Adoption and Diffusion of Electric Vehicles in Maryland

Project Abstract

Among the many approaches toward fuel economy, the adoption of electric vehicles (EVs) may have the greatest impact. However, existing studies on EV adoption predict very different market evolutions, which causes a lack of solid ground for strategic decision making. New methodological tools, based on Artificial Intelligence, might offer a different perspective. This paper proposes supervised Machine Learning (ML) techniques to identify key elements in EV adoption, comparing different ML methods for the classification of potential EV purchasers. Namely, Support Vector Machines, Artificial Neural Networks, Deep Neural Networks, Gradient Boosting Models, Distributed Random Forests, and Extremely Randomized Forests are modeled utilizing data gathered on users' inclinations toward EVs. Although a Support Vector Machine with polynomial kernel slightly outperforms the other algorithms, all of them exhibit comparable predictability, implying robust findings. Further analysis provides evidence that having only partial information (e.g., only socioeconomic variables) has a significant negative impact on model performance, and that the synergy across several types of variables leads to higher accuracy. Finally, the examination of misclassified observations reveals two well-differentiated groups, unveiling the importance that the profiling of a potential purchaser may have for marketing campaigns as well as for public agencies that seek to promote EV adoption.

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Universities and Sponsoring Organizations Involved

University of Maryland; U.S. DOT Office of the Secretary/Research

Principal Investigators

Dr. Cinzia Cirillo, University of Maryland, ccirillo@umd.edu

Funding Sources and Amounts

U.S. DOT $100,000; University of Maryland, $50,000

Start Date

7/1/2020

Expected Completion Date

6/30/2021

Expected Research Outcomes

The results from this project will help scholars and decision makers understand the potential of new vehicle technology and guide data driven policies.

Expected Equity Impacts and Benefits of Implementation

The analysis conducted for different segments of the populations will shed light on the factors hindering low- and medium-income population access to EV technology.

Subject Areas

Electric Vehicles, Social Network, Diffusion Models, Stated Preference Survey