Research on Intelligent Personalized Exercise Path Recommendation Algorithm Based on Traffic and Health Big Data

The personalized exercise path recommendation problem based on traffic and health big data is a key challenge in intelligent health management, requiring multi-dimensional optimization of factors like traffic, environment, and user health. To address high-dimensional challenges, this paper proposes the Intelligent Personalized Exercise Path Recommendation Algorithm (IPEPR Algorithm), integrating traffic flow and health data for optimized path recommendations. IPEPR algorithm dynamically adjusts routes in real-time based on user needs and changing conditions. Comparative experiments across different Body Mass Index (BMI) groups, using the traditional A-star algorithm as a control, show that IPEPR algorithm improves recommendation accuracy and real-time performance. Particularly in overweight and obese groups, it significantly increases calorie consumption and optimizes exercise intensity and efficiency, demonstrating its advantages in personalized exercise planning.