Uncovering electric vehicle ownership disparities using K‑means clustering analysis: A case study of Austin, Texas

Study Area

Elbow Curve for Optimal K Following PCA Analysis

Calculated Silhouette Scores and Cluster Distributions

Hot Spot Analysis Result

Spatial Distribution of K-Means Clusters

Abstract

Transportation electrification is promoted for its environmental benefits, but EV adoption shows complex patterns influenced by race and income disparities. Recent studies often overlook regional ownership variations and urban form measures. This study uses actual EV registration data with spatial analyses, revealing an East–West divide in Austin. West Austin has higher EV adoption, predominantly among higher-income, educated White residents living in single-family homes. East Austin has lower EV adoption, mainly among lower-income African-American and Hispanic populations in mobile homes. Land use and built environment factors, such as green spaces and urban density, significantly impact this divide. Survey preferences for EVs do not always match actual ownership, highlighting the influence of residential choices and urban form on EV adoption.

Team

Seung Jun Choi, Junfeng Jiao

Data availability

The data that support the findings of this study are available upon reasonable request.

Funding

National Science Foundation, 2125858, Junfeng Jiao, 2133302, Junfeng Jiao, 1952193, Junfeng Jiao, UT Good System Grand Challenge, USDOT Cooperative Mobility for Competitive Megaregions University Transportation Center, Energy Seed Grant Program by Energy Institute.

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For more information, please visit: https://link.springer.com/article/10.1007/s42001-024-00310-6