Towards inclusive underground public transportation: Gender differences on thermal comfort

Abstract

As urbanization and societal needs grow, the demand for inclusive, efficient, and sustainable underground public transportation has increased, necessitating a greater focus on enhancing passengers' thermal comfort. However, limited research has addressed the subway station environment, and restricted datasets are challenging to build an accurate nonlinear model. Therefore, this study proposes a thermal comfort data generation algorithm based on 5161 measured data via the Variational Autoencoder (VAE) framework to break data collection constraints and increase thermal comfort model accuracy. By employing a gradient boosting algorithm on the extensively generated dataset, this study investigated factors influencing thermal comfort in subway stations, emphasizing individual gender differences and dynamic environmental variables. The VAE model enhanced the thermal comfort dataset, resulting in a 29% and 35% increase in male and female test set accuracy, respectively. The study suggests that males with a BMI over 27 were more sensitive to temperature. As for the gender-friendly environmental features, people feel most comfortable at temperatures between 24 and 29 °C and relative humidity levels between 20% and 70%. Additionally, the study reveals that a stable environment promotes enhanced thermal comfort for passengers. These insights contribute to developing sustainable, equitable, and user-friendly underground public transportation systems.