Exploring the Influence of Environmental Characteristics on Emotional Perceptions in Metro Station Spaces

With the rapid expansion of urban metro networks, metro stations, as critical public spaces, not only influence transit efficiency and spatial layout optimization but also play a vital role in shaping users’ emotional experiences. This study examines metro station spaces as its primary research subject, exploring the correlation between physical environmental features and emotional perception within the framework of environmental psychology theory. This study adopts an innovative approach by integrating deep learning-based affective computing methods with semantic segmentation techniques in computer vision to systematically evaluate the impact of various physical environmental features and functional spaces on users’ emotional perceptions across multiple dimensions. The study provides empirical evidence for assessing and interpreting the relationship between environmental features and emotional perception, thereby enhancing the reliability of the research. The findings, quantified through deep learning methods, identify key factors influencing various emotional perception scores in metro stations. These insights will assist practitioners in gaining a deeper understanding of how metro station spaces impact users’ emotional experiences and can be applied to early-stage design and later-stage optimization of metro station spaces.

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