Predicting cycling duration for a given route is essential for training planning and event preparation. Existing solutions rely on physics-based models that require extensive parameterization, including aerodynamic drag coefficients and real-time wind forecasts, parameters impractical for most amateur cyclists. This work presents a machine learning approach that predicts ride duration using route topology features combined with the athlete's current fitness state derived from training load metrics. The model learns athlete-specific performance patterns from historical data, substituting complex physical measurements with historical performance proxies. We evaluate the approach using a single-athlete dataset (N=96 rides) in an N-of-1 study design. After rigorous feature engineering to eliminate data leakage, we find that Lasso regression with Topology + Fitness features achieves MAE=6.60 minutes and R2=0.922. Notably, integrating fitness metrics (CTL, ATL) reduces error by 14% compared to topology alone (MAE=7.66 min), demonstrating that physiological state meaningfully constrains performance even in self-paced efforts. Progressive checkpoint predictions enable dynamic race planning as route difficulty becomes apparent.

翻译：预测给定路线的骑行时长对于训练规划和赛事准备至关重要。现有解决方案依赖于基于物理的模型，这些模型需要大量参数化，包括空气阻力系数和实时风速预测，这些参数对于大多数业余自行车手而言并不实用。本研究提出一种机器学习方法，利用路线拓扑特征结合从训练负荷指标推导出的运动员当前体能状态来预测骑行时长。该模型从历史数据中学习运动员特定的表现模式，用历史表现代理替代复杂的物理测量。我们采用单受试者研究设计（N=96次骑行），使用单运动员数据集对该方法进行评估。经过严格的特征工程以消除数据泄露后，我们发现，采用拓扑+体能特征的Lasso回归实现了MAE=6.60分钟和R2=0.922。值得注意的是，与仅使用拓扑特征（MAE=7.66分钟）相比，整合体能指标（CTL、ATL）将误差降低了14%，这表明即使在自定进度的努力中，生理状态也能有意义地限制表现。渐进式检查点预测使得随着路线难度变得明显，能够进行动态比赛规划。