Dongjun Guo

The expansion of urban underground spaces has broadened the range of urban activities by accommodating functions such as transportation, retail, and entertainment. Underground shopping malls (USMs) have been widely developed as a sustainable strategy to expand urban space capacity, alleviate surface congestion, and optimize land-use efficiency. However, the development and utilization of USMs often neglect user-centered evaluations, risking mismatches between design outcomes and long-term sustainability goals such as energy efficiency, user retention, and spatial adaptability. Therefore, this study analyzes 12 typical USMs in Nanjing, China, based on environmental psychology principles, employing mixed-methods research that combines objective measurements of spatial elements with subjective user perception surveys to establish a regression model investigating correlations between USM spatial–physical environments and user comfort perception. The results show that users generally have a positive impression of the current underground environment, but there are significant differences in their subjective perceptions of the different attributes of the USMs. The USMs present a trend of humanization, human culture, and landscape in terms of spatial characteristics. These improvements are critical for fostering long-term sustainable use by minimizing vacancy rates and retrofitting needs. The findings reveal that the human-centric comfort level of the USMs is largely determined by multi-dimensional architecture-space features, as well as personal and social activity level features. Building on these insights, we propose actionable strategies to advance sustainable USM design, prioritizing adaptive reuse, energy-efficient layouts, and culturally resonant esthetics. This work clarifies the direction of USM design optimization and improvement from the perspective of users’ subjective perception and provides a theoretical foundation for aligning underground development with global sustainability frameworks like the UN SDGs.

Landscape occupation and green space reduction have decreased livability and hindered the sustainable development of cities. The urban landscape affects the quality of life and physical and mental state of the public, and urban underground space utilization is important for improving the urban landscape. Therefore, underground space utilization must be explored from a public perspective. Referring to typical underground municipal, transportation, and public space projects, an online questionnaire survey of 377 Nanjing residents was performed to construct a structural equation model investigating their perceptions and preferences regarding underground space usage to improve the landscape, focusing on the links between their preferences and opinions. Underground works that provide diverse open spaces were considered to have the most positive impact on the landscape (82.8%). The aesthetic value of the surface landscape (83.5%) and the vitality of historical sites (82.1%) were the most significantly affected, whereas stock underground space had no significant effect on landscape protection. In terms of age, profession, and place of work, the respondents demonstrated heterogeneous preferences. These findings indicate that targeted publicity and public participation are important to strengthen the cooperative utilization of space and realize the potential of the underground space, helping address actual requirements and develop sustainable cities.

The growth simulation of urban underground space (UUS) under the consideration of ecological constraints can effectively reveal the characteristics and trends of UUS changes, and provide a basis for planning the construction of sustainable and livable ecological cities. Therefore, this study considers urban ecological space as a constraint mechanism for UUS development and conducts a simulation study of the dynamic and complex UUS growth process, with a view toward guiding UUS planning under a long-term overall vision. In this study, a patch-based cellular automaton (CA) model is constructed to simulate the dynamic and complex growth process of UUS, subject to the ecological constraints generated by the agent-based land allocation optimization model. The spatial drivers of UUS growth simulation are determined based on the Random Forest (RF) algorithm. The results of the research case in Tianfu New District, Chengdu City, demonstrate that UUS expansion with ecological constraints exhibits sustainable characteristics. However, the growth rate of the UUS development scale is significantly lower when ecological constraints are present compared to when they are not. This study’s results contribute to urban management by finding a balance between UUS development and ecological space conservation, and providing theoretical support for rational UUS planning and decision making in the construction of low-carbon cities.

Gas distribution mapping (GDM) is widely used to evaluate the efficiency of environmental control systems and locate pollution sources. In this study, the optimized fan-beam measurement geometries and tomography algorithms were proposed for mapping the gas distributions indoors accurately. The airflow and gas distributions in two different types of rooms were predicted using the validated computational fluid dynamics (CFD) method. Then, the performances of five algorithms, including iterative and Tikhonov regularization methods, were quantitatively compared, and the measurement geometries (i.e., number of optical paths, grid resolution and laser emitter amount) were investigated. The effect of measurement noise on reconstruction accuracy was also analyzed. The results shown the simultaneous algebraic reconstruction technique (SART) and maximum likelihood expectation maximization (MLEM) algorithms were better than the other algorithms, and their averaged relative root mean square errors (RRMSEs) were at least 39% lower with the iteration residual of 0.0015. The optimum grid resolution and number of optical paths were 10×20 grid cells and 200 paths, respectively. In addition, the sensitivity of the SART and MLEM algorithms to noise was almost the same, and the addition of the laser emitter could improve the reconstruction accuracy of most cases in this study.

Rational use of urban underground space (UUS) and public transportation transfer underground can solve urban traffic problems. Accurate short-term prediction of passenger flow can ensure the efficient, safe, and comfortable operation of subway stations. However, complex and nonlinear interdependencies between time steps and time series complicate such predictions. This study considered temporal patterns across multiple time steps and selected relevant information on short-term passenger flow for prediction. A hybrid model based on the temporal pattern attention (TPA) mechanism and the long short-term memory (LSTM) network was developed (i.e., TPA-LSTM) for predicting the future number of passengers in subway stations. The TPA mechanism focuses on the hidden layer output values of different time steps in history and of the current time as well as correlates these output values to improve the accuracy of the model. The card swiping data from the Hangzhou Metro automatic fare collection system in China were used for verification and analysis. This model was compared with a convolutional neural network (CNN), LSTM, and CNN-LSTM. The results showed that the TPA-LSTM outperformed the other models with good applicability and accuracy. This study provides a theoretical basis for the pre-allocation of subway resources to avoid subway station crowding and stampede accidents.

Convective heat transfer on the exterior surface of the building envelope is an important component for building energy consumption. The calculation of energy consumption depends on the convective heat transfer coefficient (CHTC) of the exterior surface of the envelope. The existing research does not fully consider the effects of the airflow field around the building on the CHTC of different envelope exterior surfaces. In this paper, the relationships between the CHTC and influence factors were investigated for the isolated building. Response surface methodology (RSM) and support vector machine (SVM) algorithms were integrated with the single building simulation to build the fitting formulas. Then, the fitting correlation between CHTC and different influencing factors was validated by the heating building simulation. The results showed that the CHTC of the building exterior surface was related to the wind velocity, wind direction and temperature difference. Additionally, the fitting formulas had good accuracy in calculating the CHTC under different conditions. The SVM algorithm (averaged error: 3.34%) performed slightly better than the RSM algorithm (averaged error: 4.84%).