I wonder if there are any citybikes left on Yliopistonkatu.. 💭
Citybike predictor shows the past and predicted availability of citybikes!
NOTE: as of 4.1.2020, the backend is not functional in historical mode due to the unavailability of historical citybike availability data from HSL at https://dev.hsl.fi/citybike/stations/. The app will be in summer mode until further notice.
These examples demonstrate the usefulness predictions in various situations:
- No bikes but availability is likely to increase - wait a while and get a bike 🚲
- There are some bikes but not for long - hurry up! 🏃
- No bikes and no spikes for a while - take the bus 🚎
- The application is available at https://citybike-pred.herokuapp.com/.
- The predictions are provided from a REST API at https://citybike-helsinki-predictor.herokuapp.com/.
Please note that our current application servers might take around 30-45 seconds the start if they have gone idle.
Changing and expanding station network
- This application was created in 2018 and was trained with those stations that were in use in Helsinki both in the summers of 2017 and 2018.
- The station network expanded in Helsinki (and in surrounding cities) for 2019: the availability data and predictions will be displayed correctly for only those stations whose ID's have remained constant and that have been in use in the period 2017-.
We have combined citybike availability data from HSL and weather data from FMI. Read more about the data here: "Data wrangling and backend operation"
- Bike availability:
- City bike data source (both historical download and online): https://dev.hsl.fi/citybike/stations/
- Weather data: Ilmatieteen laitos / Finnish Meteorological Institute under license CC BY 4.0.
- Historical data loaded from: https://ilmatieteenlaitos.fi/havaintojen-lataus#!/
- Weather observations and predictions loaded from FMI open data API, see https://en.ilmatieteenlaitos.fi/open-data-manual-fmi-wfs-services
For testing and demonstration purposes we have prepared data from 7 months (2017-06, 2017-08, 2017-09, 2018-06, 2018-07, 2018-08 and 2018-09). Please see the /data-folder for more details.
- Backend source code is in this repository
- Frontend for the citybike predictor app is a separate repository.
The model is a "mixed model" consisting of a linear combination of different n-degree polynomials that were fit for each factor in relation to station bike-availability (factors: temperature, rain amount, hour of the day). The model is trained on data from 2018-08 and 2018-09. The R^2 value of the model (0.563288572018) provides a basic measure of how well the observed outcomes of bike-availability are predicted by our model. Notice: the observations for the bike-availability as a single data-series was obtained by summing the bike-availability data-series for each bike-station. We call this the "aggregate availability". Likewise, the prediction data-series was obtained by summing the bike-availability predictions for each bike station.
Read more about the model here: "Model"
Was the choice of predictors chrystal clear from the start? Nope. Read some thoughts on model variables in the early stage of the project: "Early thoughts on variables"
Backend server has REST API has endpoints predictions and past availability. For more detailed information please see: API docs
The service was a project for "Introduction to Data Science" -course at the University of Helsinki Data Science master's programme, fall 2018.
- Lea Kosonen (focus: machine learning model)
- Mikko Kotola (focus: backend and weather data)
- Juha-Pekka Moilanen (focus: frontend and bike data)
How could the project be continued? See roadmap.
To start backend server, run:
python python-src/server.py
Server will start on localhost:3001.
The server expects the environment variable FMI_API_KEY to be found in either in actual environment variables or the file .env located at the root of the project. Contents of the .env file:
FMI_API_KEY=your-fmi-apikey-here
For e.g. Heroku deployment, you can set the environment variable with the command:
heroku config:set FMI_API_KEY=your-fmi-apikey-here
NOTE: frontend is in a different repository: Citybike-front.
- Go to frontend repository root and start a local webserver to serve html and js files
python3 -m http.server
- Open browser from localhost:8000
- Click on home.html
NOTE: the frontend uses the Heroku-deployed backend. If you want to use a locally run backend, change the first line in js-src/plotter.js to const baseUrl = 'http://localhost:3001'
