Kuntao Hu

With the acceleration of urbanisation and the increased utilisation of underground space, providing a comfortable and healthy environment in public underground areas has emerged as a significant research topic. This study constructs a comprehensive decision-making framework for underground space environments by integrating human perception evaluations with physical environmental parameters. Using Shanghai Wujiaochang as a case study, field data collection and questionnaire surveys were conducted to evaluate key factors such as temperature (22.63 °C–26.39 °C), wind speed (0.26 m/s–0.67 m/s), and sound levels (59.68 dB–61.21 dB) for commercial-oriented spaces, and 63.15 dB–75.45 dB for transport-oriented spaces) to users’ perceived experiences. The appropriate ranges for key parameters were identified through single-indicator fitted regression analysis and the XGBoost machine-learning model, revealing the relationship between environmental parameters and human perception. The results indicated significant differences in user needs across various functional spaces, with commercial-oriented areas emphasising environmental attractiveness and comfort, while transport-oriented spaces prioritised access efficiency and safety. This study provided quantitative design benchmarks for underground spaces’ dynamic regulation and sustainable management, proposing a precise and adaptive environmental decision-making framework that combines physical parameters with user-perception feedback.

Urban vitality is a critical driver of sustainable urban development, significantly contributing to the enhancement of human well-being. A thorough and multidimensional comprehension of urban vitality is essential for shaping future urban planning and policy-making. This study, focused on Chengdu, proposes a framework for assessing various dimensions of UV through the distribution of milk tea and coffee shops. Using random forest and multi-scale geographically weighted regression models, this study investigates the factors influencing urban vitality from both mathematical thresholds and spatial heterogeneity, and develops spatial maps of future vitality to inform targeted urban strategies. The results show that (1) the milk tea index is effective in capturing population vitality, while the coffee index is more closely associated with economic vitality and urban renewal; (2) office buildings (13.46%) and commercial complexes (13.70%) have the most significant impact on both economic and population vitality, while the importance of transportation factors has notably decreased; (3) the influence of these factors demonstrates spatial heterogeneity and nonlinear relationships, with subway station density of 0.5–0.8 stations per kilometer being optimal for stimulating both types of vitality. The minimum threshold for economic vitality in a given unit is a housing price exceeding 6000 RMB/m²; (4) the future vitality map suggests that urban planners should pay greater attention to non-central districts with high development potential. Moreover, spontaneous social interactions and consumer behaviors stimulated by various shops are critical components of urban vitality. In designing the physical environment and urban spatial forms, special attention should be given to enhancing the attractiveness of physical spaces and their capacity to accommodate social interaction.

With the acceleration of urbanisation and the increase in underground space use, how to provide a comfortable and healthy environment in underground space has become an important research topic. This study constructed an environmental decision-making model for underground space by integrating human perception evaluation and physical environment factors. The study analysed the influence of physical environment parameters on users' perceived experience through field data collection and questionnaire surveys. The data were in-depth analysed using single-indicator fitted regression analysis and XGBoost machine learning model. The results reveal the significant influence of physical parameters such as temperature, humidity, illuminance and wind speed on the comfort of users of underground spaces and determine the range of appropriateness of these physical environment parameters. The results provide a reliable theoretical basis for optimising the design and management of underground spaces and help to enhance the environmental quality and user experience of underground spaces.

Current research on rural houses in China mainly focuses on improving energy efficiency, with relatively few studies addressing energy-saving measures and enhancing thermal comfort for residents. Therefore, this paper focuses on existing rural houses in Nantong City, Jiangsu Province, as the research object. Through on-site measurements and questionnaire surveys, it was found that the average indoor temperature of rural houses is 28.5 °C in summer and below 10 °C in winter, failing to meet the comfort needs of the villagers. To further study human thermal comfort, a linear regression method was used to establish an indoor Mean Thermal Sensation (MTS) model for Nantong’s rural houses. The neutral temperature in summer was found to be 26.46 °C, and an adaptive thermal comfort model for rural residents in the Nantong area was established. Through single-factor simulation and orthogonal experiments, the optimal comprehensive energy-saving renovation scheme was proposed. Finally, a typical rural house in Zhangzhuang Village was used as a case for building renovation practice. After the renovation, the number of thermal comfort hours increased by 145 h per year, the thermal comfort compliance rate reached 47.07%, and the overall energy-saving rate was 57.41%.

Developing and utilizing underground space is a vital direction for urban growth. Underground commercial streets, as a significant component of underground space accommodating extensive human social activities, consequently necessitate the creation of human-scale spaces. In the evolution of urban design development towards more significant, more terrific refinement, applying architectural theories and excessively subjective designs has resulted in a deficient human-centered design and a disordered spatial environment. This study merges environmental psychology and architectural theory to determine the appropriate length of spatial scale. Two experiments focusing on spatial perception evaluation were conducted using a virtual experimental platform that featured varying dimensions of spatial scale combinations. These quantified combinations were correlated with the perception evaluation, and a regression analysis was employed to identify appropriate scale ranges, which were superimposed with the range of length selection. Finally, the optimal length and scale combination for underground commercial street spaces was established, providing a reference for the human-centered design of these environments.

Developing and utilising of underground space is an vital direction for urban growth. Underground commercial streets, as a significant component of underground space accommodating extensive human social activities, and consequently necessitate the creation of human-scale spaces. In the evolution of urban design development towards greater refinement, applying architectural theories and excessively subjective designs has resulted in a deficiency of human-centered design and a disordered spatial environment. This study merges environmental psychology and architectural theory to determine the appropriate length of spatial scale. Two experiments focusing on spatial perception evaluation were conducted using a virtual experimental platform that featured varying dimensions of spatial scale combinations. These quantified combinations were correlated with perception evaluation, and regression analysis was employed to identify appropriate scale ranges, which were superimposed on the range of length selection. Finally, the optimal length and scale combination for underground commercial street spaces were established, providing a reference for the human-centered design of these environments.

The color of urban streets plays a crucial role in shaping a city’s image, enhancing street appeal, and optimizing the experience of citizens. Nevertheless, the relationship between street color environment and residents’ perceptions has rarely been deeply discussed, and most of the existing studies adopt qualitative methods. To accurately and effectively assess the connection between street color environment and residents’ emotional perceptions, this paper introduces a quantitative research framework based on multi-source data called “Color Emotion Perception with K-Means, Adversarial Strategy, SegNet, and SVM (CEP-KASS)”. By combining K-Means unsupervised machine learning and SegNet computer vision techniques, it captures and analyzes visual elements and color data from Baidu Street View Images (BSVI). It then employs a human–machine adversarial scoring model to quantify residents’ perceptions of BSVI and uses the support vector machine regression model to predict the final perception scores. Based on these data, a Pearson correlation analysis and visual analysis were conducted on the elements and color in the urban environment. Subsequently, the streets were classified based on perception frequency and perception scores by integrating multi-source data, and areas within the third ring of Xuzhou City were selected for validating the research framework. The results demonstrate that utilizing street-view images and the CEP-KASS framework can quantitatively analyze urban color perception and establish a connection with residents’ emotions. In terms of color perception, red, orange, and blue all have a strong positive correlation with the interesting score, whereas black is positively correlated with a sense of safety. Regarding color attributes, low-saturation bright colors result in higher fun perception scores in urban spaces; too low saturation and brightness can affect their attractiveness to residents; brightness has an inverse relationship with the perception of safety, and adjusting brightness inversely can improve the perceived safety experience in certain urban external spaces. The street classification criteria based on perception frequency and perception scores proposed herein can provide references for planners to prioritize color transformation decisions, with a priority on emulating HSHF streets and transforming LSHF streets. When formulating color planning, suggestions for color adjustment can be given based on the correlation study of color with visual elements and perception scores, optimizing urban residents’ spatial perception and their emotional experiences. These findings provide robust theoretical support for further enhancing the visual quality of streets and refining urban color planning.