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Determining fire source in underground commercial street fires is critical for fire analysis. This paper proposes a method based on temperature and machine learning to determine information about fire source in underground commercial street fires. Data was obtained through consolidated fire and smoke transport (CFAST) software, and a fire database was established based on the sampling to ascertain fire scenarios. Temperature time series were chosen for feature processing, and three machine learning models for fire source determination were established: decision tree, random forest, and LightGBM. The results indicated that the trained models can determine fire source information based on processed features, achieving a precision exceeding 95%. Among these, the LightGBM model exhibited superior performance, with macro averages of precision, recall, and F1 score being 99.01%, 98.45%, and 99.04%, respectively, and a kappa value of 98.81%. The proposed method for determining the fire source provides technical support for grasping the fire situation in underground commercial streets and has good application prospects. [ABSTRACT FROM AUTHOR]

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.

The complex spatial environment of underground commercial street spaces will affect users' behavior and information needs. As a medium to coordinate the interaction between the underground commercial street space environment and people, guide signs can provide useful information for users. However, the visual saliency of guide signs is the fundamental premise for determining the transmission of information to users. Based on field research, this study identified and examined the factors influencing the significance of guide signs in underground commercial streets from the user's perspective using the order relation analysis method (G1 method) and with the help of screen-based eye tracking and virtual reality (VR) eye-tracking technology, In addition, we explored the design relationship between critical influencing factors and the space between underground commercial streets, and the visual significance of the differences between each important influencing variable. The study showed that the set position, set height, and design color of underground commercial street guide signs are essential factors in their visual prominence. The prominence was more significant when the position of guide signs was located in the middle and upper area of the space, and the prominence was more significant when the set height was 2.56~2.75 m and 3.12~3.31 m. This study can provide data and theoretical support for the visual saliency design of underground commercial street guide signs and provide a reference for the humanized design of underground commercial street guide signs for public facilities in cities. [ABSTRACT FROM AUTHOR]

The current study evaluates the influence of different exit quantity and location on evacuation efficiency of underground commercial street, and analyzes the difference of evacuation efficiency. The author selected the same underground commercial street design, with different locations layout of various exports. The improved RVO is used to generate the regional evacuation path tree according to the evacuation hierarchy of underground commercial street. A node monitoring mechanism is introduced to calculate and store the evacuation path of personnel nodes to prevent “penetration” and simulation stagnation. This paper analyzes the influence of the number and location of evacuation exits on evacuation efficiency by comparing the evacuation time of personnel, and puts forward relevant design principles. The experimental results show that under the same condition of layout and the total width of the outlet, simply increasing the number of outlets cannot effectively improve the fire evacuation efficiency of the underground commercial street. With the same number of exits, symmetrical distribution of both sides with the exit location is more conducive in improving the evacuation efficiency. This paper is expected to provide suggestions for the design of the number and location of the underground commercial street exit.

The corner space, an important area of underground commercial streets, not only converts space functions but also exerts a great impact on the space atmosphere by transforming the environmental quality of the commercial street space. Based on corner space investigations of several underground commercial streets in China, this paper constructs a realistic scene model using virtual reality technology and screens. This paper classifies the corner space elements of underground commercial streets through preliminary experiments. Based on the conclusions, different morphological models of the corner space were constructed by orthogonal experiments and virtual reality technology combined with psychology. A semantic differential was used to quantitatively evaluate and analyze the spatial experience cognition of the subjects. This enabled an analysis of the of underground commercial street corners’ relationship to the component elements and the visitors’ psychological perception of their spatial form. [ABSTRACT FROM AUTHOR]