###### Abstract
Micro-level survey data are primarily collected through questionnaires and interviews, covering basic patient information, hospital grade, and key factors influencing patients' hospital choices. These data are crucial for an in-depth analysis of patient decision-making behavior regarding hospital selection. However, existing research often relies heavily on questionnaire data, which tend to provide relatively rough measurements of key characteristics such as medical level and geographic distance. Moreover, such studies are typically based on hypothetical scenarios, requiring respondents to make choices, which makes it difficult to accurately quantify the actual influence of medical quality, hospital reputation, and transportation accessibility on patients' decisions. Therefore, it is essential to adopt a multi-source data integration approach to comprehensively explore the combined effects of hospital characteristics on patient choice behaviors. This would provide robust empirical support for optimizing healthcare resource allocation and improving service efficiency. Despite the preliminary contributions made by existing studies in exploring the factors influencing hospital traffic attraction and its mechanisms, several gaps remain underexplored in the literature and need to be addressed. Firstly, most current research is focused on single cities or specific regions, lacking systematic comparisons across multiple regions and scales. This limitation has hindered a comprehensive understanding of the generalizability and spatial heterogeneity of the conclusions. Secondly, the consideration of hospital type differences remains insufficient. The distinctions between general hospitals and specialized hospitals, in terms of hospital layout, traffic accessibility, and surrounding environments, may lead to different traffic attraction patterns. This aspect has not been adequately researched or compared. Based on the above shortcomings, the research objectives are proposed as follows. Examine the effectiveness of attraction prediction indicators: This study will analyze the evolution of competitive factors during the transformation of large hospitals in China over the past two decades, from a \"resource-scarce\" to a \"resource-abundant\" environment. It will focus on investigating the impact mechanisms of emerging factors, such as traffic feature optimization and online promotion, on hospital traffic attraction. Additionally, it will assess the applicability and explanatory power of traditional prediction indicators in the new competitive environment, aiming to develop a traffic attraction prediction indicator system that fits the current landscape. Verify differences between hospital types and their key influencing factors: Given the significant regional differences in hospital types across China, this study will conduct in-depth analyses of different hospital types to validate their key influencing factors, providing theoretical support and data for accurate medical facility planning. This study takes large hospitals in the Beijing-Tianjin-Hebei region as the research subject. Based on multi-source heterogeneous data, it uses structural equation modeling to systematically explore the key influencing factors and mechanisms of hospital traffic attraction. The research content mainly includes the following four aspects: Firstly, a theoretical framework for hospital traffic attraction is constructed, and research hypotheses are proposed, stating that hospital size, medical level, and traffic accessibility all have a significant positive impact on hospital traffic attraction. Secondly, multi-source data collection and preprocessing are carried out to construct the research database. Thirdly, based on the research hypotheses and data characteristics, a structural equation model is constructed, followed by parameter estimation and model testing to explore the differences in the influencing factors of traffic attraction across different types of hospitals.