## 3 Methods  
### Study Area and Research Framework  
This study employed the Xuanwu Lake Scenic Area in Nanjing, China, as the experimental region. The area is rich in tourism resources and is especially popular among elderly tourists. Furthermore, the surrounding residential areas contain a large elderly population, making it an ideal location to accurately reflect the actual needs of elderly tourists during their visits. Moreover, the area contains a variety of rest areas, which are highly representative and applicable. Thus, the results of this study will provide valuable references for other tourist attractions in analyzing the adequacy rate of aging-friendly facilities in rest areas. The location of the study area and the distribution of relevant data are presented in Figure 1.  
This study presented an aging-friendly adequacy analysis framework for rest area facilities in tourist attractions, as illustrated in Figure 2. The framework primarily consisted of three components: (1) inputting the collected scenic area data into the object detection model YOLOv8 to detect visitor locations, automatically delineate service facility boundaries, and identify visitors occupying these facilities; (2) employing the MiVOLOv2 model to conduct age analysis of these visitors; and (3) applying the age-friendly sufficiency evaluation method for scenic area rest facilities proposed in Section 3.3 to determine the occupancy rate of each scenario and the proportion of elderly versus non-elderly visitorsat full capacity, thereby analyzing the age-friendly adequacy of the rest area facilities. A detailed explanation follows. First, three rest areas near Ginkgo Avenue, FangQiao, and LingQiao in the Xuanwu Lake Seenic Area were selected for the study. Videos were collected during peak afternoon hours on weekends, with a 2 h video recorded for each location. Every minute, three photos were taken at 0, 5, and 10 s. We adopted a nearly frontal shooting angle towards visitors, with the camera positioned approximately 15-20 m away and adjusted to align with visitors' seated body center points (at approximately 0.5 m height) to simulate a natural sitting observation perspective. Each video recording was conducted in 1920 \(\times\) 1080 full-HD resolution to ensure image clarity and detail representation. The field data were collected under favorable weather conditions (clear skies and moderate temperatures), which represent optimal meteorological parameters for outdoor recreational activities. These conditions ensure that the selected sampling days exhibit strong typicality and representativeness, effectively capturing the crowd dynamics and facility usage patterns during weekends under suitable weather. Furthermore, the data collection was conducted during non-holiday periods. Given that scenic areas typically experience significantly higher visitor volumes during holidays, if our findings indicated insufficient  
Figure 1: Location of the study area and data distribution. Figure 2: Framework for analyzing the aging-friendly adequacy rate of rest area facilities in tourist attractions. age-friendly adequacy in certain rest areas, such deficiencies would likely have been exacerbated during peak travel seasons. This sampling strategy explicitly accounted for temporal variations in visitor flow, ensuring that the research conclusions accurately reflected the routine utilization patterns of rest facilities. Consequently, the methodology enhanced the generalizability and external validity of the study outcomes. The YOLOv8 object detection model was employed to detect the positions of tourists and map out the boundaries of service facilities, identifying tourists occupying these facilities. Subsequently, the MiVOLOv2 model [13] was employed to analyze the ages of tourists, distinguishing between elderly and non-elderly groups. This enabled the calculation of facility usage across different age groups.