Hikers and hillwalkers typically use the gradient in the direction of travel (walking slope) as the main variable in established methods for predicting walking time (via the walking speed) along a route. Research into fell-running has suggested further variables which may improve speed algorithms in this context; the gradient of the terrain (hill slope) and the level of terrain obstruction. Recent improvements in data availability, as well as widespread use of GPS tracking now make it possible to explore these variables in a walking speed model at a sufficient scale to test statistical significance. We tested various established models used to predict walking speed against public GPS data from almost 88,000 km of UK walking / hiking tracks. Tracks were filtered to remove breaks and non-walking sections. A new generalised linear model (GLM) was then used to predict walking speeds. Key differences between the GLM and established rules were that the GLM considered the gradient of the terrain (hill slope) irrespective of walking slope, as well as the terrain type and level of terrain obstruction in off-road travel. All of these factors were shown to be highly significant, and this is supported by a lower root-mean-square-error compared to existing functions. We also observed an increase in RMSE between the GLM and established methods as hill slope increases, further supporting the importance of this variable.

翻译：远足者和山地徒步者通常使用行进方向上的梯度（行走坡度）作为现有步行时间（通过步行速度）预测方法中的主要变量。针对越野跑的研究提出了可能改进该场景下速度算法的额外变量：地形坡度（山坡坡度）和地形障碍程度。近期数据可用性的提升以及GPS追踪技术的广泛应用，使得在足够规模的步行速度模型中研究这些变量、检验其统计显著性成为可能。我们利用英国近88,000公里远足/徒步路线的公开GPS数据，测试了多种现有步行速度预测模型。通过数据过滤去除休息段和非步行路段，随后采用新的广义线性模型（GLM）预测步行速度。GLM与现有规则的关键区别在于：该模型同时考虑了地形坡度（不依赖于行走坡度）、越野路径的地形类型及障碍程度。结果显示所有上述因素均具有高度显著性，且相较于现有函数，该模型均方根误差更低。我们还发现随着山坡坡度增大，GLM与现有方法之间的RMSE差异进一步扩大，这进一步印证了该变量的重要性。