Python algorithm to derive bus stop locations from crowdsourced geolocalized data. The extracted bus stops are then viewable on a webGIS.

• Python 2.7
• pip
• virtualenv (recommended)

• Node.js (and npm)

IMPORTANT: Execute the following commands in the root folder of the project.

1. Create python virtual environment with virtualenv .env
2. Activate the virtual env with .env\Scripts\activate on Windows or source .env/bin/activate on Linux
3. Install all the required python dependencies with pip install -r requirements_win.txton windows or pip install -r requirements_linux.txton linux ¹
4. Run the Bus Stops Finder algorithm with python main.py
5. Follow the on-screen logging, until the proccess ends
6. Move to "website" folder with cd website
7. Install node dependencies, by executing npm install
8. Once npm has finished, start the node server with node nodeServer.js
9. Open http:\\127.0.0.1:8080 on your browser

Some dependencies in the requirements.txt might not be automatically resolved by pip. In this case, it is needed to manually install them, following the module specific documentations. For Windows, it is possible to download already built binaries from here Unofficial Windows Binaries for Python Extension Packages and then install them with pip install path\to\the\package.whl For Linux, a ready to install python dependency packages should be available in the distro repositories.

To prepare the development environment, just follow the first 3 steps of the Setup and Usage paragraph.

For testing the project, activate your virtual env ( second step of Setup and Usage) and then run the following:

python -m unittest discover

OpenStreetMap for the webGIS base map and Bus stops and stations data.

Overpass-Turbo for the bus data export from OSM.