Modeling Healthcare Accessibility with Endogenous Search Ranges: A Huff-Based Multi-Source Data Approach

This study proposes a Behavior-Calibrated Endogenous Choice 2SFCA (BCEC-2SFCA) framework for assessing spatial accessibility to tertiary hospitals. Using large-scale taxi trajectory data from Ningbo, China, we empirically calibrate the Huff model parameters (α =1.1758, β =2.9608) based on observed hospital choices and construct travel time and distance matrices from observed trips. Unlike existing Huff-based FCA approaches that assume parameter values, BCEC-2SFCA jointly estimates the attractiveness elasticity and distance-decay coefficient directly from local healthcare travel behavior and integrates these calibrated probabilities into a 2SFCA structure where hospital catchments are endogenously generated rather than exogenously imposed. Compared with conventional Gaussian 2SFCA, the BCEC-2SFCA model produces a continuously varying and behaviorally plausible accessibility surface and better replicates the relative order of hospital attractiveness (ρ = 0.527, p < 0.05), although its RMSE is slightly higher (0.02700 vs. 0.02211) while MAPE is clearly lower (32.17% vs. 42.12%). Robustness checks using all 22 hospitals confirm stable estimates, and subgroup analyses show consistent advantages across hospital scales. The framework is specifically designed for high-order medical services with strong inter-facility competition—such as tertiary hospitals—and its applicability to proximity-based services is limited.