Urban Subway Station Site Selection Prediction Based on Clustered Demand and Interpretable Machine Learning Models

With accelerating urbanization, the development of rail transit systems—particularly subways—has become a key strategy for alleviating urban traffic congestion. However, existing studies on subway station site selection often lack a spatially continuous evaluation of site suitability across the entire study area. This may lead to a disconnect between planning and actual demand, resulting in issues such as “overbuilt infrastructure” or the “island effect.” To address this issue, this study selects Kunming City, China, as the study area, employs the K-means++ algorithm to cluster existing subway stations based on passenger flow, integrates multi-source spatial data, applies a random forest algorithm for optimal positive sample selection and driving factor identification, and subsequently uses a LightGBM-SHAP explainable machine learning framework to develop a predictive model for station location based on mathematical modeling. The main findings of the study are as follows: (1) Using the random forest model, 20 key drivers influencing site selection were identified. SHAP analysis revealed that the top five contributing factors were connectivity, nighttime lighting, road network density, transportation service, and residence density. Among these, transportation-related factors accounted for three out of five and emerged as the primary determinants of subway station site selection. (2) The site selection prediction model exhibited strong performance, achieving an R 2 value of 0.95 on the test set and an average R 2 of 0.79 during spatial 5-fold cross-validation, indicating high model reliability. The spatial distribution of predicted suitability indicated that the core urban area within the Second Ring Road exhibited the highest suitability, with suitability gradually declining toward the periphery. High-suitability areas outside the Third Ring Road in suburban regions were primarily aligned along existing subway lines. (3) The cumulative predicted probability within a 300 m buffer zone around each station was positively correlated with passenger flow levels. Overlaying the predicted results with current station locations revealed strong spatial consistency, indicating that the model outputs closely align with the actual spatial layout and passenger usage intensity of existing stations. These findings provide valuable decision-making support for optimizing subway station layouts and planning future transportation infrastructure, offering both theoretical and practical significance for data-driven site selection.