A158: Artificial Intelligence Models' Impact on Mass Cycling

With the growing emphasis on health and increasing demand for sustainable travel, mass cycling (MC) has experienced rapid development. However, traditional bicycle designs exhibit notable deficiencies in functionality, personalization, and health management. While artificial intelligence (AI) technology has been extensively applied in various fields, including professional cycling competitions like the Tour de France, its potential to introduce new possibilities to MC remains uncertain. Therefore, we undertook a comprehensive investigation into the impact and application of AI on the development of MC, aiming to facilitate the digital and intelligent transformation of MC. Method: We selected representative bicycle enterprises and related projects in the application of AI, including Specialized, Trek, Giant, Garmin, and some mass-based bicycle races for research. Using the principle of comparative experiments, we collected and compared multiple data in aspects such as the evaluation of riding performance (average riding distance, average riding speed, fatigue, etc.), user satisfaction, and the participation rate in races of MC under the circumstances of traditional mode and the AI-based mode through web information queries, DeepSeek, SPSS, and Tableau. Thus, we analyzed the impact of AI on the development of MC and made an objective summary. The research shows (compared with the traditional mode): (1) AI analyzes users' body shapes, skeletal features, riding habits, etc., through deep learning algorithms to customize bicycles on a personalized basis. (User satisfaction increased by 92%, the riding comfort score rose by 40%, the average daily riding distance grew by 12%, and the fatigue after long rides decreased by 30%). (2) The AI-driven health management system can help users avoid excessive fatigue or injuries. (The average weekly riding distance increased by 25%, and the performance improved by 8%). (3) The MC events using AI algorithms have optimized the route and diversified the competition formats, attracting more participants. In previous studies, AI has brought benefits to the bicycle industry, yet there is scant research based on MC. However, this study reveals some challenges: (1) The application of AI algorithms in low-resource scenarios; (2) Issues related to user privacy protection; (3) Problems concerning the popularization of the technology. We suggest: (1) Verify the feasibility and effectiveness of AI technology in practical applications in MC through small-scale pilot experiments. (2) Strengthen user education and publicity to enhance the understanding and acceptance of AI technology.