Research on Analysis and Prediction of Electric Vehicle Ownership in Washington State, USA, Based on Data Science Technologies
Jiguo Yu · Computer Science and Technology · 2025
The rapid global shift towards sustainable transportation highlights the significance of understanding the adoption patterns of electric vehicles (EVs). This study utilizes data science techniques to analyze the trend of EV ownership in Washington State, USA, which is a frontier region for clean energy initiatives. By systematically collecting, preprocessing, and analyzing EV registration data, this study identified key factors driving market growth, including policy incentives, infrastructure development, and socio-economic variables. Exploratory data analysis and predictive modeling (utilizing linear regression and K-means clustering) revealed a significant upward trend in EV adoption, and different regional clusters highlighted different adoption rates. These findings provide actionable insights for policymakers to optimize incentive measures and enterprises to customize market strategies. Moreover, this study offers support for broader discussions on sustainable transportation by providing a data-driven framework for future research. The research results not only validate the progressive policies in Washington State but also serve as a benchmark for other regions aiming to accelerate EV adoption.