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：徒步者和山地步行者通常将行进方向上的坡度（步行坡度）作为现有方法中预测路线行走时间（通过步行速度）的主要变量。针对山地赛跑的研究提出了可能改进速度算法的其他变量：地形坡度（山坡坡度）以及地形障碍程度。近年来数据可用性的提升以及GPS追踪的广泛应用，使得在足够规模的步行速度模型中探索这些变量并检验其统计显著性成为可能。我们利用来自英国近88,000公里徒步/山径的公开GPS数据，测试了多种用于预测步行速度的现有模型。对轨迹进行过滤以去除休息和非步行路段。随后采用一种新的广义线性模型（GLM）来预测步行速度。GLM与现有规则的关键区别在于：该模型考虑了与步行坡度无关的地形坡度（山坡坡度），以及越野运动中的地形类型和地形障碍程度。所有因素均被证明具有高度显著性，且与现有函数相比，其均方根误差（RMSE）更低。我们还观察到随着山坡坡度增加，GLM与现有方法之间的RMSE差值增大，这进一步支持了该变量的重要性。