a market segmentation analysis using latent-class modeling of data from a custom-designed transportation survey of California residents. I use sociodemographics, general attitudes, and current AV familiarity to define the segments. The analysis uncovered three latent classes: (1) AV Early Adopters who are enthusiastic about the fully automated AV scenarios presented (private ownership and shared services) and are ready to adopt shared AVs instead of owning other personal/household vehicles, (2) AV Curious who are less enthusiastic about AVs than the prior class and more likely to maintain their current vehicle ownership in the future even when using shared AV services, and (3) AV Hesitant who are resistant to using either private or shared AVs and would not be interested in reducing their vehicle ownership levels if they had access to SAV services. The characteristics of the three classes provide a basis for actionable recommendations for both private companies and transportation policy makers.

Enabled by information and communication technologies (ICT) and based on the principles of the shared/gig economy, ridehailing services (e.g., Uber, DiDi, Ola) are transforming the travel patterns and the lifestyle patterns of people around the world. Other new technologies based on the same principles, including smartphone app connectivity and access to mobility services for multiple users on an on-demand basis (e.g., food delivery services, micromobility), can potentially have similar transformative impacts. It is thus important to study these services and their impacts carefully to leverage these technologies in creating a more inclusive, sustainable, and resilient transportation system. In the last two years, the COVID-19 pandemic has created an even bigger disruption to the transportation sector (including life in general). While there is a general consensus among the research community that the pandemic is fundamentally transforming the transportation sector, the post-pandemic future of the transportation system remains to be seen. What social and transportation inequities occurred during the disruption of the pandemic? And how can the post-pandemic transportation sector be shaped to become more inclusive, resilient and sustainable? The goal of my dissertation is to create a deeper understanding of these two major disruptions in the transportation sector. I do this by analyzing survey data collected in cities of the United States, Canada, Germany, Chile, Mexico, Brazil, India and China, in combination with socio-demographic and geospatial datasets available in these countries. In the first two studies (Chapter 2-3) I focus on California. First, I explore the factors that affect the use of ridehailing services (Uber, Lyft) as well as adoption of shared (pooled) ridehailing (UberPOOL, Lyft Share) by estimating a semi-ordered bivariate probit model. The model reveals the similarities and differences in the markets for each of the two services. Among the main findings from the study, I find that being white and living in a higher-income household is associated with a higher likelihood of being a frequent user of non-shared ridehailing but does not have statistically significant effects on the likelihood of adopting shared ridehailing. While the likelihood of using both non-shared ridehailing and shared ridehailing is higher in urban areas, residents of neighborhoods with higher intersection density are found to be more likely to only adopt shared ridehailing. Next, I estimate an integrated choice and latent variable (ICLV) model to develop an in-depth understanding about the effect of the built-environment on ridehailing use for non-work purposes while accounting for confounding effects such as the preference to own a vehicle and to live in urban locations. My analysis confirms that failure to consider the latent preferences for residential location can lead to biased results. This analysis results in two major findings: 1. individuals living in vibrant and walkable neighborhoods are more likely to replace other modes (possibly active modes) with ridehailing, 2. previous studies may have misestimated the relationships between public transit and ridehailing by ignoring confounding effects. In the following two studies, I move to an international perspective on the impacts of these services and other disruptions in the transportation sector. First, I focus on the adoption of ridehailing in developing countries. To do this, I compile survey datasets from Mexico City, Sao Paulo, Beijing, and Mumbai and estimate a binary logit model of the adoption of ridehailing with discrete segmentation for each country. My analysis shows that younger respondents are more likely to adopt these services in all locations. A number of other factors are found to have significant effects only in selected regions. Among other findings, in Mumbai, respondents who live in zero-vehicle households are more likely to use ridehailing, probably as an effect of social status, while this is not true in the other regions. Women are more likely to use ridehailing than men in Sao Paulo and Beijing, and this effect is significantly stronger in Mumbai. However, in Mexico City, an opposite effect was observed, i.e., men are more likely to use these services than women. In the final study, I focus on the disruption that the COVID-19 pandemic brought to society starting in early 2020. To do this, I focus on one of the major components of disruption the pandemic has caused, the heavy shift to telecommuting. I aim to understand the influence of household and individual socio-demographic characteristics on two related dependent variables: the decision to exclusively telecommute and the frequency of physical commute to work (if not exclusively telecommuting) during the first wave of the pandemic in Canada, Chile, Germany and the U.S. I jointly model the two decisions while accounting for confounding effects, including those associated with different recruiting and sampling methods for each country and unobserved country-specific attributes (e.g., COVID-19 response). In all countries, affluent workers (i.e., high-income, high-educated, or non-essential-workers) are found to have a higher propensity to exclusively telecommute and to report to work at a lower frequency if commuting physically. I also uncover that the effects of a few selected sociodemographic characteristics differ greatly by country, including household size, full/part-time worker status, gender, and vehicle ownership. This study contributes to the academic literature by comparing how the response to the global COVID-19 pandemic (in terms of telecommuting behavior) depended on the local context. Finally, the last two studies converge on one finding from my dissertation – context matters while studying individual behaviors, and it is not always easy to generalize findings and transfer the lessons learned from one location to others.

Emerging transportation services such as ridehailing, whose development and adoption have been enabled by information and communication technology, are transforming people’s travel and activity patterns. It is unclear what these changes mean for environmental sustainability, as researchers are still trying to understand how new mobility services might impact multimodal travel and reliance on private cars. A better understanding of emerging mobility patterns can improve travel demand forecasting tools, inform investment decisions, and help provide efficient, reliable, and accessible transportation solutions.

Building on a multi-year study, researchers at the University of California, Davis surveyed 4,071 California residents in 2018 about their personal attitudes and preferences, lifestyles, travel patterns, vehicle ownership, adoption and use of new mobility services, and personal and household characteristics. This brief summarizes the results of multiple studies that have used this dataset to generate insights into the impact of ridehailing services on the use of other travel modes and on car ownership prior to the COVID-19 pandemic, as well as provides policy implications.

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In recent decades, emerging technologies have significantly transformed the transportation sector, offering a wider range of vehicle options and mobility alternatives. However, the recent COVID-19 pandemic disrupted many aspects of daily life, likely leaving long-lasting effects that further complicate our understanding and prediction of individuals’ activity-travel patterns, vehicle ownership decisions, and fuel type preferences. Despite efforts by the State of California and the rest of the U.S. to promote sustainable travel, more behavioral shifts are needed to reduce car dependence, encourage multimodal transportation, and accelerate the electrification of both privately owned and shared vehicle fleets. Further research is needed to identify the factors that motivate or hinder these shifts, and to understand their heterogeneous impacts across subpopulations, which vary based on personal attitudes, socio-demographic characteristics, geographic contexts, and COVID-related factors. This dissertation aims to address these research gaps through four interconnected studies.

The first two studies examine the determinants of vehicle ownership and fuel type choice by consumers for privately owned vehicles. In the first study (Chapter 2), I construct an integrated choice and latent variable (ICLV) model to jointly model current vehicle fuel type choices and future interest in alternative fuel vehicles (AFVs) among 3,260 Californian residents. The findings highlight the critical role of latent attitudes (including those related to environmental concerns, tech-savviness, car utilitarianism, and residential location preferences) and socio-demographic factors in shaping individuals’ vehicle fuel type choices. Exposure to battery electric vehicles (BEVs) in residential locations and workplaces increases the likelihood of AFV adoption. Individuals’ current user experience with AFVs has a positive effect on their interest in these vehicles. Based on consumer intentions reported in 2018, I estimate that the potential natural ceiling for AFV adoption in California could reach 41% of the adult population.

In Chapter 3, I extend my study to examine the impacts of the COVID-19 pandemic on household vehicle ownership. Using an ICLV model and a longitudinal dataset of 1,612 US residents, I analyze changes in their household vehicle counts during the pandemic (spring 2020 to fall 2023) and expected changes after the pandemic (fall 2023 to fall 2026). The results indicate that novelty-seeking individuals are more engaged in vehicle transactions, such as adding, removing, or replacing vehicles. Younger adults, households with children, and families experiencing an increase in the number of adults or children are more likely to acquire additional vehicles to meet evolving travel needs. Entering the workforce and rising household income during the study period also contribute to vehicle acquisitions. Moreover, a higher frequency of commuting reduced the likelihood of shedding vehicles in the past and continues to increase the likelihood of vehicle purchases in the future. Finally, households that shed vehicles during the pandemic often expect to reacquire them afterward, counteracting the vehicle reductions achieved during the pandemic.

Ridehailing services have the potential to serve as a practical alternative to private vehicles and help reduce environmental impacts when powered by clean-fuel vehicles and integrated with other less-polluting modes like public transit and micromobility. Accordingly, the rest of my dissertation focuses on ridehailing riders (in Chapter 4) and ridehailing drivers (in Chapter 5). In Chapter 4, I estimate a weighted latent class cluster analysis among 5,053 California residents and identify four distinctive traveler groups. Drive-alone Users (53%) and Carpoolers (28%) are predominantly car-oriented and less multimodal, whereas Transit Users (15%) and Cyclists (4%) exhibit greater multimodality. Transit Users account for the highest rate of ridehailing adoption and usage and are also more prone to using pooled ridehailing services. If ridehailing were not available, users would generally replace ridehailing with the modes they use most frequently. For instance, car-oriented travelers are more likely to substitute ridehailing with car trips, whereas non-car-based travelers are more inclined to replace ridehailing with less-polluting modes.

Finally, Chapter 5 examines the vehicle fuel type choices of ridehailing drivers. Using data from 1,099 California ridehailing drivers, I estimate an ICLV model to explore their motivations and barriers to AFV adoption. The findings reveal that older drivers, those solely working for ridehailing, and residents in multi-family dwellings are more likely to obtain vehicles with the intention of using them for ridehailing work. Additionally, latent factors such as positive attitudes towards EVs and favorable subjective norms around EVs are positively correlated with BEV adoption, while perceived barriers to EVs hinder their adoption. Access to charging infrastructure also positively impacts BEV adoption. Home chargers have a stronger impact among drivers who obtained their vehicles without the intention of using them for ridehailing, while public chargers are more important for those who acquire vehicles with the intention of using them for ridehailing work. Federal incentives have a more substantial impact on EV adoption compared to state and local incentives, although their effectiveness depends on the driver’s familiarity with the programs. The impact of federal incentives is especially pronounced among drivers who acquired vehicles with the intention of using them for ridehailing work, with the potential to increase the BEV market share by 10 percentage points if all drivers were highly familiar with these incentives.

The COVID-19 pandemic rapidly accelerated the adoption of online shopping in early 2020. This increase raises questions about whether there are changes in e-shopping behavior, the characteristics of online shoppers, and the frequency of online purchases by different types of delivery services during that period. The e-shopping patterns during the COVID-19 pandemic also bring into question whether everyone has the accessibility to engage in online shopping and use various delivery services. This study addresses the above questions through exploratory data analyses, the estimation of binomial logistic regression models, and market segmentation using the Latent Class Cluster Analysis (LCCA) on the Spring 2020 COVID-19 Mobility Study survey data. The results show that age, educational background, type of neighborhood where the respondents live, household income, and attitudes toward technology strongly influence e- shopping behavior. The LCCA results revealed three well-defined latent classes: 1) Occasional Shoppers, who shopped not very frequently, generally used both fast and standard delivery services more than the other delivery options and often live in suburban areas, 2) Non-Shoppers, who made very few or no online purchases at all, tend to belong to the older age group, have lower education and income level, and have negative attitudes toward technology, and 3) Super Shoppers, who made more frequent purchases (usually three or more purchases in a month with any type of delivery services) tend to be younger and wealthier, live in urban areas, and usually have positive attitudes toward the adoption of technology. The results indicate that e-shopping and some delivery services during the early phase of the COVID-19 pandemic might only be available and benefit some groups, as people who had lower digital literacy and did not live in urban areas had less access to those services.

Keywords: E-shopping, COVID-19, Shopping Behavior, Delivery Services, Exploratory Data Analysis, Logistic Regression, Latent Class Cluster Analysis