Bo Wan

The urban road spatial structure is a crucial and complex component of urban design. Generative design models, such as the Stable Diffusion model, can rapidly and massively produce designs. However, the opacity of their internal architecture and the uncertainty of their outcomes mean that the results generated do not meet specific disciplinary assessment criteria, thus limiting their widespread application in planar design and planning. Additionally, traditional software processes targeting specific indicators are time-consuming and do not allow for rapid evaluation. To address these challenges, we utilized several areas of the road spatial structures in six cities and their corresponding four space-syntax parameters as training samples. We simultaneously trained two models: one is a LoRA Model based on the Stable Diffusion architecture used for generating road networks similar to those of various city road spatial structures; the other is a CoAtNet Model (Convolution + Transformer) used as an evaluation model to predict the space-syntax parameters of road structures and calculate the Mean Absolute Percentage Error (MAPE) relative to real urban samples. Subsequently, by linking these two models end-to-end, we were able to filter out generated samples with the smallest MAPE, thereby enhancing the structural similarity between the generated results and the actual urban road spatial structures. This process of rapid generation and swift evaluation of network configurations marks a critical advancement towards better performance and more customized design solutions.

The integration of railway stations into urban environments necessitates a detailed examination of their vitality and influencing factors. This study assesses urban vitality around four major railway stations in Beijing utilizing a variety of analytical models including Ordinary Least Squares, Geographically Weighted Regression, Multi-Scale Geographically Weighted Regression, and machine learning approaches such as XGBoost 2.0.3, Random Forest 1.4.1.post1, and LightGBM 4.3.0. These analyses are grounded in Baidu heatmaps and examine relationships with spatial form, functional distribution, and spatial configuration. The results indicate significant associations between urban vitality and variables such as commercial density, average number of floors, integration, residential density, and housing prices, particularly in predicting weekday vitality. The MGWR model demonstrates enhanced fit and robustness, explaining 84.8% of the variability in vitality, while the Random Forest model displays the highest stability among the machine learning options, accounting for 76.9% of vitality variation. The integration of SHAP values with MGWR coefficients identifies commercial density as the most critical predictor, with the average number of floors and residential density also being key. These findings offer important insights for spatial planning in areas surrounding railway stations.

Urban rail transportation is an important public transportation network for realizing the dual carbon strategy and sustainable transportation development. A top-stream station is an important node in this network. This paper focuses on 47 top-stream station sites during the peak hours of workdays from 2018 to 2020 in Beijing (26 inbound and 22 outbound, with Beijing South Station as a double-stream station) and collects and analyzes information such as station area spatial texture, spatial organization efficiency, land use function, land use mix, POI (Point of Interest) distribution and functional mix, construction intensity, and population heat distribution. Through an analysis of the network topological structure characteristics of each station area, comparison of spatial efficiency differences, analysis of land use function composition and mix characteristics, and distribution of spatial construction intensity, this paper discusses the relationship between the spatial structure, spatial function, spatial intensity characteristics and key indicators of the built environment of station areas from the perspective of urban design. The conclusion shows that there is a close relationship between the function, structure, and strength of the overall built environment of the station domain at the theoretical level. The regression test, to some extent, confirms the close relationship between key indicators and expands the indicator system for measuring the fit relationship. The comparison between general station sites and headstream station sites shows that the fit relationship of indicators for headstream station sites is not completely the same as that for general station sites, indicating that the influencing factors for headstream station sites are diverse. This reminds investment, design, construction, and management teams in practice that the measurement and planning of the built environment space structure of the station domain should be based on local conditions and be closely related to the topological structure of the station domain’s rail network and road network structure. At the same time, whether the stability of the indicator system and the specific R-squared value have differences in various cities requires further verification. This paper explores and tries to raise questions about the research methodology of the built environment space structure, the measurement of the station domain, and the concepts of station-city coordination and development.