Policy Briefs

Policy Briefs

A Detailed Look at How the Pandemic Changed Travel Patterns Across Regions in Northern California Megaregion

Authors: Gulhare, Siddhartha, PhD; Circella, Giovanni, PhD

Many studies have focused on the shifts in travel patterns caused by the COVID-19 pandemic and how travel demand continues to evolve in the post-pandemic era. Key metrics such as trip volume–the total number of trips within a specific area–help explain the pandemic’s impact on travel demand over time. However, to fully understand changes in travel behaviors, it is also important to analyze where trips start and end—otherwise known as Origin-Destination (OD) demand.To better understand OD demand during and after the pandemic, our research team developed a data-driven methodology to analyze travel patterns across different regions, times of day, days of the week (weekday and weekend), and trip purpose. This study used passively collected location-based data from the StreetLight Data platform (StreetLight Data, 2022) in the form of weekly OD matrices of all vehicle modes, segmented by various relevant variables. We focused on the Northern California Megaregion, which includes 21 counties from the San Francisco Bay Area to the Sacramento region and the northern part of the San Joaquin Central Valley. The study period spanned from January 2019 to October 2021.

Assessing the Shift to Remote and Hybrid Work in California throughout the COVID-19 Pandemic

Authors: Farzad Alemi and Caroline Rodier

Beginning in 2020, many in-person activities were replaced by virtual activities as a response to the COVID-19 pandemic. This affected fundamental elements of transportation systems such as trip frequency, commute distance, origins, and destinations. For example, remote work and study were widely adopted among workers and students. Still, the ways that the pandemic affected individuals’ work arrangements across different phases of the pandemic and the extent to which full remote work and hybrid work induced by the pandemic might persist in the future are unclear. In addition, recent studies are not conclusive regarding theways changes in work arrangements do/will impact travel patterns and trip making.

A Detailed Look at How the Pandemic Changed Travel Patterns Across Regions in Northern California Megaregion

Authors: Gulhare, Siddhartha, PhD; Circella, Giovanni, PhD

Many studies have focused on the shifts in travel patterns caused by the COVID-19 pandemic and how travel demand continues to evolve in the post-pandemic era. Key metrics such as trip volume–the total number of trips within a specific area–help explain the pandemic’s impact on travel demand over time. However, to fully understand changes in travel behaviors, it is also important to analyze where trips start and end—otherwise known as Origin-Destination (OD) demand.To better understand OD demand during and after the pandemic, our research team developed a data-driven methodology to analyze travel patterns across different regions, times of day, days of the week (weekday and weekend), and trip purpose. This study used passively collected location-based data from the StreetLight Data platform (StreetLight Data, 2022) in the form of weekly OD matrices of all vehicle modes, segmented by various relevant variables. We focused on the Northern California Megaregion, which includes 21 counties from the San Francisco Bay Area to the Sacramento region and the northern part of the San Joaquin Central Valley. The study period spanned from January 2019 to October 2021.

People with Disabilities in California Want Density, Improved Streets and Buses to Help Pedestrians, Bus Riders, and Car Drivers

Authors: Flynn, Justin A.; Circella, Giovanni; Venkataram, Prashanth S.

People with disabilities travel less than their peers without disabilities. Much research about the travel patterns of people with disabilities focuses on problems they experience with specific transportation modes, under the assumption that fixing those specific problems is enough to fully include people with disabilities in the broader world. This doesn’t account for the connections of different transportation modes to land use patterns, and it presumes what people with disabilities want without asking them. This project aims to understand the extent to which disability may affect the choices and desires that people have for transportation mode usage frequencies, activity frequencies, and neighborhood features.

Future Connected and Automated Vehicle Adoption Will Likely Increase Car Dependence and Reduce Transit Use without Policy Intervention

Authors: Circella, Giovanni; Jaller, Miguel; Sun, Ran; Qian, Xiaodong; Alemi, Farzad

California sits at the epicenter of self-driving vehicle technology development, with numerous companies testing connected and automated vehicles (CAVs) in the state. CAVs have the potential to improve safety and increase mobility for children, the elderly, and people with disabilities. These vehicles will operate more efficiently, use less space on the roadway, and cause fewer crashes, all of which are expected to relieve traffic congestion. However, CAVs will also likely bring about complex changes to travel demand, urban design, and land use. The degree to which these changes will affect vehicle miles traveled, energy use, and air pollution in California is unknown and could have wideranging implications for the state’s ability to meet its climate goals.

Researchers at the University of California, Davis investigated the range of potential impacts that rapid adoption of CAVs in California might have on vehicle miles traveled and emissions. The researchers estimated the vehicle miles traveled and emissions of each scenario using a statewide travel demand model, emissions factors from California agencies, and assumptions derived from the scientific literature and expert input. This policy brief summarizes the findings from that research and provides policy implications.

The Impact of Ridehailing on Other Travel Modes and on Vehicle Dependency

Authors: Iogansen, Xiatian; Circella, Giovanni

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.

Millennial Travelers Are More Multimodal than Older Travelers, but This Trend Might Change as They Age

Authors: Yongsung Lee, Patricia L Mokhtarian, Subhrajit Guhathakurta, Giovanni Circella, Xiatian Iogansen

Millennials, those who were born between the early 1980s and the late 1990s, tend to have different travel patterns than the members of the preceding generations when they were at the same age. Among various dimensions of millennial travel, multimodality—the use of multiple travel modes— has important implications for transportation sustainability. Prior research has found that members of this generation travel more by walking, bicycling, and riding public transit. Further, multimodal travelers are usually better informed about and more sensitive to level-of-service attributes of various modes than are habitual users of single modes (especially cars). Therefore, exploring trends in multimodality among millennials could inform policymakers’ efforts to encourage more sustainable travel modes for millennials and shed light on how they might respond to policy interventions.

Researchers at the University of California, Davis, compared millennials’ travel behavior to that of members of the preceding Generation X by analyzing data collected from 1,069 California commuters. The researchers analyzed the effects of individual attributes on the likelihood of different components of travel behavior, including multimodal travel. This policy brief summarizes the findings of that research and provides policy implications.

Future Connected and Automated Vehicle Adoption Will Likely Increase Car Dependence and Reduce Transit Use without Policy Intervention

Authors: Giovanni Circella, Miguel Jaller, Ran Sun, Xiaodong Qian, and Farzad Alemi

California sits at the epicenter of self-driving vehicle technology development, with numerous companies testing connected and automated vehicles (CAVs) in the state. CAVs have the potential to improve safety and increase mobility for children, the elderly, and people with disabilities. These vehicles will operate more efficiently, use less space on the roadway, and cause fewer crashes, all of which are expected to relieve traffic congestion. However, CAVs will also likely bring about complex changes to travel demand, urban design, and land use. The degree to which these changes will affect vehicle miles traveled, energy use, and air pollution in California is unknown and could have wideranging implications for the state’s ability to meet its climate goals.

Researchers at the University of California, Davis investigated the range of potential impacts that rapid adoption of CAVs in California might have on vehicle miles traveled and emissions. The researchers estimated the vehicle miles traveled and emissions of each scenario using a statewide travel demand model, emissions factors from California agencies, and assumptions derived from the scientific literature and expert input. This policy brief summarizes the findings from that research and provides policy implications.

Ride-Hailing Holds Promise for Facilitating More Transit Use in the San Francisco Bay Area

Authors: Farzad Alemi and Caroline Rodier

First-mile ride-hailing access services could reduce the generalized costs for almost one-third of drive-alone commuters. The analysis found that 31% of identified drive-alone trips could see a reduction in generalized costs (accounting for the value of time and monetary costs) by an average of $8 per trip by switching to ride-hailing and BART to get to work (Figure 1). Most of these savings would be monetary; parking costs and bridge tolls in the Bay Area are relatively high. Most commuters would not save time by switching modes. Shared ride-hailing first-mile access services could also reduce generalized costs. Low-income and single-vehicle households may be most likely to benefit from a first-mile ride-hailing service. First-mile ride-hailing access services could also contribute to significant VMT and GHG emissions reductions.

Electrifying Ride-sharing: Transitioning to a Cleaner Future

Authors: by Alan Jenn, Institute of Transportation Studies, University of California, Davis

Incentives for plug-in electric vehicles (PEVs) are typically designed to encourage broad consumer adoption of the new technology. However, maximizing electrification of the transportation sector also requires incentives targeted at stakeholders with high travel intensity, i.e., those exhibiting particularly high passenger occupancy and/or vehicle-miles traveled (VMT). This policy brief focuses on one such class of stakeholders: transportation network companies (TNCs) such as Uber and Lyft. It examines empirical data of electric vehicle use in TNCs and discusses research findings on the potential impacts of electrifying TNCs. It also raises important considerations for the development of future policy. 

Travel Effects and Associated Greenhouse Gas Emissions of Automated Vehicles

Research by Caroline Rodier Institute of Transportation Studies, UC Davis 
Policy Brief by Julia Michaels, 3 Revolutions Future Mobility Program, UC Davis

Automated vehicles (AVs) may significantly disrupt our transportation system, with potentially profound environmental effects. This policy brief outlines the mechanisms by which AVs may affect the environment through influencing travel demand, as well as the magnitude of these effects on vehicle miles travelled (VMT) and greenhouse gas (GHG) emissions. Personal AVs and AV taxis (or ride-hailing services) are likely to increase VMT and GHG emissions, exacerbate traffic congestion in city centers, and potentially lead to suburban sprawl. Electrification and vehicle sharing may reduce some of these environmental effects, but targeted policies must be put in place to ensure that these solutions are effective.

Issue Papers:

California Automated Vehicle Policy Strategies

Authors: Mollie Cohen D’Agostino, Jerel Francisco, Susan A. Shaheen, and Daniel Sperling

Senate Bill (SB) 1298 granted the California Department of Motor Vehicles (DMV) a legislative mandate to develop the Autonomous Vehicle Program. In this landmark 2012 bill, the DMV is allowed to “consult with the [California Highway Patrol (CHP)], Institute of Transportation Studies at the University of California, or any other entity DMV identifies that has expertise in automotive technology, automotive safety, and autonomous system design.”2This issue paper is offered to the State of California in the spirit of this consultation privilege. This research is also a project component of the Climate Smart Transportation and Communities Consortium (C-STACC) for the Strategic Growth Council (SGC) (Task 3.4.4). A review draft of this issue paper was submitted to the California State Transportation Agency (CalSTA) in March 2021 in response to their solicitation for feedback of the draft Automated Vehicle (AV) Framework. This paper leverages the eight principles for AV policy included in the draft CalSTA framework... 

Setting TNC Policies to Increase Sustainability

Authors: Sam Fuller, Tatjana Kunz, Austin L. Brown, Mollie C. D’Agostino

Cities and states across the U.S. are assessing fees or taxes on transportation network company (TNC) platforms, such as Uber and Lyft. The goals of these policies include traffic and emissions mitigation, as well as revenue generation, among other objectives. This research aims to assess the goals and effectiveness of these fees in achieving some of these policy objectives, primarily congestion and emissions mitigation. The analysis addresses a core difficulty in comparing TNC fees—some fees are assessed per mile and others per trip. The researchers compared 21 fees implemented by state and local governments across theUnited States and apply a methodology to compare these diverse fees and taxes based on a hypothetical ride informed by Uber’s fare calculator, as well as other sources. The findings show that when adjusted for comparison, the highest fees, by a wide margin, are assessed in downtown New York City and Chicago (during peak hours). A key policy implication of this research is that most fees or taxes are not large enough to affect enough travelers' choices to hail a TNC, and most do not differentiate between solo and pooled/shared rides. Only San Francisco, Chicago, New York City, and New Jersey differentiate between solo and shared rides, which is likely to influence travelers in choosing to share a ride. This is problematic given that increasing passengers per vehicle mile traveled is an essential strategy in managing congestion and reducing emissions associated with all vehicle travel, including TNCs.

Equitable Congestion Pricing

Authors: Mollie Cohen D’Agostino, Paige Pellaton, and Brittany White 

Congestion pricing can be an equitable policy strategy. This project consisted of a review of case studies of existing and planned congestion pricing strategies in North America (Vancouver, Seattle, and New York) and elsewhere(Singapore, London, Stockholm, and Gothenberg). The analysis shows that the most equitable congestion pricing systems include 1) a meaningful community-engagement processes to help policymakers identify equitable priorities; 2) pricing structures that strike a balance between efficiency and equity, while encouraging multi-modal travel; 3) clear plans for investing CP revenues to equalize the costs and benefits of congestion relief; and lastly,4) a comprehensive data reporting plan to ensure equity goals are achieved. This project was developed to support the San Francisco County Transportation Authority in its efforts to conduct the Downtown Congestion Project.

Policy Pathways to TNC Electrification in California

Authors: Authors:Kelly L. Fleming, Mollie Cohen D’Agostino

Contributors: Austin L. Brown , Alan Jenn, Gil Tal, Ken Kurani, Angela Sanguinetti, Giovanni Circella, Scott Hardman, Nic Lutsey

This issue paper synthesizes research related to electrification of TNC vehicles and considers policy pathways for addressing barriers to electric-vehicle (EV) use among TNC drivers. Only 0.5% of the 2–3 million TNC drivers in the United States currently drive EVs. A primary barrier to wider EV adoption is the larger upfront cost of an EV relative to a conventional vehicle, a barrier that persists despite the potential for EV ownership to yield long-term savings. The short-term nature of this barrier speaks to a need for additional policies—such as purchase incentives for used EVs or support for TNC-EV rental programs—that can make EV access more equitable for TNC drivers.

Technology is Outpacing State Automated Vehicle Policy

Author: Kelly L. Fleming; Contributors: Austin Brown, Mollie D’Agostino, Tatjana Kunz, Hannah Safford, Yoon Jae Annie Lee

Existing statutes related to Automated Vehicles (AVs) tend to be preliminary in scope. This paper creates a scale for evaluating AV policy: from most permissive to most restrictive. Our findings are that AV policy among states varies considerably, but many policies are in the middle of the road, and many states have limited legislative actions to codifying definitions or establishing exploratory committees. This assessment points to the fact that states are readying to take more decisive action. Therefore, it is critical to identify some possible best practices for AV policy development as states explore the topic. Our analysis points to guidelines for developing safe, equitable and sustainable AV policy. 

Mobility Data Sharing: Challenges and Policy Recommendations

Authors: Mollie D’Agostino, Paige Pellaton, and Austin Brown; Contributors: Hannah Safford, Kelly Fleming, and Cassidy Craford

Massive amounts of transportation data are generated every day. These data can support transportation planning, policy, and research— especially when it comes to emerging mobility options such as scootersharing, bikesharing, and ridehailing. However, there are not yet well-established mechanisms for sharing mobility data. New policy frameworks are needed to streamline and expand mobility data sharing while respecting privacy and proprietary concerns. Frameworks that achieve these goals must consider how best to (1) standardize, (2) share, (3) securely store, and (4) analyze and apply mobility data. This brief summarizes insights from the UC Davis issue paper “Mobility Data Sharing: Challenges and Policy Recommendations”, which addresses each of the above components....For a concise summary see the 2-page Policy Brief. 

Reshaping Liability and Insurance Rules for Automated Vehicles

Authors: Gordon J. Anderson, Austin L. Brown, and Hannah R. Safford 

The American civil liability framework has two basic goals: ensuring the efficient compensation of victims for their injuries and assigning the cost of compensation to the blameworthy party. When it comes to auto crashes, the existing liability system achieves these goals by assigning liability based on human fault and requiring human drivers to carry insurance. But this system, and the legal theories that support it, are predicated on the assumption that car crashes are traceable to human driver error.

In the near future, automated vehicles (AVs) capable of self-driving will come to market. These vehicles will sometimes crash while operating in a self-driving mode. The problem is that the current vehicle liability scheme does not neatly translate to a world where driving errors are made by nonhumans. Failure to update liability laws could be a missed opportunity to promote AV usage and thereby maximize the technology’s safety benefits. Furthermore, a patchwork liability scheme that varies between jurisdictions can jeopardize efficient victim compensation and fair liability assignment.

The Road to Successful Governance of Automated Vehicles

Authors: by Austin Brown, Executive Director, Policy Institute for Energy, Environment, and the Economy Greg Rodriguez, Of Counsel, Best, Best & Krieger Tiffany Hoang, Graduate Student Researcher, Policy Institute for Energy, Environment, and the Economy

Federal governance of AVs has been limited. To date, the U.S. Department of Transportation (USDOT) has issued only voluntary guidelines on AV development and deployment....So far, twenty-nine states and the District of Columbia have enacted AV-related legislation, while cities like Boston and Portland have adopted AV policy frameworks. State and local AV polices involve a variety of approaches from requiring testing permits to encouraging electric and shared AVs. This illustrates that there is no “one-size-fits-all” way to govern AVs...States and local governments have many goals for their transportation systems, including reducing congestion, improving equity, and reducing pollution. AVs will be a powerful tool to achieve these goals, but only if good governance structures empower these governments to set good policy.