published journal article

An L.A. story: The impact of housing costs on commuting

Abstract

The empirical impact of housing costs on commuting is still relatively poorly understood. This impact is especially salient in California given the state’s notoriously high housing costs, which have forced many lower- and middle-class households to move inland in search of affordable housing at the cost of longer commutes. To investigate this linkage, we relied on Generalized Structural Equation Modeling and analyzed 2012 CHTS data for Los Angeles County – the most populous county in the U.S. Our model, which jointly explains commuting distance and time, accounts for residential self-selection and car use endogeneity, while controlling for household characteristics and land use around residences and workplaces. We find that households who can afford more expensive neighborhoods have shorter commute distances (−2.3% and − 3.1% per additional $100 k to median home values around workplaces and residences respectively). Job density, distance to the CBD, and land-use diversity around workplaces have a relatively greater impact on commuting than the corresponding variables around commuters’ residences. Compared to non-Hispanics, Hispanic workers commute longer distances (+3.5%), and so do African American (+5.1%) and Asian (+2.0%) workers compared to Caucasians, while college-educated workers have shorter (−2.6% to −3.6%) commutes. Furthermore, commuters in the top income brackets tend to have faster commutes than lower-income workers. Finally, women’s commutes are ~41% shorter than men’s, possibly because they are balancing work with domestic responsibilities. A better understanding of the determinants of commuting is critical to inform housing and transportation policy, improve the health of commuters, reduce air pollution, and achieve climate goals.

policy brief

How Can California Transit Agencies Build Rail Cheaper and Faster?

Abstract

Increasing Californians’ access to and use of public transit is a key component of the state’s strategy to reduce greenhouse gas emissions (GHG) from transportation, which is the single largest source of statewide emissions. To achieve state targets of 40 percent GHG emission reduction below 1990 levels by 2030 and carbon neutrality by 2045, California leaders will need to support a range of affordable, efficient, and riderfriendly transit options—including local and regional rail networks—to replace personal vehicle use. However, rail transit projects in California and the U.S. are costly and slow to build. Most initial project budget estimates are expensive to begin with, and they often increase significantly after delays and cost overruns occur. This high-cost, slowdeployment pattern of rail transit investment risks depleting public funds available for new transit projects and the public trust necessary to ensure successful projects. With climate and urban design and livability goals demanding greater and more efficient public transit investment, what can state and local leaders do to improve project delivery in terms of cost and time? Researchers at the Center for Law, Energy and the Environment at UC Berkeley School of Law recently combined a cost baseline analysis with five California project case studies to identify the key sources of poor project delivery performance and strategies to overcome them.

policy brief

Electric Vehicle Carsharing is Helping to Fill Transit Gaps and Improve Mobility in Rural California

Abstract

In rural areas, cost-effective transit service is challenging to provide due to greater travel distances, lower population densities, and longer travel times than in cities. Access to a personal car is often essential to the quality of life for most residents, enabling them to readily access essential services. However, keeping one or two vehicles in reliable working order can be prohibitively expensive for low-income families. To address this issue, multiple organizations partnered to launch an electric vehicle (EV) carsharing pilot called Míocar in 2019. This non-profit service in the rural San Joaquin Valley of California differs from the dominant carsharing model of for-profit businesses serving affluent communities that already have high-quality transit. Míocar seeks to provide carsharing to price-sensitive populations with low transit access at a price point that is more affordable than owning a personal vehicle. The service currently has 27 EVs located at eight hubs throughout the San Joaquin Valley.

research report

Environmental Design for Micromobility and Public Transit

Abstract

Micromobility has the potential to reduce greenhouse gas emissions, traffic congestion, and air pollution, particularly when replacing private vehicle use and working in conjunction with public transit for first- and last-mile travel. The design of the built environment in and around public transit stations plays a key role in the integration of public transit and micro-mobility. This research presents a case study of rail stations in the San Francisco Bay Area, which are in the operation zone of seven shared micro-mobility operators. Nineteen stations and their surroundings were surveyed to inventory design features that could enable or constrain the use of micromobility for first- and last-mile access. Shared mobility service characteristics, crime records, and connections to underserved communities were also documented. An interactive Bay Area Micromobility Transit ArcGIS map tool was created to aid analysis and provide a useful resource to stakeholders. The map shows layers such as train stations, bike lanes, bike share kiosks, and micromobility operation zones that vary between Oakland, Emeryville, Berkeley, San Francisco, and San Jose. Key design solutions were identified based on the findings, including protected bike lanes, increased shared bike and scooter fleet size and service area, and clear signage indicating bike rack parking corral and docking points.

white paper

What Happened and Will Happen with Biofuels? Review and Prospects for Non-Conventional Biofuels in California and the U.S.: Supply, Cost, and Potential GHG Reductions

Abstract

This paper examines past and future trends for non-conventional biofuels in transportation in the next decade and beyond in California and the U.S., drawing on existing literature. It finds policy was geared toward expanding the use of technology-ready biofuels in the 2010s; hydro-processed renewable diesel from lipid feedstocks and biogas were beneficiaries alongside conventional ethanol and biodiesel. Cellulosic ventures largely failed due to a lack of technological readiness, high cost, and an uncertain and insufficient policy environment. Policy goals for competitive cellulosic fuels remain, yet fuels from technologies already in the market may suffice to meet low carbon fuel policy targets, at least in California until 2030, considerably more oilcrop-based biofuels. How much biofuel will be needed there and elsewhere to meet climate targets hinges critically on the pace and scope of zero-emission vehicles, and particularly electric vehicles, rollout. Analysis of unintended market consequences like indirect land use change has evolved over the decade but remains uncertain; current policy structures do not comprehensively safeguard against increased emissions. Market activity for non-conventional fuels has targeted jets. Pioneer plants using new conversion technologies, if successful, will take some time to scale. Technoeconomic analyses (TEAs) for such non-conventional fuels point to no clear biofuel conversion technology winner as yet, given uncertainties. techno-economic analyses are evolving to reduce uncertainty by concentrating more on robust returns in the face of uncertain policies, potential additional cost-cutting for new technologies given what is known about processes involved, and potential revenue-raising through new coproducts or shifting product slates. Policies are needed to make initial financing more secure. Additional policy and societal attention to the appropriate use of biomass, and land more generally, in a low-carbon future is needed to clarify the likely feedstock supply for biofuels that will enhance climate goals with a low risk of unintended consequences.

research report

Mobile Device Data Analytics for Next-Generation Traffic Management

Abstract

Quality data is critically important for research and policy-making. The availability of device location data carrying rich, detailed information on travel patterns has increased significantly in recent years with the proliferation of personal GPS-enabled mobile devices and fleet transponders. However, in its raw form, location data can be inaccurate and contain embedded biases that can skew analyses. This report describes the development of a method to process, clean, and enrich location data. Researchers developed a computational framework for processing large-scale location datasets. Using this framework, several hundred days of location data from the San Francisco Bay Area were (a) cleaned, to identify and discard inaccurate or problematic data, (b) enriched, by filtering and annotating the data, and (c) matched to links on the road network. This framework provides researchers with the capability to build link-level metrics across large-scale geographic regions. Various applications for this enriched data are also discussed in this report (including applications related to corridor planning, freight planning, and disaster and emergency management) along with suggestions for further work.

policy brief

Wildfire Evacuation Planning Can Be Greatly Enhanced by Considering Fire Progression, Communication Systems, and Other Dynamic Factors

Abstract

Wildfires have become a perpetual crisis for communities across California. For life-threatening wildfires, mass evacuation often becomes the only viable option to protect lives. Yet, looking back at recent events, including the devastating 2018 Camp Fire in Northern California, there are significant challenges associated with the evacuation process, such as multi-agency coordination, agency-resident communication, and management of extraordinarily high amounts of traffic within a short period of time. Currently, emergency planners use evacuation models that are typically based on existing traffic simulation models; however, it is increasingly clear that other factors need to be considered, such as fire progression and communication systems. To address this gap, UC Berkeley researchers constructed a framework and set of models that include the combined impacts of three dynamic processes on evacuations – fire progression, communication systems, and traffic flow. The framework and models were applied to two case studies in California: the town of Paradise and the unincorporated community of Bolinas. In the Paradise case, the scenarios were based on the 2018 Camp Fire event. For the Bolinas case, the scenarios were based on hypothetical wildfire events.

published journal article

Estimating short-term travel demand models that incorporate personally owned autonomous vehicles

Abstract

We estimated travel demand models that incorporate a private autonomous vehicle (AV) option using revealed preference data in which personal chauffeurs simulated a personally owned AV. We investigated four components of activity-based models (ABM): activity pattern and primary destination choice, mode choice, and time of day. We compared the chauffeur week models (“AV future”) to the non-chauffeur week models (current conditions). We found no statistically significant differences in parameters of the individual activity pattern, time of day, or destination choice. For mode choice, however, while the auto constant did not change, the mean value of time decreased by 60%. As the destination choice model included the mode choice log sum, this results in longer average tour lengths. Moreover, while the trip-making propensity of individuals did not change significantly, there was a 25% increase in systemwide trips due to “AVs” (chauffeurs) being sent on errands. This points to the importance of incorporating zero-occupancy vehicle (ZOV) trips into the ABM framework. Our findings suggest that these can be incorporated via the standard ABM development process by adding as additional model components ZOV home-based tours and ZOV subtours. Relatedly, as inter-regional travel is modeled outside the ABM framework, our results indicated that modifications should be made to account for the increase in inter-regional tours, which were 54% more frequent during chauffeur weeks. While these results are from a relatively small sample of 71 individuals, they are the first such travel demand estimation results available from a field experiment, and further studies can build on our framework.

policy brief

Key Challenges in Sanitizing Transportation Data to Protect Sensitive Information

Publication Date

November 1, 2021

Author(s)

Areas of Expertise

Abstract

As new mobility services such as ridehailing and shared micromobility have grown, so has the quantity of data available about how and where people travel. Transportation data provides government agencies and transportation companies with valuable information that can be used for identifying traffic patterns, predicting infrastructure needs, informing city planning, and other purposes. However, the data may also contain sensitive information that can identify individuals, the beginning and ending points of their trips, and other details that raise concerns about personal privacy. Even if a traveler’s name and address is suppressed, adversaries could use other parts of the information such as trip origin and destination to learn an individual’s identity and their habits. Similarly, another transportation company competing with the company that collected the data could potentially steal their customer base if they can use the data to obtain proprietary information such as frequent dropoff/pick-up locations, vehicle positioning, travel routes, or algorithms for assigning vehicles to clients.

policy brief

Real-World Simulations of Life with an Autonomous Vehicle Suggest Increased Mobility and Vehicle Travel

Abstract

Fully autonomous vehicles are expected to have a profound effect on travel behavior. The technology will provide convenience and better mobility for many, allowing owners to perform other tasks while traveling, summon their vehicles from a distance, and send vehicles off to complete tasks without them. These travel behaviors could lead to increases in vehicle miles traveled that will have major implications for traffic congestion and pollution. To estimate the extent to which travel behavior will change, researchers and planners have typically relied on adjustments to existing travel simulations or on surveys asking people how they would change their behavior in a hypothetical autonomous vehicle future. Researchers at UC Berkeley and UC Davis used a new approach to understand the potential influence of autonomous vehicles on travel behavior by conducting the first naturalistic experiment mimicking the effect of autonomous vehicle ownership. Private chauffeurs were provided to 43 households in the Sacramento, California region for one or two weeks. By taking over driving duties for the household, the private chauffeurs served the household as an autonomous vehicle would. Researchers tracked household travel prior to, during, and after the week(s) with access to the chauffeur service.