Developed in 2005, the General Transit Feed Specification (GTFS) is making transit trip planning easier by allowing public transportation agencies to share transit schedules in an electronic format that can be used by a variety of trip-planning applications, such as Google Maps. The GTFS can be used to share static transit schedules (GTFS-s) or provide real-time information on transit vehicle arrivals and departures (GTFS-r). Providing real-time updates has proven to be exceptionally valuable during the COVID-19 pandemic. For example, between January 13th and April 25th of this year Apple estimates that transit use in the United States decreased by 75%1 , which caused many public transit providers to modify their services. The California Integrated Travel Project (CITP) recently called for widespread adoption of GTFS-s and GTFS-r2 ; however, little is known about GTFS use across agencies and, in turn, the barriers to widespread adoption.
Bike-share services have rapidly expanded in cities worldwide and attracted substantial ridership, especially as electric and dockless bike- and scooter-share services have entered the market. These services have the potential to offer a healthier and more environmentally sustainable mobility option if used as an alternative to car travel and a connection to transit. However, little is known about the influence of bike-share systems on individual travel behavior; particularly if bike-share trips are replacing vehicle trips and increasing transit use. To address this knowledge gap, researchers at the University of California, Davis surveyed Sacramento-area residents before and after the 2018 implementation of a JUMP/Uberoperated dockless electric bike-share program to examine how the micromobility service influenced general travel behavior and attitudes. Surveys were sent to residents in downtown Sacramento, West Sacramento, and Davis within the bike-share service area and to a control group in Sacramento outside the service area. Key findings from the research are summarized in this brief.
Prior to the last decade, the logistics industry trended towards the development of mega-warehouse facilities in suburban settings far from core markets, creating a phenomenon known as logistics sprawl. This trend is particularly prominent in Southern California (Figure 1). Since the 2008-2009 economic crisis, however, the trend has shifted. The rise of e-commerce may have influenced supply chain decisions to locate warehouses and distribution centers closer to denser urban areas to enable faster deliveries to consumers. The changes in size and spatial distribution of warehouses and distribution centers as well as the environmental and equity implications of these changes are not fully understood. As warehouses and distribution centers locate in denser urban areas, they may introduce additional diesel truck traffic into disadvantaged and low-income communities.
To gain a better understanding of how the spatial distribution and size of freight facilities are changing and the implications of these changes for disadvantaged and low-income communities, researchers at the University of California, Davis analyzed aggregate data about the number of warehouses and distribution centers and disaggregate real estate data of purchases and leases during the last three decades in California. They also analyzed the relationship between freight facilities and communities of concern using the California Environmental Protection Agency’s CalEnviroScreen 3.0 tool. The research focused on the San Diego, Los Angeles, San Francisco, San Joaquin Valley, and Sacramento regions. Key findings from the research are presented in this brief.
Workers in Southern California currently face transportationrelated challenges accessing employment opportunities, including but not limited to high parking costs and/or limited parking availability in dense employment and residential areas; long commute distances between residential areas and employment opportunities; and poor transit service quality in many areas. These challenges are particularly burdensome for low-income households that may not have access to a personal vehicle and/or live in jobpoor neighborhoods, as having a personal vehicle may be the only viable way to get to work.
Consumers are purchasing and using partially automated vehicles, yet little research has been conducted to understand how and if these vehicles are changing travel behavior. Fully automated, or driverless, vehicles are receiving much more research and policy attention but are still many years from market introduction. Research on fully automated vehicles has shown that, without proper policies in place, these vehicles could increase vehicle miles travelled (VMT). Tesla vehicle models with the ‘Autopilot’ feature are some of the most common partially automated vehicles on the road today. A partially automated vehicle provides advanced driver assistance by controlling steering, acceleration/ deceleration, and braking; however, the human driver is still considered to be in control of the vehicle and is expected to be attentive. A previous UC Davis study found that Tesla vehicle owners with the Autopilot feature drove more than those without Autopilot, but the study did not determine whether higher VMT was caused by Autopilot. To better understand whether Autopilot influences how much individuals drive, the UC Davis research team interviewed 36 Tesla Autopilot users to evaluate whether they experienced changes to their travel, and the reasons for any reported changes. Key findings from the interviews are presented in this brief.
A California Public Utilities Commission (CPUC) rulemaking and possible legislative action in 2020 could affect data sharing requirements, with implications for shared mobility providers. The purpose of this brief is to inform this regulatory and legislative decision-making. We solicited policy and planning questions and data needs for shared mobility from within the University of California Institute of Transportation Studies research network. We defined shared mobility as including shared mobility devices, such as e-bikes and e-scooters, and transportation network companies (TNCs). We evaluated whether data shared in accordance with each of six mobility data specifications could be used to support analyses that would answer these questions. We then defined three approaches to data sharing and analysis to address these and other questions, presenting the advantages and disadvantages of each. This brief does not address the full breadth of the questions raised in the CPUC rulemaking nor does it introduce the complexities of this topic. Beyond the scope of this brief are issues of user privacy, the legal authority for sharing data, and contractual or requirements for each possible model of data sharing and analysis.
California has a number of programs intended to encourage the introduction of zero- and near-zero emission vehicle (ZEV) technologies into the medium- and heavy-duty truck markets. Meeting the goals of these programs will require the sale of large numbers of battery-electric and hydrogen fuel cell transit buses and trucks by 2025 and beyond. However, several barriers to widespread adoption of these technologies will need to be addressed, including their purchase price, utility, durability and reliability, as well as the cost of energy and the availability of refueling infrastructure. Policies such as mandates or incentives will likely be necessary to overcome these barriers and the uncertainty of adopting a new, unproven technology. These policies must make economic sense to both the bus and truck manufacturers and the vehicle purchasers if they are to be successful in the long term. To gain a better understanding of the financial barriers for ZEV bus and truck adoption, researchers at UC Davis conducted technology and cost assessments for batteryelectric and fuel cell vehicles in the medium- and heavy-duty truck sector. High-level findings and the policy implications of this research are summarized in this brief.
To better understand the equity implications of a variety of congestion management strategies, researchers at the Transportation Sustainability Research Center (TSRC) at University of California, Berkeley analyzed existing literature on congestion management strategies and findings from 12 expert interviews. The literature review applies the Spatial – Temporal – Economic – Physiological – Social (STEPS) Equity Framework1 to identify impacts and classify whether social equity barriers are reduced, exacerbated, or both by a particular strategy. The congestion management strategies of interest were categorized into six broader categories: 1) pricing, 2) parking and curb policies, 3) operational strategies, 4) infrastructure changes, 5) transportation services and strategies, and 6) conventional taxation.
