Unsupervised Analysis of Cyclist Performance for Route Segmentation and Ranking

This paper presents a study on the analysis of cycling tours along a designated route, addressing the limited attention given to non-professional cyclists in existing research. Unlike previous work focused on elite athletes, this study considers a broader population, including commuters, recreational riders, and fitness-oriented cyclists. Data was collected using advanced sensors to capture diverse ride characteristics. An unsupervised learning approach was applied to segment cyclists based on behavioral and performance patterns. Furthermore, a novel ranking method based on genetic algorithms was developed to classify and prioritize cyclist groups meaningfully. Experiments were conducted on a newly proposed dataset tailored to this objective, enabling deeper insights into cycling dynamics across user types. The results validate the effectiveness of both the segmentation and ranking methods, offering practical implications for route planning and cyclist-focused infrastructure management.