U.S. residents travel in cars for the vast majority of their trips. Yet car travel imposes costs on the individual (in terms of out-of-pocket costs) and society (via air pollution, congestion, and collision rates). Urban planners, policymakers, and elected officials have enacted many policies to subsidize and promote shared-vehicle travel – including via public transit – to mitigate these costs. Yet despite the billions of public dollars poured into public transportation, most shared travel in the U.S. occurs in private cars. And by extending mobility to people with limited car access, informal automobile sharing – with household members, friends, coworkers, and strangers – also offers benefits to disadvantaged travelers. But due to data limitations and policy emphases on public forms of shared travel, few researchers have systematically examined the relationship between transportation disadvantage and informal sharing.

In this dissertation, I use mixed methods to answer different aspects of a single question: What utility does informal vehicle-sharing offer U.S. travelers? In the first essay, I use quantitative methods to explore the factors that determine whether a traveler chooses to share. In particular, I emphasize how disadvantage (in the form of medical conditions, poverty, and vehicle access) influences informal automobile sharing. Using data from the 2017 National Household Travel Survey (NHTS), I model automobile sharing as an expression of rational choice and thus a proxy for the utility it offers travelers. I find that while transportation disadvantage is associated with certain sharing behaviors (including borrowing cars and receiving rides from people living in other households), trip purposes – particularly non-work-related ones – best predict the likelihood of sharing a private vehicle.

In the latter two essays, I analyze data from the Craigslist rideshare board to examine the opportunities and challenges people face in ridesharing with strangers. In the second essay, I use mixed methods to analyze web-scraped data. I examine the physical qualities of trips desired and offered on Craigslist and how they varied across California regions. I also measure the frequency and types of compensation that posters mentioned. In the third essay, I use qualitative methods to analyze information from surveys of and interviews with people who posted on the Craigslist rideshare board. I evaluate how often they successfully rideshared and how they balanced the risks and rewards of interacting with strangers.

Findings from the three essays highlight the potential of informal vehicle-sharing to address social and environmental challenges in the U.S. Millions of empty seats fill streets and highways every day, while many disadvantaged people struggle to meet their daily travel needs. Based on my findings, I recommend policies that provide automobile-based assistance – such as subsidized carshare programs – to low-income families. To facilitate ridesharing between strangers, I recommend that public agencies create digital applications to help people match with other travelers. I also recommend that agencies consider pricing road travel by distance, to make non-sharing – and particularly driving alone across long distances – costlier. Doing so will help increase opportunities to share for all travelers.

Problem, research strategy, and findings

Carsharing programs—subscription-based car rentals—allow users to purchase only the automobility that they need. These programs may benefit low-income travelers by increasing access at lower prices than private auto ownership. Most carshare programs, however, disproportionately serve higher-income drivers. To assess carsharing’s potential to address the accessibility needs of disadvantaged households, we interviewed members of BlueLA, an electric carsharing program in central Los Angeles (CA) that offers both subsidized and regular memberships. We found few differences in how travelers with different membership types used BlueLA. They both used the service to complement travel by other modes like public transit and ridehail. In addition, members cited the benefits of gaining car access without the financial burden of car ownership or the unpredictability of ridehail fares. Neighborhood context, including residential density and the availability of non-automobile transportation options, also increased BlueLA’s appeal. However, due to limited and unreliable vehicle availability, most users did not rely on BlueLA for time-sensitive trips. BlueLA both eased and increased access to destinations outside of the commute and complemented public transit for subsidized and regular members.

Takeaway for practice

Services like BlueLA cannot meet all transportation needs. However, subsidized electric carsharing—particularly targeted to central-city neighborhoods—may address some accessibility needs of low-income households without imposing the burdens of automobile ownership.

Daily vehicle travel collapsed with the onset of the COVID-19 pandemic in early 2020 but largely bounced back by late 2021. The pandemic caused dramatic changes to working, schooling, shopping, and leisure activities, and to the travel associated with them. Several of these changes have so far proven enduring. So, while overall vehicle travel had largely returned to pre-pandemic levels by late 2021, the underlying drivers of this travel have likely changed.

To examine one element of this issue, we analyzed whether patterns of daily trip-making shifted temporally between the fall of 2019 and 2021 in the Greater Los Angeles megaregion. We used location-based service data to examine vehicle trip originations for each hour of the day at the U.S. census block group level in October 2019 and October 2021. We observed notable shifts in the timing of post-pandemic PM peak travel, so we examined changes in the ratio of mid-week trips originating in the early afternoon (12–3:59 PM) and the late afternoon/early evening (4–7:59 PM).

We found a clear shift in the temporal distribution of PM trip-making, with relatively more late PM peak period trip-making prior to the pandemic, and more early PM peak trip-making in 2021. The peak afternoon/evening trip-making hour shifted from 5–5:59 PM to 3–3:59 PM. We also found that afternoon/evening trip-making in each year is largely explained by three workplace-area/school-area factors: (1) the number of schoolchildren in a block group (earlier); (2) block groups with large shares of potential remote workers (earlier), and (3) block groups with large shares of low-wage jobs and workers of color (later, except for Black workers in 2021). We found the earlier shift in PM peak travel between pre- and late-pandemic periods to be explained most by (1) higher shares of potential remote workers and (2) higher shares of low-wage jobs and workers of color. These findings suggest that the rise of working from home has likely led to a shift in PM peak travel earlier in the afternoon when school chauffeuring trips are most common. This is especially true for low-income workers and workers of color.

Transit patronage plunged staggeringly, from 50 to as much as 94 percent, during the first half of 2020 amidst the worst global pandemic in a century. But transit’s troubles in California date much earlier. From 2014 to 2018, California lost over 165 million annual boardings, a drop of over 11 percent. This report examines public transit in California in the 2010s and the factors behind its falling ridership.

We find that ridership gains and losses have been asymmetric with respect to location, operators, modes, and transit users. Transit ridership has been on a longer-term decline in regions like Greater Los Angeles and on buses, while ridership losses in the Bay Area are more recent. While overall transit boardings across the state are down since 2014, worrisome underlying trends date back earlier as patronage failed to keep up with population growth. But reduced transit service is not responsible for ridership losses, as falling transit ridership occurred at the same time as operators instead increased their levels of transit service.

What factors help to explain losses in transit ridership? Increased access to automobiles explains much, if not most, of declining transit use. Private vehicle access has increased significantly in California and, outside of the Bay Area, is likely the biggest single cause of falling transit ridership. Additionally, new ridehail services such as Lyft and Uber allow travelers to purchase automobility one trip at a time and likely serve as a substitute for at least some transit trips. Finally, neighborhoods are changing in ways that do not bode well for public transit. Households are increasingly locating in outlying areas where they experience longer commutes and less transit access to employment. At the same time, a smaller share of high-propensity transit users now live in the state’s most transit-friendly neighborhoods.

While the 2010s proved a difficult decade for public transit in California, and the opening of the current decade has been an even bigger challenge, transit remains an essential public service. Effectively managing transit recovery in California will require a clear-eyed understanding of the substantially altered environment within which these systems large and small must now operate.

Transit ridership in the San Francisco Bay Area is falling. Yet some operators, areas, times, directions, routes, modes, and services have fared better than others. These differences help reveal the causes of the Bay Area’s overall ridership slump and inform policy and service decisions that aim to restore Bay Area transit use. To investigate these temporal and spatial trends, we analyze ridership on the eight largest Bay Area transit operators in considerable detail in Volume II of our report.

Overall, we find a significant level of “peaking.” Ridership losses at off-peak hours, on weekends, on outlying routes, in non-commute directions, and on smaller operators account for a large and disproportionate share of the whole region’s patronage decline. Downtown San Francisco and commute-oriented rail lines like Caltrain have gained ridership as less central, lower-service routes have lost patronage. These patterns match our statistical modeling of BART ridership, on which station-area jobs had the greatest influence, one that has grown over time. The most significant exceptions to the Bay Area’s peaking problem are operators in urban cores, like Muni and AC Transit, where residential and employment density throughout the network have blunted peaking, though not necessarily overall losses.

Absolute patronage declines and peaking are intertwined but distinct problems, with cross-cutting divisions. Yet on all agencies, we see at least some evidence of peaking. The resulting dependence on peak trips both incurs high costs and depresses passenger satisfaction.