Date of Award
Level of Access Assigned by Author
Master of Science (MS)
Resource Economics and Policy
Second Committee Member
Third Committee Member
In this research, I develop then merge 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 the agents' vehicle choice sets are endogenously determined. Parameters from the first model are then applied to the choice sets determined in the second. Social network effects are utilized to endogenously determine the vehicle power types available in a consumer's choice set, the inclusion being spurred by exceeding agents' willingness-to-consider threshold through simulated idea diffusion. The merged model is highly flexible and capable of simulating several different metropolitan statistical areas, social acceptability assumptions, economic growth scenarios, battery and fuel cost assumptions, and incentive policy situations. 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. Battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) model-level percentages of vehicle stock are demonstrated to be dependent upon individual model characteristics and their respective competitive advantages in the automobile market place with respect to the mixed logit derived parameters' influence. A lower price is the main source of advantage for vehicles but other characteristics, such as vehicle classification and range, are demonstrated to influence consumer choice. Financial incentive have an overall positive effect on EV vehicle stock percentages, but BEV and PHEV model-level stock percentages have mixed resultant impacts; hybrid vehicles are demonstrated to be the most responsive to financial policy availability assumptions. 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. Results indicate that range and MPG assumptions can dramatically effect both BEV and PHEV total and model-level deployment. Pessimistic range assumptions decrease overall PHEV and BEV percentages of vehicle stock by 50% and 30% relative to the EPA-estimated range scenarios, respectively. 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.
Brown, Maxwell, "Catching the PHEVer: Simulating Electric Vehicle Diffusion with an Agent-Based Mixed Logit Model of Vehicle Choice" (2012). Electronic Theses and Dissertations. 1710.