Mass Deployment of Sustainable Transportation: Evaluation of Factors That Influence Electric Vehicle Adoption


Mass penetration of electric vehicles into the market will have a number of impacts and benefits, including the ability to substantially reduce greenhouse gas emissions from the transportation sector. Therefore, it is expected that in coming years this technology will progressively penetrate the market. This research presents an analysis of factors that influence electric vehicle adoption by modeling the conditions under which an individual, particularly one with an engineering or technical background, is more or less likely to adopt an electric vehicle. This model is developed by considering demographic determinants as well as behavioral and attitudinal measures that affect individual adoption of the technology. The methodology involves applying logistic regression to provide a good fit and predict the response given explanatory variables. Analyzing these outcomes generates empirical findings that better inform electric vehicle technology and policy development. This study takes into account preferences of potential customers and analyzes how individuals with engineering and technology background differ in electric vehicle adoption considerations compared to the general population. Therefore, this research provides both engineers and policy makers with critical information for developing future electric vehicle technology. The model results show that several factors including willingness to pay for new appealing technology, distance driven, perceptions of electric vehicles as good for the environment, perception of EV speed are statistically significant in influencing willingness to purchase an electric vehicle.


Engineering Management and Systems Engineering

Second Department

Mathematics and Statistics

Keywords and Phrases

Alternative Fuel Vehicles; Consumer Attitudes; Electric Vehicle Adoption; Electric Vehicles; Commerce; Crashworthiness; Electric Vehicles; Gas Emissions; Greenhouse Gases; Mass Transportation; Engineering And Technology; Explanatory Variables; Logistic Regressions; Sustainable Transportation; Technical Background; Transportation Sector

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Document Type

Article - Journal

Document Version


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