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.

Current research lacks focus on the vulnerability of urban river-crossing passages related to the impact of disruptions on travel. We propose a method to assess their vulnerability and impact areas by evaluating vehicle detour degrees and public transport substitution effects during possible interruptions. Applied to Wuhan, China, known for its water and bridges, the study reveals significant variations in vulnerability among different river-crossing passages. Passages in outer urban areas are more vulnerable than those in core areas, with larger impact zones and higher impact degrees. Additionally, the vulnerability-affected areas display a strip distribution, and it is harder for outer-area residents to switch from driving to public transport during interruptions. The study’s methods and findings offer valuable references for sustainable river-crossing transportation planning in riverine cities.

The aim of this study was to identify key factors affecting urban low carbon dioxide travel levels and analyse their impact mechanisms. Using Wuhan in China as a case study, a method to identify residents' travel modes through mobile signalling data and calculate regional low-carbon travel (LCT) levels was developed. Interpretable machine learning algorithms were applied to analyse the factors and mechanisms driving differences in LCT levels at a 1 km × 1 km scale. The results show that land use density and land use diversity are the most significant influences, followed by building density, surface roughness and public transportation provision. Among various points of interest considered, the density of parking lots and residential areas was found to have a notable impact. The relationship between the indicator values of influencing factors and urban LCT levels was also examined. Key points corresponding to marginal effects on LCT levels were identified, providing insights for developing precise travel strategies in urban planning.