Intelligent Travel Planning: A Restricted Boltzmann Machine Approach with Traffic Data Fusion

Travel planning can be a complex and overwhelming task due to the multitude of destination choices and variable external factors. This paper presents a machine learning-based intelligent recommendation system designed to streamline the travel planning process. Utilizing Restricted Boltzmann Machines (RBM), the system captures user preferences, historical travel data, and dynamic real-time factors such as weather conditions and local events to generate personalized destination recommendations. The architecture is implemented using Python for machine learning components, Golang for backend API services, and Docker for containerized deployment. Experimental results demonstrate that the proposed approach yields more accurate and user-centric suggestions compared to conventional recommendation methods. This work highlights the potential of artificial intelligence to enhance travel experiences, making trip planning more intuitive and efficient. Future improvements will focus on integrating richer datasets, refining user feedback mechanisms, and implementing real-time optimization techniques to further improve recommendation accuracy.