Exploring the Nonlinear Effects of Multiple Factors on Passenger Thermal Perception at Bus Stops: Evidence from Chongqing, China

With ongoing urban warming and the increasing frequency of extreme heat events, thermal comfort at highly exposed public spaces like bus stops has attracted significant attention. However, existing studies largely rely on linear assumptions and limited environmental variables, leaving the complex, multidimensional mechanisms driving thermal perception unclear. This study investigates the nonlinear impacts of microclimate, urban morphology, and station design on passengers’ thermal perception during summer in Chongqing, China. Drawing on field measurements, questionnaire surveys, and spatial data, linear regression was first applied to estimate neutral temperatures and acceptable thermal ranges. Subsequently, an interpretable machine learning framework integrating XGBoost and SHAP analysis was developed to explore the nonlinear effects and interactions mechanisms among these variables. The results reveal a dual regulatory pattern. Mechanism variables exhibit distinct nonlinear thresholds, with wind speeds above 0.98 m/s showing cooling associations and PET values exceeding 34.70 °C corresponding to more rapid increases in thermal discomfort. Concurrently, urban morphology and station design factors contextually modify these direct effects by altering their magnitude and direction. Furthermore, significant spatial heterogeneity in thermal adaptation was observed, with neutral temperatures ranging from 23.19 to 31.01 °C. These findings provide a basis for developing adaptive and context-specific thermal environment management strategies for urban bus stops.