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Modeling Individual Choices and Guiding Fleet Electrification Decisions for Sustainable Mobility

Abstract: As transportation undergoes increased electrification, individuals and fleet operators are faced with decisions across various scales. On one end, long-term considerations involve vehicle adoption and infrastructure deployment, while on the other end, short-term decisions include choices like opportunistically charging electric vehicles. Understanding and modeling individual behavior in the context of transportation electrification is crucial for developing policies that promote adoption and planning new infrastructure and e-mobility services. Simultaneously, fleet operators and public authorities seek decision support to guide their electrification strategies, ensuring cost-effectiveness, environmental responsibility, and social sustainability.
In this seminar, I will provide an overview of some of my past and current research that addresses the modeling of individual choices related to e-mobility and decision support for e-mobility service operators and regulating public authorities. The first part of the presentation will focus on charging decisions. I will discuss the modeling of individuals' charging choices and their use in choice-based optimization to leverage electric vehicle flexibility for grid support. Additionally, I will explore the application of reinforcement learning for developing charging strategies for fleets of autonomous mobility-service vehicles.
Transitioning from short-term operational decisions to longer-term ones, the second part of the talk will concentrate on my ongoing research regarding critical raw materials-aware fleet electrification planning. This line of work is motivated by the recognition that transportation electrification contributes to geopolitical tensions, especially concerning the secure supply of critical raw materials (CRM) for batteries. Given the geopolitical, economic, ethical, and environmental considerations associated with the substantial scale of CRM extraction required to achieve zero-emission goals, it is crucial to explicitly consider CRM intensity in the tradeoffs inherent in fleet electrification planning decisions. This ensures efficient utilization and prevents unnecessary inefficiencies in their use.

Bio: Dr. Daina is a Research Scientist in the Department of Civil Engineering and Engineering Mechanics, as well as at the Center on Global Energy Policy at Columbia University. Employing data science methodologies, econometric techniques, and experimentation and optimization, Dr. Daina investigates consumer behavior and the economic, environmental, and social impacts of integrated transportation and energy systems, particularly as transportation trends towards electrification. The overarching goal of his research is to provide insights for public policy decisions related to the zero-carbon transition of transportation systems. During his doctoral studies, Dr. Daina pioneered the use of discrete choice experiments (DCE) to model electric vehicle charging choices. This approach allowed capturing tradeoffs in electric vehicle charging decisions under smart charging, before smart charging services were operational. The use of DCE has since become a standard practice in charging choice studies. Dr. Daina earned his Ph.D. in Transport from Imperial College London in 2014. Following his doctoral studies, he held positions at Imperial College London's Department of Civil and Environmental Engineering, initially as a postdoctoral research associate and later as a research fellow. Before joining Columbia University, Dr. Daina served as faculty (Chancellor’s Fellow and Lecturer in Transport Policy) at the University of Strathclyde in Glasgow, United Kingdom. Currently, he holds the position of Honorary Lecturer in the School of Government and Public Policy at the University of Strathclyde. Additionally, Dr. Daina is a member of the Transportation Research Board’s Standing Committee on Alternative Fuels and Technologies in the United States.