research report

What’s Behind Recent Transit Ridership Trends in the Bay Area? Volume II: Trends among Major Transit Operators

Publication Date

February 1, 2020

Author(s)

Andrew Schouten, Brian D. Taylor, Evelyn Blumenberg, Hannah King, Jacob Wasserman, Julene Paul, Madeline Ruvolo, Mark Garrett

Abstract

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, the research team analyzes ridership on the eight largest Bay Area transit operators in considerable detail in Volume II of the report. Overall, the team finds 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 the 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 in all agencies, it can be seen that at least some evidence of peaking. The resulting dependence on peak trips both incurs high costs and depresses passenger satisfaction.

research report

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

Publication Date

February 1, 2020

Author(s)

Andrew Schouten, Brian D. Taylor, Evelyn Blumenberg, Hannah King, Jacob Wasserman, Julene Paul, Madeline Ruvolo, Mark Garrett

Abstract

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. The research finds 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).

policy brief

Life Cycle Cost Analysis Comparison Spreadsheet

policy brief

The Bay Area is Losing Transit Ridership — But Transit Commuting is Growing

policy brief

Why is Bay Area Transit Ridership Falling?

policy brief

Why is Public Transit Falling in the San Francisco Bay Area, and What Might be Done About It?

policy brief

What Can Be Done About Falling Transit Ridership in the Bay Area?

research report

Rapid Reporting of Vehicle Crash Data in California to Understand Impacts from COVID-19 Pandemic on Traffic and Incidents

Abstract

In 2015, the Road Ecology Center at UC Davis developed a web-based method to collect all incident data that appear on the CHP real-time incident-reporting website (https://cad.chp.ca.gov/). These data are assembled into a database called CHIPS, the California Highway Incident Processing System. Previous analyses suggest that these data are more spatially accurate than other state resources (e.g., the Statewide Integrated Traffic Records System (SWITRS)). Because they are collected and organized in real time, they can also be shared and queried more easily. The current project developed a web portal that supports queries for counties and specific highways (https://roadecology.ucdavis.edu/resources/covid19- traffic). The results shown make apparent the reduction in crashes and traffic during the summer 2020 peak of the COVID-19 pandemic.

research report

Uncertainty, Innovation, and Infrastructure Credits: Outlook for the Low Carbon Fuel Standard Through 2030

Abstract

California’s low carbon fuel standard (LCFS) specifies that the state’s transportation fuel supply achieves a 20% reduction in carbon intensity (CI) below 2011 levels by 2030. Reaching the standard will require substantive changes in the fuel mix, but the specifics and the cost of these changes are uncertain. The research team assesses if and how California is likely to achieve the standard, and the likely impact of infrastructure credits on this compliance outlook. The team begins by projecting a distribution of fuel and vehicle miles demand under business-as-usual economic and policy variation and transforms those projections into a distribution of low carbon fuel standard net deficits for the entire period from 2019 through 2030. The researchers then construct a variety of scenarios characterizing low carbon fuel standard credit supply that consider different assumptions regarding input markets, technological adoption over the compliance period, and the efficacy of complementary policies. In the baseline scenario for credit generation, low carbon fuel standard compliance would require that between 60% and 80% of the diesel pool be produced from biomass. The research team’s baseline projections have the number of electric vehicles reaching 1.3 million by 2030, but if the number of electric vehicles reaches Governor Jerry Brown’s goal of 5 million by 2030, then low carbon fuel standard compliance would require substantially less biomass-based diesel. Outside of rapid zero-emission vehicle penetration, compliance in 2030 with the $200 credit price may be much more difficult. New mechanisms to allow firms to generate credits by building electric vehicle charging stations or hydrogen fueling stations have minor implications for overall compliance because the total quantity of infrastructure credits is restricted to be relatively small.