Catching the PHEVer: Simulating Electric Vehicle Diffusion with an Agent-Based Mixed Logit Model of Vehicle Choice

This research develops then merges two separate models to simulate electric vehicle diffusion through recreation of the Boston metropolitan statistical area vehicle market place. The first model is a mixed (random parameters) logistic regression applied to data from the US Department of Transportation's 2009 National Household Travel Survey. The second, agent-based model simulates social network interactions through which agents' vehicle choice sets are endogenously determined. Parameters from the first model are applied to the choice sets determined in the second. Results indicate that electric vehicles as a percentages of vehicle stock range from 1% to 22% in the Boston metropolitan statistical area in the year 2030, percentages being highly dependent on scenario specifications. A lower price is the main source of competitive advantage for vehicles but other characteristics, such as vehicle classification and range, are demonstrated to influence consumer choice. Government financial incentive availability leads to greater market shares in the beginning years and helps to spread diffusion in later years due to an increased base of initial adopters. Although seen as a potential hindrance to EV diffusion, battery cost scenarios have relatively small impacts on EV diffusion in comparison to policy, range, miles per gallon (MPG), and vehicle miles travelled (VMT) as a percentage of range assumptions. Pessimistic range assumptions decrease overall PHEV and BEV percentages of vehicle stock by 50% and 30%, respectively, relative to the EPA-estimated range scenarios. Fuel cost scenarios do not considerably alter estimated BEV and PHEV stock but increase the ratio of car stock to light truck stock in the internal combustion engine (ICE) vehicle spectrum. Specifically, cars are estimated at 55% of ICE vehicle stock in the default fuel price scenario but increase to 62% of ICE vehicle stock in the high world oil price scenario, with LTs covering the appropriate differences.