Requirements:
mkdir data
virtualenv venv
source venv/bin/activate
pip install -r requirements.txt
Configuration file:
Copy the config example file :
cp config/config.yaml.dist config/config.yaml
and modify the variable as you wish :
velib_files_path: <absolute path to station*.gz files>
weather_files_path: <absolute path to paris_weather*.gz file>
logging_level: INFO
To make the import work, you should end your path with a / and compressed data in the folders.
First Launch
python run.py
It will create a training.csv file, which will be use in future runs to accelerate loading.
Modifications : I had to modify a bit the weather file to be able to read it easily
The program work this way :
- Load local challenge data (velib history + weather)
- Crawl additional data from open data paris (theatre, museum and market locations)
- Merge data using timestamps or coordinates
- Split the data into training set and test set (
split_proportionvariable) - Fit a Classifier or a Regressor the training set (here
RandomForestRegressor) - Predict on the testset
Then it will print the feature importance and plot the error rate based on the confidence level : the predictions are floats between 0 and 1. If the confidence level is 0.5, every prediction above 0.5 would mean 'There are some stands left in the station'. We then compare to the actual value.
The use case would be : I know the station near me has bikes. I will arrive to my destination in 1 hour. Would the station I'm targeting have stands left ?
### Results
The testset is a 30% subset of the trainset. We can make multiple run to have more accurate results but with few data a single run gives the same results.
Here the error rate is very low (assets/prediction_rate.png) and could be explain by an overfitting. A velib data crawler had been developped but couldn't be used because of missing weather data (even though the first 4 important features are temporal data (see below)).
I used the RandomForestRegressor for :
- its well know accuracy
- the feature importance output
- the oob estimation
Feature importance
| feature | importance |
|---|---|
| minute | 4.9833051357 |
| hour | 3.7418882181 |
| day | 1.9510148148 |
| wday | 1.0712071957 |
| tempi | 1.0058053216 |
| number | 1.0014975615 |
| tempm | 0.9662565957 |
| theatre_distance | 0.6568507767 |
| museum_distance | 0.6205061519 |
| bike_stands | 0.6125561546 |
| market_distance | 0.5977898978 |
| month | 0.539800456 |
| conds_Mostly Cloudy | 0.1460951477 |
| icon_mostlycloudy | 0.1424671599 |
| icon_clear | 0.1260777932 |
| conds_Clear | 0.1232655831 |
| conds_Scattered Clouds | 0.109913737 |
| icon_partlycloudy | 0.1092215464 |
| conds_Partly Cloudy | 0.0877612316 |
| conds_Light Rain | 0.0524947526 |
| icon_rain | 0.0461725471 |
| rain | 0.040896624 |
| conds_Overcast | 0.0368349133 |
| icon_cloudy | 0.0362478491 |
| conds_Fog | 0.024847045 |
| fog | 0.0233143817 |
| conds_Light Drizzle | 0.0201011979 |
| icon_fog | 0.0196913614 |
| conds_Rain | 0.017992301 |
| conds_Patches of Fog | 0.0171216008 |
| conds_Shallow Fog | 0.0160653025 |
| conds_Light Rain Showers | 0.0135096732 |
| icon_unknown | 0.0126847769 |
| conds_Unknown | 0.0120640566 |
| conds_Rain Showers | 0.0044508173 |
| icon_hazy | 0.0025105848 |
| conds_Mist | 0.0024687149 |
| conds_Heavy Rain Showers | 0.002114527 |
| conds_Drizzle | 0.0015543805 |
| conds_Light Fog | 0.0011291071 |
| icon_tstorms | 0.0008785905 |
| thunder | 0.0008064289 |
| conds_Light Thunderstorms and Rain | 0.0007679871 |
| precipi | 0 |
| bonus | 0 |
| snow | 0 |
| banking | 0 |
| precipm | 0 |
### Future work
- Use weather open data
- Add more relevant features
- Minutes from midnight
- Meeting area
- Be able to mix contextual predictions with historical data (active learning)
These are papers talking about bike sharing in different ways, which could help for future improvements.
- Clustering the Velib' origin-destinations ows by means of Poisson mixture models
- Analyse de données sur des stations Vélib
- Classification of the vélib stations using Kmeans, Dynamic Time Wraping and DBA averaging method
- MODELING OF REPOSITIONING ACTIVITIES IN BIKE - SHARING SYSTEMS
- Urban cycles and mobility patterns : Exploring and predicting trends in a bicycle-based public transport system
- An Analysis of Bike Sharing Usage : Explaining Trip Generation and Attraction from Observed Demand
- Modelling the Impact of Weather Conditions on Active Transportation Travel Behaviour
- Model-based count series clustering for Bike Sharing System usage mining, a case study with the Velib system of Paris
- Analyse des trajets de Vélib par clustering
- From bicycle sharing system movements to users: a typology of Vélo’v cyclists in Lyon based on large-scale behavioural dataset
- Factorisation de reseaux temporels : etude des rythmes hebdomadaires du systeme Velo'v
- Cartographie des pratiques du Vélo’v : le regard de physiciens et d’informaticiens
- Problème d’optimisation de disponibilité des véhicules partagés
- Modélisation statistique cyclique des locations de Vélo’v à Lyon
- Peut-on attraper les utilisateurs de Vélo’v au Lasso
Used
- Annuaire Immobilier de l'Enseignement Superieur
- Liste des marchés de quartier à Paris
- Salles de cinémas à Paris
- Stations Vélib - Disponibilités en temps réel
Not used