A new generation of bike-sharing services without docking stations is currently revolutionizing the traditional bike-sharing market as it dramatically expands around the world. This study aims at understanding the usage of new dockless bike-sharing services through the lens of Singapore's prevalent service. We collected the GPS data of all dockless bikes from one of the largest bike sharing operators in Singapore for nine consecutive days, for a total of over 14 million records. We adopted spatial autoregressive models to analyze the spatiotemporal patterns of bike usage during the study period. The models explored the impact of bike fleet size, surrounding built environment, access to public transportation, bicycle infrastructure, and weather conditions on the usage of dockless bikes. Larger bike fleet is associated with higher usage but with diminishing marginal impact. In addition, high land use mixtures, easy access to public transportation, more supportive cycling facilities, and free-ride promotions positively impact the usage of dockless bikes. The negative influence of rainfall and high temperatures on bike utilization is also exhibited. The study also offered some guidance to urban planners, policy makers, and transportation practitioners who wish to promote bike-sharing service while ensuring its sustainability.

The role of the built environment in improving public health through fostering physical activity has come under increased scrutiny in recent years. This study investigates relationships between walking activity and the configuration of green spaces in Greater London. Pedestrian activity for N = 54,910 walking trip stages is gathered through the London Travel Demand Survey (LTDS), with routes between origin and destination mapped onto the street network from the Integrated Transport Network of Ordnance Survey. Green spaces were extracted from UKMap and agglomerated to form London’s hundreds of parks. Regressions of pedestrian activity on park configuration, controlling for built environment metrics, revealed that catchments around smaller parks have more walking trips. Irregularity of park shape has the opposite effect. Park density, measured as number of parks inside a catchment, is insignificant in regression. Parks adjacent to retail areas were associated with pronounced increases in walking. The study contributes to landscape, urban management, environmental policy and urban planning and design literature. The evidence provides implications for performance-oriented policy and design decisions that configure a city’s green spaces to improve citizens’ public health through enhancing walkability.

Bike-sharing systems have made notable contributions to cities by providing green and sustainable mobility service to users. Over the years, many studies have been conducted to understand or anticipate the usage of these systems, with the hope to inform their future developments. One important task is to accurately predict usage patterns of the systems. Although many deep learning algorithms have been developed in recent years to support travel demand forecast, they have mainly been used to predict traffic volume or speed on roadways. Few studies have applied them to bike-sharing systems. Moreover, these studies usually focus on one single dataset or study area. The effectiveness and robustness of the prediction algorithms are not systematically evaluated. In this study, we propose a Spatial-Temporal Memory Network (STMN) to predict short-term usage of bicycles in bike-sharing systems. The framework employs Convolutional Long Short-Term Memory models and a feature engineering technique to capture the spatial-temporal dependencies in historical data for the prediction task. Four testing sites are used to evaluate the model. These four sites include two station-based systems (Chicago and New York) and two dockless bike-sharing systems (Singapore and New Taipei City). By assessing STMN with several baseline models, we find that STMN achieves the best overall performance in all the four cities. The model also achieves superior performance in urban areas with varying levels of bicycle usage and during peak periods when demand is high. The findings suggest the reliability of STMN in predicting bicycle usage for different types of bike-sharing systems.

Internal migration is critically important in China, where the fertility rate is declining and international immigration is under strict control. This study explores the massive population movement in China, examines the migration pattern of non- hukou migrants, 2010–2015 and 2014–2015 migration patterns through the urban hierarchy of the urban system using migration trajectories derived from the 2015 One Percent Population Sample Survey. Results reveal an emerging reversal from a predominantly upward pattern (e.g. most of the net flows move to high-level cities) to a downward one (e.g. from super-large/extra-large cities to large cities) in the recent migration trend. Regional disparities are significant. An upward and eastward tendency still dominates in the western, central and northeastern regions, whereas a downward and decentralised tendency has been initiated in the eastern region. The causes for the structural change include common factors found in developed countries, such as the influence of age and life courses. The age structure of China’s population caused by the ‘one-child’ policy weakened the upward momentum and led to a strengthening downward trend in the current migration pattern. The contextual and institutional factor hukou also has a significant effect on people’s migration directions. Hukou attracts people to move up or down the hierarchy to their registered place or where they can acquire registration. The characteristics of registered migrants reflect the different criteria of cities in granting hukou.

The rapid spread of infectious diseases is devastating to the healthcare systems of all countries. The dynamics of the spatial spread of epidemic have received considerable scientific attention. However, the understanding of the spatial variation of epidemic severity in the urban system is lagging. Using synchronized epidemic data and human mobility data, integrated with other multiple-sourced data, this study examines the interplay between disease spread of coronavirus disease (COVID-19) and inter-city and intra-city mobility among 319 Chinese cities. The results show a disease spreading process consisting of a major transfer (inter-city) diffusion before the Chinese New Year and a subsequent local (intra-city) diffusion after the Chinese New Year in the urban system of China. The variations in disease incidence between cities are mainly driven by inter-city mobility from Wuhan, the epidemic center of COVID-19. Cities that are closer to the epidemic center and with more population in the urban area will face higher risks of disease incidence. Warm and humid weather could help mitigate the spread of COVID-19. The extensive inter-city and intra-city travel interventions in China have reduced approximately 70% and 40% inter-city and intra-city mobility, respectively, and effectively slowed down the spread of the disease by minimizing human to human transmission together with other disease monitoring, control, and preventive measures. These findings could provide valuable insights into understanding the dynamics of disease spread in the urban system and help to respond to another new wave of pandemic in China and other parts of the world.