The development of metro-led urban underground public spaces (UUPSs) provides urban residents with extensive pedestrian-friendly activity areas sheltered from rain, snow, strong winds, and other extreme weather conditions. Although an increasing number of people are engaging in daily commercial and leisure activities within UUPSs, problems such as inconvenient transfer, poor visibility, and a lack of natural light, which indicate poor environmental quality, have led to an uneven distribution of user behavior, thereby reducing the efficiency of space utilization. Our aim in this study was to predict UUPS utilization rates by investigating the relationship between UUPS environmental attributes and user behavior characteristics and preferences. Six typical UUPSs in Wuhan were selected as case studies. User behavior data were collected using panoramic camera recordings, on-site observations, and space syntax methods, while spatial environmental factors were quantified. The correlation between various factors and multi-dimensional user behavior characteristics was discussed, and a Random Forest model was established to predict behavioral preferences. Our results indicate that accessibility and visibility are fundamental factors influencing user behavior characteristics, while the impact of landscape elements is relatively low. Regarding behavioral preference prediction, UUPS environmental features achieved the highest prediction accuracy for leisure behaviors, whereas the predictive performance for sports activities was lower. In this study, we reveal the influence of UUPS environmental factors on user behavior characteristics and predict preference patterns of different behaviors for space types. Focusing on the behavioral needs of space users, we provide a reference for the subsequent human-centered design of UUPSs.
Zhou et al. (Sat,) studied this question.
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