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Our entry for the Junction 2018 hackathon: a tool for predicting city bike station resupply demand

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Junction 2018 - Weatherall

Our entry for the Junction 2018 hackathon: an app for predicting city bike resupply demand. Consists of

  • A prediction model based on city bike usage statistics, FMI weather data and Telia Crowd Insights data
  • Integration to a Vaisala sensor device for real-time weather data
  • A map-based UI

Team

  • Fraser Barclay
  • Mohamed Karim Bouaziz
  • Mikaela Hallenberg
  • Aleksi Pekkala
  • Katri Tegel

Screenshot

Install

Setup backend

  1. Install pipenv
  2. Run pipenv install to install deps
  3. Start app with pipenv run python app.py (or open pipenv shell and run python app.py)

Setup frontend

  1. cd frontend
  2. npm install
  3. npm run serve -> App will run in http://localhost:8080/

Setup datascience

Jupyter notebook

  1. cd datascience
  2. pip install -r requirements.txt
  3. jupyter notebook -> App will run in http://localhost:8888/

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Our entry for the Junction 2018 hackathon: a tool for predicting city bike station resupply demand

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