Coupled Impacts of Urban Development Patterns and Policy Interventions on Motor Vehicle Ownership Based on Multi-Source Big Data

Understanding why cities diverge in motor vehicle ownership trajectories is critical for designing differentiated and sustainable transport policies. This study develops an integrated national–city analytical framework to examine heterogeneous urban motorization processes in China. A national Gompertz curve is first estimated to represent the benchmark income–ownership relationship. City-specific deviations are then decomposed into two interpretable dimensions: a horizontal stage parameter (h), capturing relative advancement or delay in motorization timing, and a vertical scaling parameter (s), reflecting persistent ownership intensity differences conditional on income. Results show substantial and multi-dimensional heterogeneity across cities. Stage timing (h) and ownership intensity (s) are only weakly correlated, indicating that earlier transition into higher motorization stages does not necessarily imply above-benchmark ownership intensity. Random forest models with time-forward validation demonstrate strong explanatory power (R2 ≈ 0.88 for h and 0.80 for s). SHAP-based interpretation reveals that stage deviation is primarily associated with transport supply and urban structural characteristics, whereas ownership intensity deviation is more strongly linked to urban spatial scale and economic structure. Regulatory measures, including purchase and driving restrictions, exhibit comparatively smaller and heterogeneous effects. By disentangling timing and intensity dimensions of urban motorization, this study refines conventional income-based diffusion models and provides quantitative evidence that structural urban characteristics play a more fundamental role than regulatory interventions in shaping inter-city motorization differences.