Congratulations to our 2024 Dissertation Grant and Fellowship Awardees
Congratulations to our recent dissertation and thesis grant awardees out of UC Davis, UC Riverside, and Georgia Tech. The thirteen awardees are researching everything from bike theft to electric vehicles to rail infrastructure to vehicle dispatching to road safety!
Swati Agarwal | Transportation Technology and Policy, UC Davis
NCST Dissertation Grant
The bicycling share of daily trips is relatively low in the US compared to other developed countries. One barrier to bicycle adoption and use is bike theft, although the scientific understanding of this problem is lacking. Swati’s dissertation research includes three studies specifically analyzing bicycle theft: its magnitude and equity impacts in the US, the influence of factors like parking, locking, bicycle characteristics, and area characteristics on bicycle theft, and the difference in bicycling activity before and after bicycle theft. A fourth study will also examine the barriers to e-bike adoption, including risk of theft.
Swati plans to carry out the research by using a 2024 survey to US adult residents on bicycle theft as well as leveraging data from a future survey. She will also draw on the Routine Activity Theory, which posits that the presence of suitable targets and offenders and the absence of adequate guardianship at a given place and time influence the likelihood of crime. While a previous study has drawn on this theory to provide an overview of bicycle theft, Swati’s dissertation research will constitute the first academic study of the magnitude and impacts of bicycle theft as well as provide new insights into other aspects of this barrier to bicycle adoption.
Pablo Busch | Energy Systems, UC Davis
NCST Dissertation Grant
The electric vehicle (EV) transition relies on the availability of critical materials for lithium-ion batteries, like lithium, nickel, and cobalt. Existing supply and demand models for these materials largely ignore time, geography, and mining dynamics. Pablo’s dissertation research aims to address this research gap by modeling the supply chain for critical battery materials through an international trade model, battery mineral demand model, and supply dispatch optimization model.
The international trade model connects future spatial EV demand with supply, providing information on total vehicle sales and production, as well as flows between countries. Pablo plans to make this tool available for public use, which would be able to make EV production forecasts based on market segment and countries’ preferences. The battery mineral demand model estimates global lithium, nickel, and cobalt demand at the country level based on four demand drivers, including global EV and battery sales, as well as availability of batteries to be reused, repurposed, or recycled. The supply dispatch optimization model is predicated on the idea that investments, extraction costs, and other country factors determine which mineral deposits will be exploited sooner. After constructing a comprehensive database of existing lithium deposits, Pablo intends for this model to predict the order, timing, and magnitude of the necessary deployment of lithium deposits to meet global demand.
Elizabeth DeFrance | Chemical and Environmental Engineering, UC Riverside
NCST Fellowship Award
While EVs have zero tailpipe emissions, they also weigh more than conventional vehicles and therefore produce more non-exhaust emissions. Currently, non-exhaust emissions exceed tailpipe emissions; these can lead to the degradation of local and regional air quality and affect the health and wellbeing of communities living near major roadways and freight corridors. Non-exhaust emissions are also unregulated at this point because of insufficient research on the topic. Elizabeth’s research will address this research gap, focusing on the measurement and understanding of non-exhaust emissions from mobile sources in the transportation system.
Elizabeth’s fellowship work will involve the development of measurement protocols for brake and tire wear particulate and gaseous emissions from EVs. Her experimental results can advance the current knowledge of non-exhaust emissions from mobile sources and how to measure them, informing emission inventories and future agency rulemaking. A better understanding of non-exhaust emission from EVs will help in designing a transportation sector with reduced air quality impacts and emission exposures to people living near major roadways.
Adair Garrett | Transportation Systems Engineering, Georgia Institute of Technology
NCST Dissertation Grant
The intensification of natural hazards pose risks to people and their built environment. For the transportation sector in particular, such hazards are a threat to rail assets. Previous research found that the least costly approach for rail agencies to improve resilience includes enhancing adaptive capacity: the ability to successfully and proactively respond to shocks, stressors, or opportunities through transformation or self-reorganization. Adair’s dissertation research will investigate the various factors that influence adaptive capacity in rail infrastructure as well as how to effectively measure adaptive capacity.
Adair’s research will enhance the current understanding and evaluation of climate vulnerability in rail infrastructure by providing metrics to quantify adaptive capacity. In addition, her study will result in a database of climate adaptation options and strategies to promote transportation system resilience to climate hazards. In creating this database and examining the effect of different national policy approaches in promoting adaptive capacity for rail infrastructure, Adair’s research will inform policy-making decisions to best respond in a world of changing climate.
Ghazaleh Jafarsalehi | Civil and Environmental Engineering, UC Davis
PSR Dissertation Grant
Work zones in the transportation system present increased risks of crashes and injury to both drivers and workers. Enhancing work zone safety will be especially important as aging highway infrastructure requires ongoing and future maintenance. However, a significant gap in work safety research exists, especially in the absence of a comprehensive crash dataset, modeling approach, and analysis of spatial variability in factors contributing to work zone crashes. Ghazaleh’s dissertation research will fill this gap by leveraging a robust dataset from her previous work, adopting an innovative hybrid modeling approach, and investigating regional variability in the influence of contributing factors.
Ghazaleh’s research will rely on a dataset from her previous research on work zone crashes with varying injury levels across California from 2018 to 2021 that includes environmental, roadway, and work zone characteristics. Combining this with a hybrid modeling approach, she plans to provide an interpretable framework for understanding work zone crash injury severity that accounts for previously-unobserved heterogeneity in contributing factors. This approach will improve upon traditional statistical models by improving the model’s predictive accuracy. Finally, Ghazaleh’s research will include an exploration of spatial instability between Northern and Southern California work zones to understand how factors contributing to injury severity vary across different regions. Her findings will inform decision-making on the implementation of policies to improve work zone safety.
Haishan Liu | Electrical and Computer Engineering, UC Riverside
NCST Dissertation Grant
The advent of services such as on-demand food delivery, ride-hailing, and e-commerce offer increasing convenience, but also contribute to urban congestion, excessive energy consumption, and pollutant emissions. Haishan’s research aims to mitigate the various negative impacts associated with these on-demand services and truck delivery options, namely deadheading distance, vehicle miles traveled, and emissions.
Haishan’s dissertation has three main objectives focusing on mitigating those impacts: developing efficient order bundling and dispatching policies for on-demand food delivery, creating a large-scale simulation platform incorporating new mobility technologies like ride-hailing and automation, and developing truck eco-driving strategies by analyzing signal patterns to minimize red light stops. Her research can support a better understanding of the impacts of shared mobility services and improvements in truck fuel economy.
Ziming Liu | Civil and Environmental Engineering, Georgia Institute of Technology
NCST Dissertation Grant
As EVs and hybrid EVs (HEVs) become more common on the road, researchers will need better data collection methods to distinguish between these and internal combustion engine vehicles (ICEVs). In particular, vehicle and emissions modeling accuracy depends on high-resolution and real-time vehicle fleet composition data. Ziming’s dissertation research will address this need by developing machine vision technology to identify and classify vehicles based on videos from urban traffic flow.
Traditional methods for identifying EVs and HEVs through surveys or estimations can be inaccurate and time-consuming. Other methods rely on using vehicle registration information that is not always available or up-to-date. However, machine vision techniques have been used to classify ICEV types, such as trucks vs. buses vs. sedans. Ziming’s research will apply these techniques by developing a vehicle identification and classification model, then verifying model results with vehicle registration data. He also plans on applying the fleet compositions derived from this work to an energy use and emissions modeling framework based on the MOtor Vehicle Emission Simulator model (MOVES).
Francisco Parés Olguín | Energy Systems, UC Davis
NCST Dissertation Grant
The EV transition will have a significant impact on Mexico’s economy, environment, and labor force, not least because the country is a key stakeholder in the North American light-duty vehicle (LDV) market. In addition, Mexico also receives nearly 80% of regional second-hand vehicle exports. With EVs now entering the second-hand vehicle market, there are significant implications for Mexico’s environmental health (due to the proliferation of spent batteries) as well as circularity of batteries and battery materials. Francisco’s dissertation research will investigate these issues by introducing a global LDV supply model, predicting future flows of second-hand EVs into Mexico, and developing a life cycle assessment (LCA) model of EVs from first-life use to export as second-hand vehicles to Mexico to second-life use to end-of-life.
Anticipating the flows of LDVs as well as their components utilizing his Model for iNternational EV Trade (MONET) will allow Francisco to explore tools to design policy that accelerates the EV transition in Mexico, increase its domestic auto industry, and lead to regional cooperation schemes that maximize regional benefits. Better understanding the future of second-hand EV exports will, among other things, assist in identifying impacts to communities in regions like Mexico that depend on second-hand vehicle imports. Finally, while previous LCA studies have assumed EVs operate only in the country where they were purchased, and while many of these studies focus on countries with well-established end-of-life regulations, Francisco’s LCA model will incorporate vehicle export from the US into Mexico, and their use and end-of-life there.
Yanlin Qi | Transportation Technology and Policy, UC Davis
PSR Dissertation Grant
Road safety is a critical public health issue. Traditional road safety analyses have focused on implementing spot improvements at high-crash locations. However, this approach potentially ignores broader, system-wide issues. It is susceptible to a lack of historical crash data, especially in rural areas, and is inherently reactive, not proactive, in preventing crashes. Yanlin’s dissertation focuses on a different approach: using systemic safety analyses to evaluate risk factors across the whole road network through a knowledge graph framework.
A knowledge graph organizes data as an interconnected network of entities and their relationships, offering a sophisticated way of presenting data for advanced analysis and decision-making. In Yanlin’s research, this knowledge graph will be created by aggregating a variety of data sources, such as crash databases and traffic management systems, to support analysis techniques that can identify potential risk issues system-wide, rather than just at spot locations. Such a framework and analysis approach will facilitate identifying these risk factors, mitigating them, implementing targeted safety interventions where most needed, including in previously-ignored places like rural areas, and providing a more proactive method to ensuring road safety.
Trisha Ramadoss | Transportation Technology and Policy, UC Davis
NCST Dissertation Grant
The transportation future in California is likely to be characterized by light-duty vehicle electrification due to the state’s focus on decarbonizing transportation. However, different groups will adopt EVs at different rates, and will find varying difficulty in doing so. In other words, certain groups may experience more vehicle electrification burden than others. For her dissertation research, Trisha is developing complete population tools and models to estimate vehicle electrification burdens for different households in California.
Trisha’s research will involve estimating rates of first-time EV adoption, identify groups likely to adopt multiple EVs, and detect groups that may experience significant vehicle electrification burdens. Electrification burden can depend on factors like cost or difficulty in EV charging. While it is beginning to be understood how these factors affect EV adoption, there are no tools to evaluate the combined effect of multiple factors. In achieving the EV transition, the availability of such tools is integral to decarbonizing transportation without leaving behind communities that may experience excessive vehicle electrification burdens.
Mike Stas | Electrical Engineering, UC Riverside
NCST Fellowship Award
Mike’s research goals focus on advancing Connected and Automated Vehicle (CAV) systems using Global Navigation Satellite Systems (GNSS), Aided Inertial Navigation, State Estimation, and external sensors and cameras to create safer and more reliable automated systems. Through this research, he hopes to contribute towards enhanced resilience in automated transportation systems and accelerate innovation in transportation technologies.
Mike’s work will include assessing GNSS spoofing threats and detection possibilities in transportation applications. Additionally, he plans to explore the development of a GNSS-based autonomous braking system to ensure automated vehicles brake at appropriate points, such as red traffic lights and stop signs, thereby enhancing safety and reducing accidents. His work will also integrate lane determination capabilities into a broader Advanced Driver Assistance System using additional sensors and vehicle data. This will also open the possibility of different applications which might include identifying valid parallel parking locations, lane changing and centering, and determining the correct vehicle position and action within a signalized intersection.
Christian Viteri | Mechanical Engineering, Georgia Institute of Technology
NCST Dissertation Grant
The increasing penetration of EVs is expected to strain the electric grid and degrade power quality. Concurrently, carbon-free energy resources are increasingly proliferating on the grid, such as solar photovoltaic (PV) generation and battery energy storage systems (BESS). Christian’s previous work highlighted the ability of smart charging for vehicle fleets to lower costs and grid impact. His dissertation research will build upon those findings to incorporate smart charging strategies for vehicle fleets with carbon-free energy generation at fleet depots.
Declining vehicle costs, among other factors, are leading to greater adoption of light, medium, and heavy-duty EVs. However, these have the potential to negatively impact the grid when vehicle charging leads to power demand surging. Declining costs and rising demand are also leading to greater build-out of energy resources like solar PV and BESS. Integrating these resources into smart charging algorithms can mitigate power demands on the grid, minimize EV charging and BESS operation costs, and maximize the use of clean, carbon-free energy.
Xuanpeng Zhao | Electrical and Computer Engineering, UC Riverside
NCST Dissertation Grant
As CAVs become increasingly prevalent in transportation systems, cyber attacks will pose a growing threat, which may have serious consequences for safety, energy consumption, and air quality. Xuanpeng Zhao’s dissertation research focuses on understanding and modeling the effects of cyber attacks and developing defense algorithms for detection, prevention, and mitigation.
Ensuring the further adoption of CAVs will require understanding and mitigating the potential impacts of cyber attacks. Using his planned framework to quantify the effects of cyber attacks on environmental sustainability in transportation networks and development of defense algorithms, Xuanpeng hopes to provide actionable recommendations for policymakers toward achieving a safe CAV future.