The primary mission of the UC Davis Institute of Transportation Studies is research - cross-disciplinary inquiries into emerging transportation issues with great societal significance. It draws upon campus researchers and graduate students from a variety of disciplines, and also upon other universities and research centers around the world.
Identifying and Analyzing Travel-Related Attitudinal, Personality, and Lifestyle Clusters in the San Francisco Bay Area
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.
A Thermal Model to Evaluate Sub-Freezing Startup for a Direct Hydrogen Hybrid Fuel Cell Vehicle Polymer Electrolyte Fuel Cell Stack and System
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.
Technical and Economic Studies of Regional Transition Strategies Toward Widespread Use of Hydrogen Energy
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
The Effect of Land Use Policies and Infrastructure Investments on How Much We Drive: A Practitioner’s Guide to the Literature
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.
In the US, the market share of plug-in electric vehicles (PEVs)—including battery electric and plug-in hybrid electric vehicles—has been rapidly increasing as a variety of new PEVs have been introduced. Knowing where PEV users are located is important to ensure that electric vehicle charging infrastructure is installed in areas where it is needed. Information on PEV location can also inform electricity supply planning to prepare for a future with higher PEV adoption. Previous studies have looked at the spatial distribution of new PEVs but not of used PEVs. Yet these spatial distributions will likely differ because the buyers of used PEVs have different characteristics than new PEV buyers. Therefore, planning charging infrastructure and electricity supply based solely on new PEV data may not serve both new and used PEV buyers. Policies developed to support drivers of used PEVs may ultimately attract a broader group of people into the PEV market, as used vehicles are less expensive than new ones. Researchers at the University of California, Davis used aggregated data at the zip code level to understand where buyers of second-hand PEVs are located, and to explore differences in the location and characteristics of regions with more original owners vs. second owners of PEVs. This policy brief summarizes the findings from that research and provides policy implications.
Microtransit is a new, technology-enabled, on-demand transportation mode in which small shuttles provide shared rides through flexible routing and scheduling in response to customers’ requests for rides. It can potentially offer greater efficiency and more equitable service than ride-hailing services, and it may fill gaps in traditional transit services. Thus far, the early shape of the microtransit customer market remains unclear. Specifically, why some people are interested in microtransit while others are not remains an open question. For people who have never used it, what factors could work as facilitators or barriers in their willingness to adopt microtransit? Who are early adopters of microtransit? Aiming to fill this gap, in 2021, researchers at the University of California, Davis conducted focus groups and an online survey of SmaRT Ride adopters and users of other means of transportation in the Sacramento area.
Energy-aware Trajectory Optimization of Connected and Automated Vehicle Platoons through a Signalized Intersection
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.
How Can Automated Vehicles Increase Access to Marginalized Populations and Reduce Congestion, Vehicle Miles Traveled, and Greenhouse Gas Emissions? A Case Study in the City of Los Angeles
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.
Positive reinforcement is just the beginning: Associative learning principles for energy efficiency and climate sustainability
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.
Combining stated and revealed choice research to simulate the neighbor effect: The case of hybrid-electric vehicles
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.
Practitioner Guide: An Inventory of Vehicle Design Strategies Aimed at Reducing COVID-19 Transmission in Public and Private Pooled and Shared Transportation
The COVID-19 pandemic has had dramatic impacts on transportation globally, reducing travel and deterring travelers from using shared and pooled modes such as public transit, carpooling, car-sharing, pooled ride-hailing, and micromobility. These modes are critical components of a decarbonized and equitable mobility future, but already comprised a small fraction of pre-pandemic travel in the U.S., and will likely remain further suppressed in the wake of the pandemic if people continue new mode choice habits. Those who do continue to rely on public transportation are disproportionately at risk due the degree that these modes leave them susceptible to disease transmission. For pooled and shared travel to return to and ideally surpass pre-pandemic levels, it is important to implement solutions to reduce the real and perceived risks of infectious disease transmission. This white paper presents an inventory and typology of vehicle design strategies that have been proposed or implemented with the aim of mitigating the risk of COVID-19 transmission in pooled and shared travel modes. Researchers organized these strategies into a COVID-19 Risk-mitigating Vehicle Design Typology and identified the mechanisms by which they may help diminish the risk of COVID-19 transmission. It is intended as a resource for policy-makers, transportation service operators, vehicle manufacturers, and scientists who are tasked with evaluating strategies to mitigate disease transmission risk in shared and pooled transportation services
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.
Households’ Plug-in Hybrid Electric Vehicle Recharging Behavior: Observed variation in households’ use of a 5kWh blended PHEV-conversion
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).
Related Research Centers & Groups
- 3 Revolutions Future Mobility Program
- China Center for Energy and Transportation
- National Center for Sustainable Transportation
- Energy Futures Research Center
- Hydrogen Pathways Program
- Policy Institute for Energy, Environment, and the Economy
- Plug-In Hybrid & Electric Vehicle Research Center
- Sustainable Freight Research Center
- Sustainable Transportation Energy Pathways (STEPS)
- Sustainable Transportation Center
- University of California Pavement Research Center
- Urban Land Use and Transportation Center
- Institute of Transportation Studies at UC Berkeley
- UC Irvine Institute of Transportation Studies
- UCLA Institute of Transportation Studies
- University of California Institute of Transportation Studies