Modeling railway passenger overall comfort: combining PLS-SEM and interpretable machine learning
基于刺激-机体-反应框架，结合偏最小二乘结构方程模型与可解释机器学习，识别并量化了车厢环境质量感知、效价、暴露时间等因素对乘客整体舒适度的影响，发现压力质量感知贡献最大。
Based on Stimuli-Organism-Response framework, this study examines how carriage environmental quality perception (CEQP), valence, exposure time, and individual factors affect passenger comfort. Through field investigation, PLS-SEM combined with machine learning was employed to identify and quantify the contribution of factors affecting passenger overall comfort. The results indicate that CEQP, valence, exposure time, age, agreeableness, and environmental sensitivity significantly influence overall comfort. Valence mediates the relationship between CEQP and overall comfort. Pressure quality perception (PQP) has the greatest impact on overall comfort. Significant factors were incorporated into the Adaptive Boosting (ADA) model. ADA-SHAP analysis revealed that PQP made the largest contribution (27.08%) to overall comfort, exceeding other environmental dimensions, followed by agreeableness (20.37%), valence (15.10%) and exposure time (11.47%). Other influencing factors also contributed to the model to some extent. These findings provide guidance for environmental regulation, route selection, and comfort optimization of train carriages