A variety of web-based mapping and quantitative analysis tools can help planners evaluate whether a given land use efficiency strategy can meet goals, but there has been limited information about the coverage, breadth, and availability of these tools. These tools can assist in the regional implementation of greenhouse gas reduction strategies through land use development. As such, decisionmakers would benefit from knowing which of these tools could serve their needs. Researchers at UC Davis studied methods and tools available to regional and local governments to evaluate the land use efficiency and equity of their policies and plans. The research team then conducted a workshop with regional and local government representatives to identify efficacy, gaps, and potential improvements for these tools.
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In the early stages of the COVID-19 pandemic, many cities across the US reallocated street spaces for active transportation such as walking, bicycling, and scootering, including by electric bikes and scooters. Slow Streets, projects that limit through-traffic access for motor vehicles to provide a safer space for other travelers, were implemented at an unprecedented speed and scale. This analysis of pandemic-era Slow Street dockless electric scooter (e-scooter) use offers insights that may assist decisionmakers. A research team at the University of Southern California collaborated with Lime, an e-scooter company, to analyze Slow Streets programs in the cities of Oakland, San Francisco, Los Angeles, and Portland. Using two statistical approaches, they examined dockless e-scooter travel at four different times of day and overall weekly and monthly averages of dockless e-scooter trips. This policy brief summarizes the findings from that research and provides policy implications.
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Local and state electric bike (e-bike) incentive programs offering point-of-sale or post-sale monetary discounts to consumers have been implemented across the United States since 2018. As yet, however, little is known about their effectiveness in changing travel behavior. To understand the outcomes of these incentive programs, UC Davis researchers analyzed survey data from rebate recipients in Northern California two months and one year after they acquired e-bikes. The rebate programs were evaluated for effects of e-bike ownership on travel behavior, including changes in bicycling, driving, and use of transit, and on greenhouse gas emissions. The team also suggest areas for future research.
Gig drivers who use their own vehicles to provide transportation and food delivery services face barriers to electric vehicle (EV) adoption including costs, access, and information. To move toward a sustainable transportation future, California is advancing regulations to accelerate electrification of high-mileage vehicles, such as those driven by gig workers for transportation network companies (TNCs) like Uber and Lyft. By 2030, the state istargeting 90% of passenger miles traveled on TNCs to be fueled by electricity. To support this objective, UC Davis researchers developed an online tool to help gig drivers understand their potential cost savings from EVs. This research brief summarizes the findings from that research and provides policy implications.
Rural residents face significant mobility challenges because travel destinations are far, opportunities like jobs and access to essential needs are limited, and rural roadways are more dangerous than their urban counterparts. These challenges are exacerbated when households have limited or no access to a vehicle because other transportation options are often expensive, inconvenient, or nonexistent. The confluence of not having access to a vehicle and living in rural areas is often associated with increased social isolation and difficulties in conducting basic activities like grocery shopping and accessing health care. Researchers at UC Davis used US Census microdata to describe socioeconomic and mobility characteristics of carless households and residents in rural California and conducted interviews to understand the barriers to access and travel adaptations among individuals who have limited access to a vehicle.
Traffic congestion is a significant problem in major metropolitan areas in the United States. According to the Urban Mobility Report, in 2019 commuters on average lost about 54 hours in traffic congestion. To combat this, major infrastructure projects have been undertaken. However, expansion projects cannot keep up with the increase in usage of personal vehicles and thus fail to address the traffic congestion problem. Carpool ridesharing has shown some promise in combatting this traffic congestion problem. In this system, the drivers are regular commuters who take detours to pick up and drop off passengers to decrease their transportation costs. This system increases the efficiency of the transportation system by providing flexible commutes to people, thus reducing the need for each commuter to use their own personal vehicle. The researchers developed three approaches to rideshare routing. The researchers conducted a computational study using a San Francisco taxicab dataset to determine the effectiveness of the three approaches. To show the impact of flexible meeting points, the researchers also conducted experimental simulations with and without walking and performed sensitivity analyses.
Today there are companies experimenting with autonomous mobile vehicle and equipment technologies. These technologies come in various forms, from small delivery robots to large automated heavy-duty trucks and cargo movers. Some of these have been part of the labor force in factories, warehouses, and distribution centers worldwide for some industries, and their expansion is likely. A recent white paper from UC Davis assesses the landscape for freight automation and its potential labor impacts in the freight and warehousing sector; this policy brief summarizes the key findings and policy implications of that research. While there are still more questions than answers, it is known that as the technology matures, the future for workers will depend on policymaker and industry actions. While these actions can have potentially negative effects for some workers (e.g., job loss or reduced job quality), they can have positive effects for others (e.g., improved safety, security, job quality, and new high-quality jobs).
Communication between scientists and policymakers is critical for developing effective policies grounded in scientific evidence. However, actual communication between these two groups is often difficult, due to differences in training, communication styles, and motivation. While numerous “best practices” guides provide advice on science communication, many of these recommendations are based on personal experience rather than empirical data. To remedy this gap in the literature, researchers at the University of California, Davis conducted a literature review of scholarship on best practices in science communication, with an emphasis on finding reports based on empirical data rather than personal experience. The researchers synthesized their findings into a set of best practices for science communication and considered how scientific reputation affects engagement in the policy process.
Expanding roadway capacity often leads to commensurate increases vehicle miles traveled (VMT). This is the “induced travel” effect—a net increase in VMT across the roadway network due to an increase in roadway capacity. This increase in VMT erodes any initial reduction in congestion and causes increased greenhouse gas and local air pollutant emissions. Yet highway expansion projects continue to be proposed across the US, often using congestion relief—and sometimes greenhouse gas reductions— as a justification for adding lanes. The existence of these rosy projections about highway expansion projects indicates that the induced travel effect is often not fully accounted for in travel demand models or in the environmental review process for the projects, as prior research has shown.1 With these problems in mind, researchers at the University of California, Davis developed and launched an online tool in 2019—the NCST Induced Travel Calculator—to help agencies estimate the VMT induced annually by adding lanes to major roadways in California’s urbanized counties. With Calculator use increasing, the UC Davis researchers initiated a project to update the Calculator and improve its functionality based on recent data and empirical research.
Building additional roadway capacity—via constructing entirely new roadways or extending or adding lanes to existing roadways—is often proposed as a solution to traffic congestion and even as a way to reduce greenhouse gas (GHG) emissions. The logic for the latter is that increasing roadway capacity increases average vehicle speeds, which improves vehicle fuel efficiency and reduces per-mile emissions of GHGs and local air pollutants. But that logic relies on the flawed assumption that the amount that people drive does not change when the time it takes to drive places changes. In fact, the amount that people drive does respond to changes in driving times. Empirical research demonstrates that as roadway supply increases, vehicle miles traveled (VMT) generally does, too. This is the “induced travel” effect—a net increase in VMT across the roadway network due to an increase in roadway capacity, which ultimately erodes any initial increases in travel speeds and causes increased GHG emissions. Researchers at the University of California, Davis reviewed the empirical research on induced travel to understand the likely effects of adding roadway capacity in a variety of contexts.