National Center for Sustainable Transportation

Renewable Natural Gas (RNG) is an important alternative fuel that can help the State of California meet several greenhouse gas (GHG) reduction and renewable energy targets. Despite considerable potential, current RNG use on national and state levels are not significant. RNG production potential in California through thermochemical conversion was evaluated as part of this project by assessing technical biomass availability in the state. Biomass feedstocks are defined broadly and include most carbonaceous matter including waste. The types of waste biomass available in the state are classified into three categories: municipal solid waste (MSW), agricultural residue and forest residue. A total of 32.1 million metric tonnes per year (MMT/year) of biomass is estimated to be technically available in the state. The energy content of this biomass is equivalent to approximately 602.4 million mmbtu/year. A survey of current renewable electricity generation and curtailment trends in California was conducted. Real-time data show significant curtailment throughout the year totaling more than 1,300 GWh from 2016 to early 2019. Power to gas and other forms of long-term storage integrated into the electric grid can mitigate these losses and enable smooth integration of additional renewables into the grid. Oxygen/air blown gasification, hydrogasification and pyrolysis are the three major technology options available for thermochemical biomass conversion to a gaseous fuel, including RNG. Although there are no commercial thermochemical biomass to RNG conversion facilities in operation, a number of gasification and pyrolysis technologies are undergoing pilot scale demonstration and development. Design basis for two thermochemical and power to gas conversion projects were developed as part of this project. Life cycle and economic analysis were conducted for the recommended processes.

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In 2018, pursuant to Senate Bill (SB) 743 (2013), the Governor’s Office of Planning and Research (OPR) and the California NaturalResources Agency promulgated regulations and technical guidance that eliminated automobile level of service (LOS) as a transportation impact metric for land development projects under the California Environmental Quality Act (CEQA), and replaced it with Vehicle Miles Traveled (VMT). The authors investigated how local governments have been implementing the LOS-to-VMT shift for land development projects, and how that differs from past practice. They also explored whether local governments monitor the actual VMT impacts from completed land use developments and what methods are available to do so. Their findings indicate that all responding jurisdictions acknowledged the mandatory LOS-to-VMT shift, but were in varying stages ofimplementing the shift. For those jurisdictions that had adopted VMT impact significance thresholds, most adhered closely to OPR’s recommendations. They also mostly tried to use apples-to-apples methods of calculating baseline VMT levels (for setting thresholds) and estimating project-level VMT, often relying on travel demand model outputs for both. However, most jurisdictions gave short shrift to VMT monitoring. Another important aspect of SB 743 implementation is how LOS will continue to be used outside of CEQA. The authors found that jurisdictions uniformly continue to employ LOS outside of CEQA. However, those LOS analyses are not necessarily as comprehensive and expensive as they would have been for CEQA purposes. The authors found a consensus amongst their interviewees that swapping LOS for VMT could streamline development in urban areas.

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The research team used the Los Angeles MATSim model to evaluate the travel, greenhouse gas(GHGs), and equity impacts of single-and multiple-passenger automated taxi scenarios, including free transit fares and a VMT tax. The results indicate that automated taxis increase VMT by about 20 percent across scenarios, and automated taxis mode shares more than offset reductions in personal vehicle travel. The automated taxi-only scenario also reduces transit travel by about 50 percent, but the addition of free transit fares reversed this decline and increased transit use somewhat. New empty passenger automated taxi travel compounds the impact of mode shifts in these scenarios and further increases vehicle travel. There is a slight change in mean vehicles speeds across all scenarios. When automated taxis are not battery electric vehicles (BEVs), GHG emissions increase from 16 to 18 percent across scenarios. However, GHGs decline by 23 to 26 percent when automated taxis are BEVs. The equity analysis shows that the automated taxis scenarios provide more accessibility benefits for travelers in three low-income classes than total benefits and benefits for the middle-and high-income travelers. The addition of free transit to the shared automated taxis-only scenario dramatically increases low-income benefits. The VMT tax eliminates almost all of the benefits from the automated taxi and free transit scenarios and creates losses for all three low-income groups.

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A number of state governments have recently passed legislation aiming to rein in vehicle miles traveled (VMT), and many cities have begun to take action to reduce VMT in their jurisdictions. Policymakers often want to know what they should do to encourage less driving. Unfortunately, there is no “one size fits all” solution. The effectiveness of various policy options depends critically on context: who is driving, where are they going, and what alternative modes and destinations are available.

Fortunately, there is an extensive body of academic literature on this topic that practitioners can tap into when considering various policy options. This policy brief summarizes findings from the white paper examining this literature.

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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.

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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.

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In the early years of transportation planning and highway infrastructure development in the United States the focus was on intercity or long-distance travel, a contrast to the metropolitan travel and state-based models that dominate today. Daily home and work-based travel, which have been the focus of data collection and models since the 1950s, are well-modeled by regional agencies and a limited number of state travel demand models even include some long-distance travel. Nonetheless, long-distance travel demand and factors affecting behavior are not thoroughly considered in transportation planning or behavior research. Only one recent activity-based model of national travel demand has been created and its scope was limited by a severe lack of data. The conceptualization of models to consider intercity long-distance travel has changed little since its inception in the 1970s and 1980s. In order to comprehensively consider transportation system sustainability, there is a critical need for improved nation-wide annual overnight activity data and models of overnight travel (a re-focus and important distinct re-framing of long-distance trips that this white paper suggests).

Truly addressing the economic, environmental, and social equity issues required to create a sustainable global transportation system will entail completely updating our existing planning framework to meaningfully include long-distance travel. It is clear that long-distance passenger miles must be accounted for when addressing greenhouse gas (GHG) emissions and other negative environmental externalities. Less well-known are the questions of social justice that loom large when one considers the details of long-distance travel. Travel in our society is becoming increasingly associated with quality of life. Those without intercity access may miss opportunity and social capital. However, without representative long-distance travel data it is impossible to compare the relative participation by different groups and to consider latent demand. It is difficult to measure who comprises the global mobile elite and who lacks sufficient intercity mobility for reasonable social network obligations and personal services.

This white paper suggests utilizing a common framework for long-distance data collection and tabulation that re-defines long-distance travel into daily or overnight. The author advocates using overnight as the defining characteristic for data collection, which complements existing daily travel surveys already capturing long day-trips. Within frameworks moving forward it is important to clearly characterize all trip purposes, including mixed purposes and purposeless travel, which comprise an appreciable portion of long-distance travel. Spatial data that distinguish between simple out-and-back trips and spatially complex trips are necessary and mobile devices have now made this measurement of long-distance tours feasible. In order to truly model all travel in the current system, we must move away from the idea that most travel is routine, within region, and home-based. Many people, especially the most frequent travelers, have long-distance routines including multiple home bases. Additionally, our models should not assume that travelers staying at a second home, hotel, or friend’s home travel like residents. Efforts to measure and model non-home-based travel or travel at destination are essential to accurately modeling behavior. Daily surveys such as the 2017 National Household Transportation Survey are increasingly doing this. A nation-wide annual activity model of overnight travel must fully incorporate both surface and air travel to allow full consideration of alternative future system scenarios.

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Cars provide an unparalleled level of mobility but have negative financial, public health, environmental, and social impacts. Reducing the need for driving in California would produce a range of household- and community-level benefits. Driving is associated with adverse health effects (e.g., obesity, high blood pressure, depression, injuries, fatalities), while commuting by walking or biking provides numerous physical and mental health benefits. A reduction in driving would also improve public health by decreasing air pollution and greenhouse gas emissions. It would save substantial sums of money:  households spend about $9,000/year or 16% of their expenses on private vehicle ownership (2017 data) and the state spends over $500 million per year on highway maintenance. A less car-dependent society would also be more equitable for those with limited income or limited physical abilities who cannot drive, to the benefit not just of those individuals but the community as a whole. While it is not realistic in the foreseeable future for most Californians to live without their cars, it is possible to decrease car dependence. Doing so requires a shift away from a century-old prioritization of the goal of reducing vehicle delays over other important goals. Creating a less car-dependent world is not necessarily more costly to the public and can be achieved over time through changes in land use and transportation planning practices. Answers to many of the frequently asked questions about such efforts are provided.

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In this whitepaper, the authors briefly describe the three major MOEs, followed by a categorization summary based on the most recent literature. Next, a number of typical CAV applications have been examined in depth, providing a detailed analysis of the different MOEs co-benefits and tradeoffs. Further, three representative CAV applications have been examined in detail in order to show the association between the application focus and tradeoffs/co-benefits of different performance measures. The CAV applications include High Speed Differential Warning (safety-focused), Lane Speed Monitoring (mobility-focused), and Eco-Speed Harmonization (environmental impacts-focused). The authors then highlight several future research directions, including the identification of key influential factors on system performance and how to obtain co-benefits across all key MOEs. The overall intent of this whitepaper is to inform practitioners and policy makers on the potential interactions between the safety, mobility, and environmental sustainability goals of implementing specific CAV applications as part of their ITS programs.

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