用路线地形和训练负荷预测骑行时间，更准且无需复杂参数。
Cycling Race Time Prediction: A Personalized Machine Learning Approach Using Route Topology and Training Load
- 结合路线地形与运动员训练负荷数据建模
- 误差仅6.6分钟，比仅用地形降低14%错误率
- 适合想科学规划骑行训练的业余爱好者
预测给定路线的骑行时长对训练规划和赛事准备至关重要。现有方法依赖需大量参数化的物理模型，包括空气阻力系数和实时风速预报，对大多数业余骑手不切实际。本文提出一种机器学习方法，利用路线地形特征与基于训练负荷指标推导的运动员当前体能状态进行骑行时长预测。模型通过历史数据学习个体化表现模式，以历史表现代理替代复杂的物理测量。我们在单人数据集（N=96次骑行）上采用N-of-1研究设计进行评估。经严格特征工程消除数据泄露后，发现使用拓扑+体能特征的Lasso回归模型达到MAE=6.60分钟，R²=0.922。值得注意的是，引入体能指标（慢性训练负荷CTL、急性训练负荷ATL）使误差相比仅用地形减少14%（MAE=7.66分钟），表明生理状态在自主节奏运动中仍显著影响表现。逐步检查点预测支持随路线难度变化动态调整比赛策略。
原文摘要 · Abstract (English)
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 (Chronic Training Load (CTL), Acute Training Load (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.
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