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：徒步旅行者和山地步行者通常将行进方向上的坡度（行走坡度）作为现有徒步时间（即步行速度）预测方法中的主要变量。针对山地越野跑的研究表明，引入其他变量（如地形坡度、地形阻碍程度）可能改进速度算法。近年来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检验这些变量的统计显著性成为可能。我们利用英国近88,000公里徒步/登山轨迹的公开GPS数据，测试了多种现有步行速度预测模型。通过过滤轨迹剔除休息与非步行路段，我们构建了一个新的广义线性模型（GLM）用于预测步行速度。与现有规则的关键差异在于：该GLM模型不仅独立于行走坡度考虑了地形坡度，还纳入了越野行进中的地形类型与地形阻碍程度。结果表明，所有新增变量均具有高度统计显著性，且相比现有函数具有更低的均方根误差（RMSE）。此外，随着地形坡度增加，GLM与现有方法之间的RMSE差值显著上升，进一步证实了该变量的重要性。