Density-Based Spatial Clustering and Simulated Annealing Algorithm Applied to the Travel Route Planning Scheme

With the implementation of China's visa-free entry policy, the optimization of foreign tourists' travel experience in China has become an important research topic in tourism management. The purpose of this paper is to plan a 144-hour tour route of mountain scenery in China for tourists, aiming to maximize the number of mountains visited while minimizing the total cost of admission and transportation. We first obtained the tourism data set of 352 cities from the tourism website, and then preprocessed this data set to determine the full score of 35,200 tourist attractions in 352 cities in China, and counted the number of attractions that obtained this highest score. Once the statistics are complete, we can identify the cities with the most top-rated attractions and rank them by the number of top-rated attractions they have. Through DBSCAN cluster analysis, cities with high-density mountain views are selected as the entry cities, and the simulated annealing algorithm is used to optimize the tourist routes, and the optimal tourist routes within the time limit are finally solved.