Urban vibrancy is described by the activities of residents and their spatio-temporal dynamics. The metro station area (MSA) is one of the densest and most populous areas of the city. Thus, creating a vibrant and diverse urban environment becomes an important goal of transit-oriented development (TOD). Existing studies indicate that the built environment decisively determines MSA-level urban vibrancy. Meanwhile, the spatio-temporal heterogeneity of such effects requires thoroughly exploration and justification. In this study, we first apply mobile signaling data to quantify and decipher the spatio-temporal distribution characteristics of the MSA-level urban vibrancy in Chengdu, China. Then, we measure the built environment of the MSA by using multi-source big data. Finally, we employ geographically and temporally weighted regression (GTWR) models to examine the spatio-temporal non-stationarity of the impact of the MSA-level built environment on urban vibrancy. The results show that: 1) The high-vibrant MSAs concentrate in the commercial center and the employment center. 2) Indicators such as residential density, overpasses, road density, road network integration index, enterprise density, and restaurant density are significantly and positively associated with urban vibrancy, while indicators such as housing price and bus stop density are negatively associated with urban vibrancy. 3) The GTWR model better fits the data than the stepwise regression model. The impact of the MSA-level built environment on urban vibrancy shows a strong non-stationarity in both spatial and temporal dimensions, which matches with the spatio-temporal dynamic patterns of the residents’ daily work, leisure, and consumption activities. The findings can provide references for planners and city managers on how to frame vibrant TOD communities.

Transit-oriented development (TOD) has been regarded as an effective way to improve urban vibrancy and facilitate affordable, equitable, and livable communities in metro station areas (MSAs). Previous studies placed great attention on the interplay between the MSA-level built environment and overall human activities while neglecting the heterogeneity among different age groups. To address this gap, we leverage the mobile phone signaling data to quantify the spatio-temporal distribution of the MSA-level human activities among different age groups as measured by the vibrancy index (VI). Furthermore, we investigate the impact of the MSA-level built environment on the VI and its intergenerational differences by employing multiple linear regressions based on multi-sourced data. To this end, Chengdu—a TOD-thriving megacity in China—is chosen as a case study. The results indicate that: (1) Residential and bus stop density are positively associated with the VI. And the magnitudes of the correlation coefficients are similar among different age groups. (2) Distance to CBD is negatively associated with the VI of teenagers (12–18 years), middle-aged adults (40–59 years), and older adults (above 60 years) but unrelated to the VI of young adults (19–39 years). (3) Employment density is positively associated with the VI of young and middle-aged adults but insignificantly associated with the VI of teenagers and older adults. (4) The correlations between the floor area ratio and the VI are positive for all age groups. As age increases, the significance of such correlations becomes more pronounced. (5) Streetscape greenery shows a more significant positive correlation with the VI of teenagers and older adults as compared to those of young and middle-aged adults. (6) Significant negative correlations exist between housing price and the VI of different age groups. The findings can inform the development and design of vibrant TOD communities.

Monocular image depth estimation is crucial for indoor scene reconstruction, and it plays a significant role in optimizing building energy efficiency, indoor environment modeling, and smart space design. However, the small depth variability of indoor scenes leads to weakly distinguishable detail features. Meanwhile, there are diverse types of indoor objects, and the expression of the correlation among different objects is complicated. Additionally, the robustness of recent models still needs further improvement given these indoor environments. To address these problems, a detail‒semantic collaborative network (DSCNet) is proposed for monocular depth estimation of indoor scenes. First, the contextual features contained in the images are fully captured via the hierarchical transformer structure. Second, a detail‒semantic collaborative structure is established, which establishes a selective attention feature map to extract details and semantic information from feature maps. The extracted features are subsequently fused to improve the perception ability of the network. Finally, the complex correlation among indoor objects is addressed by aggregating semantic and detailed features at different levels, and the model accuracy is effectively improved without increasing the number of parameters. The proposed model is tested on the NYU and SUN datasets. The proposed approach produces state-of-the-art results compared with the 14 performance results of recent optimal methods. In addition, the proposed approach is fully discussed and analyzed in terms of stability, robustness, ablation experiments and availability in indoor scenes.

Exploring the relationship between land use characteristics and ridership in railway station areas provides crucial decision-making support for station area planning. Previous research has mostly focused on subways, with a lack of studies on the land use characteristics and ridership of commuter rail stations, particularly in relation to the differences and impacts across various passenger catchment areas (PCAs). This study employed a multinomial logit model to evaluate the land use characteristics within 1000 m of Japan Railways (JR) stations in four different PCAs of the Tokyo metropolitan area (TMA). Additionally, regression models and a multiscale geographically weighted regression (MGWR) model were used to analyze how land use characteristics in these PCAs affected station ridership. The key findings were as follows: (1) the land use characteristics around commuter rail stations exhibit distinct zonal patterns; within 250 m, public transport stops and public service facilities are the most densely concentrated; the highest residential population density is found between 250 and 750 m; and commercial facilities are mostly clustered in the 500 to 750 m range; (2) the impact of land use factors on ridership varies in intensity across different spatial zones; the density of public transport stops and street network density is most significant within 250 m, whereas commercial facility density is greatest within the 500–750 m PCA; (3) The land use characteristics within 500 m of stations have greater explanatory power for passenger flow, and the goodness of fit of the MGWR model surpasses that of the linear regression model.

The built environment is the key to creating vibrant urban spaces that contribute to the health and sustainability of cities. Studies have demonstrated that a reasonable built environment helps to stimulate urban vitality. Nevertheless, there are limitations to the understanding that three-dimensional (3D) built environment indicators from the ‘human perspective’ can substantially affect urban vitality. This study provides an empirical analysis of Xi’an, a city with both traditional historical blocks and a modern city landscape. By applying the ordinary least square model and the geographically weighted regression model, this study explores the impacts of the two-dimensional (2D) and 3D built environments on urban vitality. Results show: (1) the urban vitality exhibits significant spatial and temporal difference characteristics; (2) the 3D built environment exerts a greater influence on urban vitality than 2D; (3) taking weekdays for instance, the indicators of green space and road space (e.g., normalized difference vegetation index (−0.092), green view index (−0.104), road density (−0.021), and enclosure (−0.089)) are negatively correlated with urban vitality, while the indicators of building space and mixed function (e.g., building density, floor area ratio, points of interest (POI) mixing degree, and 3D mixing degree) present a positive effect. To improve urban vitality, the study provides suggestions from the perspective of 3D and human perception, which will contribute to the meticulous practice of urban design.

With global warming and rapid urban development, the urban surface temperature in summer has been increasing, seriously affecting people’s work and life. The formation and changes in surface temperature are directly related to material surroundings and spatial functions. Urban construction has led to an increase in POIs (points of interest), and the POI represents the functional activity within the space to a certain extent. Therefore, this paper attempts to reproduce the process of the urban internal function development of Chengdu according to the distribution characteristics of different types of points of interest. It also delves into the influence of internal spatial functions on surface temperature in Chengdu. The results show that the surface heat values for all types of functions show a significant increase from 2009 to 2022. The rate of increase is particularly pronounced for public transportation, with temperatures increasing by an average of 0.317 °C per year. In addition, there are differences in the thermal contribution values of different functions. The residential and commercial functions have the most significant impact on surface temperature, with both accounting for more than 0.45 of all functional contribution values. Public transportation has a small thermal contribution value but shows a trend of doubling growth. The findings will provide some insights into the design of cooling in future urban planning.