UC Davis Institute of Transportation Studies

This report is part of an ongoing study of attitudes toward the act of traveling and the relationship of these attitudes to travel behavior and other characteristics. The primary purposes of this portion of the research are as follows: 1. From sets of interrelated variables, use factor analysis to identify the fundamental dimensions of Attitude, Personality, and Lifestyle characteristics relevant to this research; 2. Use cluster analysis to group respondents with similar profiles on those Attitude and Personality and Lifestyle characteristics; and 3. Analyze differences between clusters in terms of demographic traits, travel behavior, and other characteristics. The expectation is that clustering respondents with similar Attitudes and Personality and Lifestyle characteristics will offer insights into travel behavior that differ from those that can be gained from typical demographic characteristics.

Understanding and accurately predicting travel behavior can help us develop appropriate and successful policies for the future. Unfortunately, predicting human behavior has consistently proven difficult. This thesis adds to the extensive research on travel attitudes and their connections to travel behavior, through the empirical measurement of new variables and new relationships. Specifically, travel attitudes and their connection to behavior have typically been studied with an emphasis on specific travel behaviors (i.e. the amount of travel, safety and risk behavior, or behavior aimed specifically at helping the environment). This research emphasizes attitudes toward travel itself, and explores how those attitudes are related to the individual’s general travel behavior and the desire to change that behavior.

For passenger fuel cell vehicles (FCVs), customers will expect to start the vehicle and drive almost immediately, implying a very short system warmup to full power. While hybridization strategies may fulfill this expectation, the extent of hybridization will be dictated by the time required for the fuel cell system to reach normal operating temperatures. Quick-starting fuel cell systems are impeded by two problems: 1) the freezing of residual water or water generated by starting the stack at below freezing temperatures and 2) temperature-dependent fuel cell performance, improving as the temperature reaches the normal range. Cold start models exist in the literature; however, there does not appear to be a model that fully captures the thermal characteristics of the stack during sub-freezing startup conditions. Existing models do not include stack internal heating methods or endplate thermal mass effect on end cells.

The focus of this research is the development and use of a sub-freezing thermal model for a polymer electrolyte fuel cell stack and system designed for integration within a direct hydrogen hybrid FCV. The stack is separated into individual cell layers to determine an accurate temperature distribution within the stack. Unlike a lumped model, which may use a single temperature as an indicator of the stack's thermal condition, a layered model can reveal the effect of the endplate thermal mass on the end cells, and accommodate the evaluation of internal heating methods that may mitigate this effect.

This research is designed to answer the following motivating questions:

* What detailed thermal model design will accurately characterize the fuel cell stack and system during the sub-freezing startup operation? * What are the effects of different startup strategies on energy consumption and time to normal operation?

These questions are addressed in this dissertation. Major research findings include the following recommendations for the best startup strategies based on model parameter values and assumptions: 1) use internal heating methods (other than stack reactions) below 0ºC, 2) circulate coolant for uniform heat distribution, 3) minimize coolant loop thermal mass, 4) heat the endplates, and 5) use metal such as stainless steel for the bipolar plates.

This paper introduces a method to assess the reliability of hydrogen supply systems for transportation applications. It relies on a panel of experts to rate the reliability and importance of various metrics as they pertain to selected hydrogen systems. These are aggregated to develop broad reliability scores to be compared across systems. A trial application of the methodology is presented, where a group of hydrogen researchers at the Institute of Transportation Studies at the University of California, Davis comprise the expert panel. Two hydrogen pathways supplying a hypothetical network of refueling stations in Sacramento were compared. The first uses centralized steam reforming of imported liquefied natural gas and pipeline distribution of hydrogen. The second electrolyzes water onsite from electricity produced independent of the grid, and no hydrogen transport is required. The panel determined the second pathway to be more reliable, primarily due to the lack of imports, the distributed nature of the system, and the lack of hydrogen transport. This preliminary application only intends to demonstrate how the method is applied, however, and the results presented here should not be taken as definite.

The current lack of an extensive (H2) infrastructure is often cited as a serious barrier to the introduction of H2 as an energy carrier, and to the commercialization of technologies such as H2 vehicles. Because H2 can be made at a wide range of scales (from household to large city) and from a variety of primary sources (fossil, renewable and nuclear), there are many possible pathways for producing and distributing H2 to users. The DOE has identified the need to find viable transition strategies toward widespread use of H2.

In this work, we developed and applied simulation tools to evaluate alternative pathways toward widespread use of H2 under various demand scenarios and regional conditions. Geographic information system (GIS) data are utilized as input to analysis, and to visualize results. The use of mathematical programming or other methods to screen the large design space of possible transition pathways for optimum solutions is employed. Using these techniques we carried out a series of regional case studies for H2 infrastructure development. The goal is to understand which factors are most important in finding viable transition strategies under different regional conditions and to develop rules of thumb for future H2 infrastructure development.

Presented to the Hydrogen and Fuel Cell Caucus, Washington, DC, January 11, 2005

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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Traffic signals, while serving an important function to coordinate vehicle movements through intersections, also cause frequent stops and delays, particularly when they are not properly timed. Such stops and delays contribute to significant amount of fuel consumption and greenhouse gas emissions. The recent development of connected and automated vehicle (CAV) technology provides new opportunities to enable better control of vehicles and intersections, that in turn reduces fuel consumption and emissions. In this paper, we propose platoon-trajectory-optimization (PTO) to minimize the total fuel consumption of a CAV platoon through a signalized intersection. In this approach, all CAVs in one platoon are considered as a whole, that is, all other CAVs follow the trajectory of the leading one with a time delay and minimum safety gap, which is enabled by vehicle to vehicle communication. Moreover, the leading CAV in the platoon learns of the signal timing plan just after it enters the approach segment through vehicle to infrastructure communication. We compare our PTO control with the other two controls, in which the leading vehicle adopts the optimal trajectory (LTO) or drive with maximum speed (AT), respectively, and the other vehicles follow the leading vehicle with a simplified Gipps’ car-following model. Furthermore, we extend the controls into multiple platoons by considering the interactions between the two platoons. The numerical results demonstrate that PTO has better performance than LTO and AT, particularly when CAVs have enough space and travel time to smooth their trajectories. The reduction of travel time and fuel consumption can be as high as 40% and 30% on average, respectively, in the studied cases, which shows the great potential of CAV technology in reducing congestion and negative environmental impact of automobile transportation.

This pilot study applies the principles of Vehicle-Pavement Interaction (V-PI) and state-of-the practice tools to simulate and measure peak loads and vertical acceleration of trucks and their freight on a selected range of typical pavement surface profiles on the State Highway System. Outputs from the pilot study are expected to provide input for planning and economic models to enable an improved evaluation of the freight flows and costs in selected regions. The San Joaquin Valley corridor, a major production and transportation corridor in California, is identified as well-suited to be the pilot area for the remainder of this project.

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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A major cause of global climate change, human behavior has long been recognized as an essential part of the solution as well. Behavior change methods in turn rely in part on associative learning principles. Some learning principles, such as positive reinforcement and delay discounting, are already integrated into energy research and interventions. However, others remain underutilized. In this paper, we review selected learning principles, suggesting how they can enhance both our understanding of the behavioral challenges and our effectiveness in addressing them. We seek to interest and involve researchers and practitioners in a variety of energy and sustainability specializations.

According to intuition and theories of diffusion, consumer preferences develop along with technological change. However, most economic models designed for policy simulation unrealistically assume static preferences. To improve the behavioral realism of an energy-economy policy model, this study investigates the "neighbor effect," where a new technology becomes more desirable as its adoption becomes more widespread in the market. We measure this effect as a change in aggregated willingness to pay under different levels of technology penetration. Focusing on hybrid-electric vehicles, an online survey experiment collected stated preference (SP) data from 535 Canadian and 408 Californian vehicle owners under different hypothetical market conditions. Revealed preference (RP) data was collected from the same respondents by eliciting the year, make and model of recent vehicle purchases from regions with different degrees of HEV popularity: Canada with 0.17% new market share, and California with 3.0% new market share. We compare choice models estimated from RP data only with three joint SP-RP estimation techniques, each assigning a different weight to the influence of SP and RP data in coefficient estimates. Statistically, models allowing more RP influence outperform SP influenced models. However, results suggest that because the RP data in this study is afflicted by multicollinearity, techniques that allow more SP influence in the beta estimates while maintaining RP data for calibrating vehicle class constraints produce more realistic estimates of willingness to pay. Furthermore, SP influenced coefficient estimates also translate to more realistic behavioral parameters for CIMS, allowing more sensitivity to policy simulations.

Abstract Air pollution from motor vehicles, electricity-generating plants, industry, and other sources can harm human health, injure crops and forests, damage building materials, and impair visibility. Economists sometimes analyze the social cost of these impacts, in order to illuminate tradeoffs, compare alternatives, and promote efficient use of scarce resource. In this paper, we compare estimates of the health and visibility costs of air pollution derived from a meta-hedonic price analysis, with an estimate of health costs derived from a damage-function analysis and an estimate of the visibility cost derived from contingent valuation. We find that the meta-hedonic price analysis produces an estimate of the health cost that lies at the low end of the range of damage-function estimates. This is consistent with hypotheses that on the one hand, hedonic price analysis does not capture all of the health costs of air pollution (because individuals may not be fully informed about all of the health effects), and that on the other hand, the value of mortality used in the high-end damage function estimates is too high. The analysis of the visibility cost of air pollution derived from a meta-hedonic price analysis produces an estimate that is essentially identical to an independent estimate based on contingent valuation. This close agreement lends some credence to the estimates. We then apply the meta hedonic-price model to estimate the visibility cost per kilogram of motor vehicle emissions.

This paper concerns the economic and environmental challenges confronting California and the potential role for clean energy systems and hydrogen as an energy carrier in helping to address these challenges. Hydrogen in particular has recently gained great attention as part of a set of solutions to a variety of energy and environmental problems — and based on this potential the current high level of interest is understandable. In our view, however, full realization of the benefits that hydrogen can offer will not be possible without a clear strategy for producing hydrogen from clean and sustainable sources and in a cost-effective manner. One of hydrogen's greatest benefits — having a wide range of potential feedstocks for its production — also complicates the issue of how hydrogen use may be expanded and necessitates careful forethought as key technology paths unfold. We must remember that the additional cost and complexity of building a hydrogen infrastructure is only justified if significant benefits to society are in fact likely to accrue.

This paper has been written for two primary purposes. First, we argue that the time is ripe for an expanded science and technology initiative in California for clean energy development and greater end-use energy efficiency. This initiative should span transportation systems, electrical power generation, and natural gas and other fuel use, and should place the potential for expanded use of hydrogen within this broader context. Second, we specifically discuss potential concepts and strategies that California might employ as it continues to explore the use of hydrogen in transportation and stationary settings. The authors believe that at this stage the question is not if California should continue with efforts to expand hydrogen use, because these efforts are already underway, but how these efforts should be structured given the level of effort that ultimately emerges through various political and corporate strategy processes. However, we feel that it is critical that these efforts take place in the context of a broader "no regrets" clean energy strategy for California.

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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The expansion of automation in the U.S. economy is increasingly tangible and will presumably entail positive and negative impacts that are not yet well understood. In the freight sector, there is uncertainty about how and when automation will impact labor. Beyond this, there are further unknowns about what the impacts will be on such freight subsectors as warehousing, long- and short-haul. It is expected that penetration rates of freight automation will vary across subsectors. In some subsectors, new jobs will be created and/or working conditions will improve. Other subsectors will see declining job quality and/or job losses that require workers to transition to new roles or sectors entirely, when possible. Changes in job opportunities and quality will vary within sectors and subsectors, by region, and/or by firm. This study offers an overview and recommendations in three directions. First, despite the uncertainties and based on past and present examples of automation, it provides some insights about strategies that may help impacted workers within and outside of the heavy freight sector transition. Second, it discusses examples of existing public policies that can support a transition for automation-impacted workers. And third, it provides insights on how different freight subsectors are likely to be impacted by automation.

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Annual electric bike (e-bike) sales in China grew from 40,000 in 1998 to 10 million in 2005. This rapid transition from human-powered bicycles and gasoline-powered scooters to an all-electric vehicle/fuel technology system is special in the evolution of transportation technology and, thus far, unique to China. We examine how and why e-bikes developed so quickly in China with particular focus on the key technical, economic, and political factors involved. This case study provides important insights to policy makers in China and abroad on how timely regulatory policy can change the purchase choice of millions and create a new mode of transportation. These lessons are especially important to China as it embarks on a large-scale transition to personal vehicles, but also to other countries seeking more sustainable forms of transportation.

Plug-in hybrid electric vehicles (PHEVs), which run on both electricity from the grid and gasoline, are touted as providing some of the societal and environmental benefits of electric vehicles for a large portion of motorists’ daily travel, while also acting as a transitional technology toward fully electric vehicles. To test analysts’ assumptions about how PHEV users will recharge their vehicles, the observed recharging behaviors of forty households that participated in a PHEV demonstration in Northern California are reported. Recharging behavior is summarized across all households’ last week of their four-week PHEV trial period with regards to the time-of-day, frequency of plugging-in, and electricity demand to recharge the vehicles. While the means of the frequency distribution of plug-in events among demonstration households is similar to prior recharging assumptions made by analysts, the distributions are not symmetrical about the mean and there exists a large variation in both the average number of times households plugged-in per day and the average energy per plug-in event. Further, there is no strong correspondence between the number of daily plug-in events and total daily electricity demand. The range of behaviors reported here support the contention that the success of PHEVs in meeting energy and emissions goals relies on PHEV users’ recharging and driving behavior as much or more as on PHEV designs.

A low carbon fuel standard (LCFS) seeks to reduce greenhouse gas emissions by capping an industry’s carbon emissions per unit of output. California has launched an LCFS for automotive fuels; others have called for a national LCFS. We show that this policy causes production of high-carbon fuels to decrease but production of low-carbon fuels to increase. The net effect of this may be an increase in carbon emissions. The LCFS may also reduce welfare, and the best LCFS may be no LCFS. We simulate the outcomes of a national LCFS, focusing on gasoline and ethanol as the high- and low-carbon fuels. For a broad range of parameters, we find that the LCFS is unlikely to increase CO2 emissions. However, the surplus losses from the LCFS are quite large ($80 to $760 billion annually for a national LCFS reducing carbon intensities by 10 percent), and the average carbon cost ($307 to $2,272 per ton of CO2 for the same LCFS) can be much larger than damage estimates. We propose an efficient policy that achieves the same emissions reduction at a much lower surplus cost ($16 to $290 billion) and much lower average carbon cost ($60 to $868 per ton of CO2).