Wuhan encountered a serious attack in the first round of the coronavirus disease 2019 (COVID-19) pandemic, which has resulted in a public health social impact, including public mental health. Based on the Weibo help data, we inferred the spatial distribution pattern of the epidemic situation and its impacts. Seven urban factors, i.e., urban growth, general hospital, commercial facilities, subway station, land-use mixture, aging ratio, and road density, were selected for validation with the ordinary linear model, in which the former six factors presented a globally significant association with epidemic severity. Then, the geographically weighted regression model (GWR) was adopted to identify their unevenly distributed effects in the urban space. Among the six factors, the distribution and density of major hospitals exerted significant effects on epidemic situation. Commercial facilities appear to be the most prevalently distributed significant factor on epidemic situation over the city. Urban growth, in particular the newly developed residential quarters with high-rise buildings around the waterfront area of Hanyang and Wuchang, face greater risk of the distribution. The influence of subway stations concentrates at the adjacency place where the three towns meet and some near-terminal locations. The aging ratio of the community dominantly affects the hinterland of Hankou to a broader extent than other areas in the city. Upon discovering the result, a series of managerial implications that coordinate various urban factors were proposed. This research may contribute toward developing specific planning and design responses for different areas in the city based on a better understanding of the occurrence, transmission, and diffusion of the COVID-19 epidemic in the metropolitan area.

In recent years, machine learning has gradually been applied to building energy-saving designs to reduce the time consumption of the optimization screening stage. However, since most of the existing research scholars come from the fields of computers and engineering, the application of machine learning technology mostly involves complex programming as well as software in the field of engineering, which requires multiple software to be coupled to achieve. In view of the differences between disciplines and the high learning threshold, these theories are difficult to apply and promote in practical work in the field of architecture. In this regard, this paper focuses on the improvement of methods, based on the Grasshopper platform, proposes a detached energy-saving residential form generation design method and process, to explore the optimal energy-saving building form in a more concise and efficient way. Based on this new method, on the basis of verifying its feasibility through a residential building case, two machine learning algorithms, neural network (ANN) and support vector machine (SVM), are compared and studied, and the applicability of these two algorithms in different building performance indicators is further discussed. The results show that the ANN model has the highest accuracy and is more suitable for the prediction of building energy consumption; in view of the simple and fast operation of SVM, it is more suitable for comfort prediction with relatively low accuracy requirements. By combining the above two machine learning methods, work efficiency can be improved while satisfying the prediction of relevant performance indicators. This method can help architects quickly search for the best building energy-saving form design scheme in the scheme design stage and provide data support and information feedback for architects in design conception and deepening.

Throughout history, rivers and lakes have wielded a profound influence on the dynamics of urban transportation expansion. To illustrate this phenomenon, we turn to the century-long evolution of the road network in Wuhan, China, as a case study. The study aims to explore the relationship framework between water bodies and urban transportation, characterized by the sequence of “strong connection” to “weakened connection”, then to “mutual restriction”, and ultimately to “mutual benefit”. Additionally, the analysis of the impact mechanisms of rivers and lakes on urban transportation at different stages of development is also a key research objective. To facilitate our exploration, we select the road networks in Wuhan from four years of 1922, 1969, 1995, and 2023 as the primary research subjects. By establishing water buffers, we scrutinize the evolving characteristics of riverside and lakeside transportation amidst the city’s expansion. Based on the modified shortest path model, we introduce the innovative concepts of “Detour Index” and “Weighted Detour Index” to assess the road accessibility of each node in the city based on its inherent environmental conditions. This allows for the effective analysis of the potential impact of water bodies as “obstacles” on the road network at different stages of urban development. The study found that in the areas adjacent to the rivers and lakes in Wuhan, there is insufficient road accessibility based on their inherent environmental conditions. Particularly, some areas along the rivers may become “terminals” in the urban road network. Furthermore, during the process of urban expansion, the correlation between the urban road network and rivers continues to weaken, while the correlation with lakes continues to strengthen. These conclusions can provide valuable insights for the planning of urban roads near water bodies.

The COVID-19 pandemic has led to thinking about the response to sudden-onset disasters, for which the transportation resilience of urban areas is crucial. The purpose of paper is to provide a graded assessment of urban transportation resilience to help city managers target policies and plans. Wuhan, the first city in China to be severely hit by COVID-19, was selected as the case study for this research. Based on an extensive survey of the travel characteristics of residents in central urban areas, the concept of “travel mode shift” was combined to classify residents into four modes, including non-motorized conventional travel, non-motorized over-distance travel, motorized adaptable travel and motorized non-substitutable travel. The potential transportation stoppages in different levels of epidemic impact were then divided into three scenarios, corresponding to each of the city’s three levels of transportation resilience. The concept of MWD (Maximum Willingness Distance) in active travel mode was further developed, which was divided into WMWD (Walking Maximum Willingness Distance) and RMWD (Riding Maximum Willingness Distance). Finally, a hierarchical assessment model of urban transportation resilience is developed based on the MWD distance threshold. Besides, the average income level of urban residents was also included in the assessment system. The following research conclusions were drawn: (1) The degree of transportation resilience in Wuhan showed an “S-curve” relationship with RMWD, with thresholds at RMWD = 2.5 km, 11 km and 23 km respectively. (2) The resilience of transportation in the suburbs of the city was weaker than in the city center, and the gap between the two increases as the RMWD increases, but the share of motorized transportation in short distance trips in the city center was still higher than desirable. (3) The upper-income groups in the city had more flexible travel options, while the lower income groups were less resilient to travel. Based on the results of the study, it is recommended that city managers can identify areas of low resilience and critical distance thresholds that may lead to sudden changes in transportation resilience in the event of a sudden disaster. This will lead to the development of improved policies. The special needs of socially disadvantaged groups should also be taken more into account in this process.

Introduction: The transition to carbon-centric urban policies requires innovative strategies to reduce transportation-related emissions. One such strategy is to shift travelers from high-carbon to low-carbon modes of transportation, a potential that has not been thoroughly quantified in urban planning frameworks.Methods: This study introduces the concept of urban transportation carbon reduction potential, defined as the share of travelers who could feasibly switch to low-carbon modes under optimized public transit conditions. A dynamic evaluation model was developed, integrating key factors such as connection, detour, and transfer barriers that influence residents’ public transit adoption. The model was applied to cross-river travel in Wuhan, China, using 10,327 Origin-Destination (OD) survey data points collected between 2023 and 2024. Multiple optimization scenarios were simulated to assess responses to transit improvements.Results: The analysis revealed three distinct zones of carbon reduction potential: stable, unlocking, and re-stable. Particularly, the unlocking zone exhibited substantial carbon reduction effects when targeted public transit improvements were implemented. Key thresholds were identified where small enhancements in transit connectivity led to sharp increases in low-carbon travel adoption.Discussion: This study presents a novel optimization model for urban transit planning, which explicitly incorporates natural environmental constraints and provides quantitative decision support for achieving SDG11 targets on sustainable cities and communities. It offers valuable insights for cities within the Yangtze River Economic Belt, aiding strategic investment in public transit to enhance low-carbon mobility.