Reaching net-zero carbon emissions by 2050 is an overarching goal for sustainable development in United States. Electric Vehicles (EVs), including hybrid-electric vehicles (HEVs), plug-in hybrid-electric vehicles (PHEVs), battery electric vehicles (BEVs), and fuel-cell electric vehicles (FCEVs), are being developed to reduce the energy consumption during on-road operations and have become the cornerstone of sustainable transportation systems. While researching how multiple factors impact a households’ EV purchase decisions, and how people adapt to the expansion and development of EVs have been important topics in transportation related studies, there is limited research focusing on comprehensive studies of how EV adoption and use has expanded over time. Moreover, the fact that customer preferences have changed over time and how these factors have influence purchase decisions over the years has not been explicitly examined. On the contrary, most research papers assume a static influence of multiple variables, which may fail to capture the fact that customers’ preferences evolve as relevant vehicle and infrastructure technologies change over time.
This study aims to conduct a time-series to explore how EV adoption has increased over time and to predict how future EV adoption will continue to expand in the future. In the first research effort, EV purchasing trends over the past years will be captured through a dynamic discrete choice model using Puget Sound household panel monitored travel data to assess the factors that changed over time relative to EV purchase decisions. Using dynamic factors, both internal influences covering the imitation and expansion by population and external influences including incentive policies, technology advancements and expansion of infrastructure on EV expansion will be quantified and their level of importance in the purchase decisions will be reflected. In the second part of the research, a two-phase model for predicting EV purchase on household level and then on-road vehicle activity as a function of demographic, technology, and household activity factors will be derived from the research in the first phase and used to predict EV adoption and use under future techno-policy scenarios. Sensitivity analysis regarding technology breakthroughs, deployment of EV infrastructure, tax credit and purchase incentive policy changes and implementation, and travel behavior change will be performed in future scenario predictions for optimistic (best case), neutral, and pessimistic (worst case) assumptions.
The Puget Sound Regional Council (PSRC) three-wave travel survey will be adopted as the main dataset for this research. The household travel diary surveys and vehicle activity monitoring were conducted over a period of eight years (2014~2015, 2017~2019, and 2021). The data set contains household-level and person-level demographics, vehicle information, and monitored vehicle activity data. The research methodology will supplement the existing studies on EV expansion and penetration over time, and will specifically account for parameter-driven preference dynamics. Prediction results will also provide substantial research findings on understanding future EV market and possible impacts and turbulences, thusly helping with better transportation planning and policy design.