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：徒步者和山地行走者通常将行进方向上的坡度（行走坡度）作为现有方法中预测行进时间（通过行进速度）沿路线行进的主要变量。针对山地奔跑的研究提出了可能改进该背景下速度算法的其他变量：地形坡度（山体坡度）和地形障碍程度。近年来数据可用性的提升及GPS追踪的广泛使用，使得在足够规模的行走速度模型中探索这些变量以检验统计显著性成为可能。我们利用来自英国近88,000公里徒步/山地行走轨道的公共GPS数据，对多种用于预测行走速度的现有模型进行了测试。轨道经过筛选以去除休息和非行走段。随后采用一种新的广义线性模型（GLM）来预测行走速度。GLM与现有规则的关键区别在于：GLM考虑了地形坡度（山体坡度）而与行走坡度无关，同时考虑了越野行进中的地形类型及地形障碍程度。研究表明所有上述因素均具有高度显著性，且相较于现有函数，模型均方根误差（RMSE）更低也支持了这一点。我们还观察到随着山体坡度增加，GLM与现有方法之间的RMSE差距增大，这进一步证实了该变量的重要性。