This repository provides files and notebooks for predicting hiking times, using GPX tracks as inputs. It contains notebooks for training of simple models and scripts for running inference on new GPX tracks. The hiking time is estimated as the moving / walking time, to which times for breaks / pauses need to be added in order to get the duration of a hike. When using own records of GPX tracks for training, the models estimate personalized walking times.
The estimates are based on simple statistics about GPX tracks, GPX tracks themselves, or a combination of the two. The statistical features include the length of tracks, the total elevation gain and the total elevation loss. In order to obtain good predictions based on a relatively small number of GPX tracks, the latter are split into smaller pieces which are used as inputs for the model. The same preprocessing steps are applied during inference. A dataset of 40-50 GPX tracks allows to train models that predict walking times much better than standard formulas.
The repository includes files for extracting data from tracks, training models and making predictions.
Run
conda env create -f environment.yml
and activate environment hikingTimePredictor by typing
source activate hikingTimePredictor
and run
pip install -r requirements.txt.
Run
python prepareData.py base_folder filter_key
where base_folder is the folder in which the program searches recursively
for GPX tracks (with file ending .gpx). It filters for files that contain
filter_key in their path, which could for example be Hiking.
Open the notebook hikingTimeRegression_v1.ipynb (or v2, v3) and follow the steps described there.
Run
python inference.py input_file model_type
where input_file is the name of the GPX track for which walking times should be estimated. model_type is either 'simple' for v1, 'recurrent' for v2 and 'mixed' for v3.
MIT, see LICENSE for more information