To explore the reasonable width of urban road green belts under the background of carbon peak policy and provide quantitative design guidance for urban green-belt planning, a closed-loop design that integrates urban traffic, carbon emissions, urban greening, and carbon absorption was established at the road network level. First, the factors affecting urban transport carbon emissions were analyzed from the aspects of environment, economy, population, and technology. A carbon emission prediction model was constructed based on the STIRPAT (stochastic impacts by regression on population, affluence, and technology) model. The path of the carbon peak in the transportation sector was simulated. A scenario under the carbon peak target was designed to determine the development trend of each factor. The layout forms and design requirements of urban road greening were then summarized. The annual carbon sequestration amounts of different plant types were calculated. An objective optimization model was constructed with the lowest cost of greening as the objective function. The constraint conditions specify that the carbon absorption be greater than the carbon emissions, in addition to specifying the basic greening design requirements. Finally, an empirical study was conducted on a road network area in Xi’an. According to the results, the traffic carbon emissions of Xi’an City will be 2.71 Mt in 2025, with road traffic accounting for 1.94 Mt. Without considering the road-measurement green-width constraint, the proportions of the road green belt for six road classes and the red-line width under the carbon peak target are 0.31, 0.33, 0.40, 0.22, 0.21, and 0.23. These research results provide a quantitative and reliable basis for designing the width of an urban green belt under the background of carbon peak policy. Under proportion restrictions, road greening yields better performance by considering both aesthetics and road characteristics.

Travelers’ attention to high-quality human habitats is increasing, and the role of urban greenways in improving the quality of travelling spaces has also been appreciated. This research aims at making the weight calculation of suitability more scientific and reasonable, clustering the shared bicycle travelling OD points according to suitability, and analyzing the distribution of OD points. Taking Xiamen as an example, multiscale geographically weighted regression and entropy weight methods were used to calculate the weights of variables using multi-source big data. The clustering of origin-destination (OD) points for shared bicycle travel are identified using the DBSCAN clustering algorithm, which can provide accurate support for greenway planning and shared bicycle placement. The results show that the density of tourist attractions, POI entropy index, road density, and intermediate are four important factors affecting the suitability of greenways. The clustering results of the shared bicycle OD points show that the high-aggregation areas of origin and destination points are located in the northeast and southwest directions as well as west and east directions. This study provides a theoretical and modelling analysis reference for greenway planning and design.

Mechanical parking lots and spaces are known as the “energy saver” of urban space because of their small footprint, high efficiency, and environmental protection. However, the location and number of mechanical parking lots and space planning have become an important part of effectively exerting the function of mechanical parking lots. In order to explore the planning problem of mechanical parking lots, this study used the gradient boosting decision tree–Shapley additive explanations (GBDT-SHAPs) to measure the non-linear impact of the urban built environment on the mechanical parking spaces ratio and extract the optimal threshold of key variables. The results show that land use mix and distance to Bell Tower (CBD) are two key variables affecting mechanical parking space planning, and both have a non-linear relationship with the built environment. The threshold values are 0.83 and 7 km. The results will provide urban and transport planners with strategies for planning mechanical parking lots and spaces.

Despite the critical role of parking supply in urban transportation, the nonlinear relationship between parking facilities and commute mode choice remains poorly understood. This study systematically examines the nonlinear influences of the built environment, with a focus on parking facilities, on commuting mode choice using 2019 survey data from Xi’an. A Gradient Boosting Decision Tree (GBDT) model combined with Accumulated Local Effects (ALE) analysis was applied to capture complex relationships. The parking-related variables encompass factors such as parking fees, distance to the nearest parking lot, number of parking spaces, and parking density. Key findings indicate that car ownership, gender, land use mix-work, and distance to CBD-work, distance to CBD-home, and number of parking spaces-home at home are significant predictors. Notably, the number of parking spaces proved more influential than parking density. A positive correlation was observed between parking supply at workplaces and car usage, with a sharp increase in the probability of car ownership when supply exceeds 2800 spaces/km². Similarly, a threshold of 7500 spaces/km² around residences significantly promotes car dependence. The results underscore the importance of incorporating nonlinear parking supply effects into travel demand forecasting and provide insights for developing targeted parking management policies.

Low-altitude tourism, which integrates the low-altitude economy with cultural tourism, is a key driver of the sustainable transformation of county-level tourism, offering a low-carbon alternative to ground-based travel in topographically constrained regions. Taking Huangling County, Shaanxi Province, as a case study, this research develops a systematic framework for siting takeoff and landing points and planning low-altitude tourism routes based on suitability analysis and the minimum cumulative resistance (MCR) model. Fourteen indicators across four dimensions—topography, land use, transportation accessibility, and scenic connectivity—were evaluated, with weights objectively determined by the entropy weight method. GIS-based spatial analysis identified six candidate points. The results show that safety-sensitive factors (distances to residential areas, schools, and hospitals) carry the highest combined weight (40.97%), followed by terrain factors (34.91%). An MCR model was then applied to extract preliminary spatial corridors via least-cost path analysis. The integrated ‘suitability evaluation–resistance modeling–network analysis’ approach offers a replicable framework for low-altitude tourism planning in similar regions.