A Machine Learning Approach to Understanding Sociodemographic Factors in Electric Vehicle Ownership in the U.S

Eazaz Sadeghvaziri, Ramina Javid, Hananeh Omidi and Mahmoud Arafat ()
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Eazaz Sadeghvaziri: Department of Environmental and Civil Engineering, School of Engineering, Mercer University, Macon, GA 31207, USA
Ramina Javid: Department of Transportation and Urban Infrastructure Studies, Morgan State University, Baltimore, MD 21251, USA
Hananeh Omidi: Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, OK 73019, USA
Mahmoud Arafat: Frederick County Government, Division of Planning & Permitting, Frederick, MD 21701, USA

Sustainability, 2024, vol. 16, issue 23, 1-17

Abstract: Electric vehicles (EVs) are rapidly gaining popularity due to their environmental benefits, such as reducing greenhouse gas emissions. Considering the sociodemographic factors that influence the adoption of EVs is essential when developing equitable and efficient transportation policies. This article leverages the National Household Travel Survey (NHTS) 2022 data to analyze the sociodemographic factors influencing the adoption of EVs in the U.S. A binary logistic regression model and three machine learning models were employed to predict EV ownership in the U.S. The results of the regression model suggested that the Pacific division leads in EV adoption, most likely due to legislation and improved infrastructure, while regions such as East South Central suffer from lower EV adoption. The findings indicate that higher household income and home ownership significantly correlate with increased EV adoption. In contrast, renters and rural households exhibit lower adoption rates suggesting an increase in charging facilities in these regions can promote EV adoption. The Random Forest model outperforms others with an accuracy of 82.72%, suggesting its robustness in handling complex relationships between variables. Policy implications include the need for financial incentives for low-income households and increased charging infrastructure in rural and underserved urban areas to promote equitable EV adoption.

Keywords: electric vehicles; EV adoption; national household travel survey; machine learning; socioeconomic factors (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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