Route Planning for Ice and Snow Tourism Based on Path Optimization Algorithm

With the booming global ice and snow tourism, ice and snow activities have become a popular choice for many travel enthusiasts to relax and vacation. It is particularly important to plan efficient and attractive tourist routes in order to enhance the ice and snow tourism experience for tourists. However, the planning of ice and snow tourism routes is a complex and meticulous task that not only considers the distribution and characteristics of attractions, but also closely monitors the convenience of transportation networks, the unpredictable weather conditions, and the personalized needs of tourists. In the face of these challenges, this article proposes an ice and snow tourism route planning algorithm that combines ant colony algorithm (ACO) and deep learning (DL) techniques. ACO, with its powerful search capability and optimization characteristics, can find the optimal path in complex spaces; DL can process and analyze large amounts of data, improving the accuracy of predictions. The combination of the two makes tourism route planning more intelligent and personalized. The experimental results show that the algorithm not only significantly improves the efficiency of route planning, but also effectively optimizes the travel experience of tourists, injecting new vitality into the sustainable development of the ice and snow tourism industry.