Transit-oriented development (TOD) has been widely adopted as a primary urban planning strategy to better integrate transit and land use; further, the pedestrian-oriented perspective has been receiving increasing attention. However, most studies so far have only focused on few features and fail to capture comprehensive perceptions in the transportation (T), pedestrian-oriented accessibility (O), and urban development (D) dimensions. New emerging urban datasets provide a more refined and systematic approach to quantify the characteristics of metro station areas. This study offers a more efficient and convenient process and comprehensive approach to measure TOD performance. With a combination of traditional data collected by an official department, high-resolution open data, and innovative technology, large-scale analyses of 347 metro stations in Shanghai were conducted. Fifteen indicators for T, O, and D were chosen to categorize TOD performance into five clusters. Radar charts, boxplots, and colored maps were used to display numerous quantitative factors for each type. Combining the results with the Shanghai Comprehensive Plan (2017–2035) showed that the majority of Cluster 4 is located at the center of the Five New Towns. The correlation analysis between ridership and TOD performance showed that the transportation dimension indicator has a strong correlation with daily ridership, followed by the O and D indicators. Moreover, ridership per capita was found to be affected by resident density, employment density, O value, and D value, whereas no significant correlation was found between ridership per capita and T value. Population plays a pivotal role in metro passenger traffic, indicating ridership per capita had a high, strong correlation with resident density, with R = 0.658 for weekdays and R = 0.654 for weekends. This study reinterpreted the node-place method and 5Ds framework, resulting in a renewal method with new datasets and analysis tools. It contributes to providing pedestrian-oriented TOD planning methodology for urban planners and policymakers by combining T, O, and D dimensions and visualizing the results with current urban planning.

Transit-oriented development (TOD) construction is considered critical for economic growth and the population’s daily well-being in suburban development. However, there are few empirical evaluations of TOD performance and typology in suburban areas of high-density cities. In this study, we selected 23 metro stations in the five new towns in Shanghai as research objects to understand their TOD characteristics. By proposing a data-driven framework built on points of interest (PoIs) to characterize urban functions of metro stations in new towns, four thematic topic functions were extracted by implementing Latent Dirichlet Allocation (LDA) topic modeling. Five types of stations were revealed through a hierarchical cluster analysis based on their main functions. Then, an extended “Node-Place” model with a third “design” dimension was applied to classify TOD typologies. After establishing an evaluation framework by calculating the results of 15 indicators, five TOD topologies were identified through hierarchical cluster analysis. In addition, results from the ANOVA analysis showed that the classification according to thematic topics changes according to the “place” dimension indicators. Ultimately, the identified urban thematic function types and TOD types provide a useful tool for planners and governors to diagnose common problems and design targeted strategies in new towns.

In the context of intelligent and fine-grained urban governance, the coordinated configuration of spatial capacity and locational value has become a key proposition for optimizing urban resource utilization. Using Shanghai as a case study, this paper represents spatial capacity with population density and locational value with land values. By quantifying the degree of spatial mismatch between population density and land values, the study reveals the imbalance between spatial capacity and locational value. First, the research calibrates the population grid data to obtain the population distribution within the study area; subsequently, a composite land value prediction model is constructed to compute the land value distribution across the study region; finally, the spatial mismatch index is calculated using the regression residual method to quantify the degree to which population density deviates from land values. The results indicate that there is a significant spatial mismatch between population density and land values in Shanghai, which unveils an imbalance in the allocation of spatial capacity within the city. This framework can be integrated into smart city digital twins and real-time monitoring platforms, providing 100 m resolution decision support for spatial resource optimization.

Uneven distribution of passenger flows across metro entrances/exits is prevalent. Previous studies primarily examined built-environment factors influencing established exit-level flow disparities from an objective perspective. This study, however, incorporates passengers’ subjective preferences to provide a more comprehensive understanding of the environment–behavior mechanisms shaping entrance/exit choice. A visual stated preference method was employed to construct choice scenarios with 12 environmental attributes grouped into two complementary dimensions of path accessibility and environmental quality. Multinomial logit models were then applied to estimate passengers’ entrance/exit choice preferences, and the results informed a two-dimensional exit-level evaluation framework, demonstrated through a case study of Xujiahui Station in Shanghai. Compared with empirical studies, this study employs a discrete choice experiment, which circumvents the modeling challenges posed by the limited number of entrances/exits at individual stations and systematically integrates a range of station-internal and urban environmental attributes into a unified utility-based framework to evaluate their contributions. The results reveal the relative importance of various environmental attributes, together with their varying levels, in shaping passengers’ entrance/exit choices and indicate that path accessibility exerts a stronger influence on decision-making than environmental quality. The proposed exit-level evaluation framework also serves as a practical tool for assessing resource allocation status at individual station areas, providing a foundation for policy formulation to support more human-centered, equitable, and fine-grained station-area governance.

Measuring similarity in urban spatial networks is key to understanding cities as complex systems. Yet most existing methods are not tailored for spatial networks and struggle to differentiate them effectively. We propose GCA-Sim, a similarity-evaluation framework based on graph cellular automata. Each submodel measures similarity by the divergence between value distributions recorded at multiple stages of an information evolution process. We find that some propagation rules magnify differences among network signals; we call this "network resonance." With an improved differentiable logic-gate network, we learn several submodels that induce network resonance. We evaluate similarity through clustering performance on fifty city-level and fifty district-level road networks. The submodels in this framework outperform existing methods, with Silhouette scores above 0.9. Using the best submodel, we further observe that planning-led street networks are less internally homogeneous than organically grown ones; morphological categories from different domains contribute with comparable importance; and degree, as a basic topological signal, becomes increasingly aligned with land value and related variables over iterations.