Xinge Ji

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.

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.

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.