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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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yu, Cui, Liu, Luo, Tian and Yang.)

Walking, a fundamental activity and travel mode for the elderly, plays a crucial role in improving their physical and mental health. However, previous studies on the relationship between elderly walking and the built environment have paid limited attention to the indicator of daily walking time and its human-scale built-environment correlates. This study uses extensive survey data on residents'walking behavior in Chengdu to analyze the daily walking time of individuals aged 60 years or above, adopts multi-source data such as street view imagery and remote sensing data to assess numerous community-level built-environment factors, and establishes a multiple linear regression model to investigate the relationship between the elderly's walking time and various built-environment factors. The results reveal positive correlations between walking time among elderly individuals and community-level built-environment factors such as population density, road density, NDBI, the number of recreational facilities, and the green view index. Finally, the study proposes improvement strategies for age-friendly environments, which focus on three aspects:"living circles,""public spaces,"and"slow transportation system." [ABSTRACT FROM AUTHOR]

• Use mobile phone signaling data to assess urban vitality. • Call for temporally and spatially varying policies for urban vitality enhancement. • The spatial distribution of urban vitality is clustered and polycentric. • Urban vitality varies considerably throughout the day. • The effects of metro accessibility are more evenly distributed on weekends. Urban vitality has long been a focus of urban planners and designers. This study portrays the spatiotemporal patterns of urban vitality in Chengdu, China, on weekdays and weekends and examines their relationship with a set of factors using spatial regression and geographically weighted regression models. The results reveal that the spatial distribution of urban vitality displays a clustered and polycentric pattern, which can be attributed to urban spatial structure. Urban vitality varies considerably throughout the day on weekdays and weekends, and it consistently decreases as the distance to the city center increases. Furthermore, weekday vitality is much more predictable (less random) than weekend vitality, indicating that human activity patterns present higher regularity on weekdays than on weekends. The normalized difference vegetation index (NDVI), road density, the number of residential facilities, and land-use mix have positive influences on urban vitality on weekdays and weekends, while the distance to the metro station has negative influences. The influences of NDVI on urban vitality in the city center are negative on weekdays but become positive on weekends. The influences of the distance to the metro station are more homogeneously distributed on weekends than on weekdays. Finally, this study calls for temporally and spatially varying policies for urban vitality enhancement. [ABSTRACT FROM AUTHOR]

Optimizing the built environment helps alleviate the adverse impact of extreme heat on public health and sustainable urban development. To this end, this study measured the daily land surface temperature (LST) in August 2022 (when extreme heat occurred) in Chengdu and introduced the 3D street view characteristics extracted from street view images. By employing OLS and spatial regression models, we probed the combined effects of 2D and 3D built environments on LST and their relationships under disparate circle spaces. The results show that: (1) compared with the 2D built environment, the 3D built environment presents a superior impact on LST. (2) urban land use that is 3D, compact, and has a high-floor area ratio diminishes LST, as evidenced by the finding that floor area ratio (FAR) reduces LST, but building density enhances it. (3) the green view index exhibits a stronger cooling effect than green space areas. (4) the built environment beneath distinct circle spaces displays a spatially varying impact on LST. For example, FAR is positively correlated with LST in the urban center, while in the urban periphery, it is prominently negative. Our empirical findings are conducive to formulating strategies from both 2D and 3D built environments for the formation and implementation of healthy urban design and development. • In 2022, extreme heat has risked public health and urban sustainable development. • 3D, rather than 2D, environment has superior impacts on LST. • FAR reduces—whereas building density increases—LST. • The green view index shows a stronger cooling effect than that of green space area. • Built environment and LST have spatially varying impacts in distinct circle spaces. [ABSTRACT FROM AUTHOR]

• Temporal heterogeneity exists in the associations between metro ridership and the built environment. • Built environment variables have non-linear and threshold effects on metro ridership. • Distance to the city center and population density are strong predictors of metro ridership in the morning peak hour. • Employment density, enterprise density, and road density are strong predictors in the evening peak hour. • Critical TOD planning parameters are identified. The metro is the backbone of the transport system in many cities. Analyzing the built-environment correlates of metro ridership is crucial for transit-oriented development (TOD) planning and practice. Although numerous studies went along this line, they have rarely considered the non-linearity and temporal heterogeneity in the association of metro ridership with the built environment. After collecting transit smart card data, geo-data, and mobile phone signal data, this study adopts the random forest model to reveal the complex association of hourly metro ridership in November 2019 in Chengdu (China) with the built environment in three times of day (i.e., morning peak, noon off-peak, and evening peak hours). Notably, the contribution of several variables, such as the number of station entrances/overpasses and parking density, has rarely been considered in the literature. The results confirm the presence of non-linearity and temporal heterogeneity in the aforementioned association. Access to the city center and population density are strong predictors of metro ridership in the morning peak hour, whereas employment density, enterprise density, and road density are strong predictors in the evening peak hour. There are great differences in the correlates of metro ridership in different periods. Critical TOD planning parameters are also identified from the partial dependence plots obtained from random forest modeling. [ABSTRACT FROM AUTHOR]