The construction of new urban areas has become one of the important ways for urban regeneration throughout the process of polycentric urban development. New urban area construction following transit-oriented development (TOD) conception advocates development around the public transportation stations. To gain more benefits around the stations, it is necessary to conduct an ex-ante evaluation of TOD projects in the new urban area. The Node-place (NP) model is a commonly used method for TOD evaluation and classification, which essentially designs an analytical framework for assessing station areas in both transport (node) and land use (place) aspects. The objective of our study is twofold. First, based on the original NP model, we propose the node-place-system support (NPS) model by introducing a novel evaluation dimension—system support—which quantitatively describes the relationship between local stations and the overall urban system. Second, taking advantage of multi-sourced data and Geographical Information System (GIS) techniques, we employ the proposed NPS model to evaluate and classify the metro stations in the Tianfu New Area of Chengdu, China. The results show that most stations present a balanced relationship between transport and land use performances. However, for a fraction of these balanced stations, we observe a mismatch between the system support and NP performances. Accordingly, we identify the system-mismatched stations and provide targeted improvement strategies for urban design.

Urban Agglomerations (UAs), as the primary form of China’s new urbanization and an essential spatial unit for promoting coordinated regional development, play a crucial role in measuring the sustainable and healthy development of urban clusters through the assessment of spatial network connections among cities within the UAs. Taking the 16 prefecture-level cities of the Chengdu-Chongqing Urban Agglomeration (CCUA) as the research subject, this study constructs six types of element flow networks, including population flow, logistics, and information flow. Employing network visualization analysis, the Self-Organizing Maps (SOM) neural network machine learning models, and Quadratic Assignment Procedure (QAP) relational regression models, the research analyzes the spatial network characteristics of the CCUA from the perspective of multi-dimensional element flows and explores the influencing factors of the UA’s connectivity pattern. The results indicate that: The various element flows within the CCUA exhibit a bipolar spatial network characteristic with Chengdu and Chongqing as the poles. In the element network grouping features, a multi-centered group differentiation structure is presented, and the intensity of internal element flow varies. Based on the results of the SOM neural network machine learning model, the connectivity capabilities of cities within the CCUA are divided into five levels. Among them, Chengdu and Chongqing have the strongest comprehensive connectivity capabilities, showing a significant difference compared to other cities, and there is an imbalance in the connectivity capabilities between cities. In terms of the influencing factors of the urban connectivity pattern within the CCUA, the differences in permanent population size and urbanization rates have a significant negative impact on the information flow network, technology flow network, and capital flow network. The differences in the secondary industrial structure and public budget expenditures have a significant positive impact on the intensity of inter-city element flows, and the differences in per capita consumption expenditures have a significant negative impact, collectively influencing the formation of the spatial connectivity pattern of the CCUA. The findings of this study can provide a scientific basis for the construction and optimization of the spatial connectivity pattern of the CCUA.

An urban central metro station area is a core hub within the high-quality Transit-Oriented Development (TOD) model. This study explores users’ perceptions of built environments around urban central metro stations to investigate the critical determinants of user satisfaction and proposes strategies to enhance the quality of these environments. First, a comprehensive perception system, including location situation, field environment, and urban aesthetics, was developed through literature reviews and expert consultation. Secondly, three typical central metro station areas in Chengdu were selected as study cases, and 425 questionnaires were collected from August to October 2024. The data were analyzed using a structural equation model (SEM) to reveal the impact of built environment perception on overall satisfaction. The results indicate that the field environment has the strongest direct influence on satisfaction. Urban aesthetics impacts satisfaction both directly and indirectly, making its overall effect the most significant. While the location situation does not directly affect satisfaction, it indirectly influences satisfaction through its impact on the field environment and urban aesthetics. Subsequently, based on the satisfaction performance and SEM outcomes, an importance–performance analysis (IPA) was conducted to identify specific areas needing enhancement. Finally, we integrated environmental assessments with the above findings and put forth strategic recommendations to enhance the quality of the built environment.

As metropolitan regions expand, railway station areas (RSAs) encounter unprecedented challenges and opportunities regarding sustainability, influenced by transportation, location, and urban factors. However, there is currently a lack of models that assess the sustainable development status of RSAs from a macro perspective of regional integration. This study innovatively incorporates the dimension of city support into the traditional Node–Place (NP) model, constructing the “Node–Place–Support” (NPS) model. This model consists of three dimensions and 21 indicators, utilizing the entropy weight method and AHP methods to conduct a comprehensive assessment of the sustainable development levels of 12 RSAs within the Guangzhou metropolitan area. The results indicate that, compared to the traditional NP model, the NPS model effectively reveals the unique factors influencing the development of RSAs within the metropolitan area, emphasizing the collaborative importance of city support, transportation capacity, and station attractiveness. Furthermore, according to the new classification method of the NPS model, the 12 research samples can be categorized into four categories and seven subcategories. Lastly, optimization strategies should be tailored to the specific development status of RSAs and the characteristics of the city. This study presents a new paradigm for assessing and enhancing RSA sustainability, advocating for strategies tailored to local conditions.

Background and objectives: The urban underground complex integrates comprehensive architecture and subterranean space, presenting physical and psychological complexities. To mitigate the negative experiences associated with this complexity, we need to explore, in depth, how the built environmental elements of underground complexes affect user perception to design a comfortable, healthy, and biophilic underground space. Methods: We selected Tianfu Square, a typical underground complex in Chengdu, China, as the empirical case. We identified 26 key environmental indicators affecting user perception and designed Kano and satisfaction questionnaires based on these indicators. A total of 476 questionnaires were distributed to collect data on users’ attitudes towards changes in element quality and their satisfaction levels. By integrating the Kano model and IPA method, we defined the perceptual attributes of elements based on their impact on user experiences and assessed the alignment between element attributes and satisfaction. Results: We categorized the elements into five perceptual types: attractive, one-dimensional, indifferent, must-be, and reverse attributes, and assigned corresponding importance levels. We then compared the importance and performance scores of these elements, evaluated their satisfaction alignment, identified elements needing optimization, and proposed improvement strategies. Implications: This research provides valuable guidance for designers on how various built environment elements in underground spaces influence user perceptions, with practical implications for urban underground complex planning and design.

Railway stations are key nodes connecting urban space with regional transport networks. In the context of compact city development and station–city integration, developing railway stations as compact, three-dimensional composite spaces is an important means of improving land-use efficiency and coordinating transport and urban functions. To quantitatively evaluate the compactness of three-dimensional composite space and the level of station–city coordination, this study develops an evaluation framework comprising 26 indicators across three dimensions: functional configuration, spatial organization, and efficiency and accessibility. Following indicator selection and data standardization, the entropy weight method is used to derive objective weights, which are incorporated into a TOPSIS model. Eighteen representative railway stations are evaluated and ranked using the model, while their development levels are classified using the rank-sum ratio (RSR) method. The results show that Kyoto Station, Osaka Station, Shanghai Hongqiao Railway Station, and Berlin Central Station exhibit relatively high spatial compactness in their three-dimensional composite spaces, whereas Milan Central Station and London King’s Cross Station show comparatively weaker levels. The five clusters identified through K-means clustering were interpreted in light of the evaluation results and spatial characteristics, and subsequently consolidated into four spatial development types: High-Compactness Urban Comprehensive Type, High-Compactness Underground Station Type, Moderate-Compactness Transport-Dominant Type, Low-Compactness Regionally Oriented Type. The study identifies key spatial factors influencing the three-dimensional integrated development of railway stations. The findings provide a quantitative basis for the intensive space use, station–city coordination strategies, and architectural design optimization.