Evaluating private vehicle access to emergency care is crucial for public health resource planning. However, conventional models frequently depend on static traffic data and subjective parameters, neglecting the behavioral complexities involved in hospital selection under time constraints. This study introduces a Gaussian Probability Two-Step Floating Catchment Area (GP2SFCA) framework aimed at addressing these deficiencies. The framework enhances the analysis by: (i) incorporating multi-period real-time traffic data to capture dynamic accessibility; (ii) introducing a two-stage decision process—geographic screening followed by probabilistic choice—to explicitly model how residents select hospitals in urgent situations; and (iii) implementing an objective, multi-constraint calibration to derive the distance-decay coefficient (λ) free from subjective bias. When applied to Chengdu, a megacity characterized by a concentrated distribution of top-tier hospitals, the analysis reveals a dual vulnerability pattern that highlights the significance of this behavioral decomposition. A relatively predictable decline in accessibility, driven by congestion, was observed across suburban areas during weekday peak hours. In contrast, more volatile and localized deficits appeared within the central city on weekends, likely reflecting demand displacement and adaptive choices among high-reputation hospitals. The calibrated λ increased with time pressure, rising from 0.92 at 30 min to 2.68 at 5 min, a trend consistent with heightened sensitivity to travel time under urgent conditions. By differentiating between feasibility-driven and choice-driven disparities, the GP2SFCA framework provides a transferable approach for examining how fixed resource configurations and dynamic human behavior interact to influence access to emergency care.
Luo et al. (Thu,) studied this question.