UC Davis Institute of Transportation Studies

This thesis is a preliminary investigation into the technical feasibility and cost effectiveness of a hydrogen-fueled aviation system. A review on hydrogen aircraft reveals that designing and manufacturing hydrogen-powered aircraft is technically feasible. Major hydrogen supply technologies are available, but their capacity is far below the need of a hydrogen aviation system. A large airport such as San Francisco International Airport (SFO) can consume over 3000 metric tons of hydrogen per day, if its air traffic is entirely fueled by hydrogen. Such an energy flow could support over 3 million typical hydrogen fuel cell cars’ normal use. Airport liquid hydrogen cost modeling provides an estimation of hydrogen fuel cost as an aviation fuel. The cost is found to be 20%-90% higher than conventional jet fuel on a per energy basis, and supplying liquid hydrogen creates major electric power and land use challenge to the airport. The economies of scale are limited when hydrogen is supplied at an airport level scale, given hydrogen production, liquefaction, delivery, and storage technologies available today. Compared to other alternative aviation fuels (e.g. biofuel and LNG), hydrogen is highly costly but offers huge GHG saving potentials.

Crumb rubber modifier (CRM) produced from waste tires has been used in pavement engineering for more than half a century. This recycling methodology improves sustainable development because of environmental benefits of recycling scrap tires and because of improved performance of pavement materials when the recycled tire rubber is used as modifier in asphalt binders. This application improves both rutting and cracking resistance of asphalt pavement when an appropriate design is followed.

Adding the CRM to asphalt binders leads to modification of binder properties, including rheological properties and aging resistance. This modification alters pavement performance in-service. Previous studies found that rubber-modified binders had better aging resistance than their base binders. However, the mechanism of rubber modification on aging resistance was not well understood. This study aimed to explore this mechanism for rubber-modified binders. This study also evaluated the performance-related properties of dense-graded asphalt mixes using smaller quantities of CRM than are used in current applications, and their expected performance in different structural applications.

The main goal of this dissertation was to develop frameworks, quantitative models, and databases needed to support data-driven, informed, and integrated decision-making in managing the vast transportation infrastructure in California. Such a management system was envisioned to consider both costs and environmental impacts of management decisions, based on full life cycles of the infrastructure, and using reliable, high quality data that well represent local conditions in terms of materials and energy sources, production technologies, design methods, construction practices, and other critical parameters.

This PhD research consisted of three parts: 1) development of a comprehensive life cycle inventory (LCI) database for implementation of life cycle assessment (LCA) methodology in transportation infrastructure management in California. 2) Evaluation of current and potential sustainability actions at the state and local government levels through the development of frameworks, models, and datasets needed for objective and accurate quantification of the impacts of management decisions. 3) Assessment of recycling practices available for pavements at their end of life to quantify changes in environmental impacts compared to conventional methods, considering the effects of recycling through the use stage.

Changes are coming to housing development and demand in California. The state’s sprawling development patterns have come under increasing scrutiny as the state struggles to reduce its greenhouse gas emissions, abate a decades-long housing supply and affordability crisis, and meet the needs of the largest generation in American history – the millennials (Generation Y). In this dissertation, I explore three ways in which residential development and demand in California could change going forward.

In my first study (Chapter 2 of this dissertation), I investigate how an upcoming change in California’s project-level environmental review law (the California Environmental Quality Act or CEQA) could affect the approval process for urban development. The state recently mandated that local, regional, and state agencies must replace “level of service” (LOS) with vehicle miles traveled (VMT) as the primary measure – and basis for mitigation – of transportation impacts under CEQA by July 1, 2020. I use a historical counterfactual approach to assess how replacing LOS with VMT could have impacted the approval process for 153 land development projects over 16 years in the City of Los Angeles. I find that most projects could have qualified for at least some environmental review streamlining under the VMT-based framework recommended by the state, including over 75 percent of residential-containing projects. My results suggest that swapping LOS for VMT could reduce the environmental review burden for development in urban areas and provide some of the approval process streamlining necessary to increase housing production in California. And because the streamlined development would be in areas characterized by lower VMT per capita than the regional average, it would likely contribute to reducing VMT per capita in line with state targets. 

In my second study (Chapter 3 of this dissertation), I look at accessory dwelling units (ADUs). How much ADUs can help with California’s housing supply and affordability crises depends on the homeowners who do not yet own one – their willingness and ability to build an ADU will determine the ceiling for ADU construction. I use a survey of 502 single-family homeowners in the Sacramento metropolitan area to investigate homeowners’ willingness to consider building an ADU, and the motivations and barriers they face. I find that as many as 54.1% of Sacramento city single-family detached homeowners could either have an ADU or be open to creating one. Familiarity with ADUs has the strongest association with openness to building an ADU in my logistic regression model. And homeowners’ top-ranked motivation for creating an ADU is housing family or friends. Cost-related concerns ranked as the biggest obstacles to creating an ADU, followed by permitting and regulatory issues. My findings suggest that ADUs have significant potential to help California close its housing supply gap.

In my third study (Chapter 4 of this dissertation), I explore how millennials – people born between 1982 and 2000 – choose where to live. Surveys suggest that millennials have a stronger preference than previous generations for urban amenities. But studies also indicate that most millennials will eventually settle in a suburb. That raises big questions for urban planners and policymakers, as well as for the future of sustainable urbanism. If most millennials will end up suburbanizing, what happens to their erstwhile preferences for urban amenities? Do they seek out suburban neighborhoods with urban amenities? Do their preferences simply change with time and major life events? I use in-depth interviews of 20 households who recently purchased homes in the San Francisco Bay Area to explore how millennials choose where to live when they reach the life cycle stages typically associated with bigger homes in suburban areas. I find that life cycle effects emerged in different ways for the households I interviewed. As they partnered and began having or thinking about having children, most households suburbanized or planned to suburbanize in the future. The households still valued urban amenities, but they generally did not prioritize urban amenities when searching for their suburban homes, with one exception – proximity to commuter transit.

Accelerating the adoption of plug-in electric vehicles (PEVs), is critical to reduce GHG emissions in the light duty vehicle sector. Conventional PEV usage and GHG assessments are largely based on assumptions drawn from stated preferences and choice experiments of potential or current PEV owners, or self-reported travel and refueling diaries of mainstream internal combustion engine(ICE) users. This dissertation focuses on observed behavior of current PEV users. I present three studies that seek to improve our understanding of PEV driving and charging typified by two levels of disaggregation- vehicle level and household level.

First study develops an analytical procedure to quantify what aspects of driving and charging behavior contributes to the gap between observed PHEV Utility Factors and Society of Automotive Engineers (SAE) J2841 expectations. Results indicated that depending on the PHEV range, roughly ±45% of deviations is attributable charging behavior. Daily mileage was responsible for -20% to +3% of deviation. Annual mileage and effective charge depleting range achieved on-road influenced the UF deviation by ±25% and -20% to -4% respectively.

In the second study, driving and charging behavior differences between short-range (20 miles or less) and long-range (35 miles or more) PHEVs are investigated. It was found that diversity of charging locations is positively associated with electric miles from short-range PHEVs whereas encouraging more home charging increases the electrification benefits of longer-range PHEVs.

Third study quantifies the well-to-wheel GHG mitigation potential of Nissan Leaf, Chevrolet Bolt and Tesla Model S at the household level using a multi-year actual usage data from 73 two-car (single BEV and single ICE) California households. Analysis shows that on average 25% of Leaf and Bolt, and 30% of Tesla household’s GHG can be reduced from their current levels by driving the BEV instead of the ICE. Upgrading to a longer-range efficiency oriented BEV and fully charging overnight can mitigate an additional 10-15% household GHG. Upgrading to longer-range sportier performance oriented BEV nearly offset the GHG abatement benefits, but it electrifies the highest share of household miles.

This study approaches the problem of quantifying the network sensor errors as a supervised learning problem and leveraging deep neural networks to map observed traffic flow counts to the systematic errors in the sensors. The author aims at building a model that could reconstruct the erroneous flow irrespective of the level of random noise in the sensors, which is unknown in the real-world. By reconstructing the erroneous flow with high accuracy, the transportation planners could gauge the true traffic flow demand in the network and can make informed infrastructure related decisions.

The study begins by simulating the traffic network under dynamic flow assignment settings to generate the base flow that is treated as the ground truth. The authors then introduce measurement errors to the base flow to generate the observed flow which is transformed into a multi-dimensional time-series tensor data, where each time step has dimension equal to the number of sensors in the network. Next, they introduce deep neural network comprising of 1-Dimensional Convolutional Neural Networks (1-D CNNs) to extract high-level spatial-temporal features from the observed flow time series data. To understand the generalization capability of the deep learning model, they deploy it against numerous test cases with varied levels of random errors and proportion of malfunctioning sensors in the network. Results indicate that the flow reconstructed using the deep learning model is very close to the ground truth flow and that the model predicts the systematic errors in the test cases with high accuracy.

The major advantages of this study are that, firstly, the model is robust to the flow imbalance in the network unlike most of the network sensor health studies in the past. Secondly, the approach escapes dealing with complicated flow-density relations one might encounter while modeling dynamic flow using traditional analytical statistical approaches.

Habitat loss and fragmentation is currently the primary driver of biodiversity decline. Community forest management and wildlife crossing structures are two common conservation strategies applied to mitigate habitat loss and fragmentation. Community forest management is an approach that enables local communities to participate in forest management in order to reduce deforestation, and crossing structures are intended to mitigate the negative impacts of roads in fragmenting the landscape. To implement efficient design, their effectiveness needs to be examined using rigorous and appropriate methods. Herein, I assess the efficacy of each in the context of counterfactual assessments and baseline conditions. Using Pemba Island, Tanzania, as a case study, I monitor Community forest management, and use unprotected areas as the baseline. For wildlife crossing structures I examine structures along California highways, and use adjacent wildland areas absent of roads as the baseline. I employ methods such as remote sensing and hierarchical modeling to decipher forest cover change, wildlife movement, and behavioral responses within a fragmented habitat. I focus on particular anthropogenic stressors that may contribute to the efficacy of Community forest management and wildlife crossing structures, such as human population density, and light and noise pollution. The results offer solutions to the broader conservation community in how to evaluate the conservation tools we are currently utilizing. Furthermore, results guide the decision-making process for wildlife managers, practitioners, and agencies specific to these case studies and future conservation projects.

The last century brought a shift in rail propulsion from the (typically) coal-powered steam engine to a combination of the diesel-electric locomotive and the electrified locomotive running under electrified overhead lines. While, no doubt, an advance over the earlier technology, the two incumbent technologies are not without their shortcomings.

In the current era, rapid technological developments and increased concerns about climate change have also spurred interest away from the internal combustion engine and the use of fossil fuels in various applications. These same technologies hold promise in a rail context, a mode of transportation that relies on a smaller number of more centralized operators.

With the tremendous investment of time, cost, and other resources that can go into a pilot experiment of a fuel technology and, often, related regulatory processes, it makes sense to determine the key candidates for such pilots. A major goal of this work is to help industry and government narrow down the key technologies, in terms of cost, viability, and environmental impacts, and simultaneously identify the challenges that may be encountered by a given technology that otherwise appears to hold significant promise. This study focuses on a U.S. context, and on the period between 2022 and 2038. Passenger and freight rail routes and systems were examined, each with different characteristics, via simulations of a single rail trip, A general environmental analysis was also performed on freight switcher locomotive activity.

The fuels examined included diesel, natural gas, Fischer-Tropsch diesel, hydrogen, and, in a passenger rail and switcher context, diesel and hydrogen powertrains paired with batteries to take in regenerative braking energy. The study finds cost reductions with both natural gas and (natural gas-derived) Fischer-Tropsch diesel, but with limited environmental benefits. Hydrogen via fuel cell has significant promise to reduce GHG and criteria pollutant emissions. That technology’s costs, both fuel and equipment, are highly uncertain; however, the study finds that, with lower bound projected costs, it could be competitive with diesel-electric costs; in the case of passenger rail, hybridization with batteries is also compelling. Hybridized hydrogen also was found to demonstrate a clear environmental benefit in switcher locomotive applications.

Biofuels have been promoted by governmental policies for reducing fossil fuel dependency and greenhouse gas emissions, as well as facilitating regional economic growth. Comprehensive model analysis is needed to assess the economic and environmental impacts of developing bioenergy production systems. For cellulosic biofuel production and supply in particular, existing studies have not accounted for the inter-dependencies between multiple participating decision makers and simultaneously incorporated uncertainties and risks associated with the linked production systems.

This dissertation presents a methodology that incorporates uncertainty element to the existing integrated modeling framework specifically designed for advanced biofuel production systems using dedicated energy crops as feedstock resources. The goal of the framework is to support the bioenergy industry for infrastructure and supply chain development. The framework is flexible to adapt to different topological network structures and decision scopes based on the modeling requirements, such as on capturing the interactions between the agricultural production system and the multi-refinery bioenergy supply chain system with regards to land allocation and crop adoption patterns, which is critical for estimating feedstock supply potentials for the bioenergy industry. The methodology is also particularly designed to incorporate system uncertainties by using stochastic programming models to improve the resilience of the optimized system design.

The framework is used to construct model analyses in two case studies. The results of the California biomass supply model estimate that feedstock pretreatment via combined torrefaction and pelletization reduces delivered and transportation cost for long-distance biomass shipment by 5% and 15% respectively. The Pacific Northwest hardwood biofuels application integrates full-scaled supply chain infrastructure optimization with agricultural economic modeling and estimates that bio-jet fuels can be produced at costs between 4 to 5 dollars per gallon, and identifies areas suitable for simultaneously deploying a set of biorefineries using adopted poplar as the dedicated energy crop to produce biomass feedstocks. This application specifically incorporates system uncertainties in the crop market and provides an optimal system design solution with over 17% improvement in expected total profit compared to its corresponding deterministic model.

Bikeshare programs are increasingly popular in the United States and they are an important part of sustainable transportation systems, offering a viable mode choice for many types of last-mile trips. This popularity means that an increasing number of people can enjoy the convenience of cycling and the associated physical health benefits without actually owning a bike (or having access to their own bikes). However, bikeshare systems have not captured high levels of ridership from disadvantaged populations. Many barriers exist that prohibit residents from disadvantaged communities from accessing bikeshare services. These barriers include absence of bikeshare stations within walking distances, lack of financial resources, cultural barriers, and/or unsafe cycling environments. Most of the current research on bikeshare programs focuses on societal benefits (e.g. reducing greenhouse gas emissions by replacing auto trips with bike trips) and bikeshare system management (e.g., bike repositioning between stations). There is some emerging research focused on equity issues in developing bikeshare. However, far less attention has been paid to bikeshare programs’ potential benefits for disadvantaged communities and virtually no quantitative research on how to design bikeshare systems to improve access for these populations. This dissertation work addresses three fundamental bikeshare equity problems.