###### Abstract
However, with the acceleration of urbanization and the growing demand for medical services, the predictive accuracy of these traditional metrics has gradually declined, especially in large cities and densely populated areas, where expanding hospital size does not necessarily result in a significant increase in patient volume [9]. In recent years, many developed countries have promoted competition among medical institutions by granting patients more autonomy in their healthcare choices. This shift has sparked widespread academic interest in factors such as the spatial distribution of medical resources [10], healthcare quality [11;12], costs [13], and reputation [14], and their influence on patient healthcare choices. These studies have provided deep insights into improving the fairness of healthcare accessibility and improving the relationship between patients and healthcare providers. With the development of transportation infrastructure, traffic accessibility has increasingly become an important factor influencing patient decision-making. A large body of research has shown that the extent of public transportation network development, especially the number of stations [15] and route density [16], significantly impacts patient choices. This effect is particularly prominent in Asian countries, where public transportation is the preferred mode of travel for the elderly due to aging demographics [17]. Moreover, in China, most large, high-level hospitals are located in major cities and densely populated areas, and public transportation offers patients more equitable access to healthcare [18]. In the context of growing cross-provincial medical demand, private car travel has a significant impact on patient satisfaction with healthcare due to its convenience and efficiency [19;20]. Exploring the factors influencing patients' hospital choices and their underlying mechanisms has long been a focus of research. Traditional studies have primarily relied on statistical learning [21] and regression models [22], such as classification trees and logistic regression [23], which are widely used to identify the main effects of influencing factors and their interactions. These methods allow for quantitative analysis of the magnitude and direction of factors influencing hospital traffic attraction. However, they face limitations in estimating the effects of latent variables, such as healthcare quality. In recent years, with the rapid development of artificial intelligence technologies, machine learning methods [24] have been extensively applied in research on hospital choice. Compared to traditional methods, machine learning offers superior nonlinear fitting capabilities and can handle large-scale, multidimensional datasets, demonstrating great potential in predicting patients' hospital choices. However, its limitations cannot be ignored. Machine learning modelsrely heavily on large-scale, high-quality datasets, which are often incomplete or noisy in the medical field, particularly in studies related to patient hospital choice [25]. Moreover, machine learning struggles to provide clear causal mechanisms or actionable insights regarding patients' hospital selection, which poses a challenge for hospital administrators and urban traffic management authorities. In terms of hospital information data collection, the main methods include cross-sectional surveys [26], questionnaire surveys [27], interviews [28], and online data retrieval [29]. The data are categorized into two levels: macro-level statistical data and micro-level survey data. Macro-level statistical data are typically sourced from health statistics yearbooks or hospital management databases, including indicators such as outpatient volume, hospitalization rates, and bed utilization rates. These data are used to reflect the overall supply-demand trends and resource utilization in healthcare services.