Understanding household vehicle ownership dynamics in the post-COVID-19 era is critical for designing equitable, resilient, and sustainable transportation policies. This study employs an interpretable machine learning framework to model household vehicle ownership using data from the 2022 National Household Travel Survey (NHTS)—the first nationally representative U.S. dataset collected after the onset of the pandemic. A binary classification task distinguishes between single- and multi-vehicle households, applying an ensemble of algorithms, including Random Forest, XGBoost, Support Vector Machines (SVM), and Naïve Bayes. The Random Forest model achieved the highest predictive accuracy (86.9%). To address the interpretability limitations of conventional machine learning approaches, SHapley Additive exPlanations (SHAP) were applied to extract global feature importance and directionality. Results indicate that the number of drivers, household income, and vehicle age are the most influential predictors of multi-vehicle ownership, while contextual factors such as housing tenure, urbanicity, and household lifecycle stage also exert substantial influence highlighting the spatial and demographic heterogeneity in ownership behavior. Policy implications include the design of equity-sensitive strategies such as targeted mobility subsidies, vehicle scrappage incentives, and rural transit innovations. By integrating explainable artificial intelligence into national-scale transportation modeling, this research bridges the gap between predictive accuracy and interpretability, contributing to adaptive mobility strategies aligned with the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities), SDG 10 (Reduced Inequalities), and SDG 13 (Climate Action).

The transition to green energy and sustainable urban planning, guided by EU policies and local community perceptions, has positioned the concept of the “green city” as a key topic in both academic and practical discourse. This study provides an in-depth analysis of the current understanding of green cities, while also identifying cities structurally prepared for this transition. The literature review highlights the significance of the green city concept, the strategic directions of the EU, and the perspectives of residents regarding what constitutes a green city. The research outlines two primary objectives: (1) identifying urban development models within a selected sample of cities, and (2) determining cities or groups of cities with the potential to implement the green city concept. Using data from the Organisation for Economic Co-operation and Development (OECD) and Open Street Map, we employed spatial indicators and calculated average distances to key points of interest, using R software for analysis. Principal Component Analysis and Random Forest predictive analysis algorithm were applied to classify urban development models, facilitating the identification of cities best equipped for green city implementation. The findings offer valuable insights into sustainable urban transitions, providing a data-driven approach to guide policymakers and city planners in fostering resilient and green urban environments. This research emphasizes the importance of integrating technological tools with human-centered urban planning for a sustainable future.