Open Data Bikes Analysis

Analyze bikes sharing station data from Bordeaux and Lyon Open Data (French cities).

Use the Python 3 programming language in Jupyter notebooks and the following libraries: pandas, numpy, seaborn, matplotlib, scikit-learn, xgboost.

See the requirements.txt file for the dependencies. If you use conda and the conda environment, you can just do: conda env create -f environment.yml and the source activate bikes.

Clustering

Highly inspired by the Usage Patterns Of Dublin Bikes Stations article and his great notebook.

Analyze the daily profile and plot a map with a color for each usage pattern.

• Notebook for usage pattern in Bordeaux
• Notebook for usage pattern in Lyon

Example of pattern

You can see the percentage of available bikes for 4 different daily profiles. Note the analysis only keep job days.

• Blue profile: people who take bikes in the morning, roll them into 'green' stations and go back home in the evening.
• Green profile: opposite of the blue profile.
• Orange profile: not very used stations. Sometimes too far from city center. Sometimes very close the tramway stations.
• Red profile: stations where people go in the evening

Maps

Bordeaux Map Clustering

Lyon Map Clustering

Predict (draft)

Play with some different models to predict the number of available bikes (or a kind of availability).

• See the script prediction.py which uses XGBoost to predict the bicycle-station availability
• Notebook for prediction in Lyon

Prediction Map

From history data (two weeks), prediction at T+30 minutes for every station in Lyon (France).

• Blue means there are several available bikes
• Red means there are just a few available bikes

Data

• Data Grand Lyon Website
• Data Bordeaux Website

See the lyon.tar.gz and bordeaux.tar.gz.