Skip to main content
eScholarship
Open Access Publications from the University of California
Cover page of Transit Blues in the Golden State: Analyzing Recent California Ridership Trends

Transit Blues in the Golden State: Analyzing Recent California Ridership Trends

(2020)

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.

Cover page of Learning to Collaborate: Lessons Learned from Governance Processes Addressing the Impacts of Sea Level Rise on Transportation Corridors Across California

Learning to Collaborate: Lessons Learned from Governance Processes Addressing the Impacts of Sea Level Rise on Transportation Corridors Across California

(2020)

This study was designed to identify lessons learned from experiences of multi-stakeholder collaboration in governance processes focused on adaptation to sea level rise for specific transportation corridors/assets across different areas of California. Four transportation assets in California were selected as case studies: State Route 37 in the Bay Area; the Cardiff Beach Living Shorelines Project and the LOSSAN railroad at Del Mar in San Diego County; and the Port of Long Beach in Los Angeles County. The study methods included attendance of policy meetings; document analysis; and interviews of staff at (local, regional, and state) government bodies, transportation agencies, climate collaboratives, etc. The study identified three major governance challenges shared among these cases: (1) stakeholder involvement or collaboration with ‘unusual’ partners; (2) jurisdictional fragmentation; and (3) lack of funding. The lessons learned to address these challenges were: (a) include a wide range of stakeholders early on in the project; (b) identify an intermediary or facilitator with relevant knowledge and social capital with the stakeholders; (c) establish a forum for negotiations and information exchange; (d) draft a memorandum of understanding with the rules of collaboration; (e) appoint a project manager to tie all the project parts and stakeholders together and sustain engagement; (f) structure the collaboration in tiers from technical/operational to executive/political; (g) explore options to make any given project a multi-benefit project; (h) advocate for a multi-year stream of funding rather than a lump sum; (i) leverage collaboration for funding and highlight, to potential funders, the collaborative element as a means to increase the efficiency of their investment. Issues to consider when deriving lessons from other jurisdictions were: differences in capacity, or available resources and staff; the numbers of actors involved; pre-existing positive collaborative relationships between the actors; exposure of transportation assets to sea-level rise; existing vulnerabilities of the corridor/asset; and the economic relevance of the corridor/asset.

Cover page of Travel Behavior Changes Among Users of Partially Automated Vehicles

Travel Behavior Changes Among Users of Partially Automated Vehicles

(2020)

Partially automated battery electric vehicles (BEVs) are being sold to and used by consumers. Estimates indicate that as of the end of 2019, there were over 700,000 Partially Automated Tesla Vehicles—the subject of this study—on the roads globally. Despite this, little research has been done to understand how they may be changing travel behavior. In this study, qualitative interviews with 36 users of Tesla BEVs with Autopilot were conducted. The goal of this was to understand how Autopilot is used, user experiences of the system, and whether the system has any impact on drivers’ travel behavior. The focus of the last of these aims was to determine whether Autopilot could cause or was causing an increase in vehicle miles traveled (VMT) among the study participants. Results from the interviews showed that partial automation leads to consumers travelling by car more and being more willing to drive in congested traffic. These changes are due to increased comfort, reduced stress, and increased relaxation due to the partial automation system, and because of the lower running costs of a BEV. The results also point to a need for further research of partially automated vehicles that are already on the market, as 11 of 17 reasons for increased VMT that have been identified in modeling studies of fully automated vehicles (not yet commercially available) applied to users of Autopilot.

Cover page of Research Synthesis for the California Zero Traffic Fatalities Task Force

Research Synthesis for the California Zero Traffic Fatalities Task Force

(2020)

This research synthesis consists of a set of white papers that jointly provide a review of research on the current practicefor setting speed limits and future opportunities to improve roadway safety. This synthesis was developed to inform thework of the Zero Traffic Fatalities Task Force, which was formed in 2019 by the California State Transportation Agencyin response to California Assembly Bill 2363 (Friedman). The statutory goal of the Task Force is to develop a structured,coordinated process for early engagement of all parties to develop policies to reduce traffic fatalities to zero. Thisreport addresses the following critical issues related to the work of the Task Force: (i) the relationship between trafficspeed and safety; (ii) lack of empirical justification for continuing to use the 85th percentile rule; (iii) why we need toreconsider current speed limit setting practices; (iv) promising alternatives to current methods of setting speed limits;and (v) improving road designs to increase road user safety.

Cover page of Supercharged? Electricity Demand and the Electrification of Transportation in California

Supercharged? Electricity Demand and the Electrification of Transportation in California

(2020)

The rapid electrification of the transportation fleet in California raises important questions about the reliability, cost, and environmental implications for the electric grid. A crucial first element to understanding these implications is an accurate picture of the extent and timing of residential electricity use devoted to EVs. Although California is now home to over 650,000 electric vehicles (EVs), less than 5% of these vehicles are charged at home using a meter dedicated to EV use. This means that state policy has had to rely upon very incomplete data on residential charging use. This report summarizes the first phase of a project combining household electricity data and information on the adoption of electric vehicles over the span of four years. We propose a series of approaches for measuring the effects of EV adoption on electricity load in California. First, we measure load from the small subset of households that do have an EV-dedicated meter. Second, we estimate how consumption changes when households go from a standard residential electricity tariff to an EV-specific tariff. Finally, we suggest an approach for estimating the effect of EV ownership on electricity consumption in the average EV-owning household. We implement this approach using aggregated data, but future work should use household-level data to more effectively distinguish signal from noise in this analysis. Preliminary results show that households on EV-dedicated meters are using 0.35 kWh per hour from Pacific Gas and Electric (PGE); 0.38 kWh per hour from Southern California Edison; and 0.28 kWh per hour from San Diego Gas and Electric on EV charging. Households switching to EV rates without dedicated meters are using less electricity for EV charging: 0.30 kWh per hour in PGE. Our household approach applied to aggregated data is too noisy to be informative. These estimates should be viewed as evidence that more focused analysis with more detailed data would be of high value and likely necessary to produce rigorous analysis of the role EVs are playing in residential electricity consumption.

Cover page of Review of California Wildfire Evacuations from 2017 to 2019

Review of California Wildfire Evacuations from 2017 to 2019

(2020)

Between 2017 and 2019, California experienced a series of devastating wildfires that together led over one million people to be ordered to evacuate. Due to the speed of many of these wildfires, residents across California found themselves in challenging evacuation situations, often at night and with little time to escape. These evacuations placed considerable stress on public resources and infrastructure for both transportation and sheltering. In the face of these clear challenges, transportation and emergency management agencies across California have widely varying levels of preparedness for major disasters, and nearly all agencies do not have the public resources to adequately and swiftly evacuate all populations in danger. To holistically address these challenges and bolster current disaster and evacuation planning, preparedness, and response in California, we summarize the evacuations of eleven major wildfires in California between 2017 and 2019 and offer a cross-comparison to highlight key similarities and differences. We present results of new empirical data we collected via an online survey of individuals impacted by: 1) the 2017 October Northern California Wildfires (n=79), 2) the 2017 December Southern California Wildfires (n=226), and 3) the 2018 Carr Wildfire (n=284). These data reveal the decision-making of individuals in these wildfires including choices related to evacuating or staying, departure timing, route, sheltering, destination, transportation mode, and reentry timing. We also present results related to communication and messaging, non-evacuee behavior, and opinion of government response. Using the summarized case studies and empirical evidence, we present a series of recommendations for agencies to prepare for, respond to, and recover from wildfires.

Cover page of Investigating the Influence of Dockless Electric Bike-share on Travel Behavior, Attitudes, Health, and Equity

Investigating the Influence of Dockless Electric Bike-share on Travel Behavior, Attitudes, Health, and Equity

(2020)

Cities throughout the world have implemented bike-share systems as a strategy for expanding mobility options. While these have attracted substantial ridership, little is known about their influence on travel behavior more broadly. The aim of this study was to examine how shared electric bikes (e-bikes) and e-scooters influence individual travel attitudes and behavior, and related outcomes of physical activity and transportation equity. The study involved a survey in the greater Sacramento area of 1959 households before (Spring 2016) and 988 after (Spring 2019) the Summer 2018 implementation of the e-bike and e-scooterservice operated by Jump, Inc., as well as a direct survey of 703 e-bike users (in Fall 2018 & Spring 2019). Among householdrespondents, 3–13% reported having used the service. Of e-bike share trips, 35% substituted for car travel, 30% substituted for walking, and 5% were used to connect to transit. Before- and after-household surveys indicated a slight decrease in self-reported (not objectively measured) median vehicle miles traveled and slight positive shifts in attitudes towards bicycling. Service implementation was associated with minimal changes in health in terms of physical activity and numbers of collisions. The percentages of users by self-reported student status, race, and income suggest a fairly equitable service distribution by these parameters, but each survey under-represents racial minorities and people with low incomes. Therefore, the study is inconclusive about how this service impacts those most in need. Furthermore, aggregated socio-demographics of areas where trips started or ended did not correlate with, and therefore are not reliable indicators of, the socio-demographics of e-bike-share users. Thus, targeted surveying of racial minorities and people with low-incomes is needed to understand bike-share equity.

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

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

(2020)

California’s low carbon fuel standard (LCFS) specifies that the state’s transportation fuel supply achieve 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. We assess if and how California is likely to achieve the standard, and the likely impact of infrastructure credits on this compliance outlook. We begin by projecting a distribution of fuel and vehicle miles demand under business-as-usual economic and policy variation and transform those projections into a distribution of LCFS net deficits for the entire period from 2019 through 2030. We then construct a variety of scenarios characterizing LCFS credit supply that consider different assumptions regarding input markets, technological adoption over the compliance period, and the efficacy of complementary policies. In our baseline scenario for credit generation, LCFS compliance would require that between 60% and 80% of the diesel pool be produced from biomass. Our 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 LCFS 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.

Cover page of Generalized Costs of Travel by Solo and Pooled Ridesourcing vs. Privately Owned Vehicles, and Policy Implications

Generalized Costs of Travel by Solo and Pooled Ridesourcing vs. Privately Owned Vehicles, and Policy Implications

(2020)

The emergence of “3 Revolutions” in transportation (automation, electrification and shared mobility) presents a range of questions regarding how consumers will travel in the future, and under what conditions there may be rapid adoption of various services. These include individual on-demand taxi-style services, shared mobility in pooled services, and use of public transit, all with or without drivers. There is now enough data and estimates on the costs of these service combinations, and in some cases ridership data, to consider how consumers are making choices and could do so in the future as things evolve. This project involved: (a) reviewing existing literature and data on consumer mode and vehicle choice; (b) developing new “generalized cost” estimates that combine monetary and non-monetary (e.g., hedonic) components of travel choice, notably incorporating value of time; and (c) conducting a comparison of monetary and generalized trip cost for a range of trip types across travel options in the near term (2020) and longer term (2030-35). Three main travel options were considered: privately owned vehicles, ridesourced solo trips, and ridesourced pooled trips. Consideration of internal combustion vs. battery electric and, in the longer term, automated technology was also core to the analysis. The trips considered include urban and suburban types in the San Francisco metro area, using actual trip characteristics. The results suggest that in the near-term, solo ridesourcing is likely to be perceived as significantly more expensive (in terms of monetary and time costs) than pooled ridesourcing or solo private vehicle trips except for those with a very high value of time. Solo ridesourcing does better in dense, slow, urban trips than in faster suburban trips. In the longer term, with automated driverless vehicles, solo ridesourcing could become the cheapest mode for many travelers in a range of situations. This report includes an initial consideration of the implications of these policies for affecting travel choices, presumably to push choices toward pooled ridesourcing as a sustainable option. VMT-based pricing, pricing that could be adjusted with vehicle occupancy, and parking-related approaches are described. A large price signal might be needed to shift travel, given some of the differences in generalized cost found in this analysis.

Cover page of Assessment of the Employment Accessibility Benefits of Shared Autonomous Mobility Services

Assessment of the Employment Accessibility Benefits of Shared Autonomous Mobility Services

(2020)

The goal of this study is to assess and quantify the potential employment accessibility benefits of Shared Autonomous Mobility Service (SAMS) commute modes across a large diverse metropolitan region considering heterogeneity in the working population. To meet this goal, this study employs a welfare-based (i.e. logsum-based) measure of accessibility, obtained via estimating a hierarchical work destination-commute mode choice model. The employment accessibility logsum measure incorporates the spatial distribution of worker residences and employment opportunities, the attributes of the available commute modes, and the characteristics of individual workers. This research further captures heterogeneity of workers using latent class analysis (LCA). The LCA model inputs include the socio-demographic characteristics of workers to subsequently account for different worker clusters valuing different types of employment opportunities differently. The accessibility analysis results indicate: (i) the accessibility benefit differences across latent classes are modest but young workers and low-income workers do see higher benefits than high- and middle-income workers; (ii) there are substantial spatial differences in accessibility benefits with workers living in lower density areas benefiting more than workers living in high-density areas; (iii) nearly all the accessibility benefits come from the SAMS-only mode as opposed to the SAMS+Transit mode; and (iv) the SAMS cost per mile assumption significantly impacts the magnitude of the overall employment accessibility benefits.