# Research on Automatic Generation of Park Road Network Based on Skeleton Algorithm
## 2 Related Research Status  
The organization and analysis of traditional park roads frequently employ qualitative research approaches. Along with the advancement of computer science, geography information, and other disciplines, numerous accomplishments have been attained in the research on the automatic generation of road networks. The technologies utilized for road network generation can be approximately categorized as follows. One type is the road network generation approach based on urban planning and urban evolution. Such approaches focus on how to generate road networks that are in line with reality based on urban planning or urban historical evolution data. Commonly utilized methods encompass simulation techniques such as L-system, the Ant Colony Algorithm, and Cellular Automata. In 1958, [PERSON] [3] initially proposed the utilization of the Digital Terrain Model (DTM) to accomplish the automatic generation of highway roads; [PERSON] et al. [4] introduced the L-system into road network generation in 2001 and incorporated objective factors such as the environment and personnel distribution; [PERSON] et al. [5] and [PERSON] [6] employed the Ant Colony Algorithm to investigate the road network planning issue; [PERSON] [7] utilized Cellular Automata, which are conceived and developed by the High-Quality Laboratory at Politecnico di Torino, to generate urban models on urban and regional scales. Some researchers apply GIS technology for the path planning of park road systems, yet GIS technology merely plays an auxiliary analysis role in the midst of this, and numerous artificial planning adjustments are still necessary when confronted with complex terrains. These tools offer highly significant support for technicians, but appropriate road generation methods remain limited. Another type of technology is the road network generation approach based on measured data. This type of approach makes use of measured data, such as urban maps and traffic flows, and extracts the characteristics of the road network through technologies like big data and machine learning to generate artificial road networks that are closer to reality. For instance, [PERSON], [PERSON] et al. [8] employed deep neural networks and planning-guided methods to automate the generation of street networks; [PERSON] et al. [9] and [PERSON] and [PERSON] [10] endeavored to automatically generate road network layouts through deep generative models; [PERSON] et al. [11] proposed approaches for mining regional traffic connection intensity and evaluating urban road networks based on mobile phone data. The application of deep learning methods in road design has become a possibility, yet such methods still have numerous problems. Deep learning and other methods have large sample sizes and typically require a vast amount of data for training, and obtaining sufficient data can be extremely challenging. Training deep learning models usually demand a considerable amount of computing resources and have high requirements for research costs. More significantly, deep learning models are often regarded as \"black box\" models, and their decision-making processes are difficult to comprehend and explain, and it is challenging to carry out refined control. This aspect is of great significance for the design field, which severely affects the design quality. Furthermore, deep learning methods encounter significant challenges in addressing changes in the actual environment, such as diverse terrain variations, lighting conditions, and other scenarios not encompassed in the training data. In contrast to the regular urban layout, the layout within parks is more flexible and subjective. Essentially, the existing research outcomes are scarcely directly transferable to the automatic generation of park road networks. In recent years, numerous scholars have also employed the skeleton algorithm in domains such as transportation and road design. The practicality and mapping capability of the skeleton algorithm in computer graphics hold significant potential [12].