Plug-in electric vehicle (EV) sales are on the rise and growing EV adoption can be a key factor in helping regions achieve national- and state-level air quality standards for ozone and particulate matter, and ultimately carbon-emissions standards. In the Mid-Atlantic region, purchasing an EV over a gasoline-powered medium sedan can reduce transportation greenhouse gas emissions by 60 percent. The ability to predict which households in which neighborhoods are most likely to own such vehicles can provide important insights and opportunities for power-grid planning, transportation investments, and air quality policy-making. However, unlike the choice to purchase a traditional gasoline-powered vehicle, the decision to adopt an EV is complicated by technology familiarity, vehicle availability, and charging infrastructure provision. Previous quantitative research forecasting regional or household EV ownership trends tend to neglect one or more of these influence factors. The research proposed here aims to enrich the existing understanding of EV adoption by incorporating a combination of revealed-preference (RP) and stated-preference (SP) data that captures household demographics, vehicle traits, and zone-level infrastructure and land use considerations, all of which influence vehicle choice. The latent choice model proposed here minimizes SP data bias by recognizing differences in individual households' vehicle choice set while incorporating a spatial analysis component which accounts for 'neighbor effects' that are commonly present in early technology diffusion.
|Effective start/end date||5/1/17 → …|
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