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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]

Many studies have disentangled the perceived benefits of vegetation on subjective well‐being (SWB). Yet, scant attention has been paid to the joint effect of vegetation and building density on SWB. This study explores the relationship between streetscape vegetation (SV), building density and SWB in Beijing, China. Our analysis relies on rich measures of street view data to assess SV exposure at the neighbourhood level. Notably, we distinguish between trees (SV‐tree) and grasses (SV‐grass) when evaluating SV metrics. The results suggest that streetscape trees and grasses are positively associated with SWB, though estimated effects are dependent upon tree and grass density exposures. We also find that the effects of streetscape trees and grass are moderated by building density in the neighbourhood. Additional decomposition analysis provides the insight that the well‐being implications of street vegetation and building density are varied significantly by individual sociodemographic characteristics such as sex, age and income. The findings of this study suggest the importance of considering density in SV planning and land use policies to enhance people's quality of life. [ABSTRACT FROM AUTHOR]

Bike-sharing holds promise for available and healthy mobility services during COVID-19 where bike sharing users can make trips with lower health concerns due to social distancing compared to the restricted transportation modes such as public transit and ridesharing services. Leveraging the trip data of the Divvy bike-sharing system in Chicago, this study exploresspatially heterogeneous effects of built environment on bike-sharing usage under the pandemic. Results show that the average weekly ridership declined by 52.04%. To account for the spatially heterogeneous relationship between the built environment and the ridership, the geographically weighted regression (GWR) model and the semiparametric GWR (S-GWR) model are constructed. We find that the S-GWR model outperforms the GWR and the multiple linear regression models. The results of the S-GWR model indicate that education employment density, distance to subway, COVID-19 cases, and ridership before COVID-19 are global variables. The effects between ridership and the built environment factros (i.e., household density, office employment density, and the ridership) vary across space. The results of this study could provide a useful reference to transportation planners and bike-sharing operators to determine the high bike-sharing demand area under the pandemic,thus adjusting station locations, capacity, and rebalancing schemes accordingly. [ABSTRACT FROM AUTHOR]

As the most prevalent physical activity and transportation mode for older people, walking is considered to have multiple health and well‐being benefits. Previous studies used separate models to assess the built‐environment determinants of a battery of walking behavior measures, such as walking frequency and duration. In a departure from them, this study develops a system of equations, which is estimated by seemingly unrelated regression, to determine the built‐environment factors that significantly influence two correlated walking behavior measures (including walking frequency and duration) of older adults in Xiamen (a medium‐sized Chinese city) based on data from the Travel Survey of Xiamen Residents 2015 and built‐environment geo‐data. The results show the following: (1) the walking frequency and duration of older adults are affected by the built environment and socio‐demographic characteristics; (2) land‐use mix, intersection density, and bus route density positively influence older adults' walking frequency and duration; (3) distance to the commercial center adversely impacts the walking frequency and duration; and (4) the built environment has similar effects on the two measures. This study offers a worthwhile reference for policy intervention to promote older adults' walking activities, thereby contributing to active and healthy aging. [ABSTRACT FROM AUTHOR]

Numerous cities are undergoing the ageing of populations. Developing a transport system that incorporates mobility needs, preferences, and demands of elderly people is crucial to such a demographic shift. Understanding the travel behavior and characteristics of elderly people is the first and foremost step towards this goal. In stark contrast with Western car-dominant places, numerous East Asian modern cities (e.g., Hong Kong, Beijing, and Shanghai) have high levels of public transportation use. Experience gained from existing studies in the West cannot be applied to Hong Kong, a typical transit-dependent city. In light of this, based on the 2011 Travel Characteristic Survey data and Poisson and negative binomial regression models, this study identifies factors that significantly affect the number of daily trips taken by (or trip frequency of) elderly people in Hong Kong. The paper finds that both socio-demographic (e.g., age and housing type) and built environment characteristics significantly affect the trip number of elderly people. Interestingly, it is determined that car availability does not play a significant role in the trip generation process, but public transportation accessibility truly matters. This outcome remarkably differs from findings in Western car-dominant cities. Moreover, to discern whether the elderly are a homogenous group, this study estimates separate models for the young elderly (aged between 60 and 74) and the very old (aged 75 or above). It is observed that the effect of public transportation accessibility on elderly mobility varies among elderly subgroups: public transportation accessibility significantly affects the trip frequency of the young elderly, but not that of the very old. Finally, a multitude of policy measures with the aim of upgrading elderly mobility is proposed. [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]

Understanding the relationships between built environment features and metro ridership is crucial to transit-oriented development (TOD). This study proposes a geographically weighted regression model based on metro network distance (ND-GWR) to capture the local patterns of relationships between the built environment (BE) and station-level metro ridership. The ND-GWR calculates network distances rather than traditional distance metrics (e.g., Euclidean, Manhattan, or Minkowski) to calibrate the spatial weight matrix, which can more truly characterize the associations among stations in the real world. Five types of independent variables, including demographic, land use, network, transfer, and station characteristics, are calculated by an exponential distance-decay function. This study also compares the modeling results across holidays, weekdays, and weekends to analyze temporal variations of BE's effects. Using data collected in 2019 in Shenzhen, China, the modeling results show that (1) the integration of ND-GWR and the decay-distance function can estimate the relationships between BE variables and metro ridership with higher accuracy; (2) bike-metro access-integrated use has positive impacts on metro ridership, and it presents more significant in the city periphery and on weekdays; (3) senior citizens (aged over 60) are unwilling to use the metro service due to the defect of their physical functions. The negative effects exhibit smaller in the city center and on holidays; (4) whether the effects of BE variables vary over space is highly affected by when the event happened. The findings are expected to provide insights for calibrating spatial models and increasing the attractiveness of metro systems. [ABSTRACT FROM AUTHOR]

Population aging has become a notable and enduring demographic phenomenon worldwide. Older adults' walking behavior is determined by many factors, such as socioeconomic attributes and the built environment. Although a handful of recent studies have examined the influence of street greenery (a built environment variable readily estimated by big data) on older adults' walking behavior, they have not focused on the spatial heterogeneity in the influence. To this end, this study extracts the socioeconomic and walking behavior data from the Travel Characteristic Survey 2011 of Hong Kong and estimates street greenery (the green view index) based on Google Street View imagery. It then develops global models (linear regression and Box–Cox transformed models) and local models (geographically weighted regression models) to scrutinize the average (global) and location-specific (local) relationships, respectively, between street greenery and older adults' walking time. Notably, green view indices in three neighborhoods with different sizes are estimated for robustness checks. The results show that (1) street greenery has consistent and significant effects on walking time; (2) the influence of street greenery varies across space—specifically, it is greater in the suburban area; and (3) the performance of different green view indices is highly consistent. [ABSTRACT FROM AUTHOR]

• We explored the moderation effect of urban density on changes in physical activity during the COVID-2019 pandemic. • The results were based on a longitudinal study. • Urban density mitigated the decrease in physical activity caused by the pandemic. • The duration of physical activity performed in country parks increased during the pandemic. • A flexible and porous urban development strategy could enhance the resilience of a city during the pandemic and beyond. Various social distancing measures were carried out in many cities worldwide during the coronavirus disease 2019 pandemic (COVID-19). These measures have led to decreased physical activity levels and higher health risks among urban populations. Strong evidence has been established that built environment characteristics can stimulate physical activity and thus improve public health during non-pandemic periods. Urban density was arguably one of the most important built environment characteristics. However, little is known about whether high urban density amplifies or attenuates the decline in physical activity during the pandemic. Based on two-wave physical activity data collected before and during the pandemic (in January and May 2020, respectively), we used moderation analysis to compare the changes in physical activity levels between people living in low- and high-density neighborhoods. Our results showed that people living in low-density areas have a smaller decrease in physical activity conducted in neighborhood, compared to those living in high-density areas. Our findings suggest that a flexible and porous urban development strategy could enhance the resilience of a city during the coronavirus pandemic and beyond. [ABSTRACT FROM AUTHOR]

Understanding the associations between demographic groups' metro travel behaviors and the built environment is crucial for addressing automobile dependence and promoting transportation equity and reasonable urban construction. This study examines the nonlinear relationships and threshold effects of the built environment on the metro travel patterns of three groups (i.e., commuters, seniors, and students) by applying smart card data in Kunming, China. We select the optimal machine learning model—gradient boosting decision trees (GBDTs)—and consider various built environment attributes. Our findings indicate that: 1) built environment attributes universally have nonlinear and threshold effects on metro travel for all groups; 2) the collective contributions of density and diversity differ greatly across groups compared to other attributes; and 3) only a few built environment attributes have similar effect directions and degrees across all three groups, while most have unique effects on each group. The findings suggest metro station area planning strategies to promote metro use and transportation equity for different groups. • Analyze the relationship between the built environment and different metro travelers. • Apply machine learning methods to evaluate nonlinearity. • Built environment attributes universally have nonlinear effects on all groups' metro travel. • A few built environment variables have similar effect directions and degrees for all three groups. • The effects of most variables vary across groups. [ABSTRACT FROM AUTHOR]

• Propose a people – metro – bike – route – urban space framework to describe the built environment. • Introduce the multilevel modeling framework to the studied problem. • Compare the integrated usage of dockless bike-sharing and the metro in different scenarios. • Investigate built environment effects on using dockless bike-sharing to connect the metro. • Discuss practical implications drawn from the empirical findings. The newly prevailing dockless bike-sharing system offers a decent solution to the first- and last-mile problem and connects trip origins/destinations and transit (mostly metro) stations. Few studies, however, have explored the effects of built environment characteristics on the integrated usage of dockless bike-sharing and the metro, especially in different conditions (e.g., access versus egress and morning peak versus evening peak) and using panel data. To fill the gap, this study proposes a people – metro – bike – route – urban space framework to describe the feeder-related built environment from the perspective of the feeder process. Using 3-day data of ofo bikes in Shenzhen, China, this study then develops multilevel negative binomial models that incorporate random effects and address the intracluster correlation attributed to repeated measures to scrutinize the feeder-related built environment effects on the integrated usage. The findings are listed as follows: (1) The majority of access and egress integrated trips have a distance range of 500–2000 m and a duration range of 2.5–10 min; (2) Popular metro stations (with a large ridership) are positively related to the access integrated usage; (3) The number of available shared bikes and the length of bikeway in the catchment areas of the metro are positively related to the integrated usage; and (4) Mixed land use increases the integrated usage, whereas urban villages are places with few demands for the integrated usage. These findings are beneficial in developing a bike-friendly built environment that facilitates the seamless connection between dockless bike-sharing and the metro. [ABSTRACT FROM AUTHOR]

Walking and cycling are not only frequently-used modes of transport but also popular physical activities. They are beneficial to traffic congestion mitigation, air pollution reduction, and public health promotion. Hence, examining and comparing the built environment correlates of the propensity of walking and cycling is of great interest to urban practitioners and decision-makers and has attracted extensive research attention. However, existing studies mainly look into the two modes separately or consider them as an integral (i.e., active travel), and few compare built environment correlates of their propensity in a single study, especially in the developing world context. Thus, this study, taking Xiamen, China, as a case, examines the built environment correlates of the propensity of walking and cycling simultaneously and compares the results wherever feasible. It found (1) built environment correlates of the propensity of walking and cycling differ with each other largely in direction and magnitude; (2) land use mix, intersection density, and bus stop density are positively associated with walking propensity, while the distance to the CBD (Central Business District) is a negative correlate; (3) as for cycling propensity, only distance to CBD is a positive correlate, and job density, intersection density, and bus stop density are all negative correlates. The findings of this study have rich policy implications for walking and cycling promotion interventions. [ABSTRACT FROM AUTHOR]

Air pollution, an unexpected event, poses a significant threat to public health and affects human mobility. Ride-hailing provides an effective way to understand how human mobility adapts to air pollution. This study examines a week-long ride-hailing demand dataset from Chengdu, China, to evaluate the resilience of ride-hailing services (or ride-hailing resilience) in the face of poor air quality. A gradient boosting decision tree model is developed to explore the non-linear and interaction effects of air pollution, the built environment, and socioeconomic characteristics on ride-hailing demand and resilience. The results show that the relative importance and impact of independent factors on ride-hailing demand and resilience vary. Specifically, the density of residence facilities and air pollution are the most important predictors of ride-hailing demand and resilience, respectively. The non-linear and interaction effects of air pollution and selected built-environment and socioeconomic characteristics on ride-hailing resilience are presented. We recommend that urban planners and policymakers address the vulnerability of regions to air pollution, optimize the allocation of ride-hailing resources, and develop strategies to improve regional resilience. [ABSTRACT FROM AUTHOR]

Neighborhood compactness has been identified as a sustainable approach for fostering social relationships. However, existing studies have not clearly demonstrated whether neighborhood compactness in high-density cities directly influences the mental distress of community-dwelling older adults, or whether it indirectly influences mental distress through perceived accessibility to urban amenities (PA). Using a composite score of three dimensions (depressive symptoms, loneliness, and social isolation) to measure mental distress, and applying a mediation model within a cross-sectional design involving 947 older adults (aged ≥ 60) with at least mild symptoms in Hong Kong, our findings suggest an indirect pathway between neighborhood compactness, PA, and mental distress. Neighborhood compactness was positively associated with PA (B = 7.791, p < 0.001), and PA was negatively associated with mental distress (B = −0.12, p = 0.017). However, no direct impact of neighborhood compactness on mental distress was found. Moderated mediation analysis further indicated that neighborhood compactness and PA supported older females but not males. These results align with our hypothesis that (1) neighborhood compactness may not always be beneficial for older adults with mental distress in high-density cities unless compactness itself enhances PA and facility usage to support community-dwelling individuals, and (2) gender differences may result in varying interactions and perceptions of urban amenities and the built environment. To support the concept of "aging in place" in the future, urban plans aimed at enhancing neighborhood compactness and its social impacts should focus on addressing social inequality, including strategies to improve urban design, social participation, and gender-specific protocols, so that older adults can achieve better community awareness, environmental satisfaction, and facility usage, ultimately reducing mental distress. • An indirect pathway between neighborhood compactness, PA, and mental distress of older adults is found. • A mediation model is applied to a cross-sectional design of a high-density city. • Higher neighborhood compactness correlating with higher PA leads to alleviated mental distress. • Neighborhood compactness is associated with improved mental conditions among female older adults. • PA is associated with alleviated mental distress among females but not males. [ABSTRACT FROM AUTHOR]

Urban resilience studies often prioritize the investigation of sudden and short-lived natural disasters, while overlooking the gradual emergence and enduring nature of pandemics. The COVID-19 crisis, a quintessential example of such a challenge, has elicited significant but underexplored shifts in the landscape of urban public events. This research, drawing on social sensing geospatial datasets, aims to examine the changes in public events prior to, during, and after the fifth wave of COVID-19 in Hong Kong. Employing a dynamic time warping-based clustering algorithm alongside multiple linear regression analysis, we endeavor to address two pivotal questions: (1) How have the patterns of public events transformed in response to the pandemic? (2) What factors significantly influence these changes? The findings reveal that: Temporally, the implementation of social distancing measures is correlated with a marked reduction in public event frequency, reflecting the extensive impact of the COVID-19 crisis. Spatial analysis reveals that while public events are concentrated in Hong Kong's central areas, the spread of confirmed COVID-19 cases is more evenly distributed, with only a weak correlation observed between event hotspots and case distributions. Furthermore, our analysis identifies three distinct temporal patterns of public event changes, underscoring a higher resilience of events in urban centers against pandemic-induced disruptions. Additionally, built environment factors are found to be positively correlated with the decline in events as per the impact ratio, while socio-demographic factors more significantly affect the recovery ratio of events post-pandemic. This study not only pioneers in providing a comprehensive framework for monitoring urban events during a pandemic but also offers crucial insights into urban dynamics and vitality. These findings are invaluable for shaping more effective public health policies and crisis management strategies, enhancing urban resilience in face of future pandemics. • This study addresses resilience from a slow-onset and long-standing pandemic perspective. • Social sensing geospatial datasets enhance fine-grained tracking of public activities. • Activity resilience is defined by COVID-19's impact and the recovery extent after COVID-19. • The driving mechanism of activity resilience is explored from the built environment and socio-demographic aspects. • The research framework provides insights into urban dynamics and allows policymakers to introduce better policies. [ABSTRACT FROM AUTHOR]

• Waiting time (WT) is used as an indicator of equity of ridesouricng service. • Trip, socioeconomic and built environment factors influence WT in Austin, TX. • WT is positively related to traffic condition, surge multiplier, and rainy weather. • WT is negatively related to road density, transit stop density, and land-use entropy. • WT is higher in areas with high fraction of Hispanic/Latino and Black residents. Waiting time (WT) is an important measure that can reflect accessibility to ridesourcing service. Previous studies explored the effects of built environment factors on WT based on estimated WT but did not control for trip-level characteristics, which may lead to biased parameter estimation. Thus, we further study this topic by using the actual WT recorded by the RideAustrin platform and considering trip-level variables. The single-level and multilevel proportional hazards models are constructed, and model comparison shows that the multilevel model performs better. We find that waiting time is positively correlated with trip-level characteristics such as traffic conditions, surge multiplier, and rainy weather. Regarding built environment factors, WT is positively related to distance to CBD and negatively related to road density, transit stop density, and land-use entropy. WT is also higher in areas with a high fraction of Hispanic/Latino and Black residents but lower in areas of low income. [ABSTRACT FROM AUTHOR]

• Relationship between the built environment (BE) and metro ridership (MR) is explored. • Four calendar events are considered during the modeling of the BE-MR relationship. • Global (OLS) and local (GWR) regression approaches are adopted. • BE factors that significantly affect MR are unlike between workdays and non-workdays. This study examines the associations between the built environment (BE) and metro ridership (MR) during different calendar events (workdays, weekends, Spring Festival, and Valentine's Day) in Shenzhen. Utilizing 18-day metro smart card and points of interest (POIs) data, we employ ordinary least squares (OLS) and geographically weighted regression (GWR) models to compare the BE-MR relationship across different calendar events. The findings show that MR during different calendar events follows the pattern: unofficial holiday (Valentine's Day) > workdays > weekends > public holidays (Spring Festival). Moreover, BE factors significantly affecting MR differ between workdays and non-workdays, with slight differences observed among non-workdays (i.e., weekends and holidays). Specifically, during non-workdays, especially Spring Festival, metro trips related to leisure and tourism activities are more prevalent in the southwest area of Shenzhen, while potential cross-city travel is more active in the north. On workdays, metro trips associated with hotel check-in activities concentrate in central urban areas (e.g., Luohu and Futian districts), whereas they are more active in the northeast suburbs during the Spring Festival. These findings can aid in predicting metro usage, enabling more efficient and productive resource allocation. [ABSTRACT FROM AUTHOR]

Understanding the relationship between the built environment and e-scooter sharing (ESS) usage is important because it could help planners determine the high-demand area and design an effective investment plan to promote the use of micromobility. Previous studies usually assume that the relationship is linear, which may lead to inaccurate ridership prediction and ineffective policy. Thus, this study explores the nonlinear and threshold effects of the built environment on ESS ridership in Los Angeles using the gradient boosting decision tree. Fourteen built environment and ten demographic variables are selected as independent variables. We find that the built environment accounts for 91.66% of the total relative importance. The ten most important variables are intersection density, road density, public transit station density, restaurant density, employment density, distance to the center, population density, proportion of park area, parking density, and bike lane density. The nonlinear and threshold effects of the built environment on ESS ridership are determined. By using two spatial analysis units (census tract and census block group) and four temporal analysis units (spring, summer, autumn, and winter), the modifiable areal unit problem and the modifiable temporal unit problem are revealed. [ABSTRACT FROM AUTHOR]

Many studies have confirmed that the characteristics of the built environment affect individual walking behaviors. However, scant attention has been paid to population-level walking behaviors, such as pedestrian volume, because of the difficulty of collecting such data. We propose a new approach to extract citywide pedestrian volume using readily available street view images and machine learning technique. This innovative method has superior efficiency and geographic reach. In addition, we explore the associations between the extracted pedestrian volume and both macro- and micro-scale built environment characteristics. The results show that micro-scale characteristics, such as the street-level greenery, open sky, and sidewalk, are positively associated with pedestrian volume. Macro-scale characteristics, operationalized using the 5Ds framework including density, diversity, design, destination accessibility, and distance to transit, are also associated with pedestrian volume. Hence, to stimulate population-level walking behaviors, policymakers and urban planners should focus on the built environment intervetions at both the micro and macroscale. • Population-level walking behaviors were represented by street-level pedestrian volume at a large scale in Shanghai, China. • Pedestrian volume and street-level built environment were extracted from street view images using machine learning. • Both macro- and micro-scale built environment were simultaneously considered including 5D variables and street-level characteristics at a large scale. • Macro-scale built environment measured by 5Ds framework were associated with pedestrian volume. • Micro-scale built environment such as street-level greenery, open sky, and sidewalk were associated with pedestrian volume. [ABSTRACT FROM AUTHOR]

Bicycle-metro integration is an efficient method of solving the "last mile" issue around metro stations. Built environment is believed to have a significant effect on cycling behavior. However, transfer cycling around metro stations, as a specific type of cycling behavior, has often been overlooked in transport research. In addition, static contextual units such as circular or street-network buffers are typically used to delineate metro catchment areas of transfer cycling trips. These methods are inaccurate to represent the actual geographic contexts of cycling trips, according to the uncertain geographic context problem (UGCoP). Thus, in this study, bicycle-metro catchment areas are delineated based on aggregating the end points of over three million transfer cycling trips. The impact of the built environment on transfer cycling behavior is also explored. First, we find that the aggregate-points buffer outperforms traditional static buffers in predicting transfer cycling trips. Second, we also identify a high level of spatial heterogeneity in catchment area and transfer cycling density between urban and suburban areas. Third, residential and working population density, bus stop density, and metro stations accessibility have a significant effect on bicycle-metro transfer cycling. [ABSTRACT FROM AUTHOR]

Electric scooter (e-scooter) sharing systems (ESSs) have been widely adopted by many cities around the world and have attracted a growing number of users. Although some studies have explored the usage characteristics and effects of the built environment on ESS ridership using one city as an example, few studies have considered multiple cities to obtain generalizable and robust results. To fill this research gap, we collect the ESS trip data of five cities in the U.S., namely Austin, Minneapolis, Kansas City, Louisville, and Portland, and explore the effects of the built environment on ESS ridership after controlling for socioeconomic factors. The temporal distributions of e-scooter ridership of different cities are similar, having a single peak period on weekdays and weekends between 11:30 and 17:30. In terms of spatial distribution, the ESS ridership is higher in universities and urban centers compared to other areas. Multilevel negative binomial model results show that ESS trips are positively correlated with population density, employment density, intersection density, land use mixed entropy, and bus stop density in the census block group. E -scooter ridership is negatively correlated with the median age of the population in the census block group and distance to the city center. The findings in this article can help operators understand the factors that affect the ridership of shared e-scooters, determine the changes in ridership when the built environment changes, and identify high-ridership areas when ESS is implemented in new cities. • The effect of built environment on e-scooter ridership (ER) is studied. • Trip data of five cities are analyzed to obtain more generalizable and robust result. • Temporal variation of ER of different cities exhibits similar pattern. • ER increases with population, employment, intersection density, and mixed land use. • ER decreases with median age of the population and distance to the city center. [ABSTRACT FROM AUTHOR]

China has entered a stage in which new rural construction and urbanization are rapidly developing. Considerable changes are occurring in rural China, and the built environment is different from that in the past; such difference directly influences the travel mode choice of rural residents. However, our knowledge on how the rural built environment influences the travel mode choice of rural residents in China remains limited. To fill this gap, this study combines on-site measurement methods, geographic information system (GIS) technology, and activity diary survey to obtain basic data regarding the built environment and the daily activities of rural residents. The multinomial logit (MNL) model is used to explore the relationship between the rural built environment and the travel mode choice of rural residents. Results show that building density significantly positively affects private car trips. This finding challenges earlier urban built environment research due to the considerable gap between rural and urban areas. An increase in road density increases the travel frequency of electric bicycles and motorcycles. Accessibility perception and preferences positively affect the probability of choosing to walk. Safety and neighborhood harmony perception positively affect the travel frequency of motorcycles and private cars. Rural residents who prefer a safe living environment are likely to choose walking for their daily travel. Despite the considerable achievements in the construction of rural roads, the frequency of public transportation remains low for rural residents. Therefore, additional attention should be given to the investment and construction of public transport facilities during rural urbanization. [ABSTRACT FROM AUTHOR]