This project is designed to help to improve the bikeability of cities. Therefore, it combines a route choice model for the cyclists, based on topography, street/intersection size and presence or absence of bike paths along streets, OpenStreetMap data and the cyclists demand.

The setup is written for Linux/macOS systems. For Windows systems some commands might be slightly different, but the general setup structure stays the same. You need Python in version 3.12 and Julia in version 1.10 installed.

1. Create a Python virtual environment (named venv from here on) and add all packages from the requirements.txt.

   python3 -m venv /path/for/venv
   source /path/to/venv/activate
   pip install -r requirements.txt

2. Install julia packages via the julia REPL (in Julia REPL press ])

   pkg> add DrWatson
   pkg> activate path/to/this/project
   pkg> instantiate

3. Optional: If you want to include elevation/slope data for the algorithm you need to download world elevation data provided by the NASA Shuttle Radar Topography Mission (SRTM). This project only utilises the more precise 1 arc-seconds (30 meters) data. After downloading the data add the location to the environment variables.

   export SRTM1_DIR=/path/to/srtm1_data/

   If you don't want to use the elevation data you can skip this step, all node elevations will be set to 0.

All commands assume you are in the project folder.

1. Prepare data for algorithm with given street networks.

   python3 scripts/hh_prep.py

   It will take only a couple of seconds.
2. Run the simulations.

   julia scripts/hh_run.jl

   If you want to make use of parallelizing parts of the calculations, specify the number of threads (e.g. 8) available to julia.

   julia --threads 8 scripts/hh_run.jl

   The calculations take 5-15 minutes depending on the given number of CPU cores and their speed.
3. Now the figures can be generated, some additional information will be printed in the commandline.

   python3 examples/hh_plot.py

   The plots will be saved in the examples/plots/ folder. Should take less than 5 minutes for all plots.

The notebooks for Copenhagen cannot be executed as most of the data is not publicly accessible.

You may notice that the julia script start with the commands:

using DrWatson
@quickactivate "BikePathNet"

which auto-activate the project and enable local path handling from DrWatson.

1. Python 3.12.3
2. Julia 1.10.0
3. Python packages and dependencies: See requirements.txt
4. Julia packages and dependencies: See Mainfest.toml

The data used for Hamburg (hh) in the example folder is extracted from a publicly available data set provided by Call a Bike by Deutsche Bahn AG (License: CC BY 4.0).