Adoption and Diffusion of Electric Vehicles in Maryland

US Transportation Collection2021PDFTech ReportCirillo, CinziaBas, JavierUniversity of Maryland (College Park, Md.). Department of Civil and Environmental EngineeringUrban Mobility & Equity CenterUnited States. Department of Transportation. Office of the Secretary for Research and TechnologyUnited States. Department of Transportation. University Transportation Centers (UTC) ProgramUrban Mobility & Equity CenterMarylandUnited StatesElectric vehiclesForecastingImplementationMarket penetrationMachine learningCluster analysisAdoptionPredictionImputation69A43551747123Final ReportAmong 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\u2019 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.120