Research on Algorithms for Optimizing Tourism Route Planning Using Artificial Intelligence

This study proposes a tourism route optimization algorithm based on reinforcement learning, which achieves multi-objective optimization (such as shortest time, lowest cost, best experience, etc.) through intelligent learning and decision-making. This algorithm combines multi-dimensional data such as scenic spot information, traffic flow and weather, and adjusts the path planning strategy in real time through the reinforcement learning model to optimize the travel experience of tourists. The experimental results show that the algorithm can significantly improve the efficiency and accuracy of path planning when processing large-scale tourism data. Compared with traditional path planning algorithms (such as Dijkstra, A=, etc.), the reinforcement learning-based model shows stronger adaptability in multi-objective optimization and dynamic adjustment. In the experiment, the travel time of the reinforcement learning algorithm was shortened by an average of 15.8%, and the total cost was reduced by 12.4%. In addition, the system can dynamically adjust the route according to real-time traffic conditions and weather changes to ensure the best travel experience. This study provides a new idea for tourism route planning and demonstrates the great potential of artificial intelligence in tourism management, which has broad application prospects.