Transit-oriented development (TOD) has been recognised as a sustainable planning approach and that is typically designed for a whole city. Individual land use characteristics and the causations have often been ignored. Therefore, the primary objective of this study was to explore the factors that influence the land use catchment area (LCA) characteristics at a station neighborhood level. First, it contributes a methodology to measure the LCA by introducing a new concept. The density gradient was introduced to generate the scale and compactness degree of each station. Second, it provides a theoretical framework for understanding the causes of different LCAs. The partial least squares (PLS) regression model was employed to explore the accessibility effects. By analysing density gradient curves, it reveals that stations grew to fit the negative exponential function. Regarding the scale and form degree of LCAs, the impact of accessibility before and after a station construction have been corroborated. Moreover, the effects of facilities function before construction, distance from main roads, and elevated stations have been emphasized. The results provide support for a more sophisticated concept of catchment area relating to land use at the level of an individual TOD station, while shedding light on the benefits of those engaged in the future design of TOD with due consideration of the local physical environments.

Introduction: Current studies estimated a general incubation period distribution of COVID-19 based on early-confirmed cases in Wuhan, and have not examined whether the incubation period distribution varies across population segments with different travel histories. We aimed to examine whether patients infected by community transmission had extended incubation periods than the early generation patients who had direct exposures to Wuhan. Methodology: Based on 4741 patient case reports from municipal centers of disease control by February 21, 2020, we calculated the incubation periods of 2555 patients with clear epidemiological survey information and illness development timeline. All patients were categorized into five groups by their travel histories. Incubation period distributions were modeled for each group by the method of the posterior Weibull distribution estimation. Results: Adults aged 30 to 59 years had the most substantial proportion of confirmed cases in China. The incubation period distribution varied slightly across patient groups with different travel histories. Patients who regularly lived in Wuhan and left to other locations before January 23, 2020 had the shortest posterior median value of 7.57 days for the incubation period, while the incubation periods for persons affected by local community transmission had the largest posterior median of incubation periods, 9.31 days. Conclusions: The median incubation period for all patients infected outside Wuhan was 9 days, a bit of more extended than the early estimated 5-day incubation period that was based on patients in Wuhan. Our findings may imply the decreases of virulence of the COVID-19 virus along with intergenerational transmission.

Efficiency and equity have always been the two points of focus of transport projects. Compared with efficiency, equity is easily overlooked in the evaluation of transport projects. Many studies emphasize that defining and operationalizing costs and benefits and the distributive principle are critical parts in the assessment of transportation equity. However, the scope and time frame of the assessment target are also critical. In this paper, we took China’s fastest urbanizing city, Shenzhen, as a case study to assess transport equity by comparing accessibility among groups. First, the public transport system was divided into bus and subway, and the residents were divided into two groups: urban village and nonurban village. Second, we adopted an enhanced potential opportunity model to measure residents’ bus and subway accessibility and summarized them as transit opportunity. Third, we used the Dagum Gini coefficient decomposition and kernel density estimation method to explore the fair distribution of transit opportunity among groups and districts from 2011 to 2020. Decade-long changes in disparity and distribution of transit opportunity gave us a clear picture. On the one hand, the development of Shenzhen public transport system had a positive effect. All populations are benefiting, and their accessibility is increasing. On the other hand, it also had a negative effect to exacerbate inequality between populations. For the absolute value of the opportunity, Shenzhen’s urban village populations do have fewer transportation opportunities than nonurban villages, and this gap between them will be wider more and more. The public transport system is more inclined to improve the population with high initial opportunity and make them higher. The results illustrated the importance of examining transportation equity over an extended period and could provide information on urban development strategies.

Since the number of bicycles is critical to the sustainable development of dockless PBS, this research practiced the introduction of a machine learning approach to quantity management using OFO bike operation data in Shenzhen. First, two clustering algorithms were used to identify the bicycle gathering area, and the available bike number and coefficient of available bike number variation were analyzed in each bicycle gathering area’s type. Second, five classification algorithms were compared in the accuracy of distinguishing the type of bicycle gathering areas using 25 impact factors. Finally, the application of the knowledge gained from the existing dockless bicycle operation data to guide the number planning and management of public bicycles was explored. We found the following. (1) There were 492 OFO bicycle gathering areas that can be divided into four types: high inefficient, normal inefficient, high efficient, and normal efficient. The high inefficient and normal inefficient areas gathered about 110,000 bicycles with low usage. (2) More types of bicycle gathering area will affect the accuracy of the classification algorithm. The random forest classification had the best performance in identifying bicycle gathering area types in five classification algorithms with an accuracy of more than 75%. (3) There were obvious differences in the characteristics of 25 impact factors in four types of bicycle gathering areas. It is feasible to use these factors to predict area type to optimize the number of available bicycles, reduce operating costs, and improve utilization efficiency. This work helps operators and government understand the characteristics of dockless PBS and contributes to promoting long-term sustainable development of the system through a machine learning approach.

This paper corrected the long‐term misunderstanding of the land utilization efficiency concept. The Undesirable‐Window‐DEA model, Dagum Gini coefficient, and spatial panel autoregressive model with fixed effect were used to explore the spatial heterogeneity and influencing factors of urban construction land utilization efficiency in China from 2004 to 2016. The results show the following: (1) China’s overall utilization of urban construction land is still at a low level. It decreased first and then rose, with a “flat V‐shaped” evolution pattern. (2) During the study period, the Gini coefficient of urban construction land utilization efficiency for all provinces decreased first, then rose, and decreased again. The utilization efficiency Gini coefficients within provincial grouping were eastern region (0.063), central region (0.101), and western region (0.128). The Gini coefficients within provincial grouping were central versus western (0.121), eastern versus western (0.161), and eastern versus central (0.168). For the contribution to overall inequality of land utilization efficiency, the inequality within groups contributes the most (57.57%), and the inequality between groups accounts for about 25.62%. The overall efficiency is improved, with an evolution pattern of “difference narrowed–differences expanded–difference narrowed.” (3) “economic development,” “industrial structure,” “research development investment,” and “land urbanization level” have significantly positive effect on urban construction land utilization efficiency, while other factors have a negative effect, including “urbanization level of population,” “urban population density,” “cultivated field resources level,” “government influence level,” “land urbanization level,” and “financial dependence level.” This study could provide theoretical support for the implementation of cross‐provincial/regional urban construction land quotes reallocation and differential construction land management policies.