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Housing plays a critical role in health and has real consequences on people's wellbeing. Numerous studies have provided extensive insights into the housing–health–wellbeing association. However, in the Chinese context, the unique housing allocation system with institutional force may present a distinct picture from that based on international knowledge. Using sample data from housing surveys, this study employs statistical analysis and the Tobit model to explore the links between the physical environment, socio-psychological dispositions, and residential satisfaction at the neighborhood level across different housing types in the unique Chinese context. The analyses reveal that (1) residents' socio-psychological dispositions and residential satisfaction vary remarkably across different housing types, with residents in replacement housing experiencing particularly low levels of residential satisfaction, presenting a regionalization trend; (2) housing types appear to have a significant effect on socio-psychological dispositions; and (3) physical environment characteristics in conjunction with socio-psychological dispositions significantly affect residential satisfaction. These findings suggest that social housing development should integrate socio-psychological initiatives with physical environment improvement, particularly for disadvantaged groups in public and replacement housing, to achieve more livable communities with better residential satisfaction and higher social resiliency and sustainability. [ABSTRACT FROM AUTHOR]

Reducing car dependence is the key to achieving the goal of green and sustainable development. Compared with the existing studies, which mainly focus on administrative areas, this study takes residential areas as the research unit. Four spatial regression models were used to investigate the effect on car dependence of six factors of the built environment (land use mix, population density, jobs–housing balance, bus stop density, metro station density, and road network density). Various test results show that the geography-weighted regression (GWR) model has more substantial explanatory power and that the estimated coefficients of built environment characteristics vary positively or negatively in diverse residential communities. The findings demonstrate that the impact of built environment characteristics on car dependence is significantly spatially heterogeneous. These results are conducive to better comprehending how built environment factors affect car dependence and help establish policies and strategies to promote sustainable transportation. [ABSTRACT FROM AUTHOR]

For safety and energy reasons, most metro stations in China choose to install platform screen doors (PSDs), which make passengers not distribute evenly on the platform, but instead gather into clusters in front of screen doors to wait for a train. However, the traditional evaluation method considers passengers evenly distributing on platform and does not reflect the actual status of waiting passengers. In addition, the traditional method is limited to accurately describe the crowdedness level on platform and merely considers per capita space and walking freedom, neglecting the service quality in the boarding process and safety evacuation in an emergency. Subsequently, the paper introduces the new classification method of crowdedness levels, which not only considers pedestrian flow characteristics but also takes the concepts of boarding service for passengers and safety evacuation for managers into account. From the perspectives of passengers and managers, this paper defines and calculates four key indicators-per capita space, per capita longitudinal distance, boarding coefficient, and evacuation coefficient-and then classifies the crowdedness level with the -mean cluster analysis method. Finally, the paper verifies the applicability and feasibility with an actual case study. [ABSTRACT FROM AUTHOR]

A metro station island platform in China takes a high density of passenger flow. For security and energy reasons, most metro stations have installed fully enclosed and semienclosed platform screen doors (PSDs), which causes passengers to gather into clusters in front of the screen doors to await the train rather than distributing randomly and evenly on the platform. This paper proposes a new waiting area division method on a metro station island platform, which can obviously improve the evaluation of the level of service at such stations and the accuracy of simulation software. The paper first analyzes the changes in waiting behavior after the installation of PSDs and the regional function transition during the three processes of waiting, alighting and boarding, and dissipation. Then, by means of video identification and field observations during the peak hours (7-9 a.m. and 5-7 p.m.), determines the shape of the waiting area to be a rectangle on the basis of the distribution pattern of passengers on platform. In addition, the paper presents the notion that the size of the waiting area varies with the different volume of waiting passengers and thereby establishes and validates the mathematical models for length and width of the single waiting area, which concludes that the waiting area width model is in the form of a logarithmic function and the length model is in the form of a power function. Finally, it shows the practicality of the new method in the level of service (LOS) evaluation and simulation accuracy. The result verifies that the new proposed method can truly reflect the feelings of waiting passengers and provide a significant basis for platform operation management. According to comparisons, the traditional method evaluating the whole platform LOS is overly optimistic, whereas the proposed method based on a separate waiting area can truly reflect the feelings of waiting passengers. In the simulation software Legion, the precise division of the platform waiting area with installation of PSDs improves the accuracy of the simulation results. [ABSTRACT FROM AUTHOR]