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:徒步旅行者通常使用沿路走的坡度(行走坡度)作为预测步行时间(通过步行速度)的主要变量。至于在摔跤运动的研究中,人们提出了进一步的变量来改进此情况下的速度算法,如地形的坡度(山坡)和地形阻塞水平。近年来,数据可及性的改善及GPS跟踪的广泛应用,使得可以对这些变量在足够规模的步行速度模型中进行探索,以测试其统计显著性。我们针对来自近88000公里英国行走/徒步追踪公共GPS数据测试了各种已建立的用于预测步行速度的模型。追踪记录被过滤以移除中断和非步行部分。然后采用了一个新的广义线性模型(GLM)来预测步行速度。GLM和已有规则之间的主要差异在于,GLM考虑了地形的坡度(山坡),并且是不考虑行走坡度的; 并且在越野旅行中,还考虑了地形类型和地形阻碍水平。所有这些因素都被证明是非常显著的,这得到了比现有函数更低的均方根误差的支持。我们还观察到,在GLM和已有方法之间的RMSE随山坡的增加而增加,进一步支持了这个变量的重要性。