What’s Behind Recent Transit Ridership Trends in the Bay Area? Volume I: Overview and Analysis of Underlying Factors

Public transit ridership has been falling nationally and in California since 2014. The San Francisco Bay Area, with the state’s highest rates of transit use, had until recently resisted those trends, especially compared to Greater Los Angeles. However, in 2017 and 2018 the region lost over five percent (>27 million) of its annual riders, despite a booming economy and service increases. This report examines Bay Area transit ridership to understand the dimensions of changing transit use, its possible causes, and potential solutions. We find that: 1) the steepest ridership losses have come on buses, at off-peak times, on weekends, in non-commute directions, on outlying lines, and on operators that do not serve the region’s core employment clusters; 2) transit trips in the region are increasingly commute-focused, particularly into and out of downtown San Francisco; 3) transit commuters are increasingly non-traditional transit users, such as those with higher incomes and automobile access; 4) the growing job-housing imbalance in the Bay Area is related to rising housing costs and likely depressing transit ridership as more residents live less transit-friendly parts of the region; and 5) ridehail is substituting for some transit trips, particularly in the off-peak. Arresting falling transit use will likely require action both by transit operators (to address peak capacity constraints; improve off-peak service; ease fare payments; adopt fare structures that attract off-peak riders; and better integrate transit with new mobility options) and public policymakers in other realms (to better meter and manage private vehicle use and to increase the supply and affordability of housing near job centers)

Temporary versus Permanent Pandemic Transit Leavers: Findings from the 2022 US National Household Travel Survey

Using data from the 2022 National Household Travel Survey, I explore the socio-demographic characteristics of Americans who reduced their use of public transit during the latter stages of the COVID-19 pandemic. I also examine differences between travelers whose reduced transit use was temporary versus permanent. Using adjusted Wald tests and multinomial logistic regression, I find significant differences between people who did not leave transit and those who did, as well as between temporary and permanent transit leavers. Notably, owning a vehicle, having a disability, and working from home were associated with leaving public transit permanently rather than temporarily

Sharing In and Sharing Out: The Equity Implications of Informal Vehicle-Sharing

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

Vehicle ownership rates: The role of lifecycle, period, and cohort effects

Researchers and policymakers often attempt to forecast trends in automobile ownership. But to understand recent changes in demand for cars, researchers must account for behaviors specific to different generations, while simultaneously controlling for the influence of lifecycle and historical effects. To overcome the analytical challenges of cross-sectional data in Age-Period-Cohort (APC) analysis, we apply three different approaches largely used by biostatisticians to isolate how cohort effects influence the likelihood that a U.S. adult lives in a zero-vehicle household.Our analyses draw on data from the U.S. Census Public Use Microdata Samples (PUMS) from 1970 to 2019. To test for cohort effects, we use constraint-based binary logistic regression, a nonlinear parametric approach to log-linear models, and median polish analysis. We find that people born from 1935 to 1944 experienced the strongest negative cohort effect of all groups, and thus were least likely to live in zero-vehicle households (after accounting for age and period effects). Compared to this cohort, persons born before 1924 and after 1955 saw higher likelihoods of living in zero-vehicle households, all else equal.The peak cohort effect of people born in the 1930s to 1940s may please those interested in reducing automobile use. But because automobiles offer access benefits, more recent cohorts may experience transportation challenges. Negative effects may be especially salient for Millennials, a group faring worse economically than previous generations. Further, recent changes in the transportation landscape – including the growth of services like carshare and ride-hail and behavioral changes emerging from the COVID-19 pandemic – complicate efforts to forecast demand for automobiles

Pandemic transit: examining transit use changes and equity implications in Boston, Houston, and Los Angeles.

UNLABELLED: While the COVID-19 pandemic upended many aspects of life as we knew it, its effects on U.S. public transit were especially dramatic. Many former transit commuters began to work from home or switched to traveling via private vehicles. But for those who continued to work outside the home and could not drive-who were more likely low-income and Black or Hispanic-transit remained an important means of mobility. However, most transit agencies reduced service during the first year of the pandemic, reflecting reduced ridership demand, increasing costs, and uncertain budgets. To analyze the effects of the pandemic on transit systems and their users, we examine bus ridership changes by neighborhood in Boston, Houston, and Los Angeles from 2019 to 2020. Combining aggregated stop-level boarding data, passenger surveys, and census data, we identify associations between shifting travel patterns and neighborhoods. We find that early in the pandemic, neighborhoods with more poor and non-white households lost proportionally fewer riders; however, this gap between high- and low-ridership-loss neighborhoods shrank as the pandemic wore on. We also model ridership change controlling for multiple factors. Ridership in Houston and LA generally outperformed Boston, with built environment and demographic factors accounting for some of the observed differences. Neighborhoods with high shares of Hispanic and African American residents retained more riders in the pandemic, while those with higher levels of auto access and with more workers able to work from home lost more riders, all else equal. We conclude that transits social service role elevated during the pandemic, and that serving travelers in disadvantaged neighborhoods will likely remain paramount emerging from it. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11116-022-10345-1

Who lives in transit-friendly neighborhoods? An analysis of California neighborhoods over time

In this paper we examine social and economic trends in California’s transit-friendly neighborhoods since 2000. In particular, we explore the relationship between high-propensity transit users – who we define here as members of households classified as poor, immigrant, African-American, and without private vehicles – and high-transit-propensity places – which are neighborhoods that regularly host high levels of transit service or use. As housing costs have increased dramatically in California and neighborhoods change, many planners and transit advocates reasonably worry that in transit-friendly neighborhooods, lower-propensey transit users may replace residents who tend to ride transit frequently. Such changes in residential patterns could help to explain sharp transit ridership declines in California in the 2010s ahead of much sharper pandemic-related ridership losses in 2020. Indeed, we find that California’s most transit-friendly neighborhoods have changedin ways that do not bode well for transit use. The state's shares of poor, immigrant, African American, and zero-vehicle households have all declined modestly to substantially since 2000. Collectively, these trends point to changes in California’s most transit-friendly neighborhoods that are not very, well, transit-friendly

The Hangover: Assessing Impact of Major Service Interruptions on Urban Rail Transit Ridership

Driven by several factors, transit ridership has increased dramatically in some major U.S. urban areas over the past several years. Developing accurate econometric models of system ridership growth will help transit agencies plan for future capacity. As major weather events and maintenance issues can affect transit systems and have large impacts on the trajectory of ridership growth, this study examined the effect of major and minor service interruptions on the PATH heavy rail transit system in northern New Jersey and New York City. The study, which used PATH ridership data as well as data on weather, economic conditions, and fares for both PATH and competing services, concluded that Hurricane Sandy likely dampened ridership gains. Other major service interruptions, which lasted only hours or days, had little effect on long-term ridership growth. Suggestions for further study of service interruptions, especially in the face of climate change and resiliency issues in coastal regions, are presented