Data-driven micromobility network planning for demand and safety

This is the source code for the scientific paper Data-driven micromobility network planning for demand and safety by P. Folco, L. Gauvin, M. Tizzoni and M. Szell.

The notebooks are divided in 3 sections.

Section 1: collect e-scooter position data from the Bird API.

• Run the notebook "1a_locations-for-requests.ipynb" to define the location of your queries.
• Run the notebook "1b_collect-escooter-data.ipynb" to collect the real-time Bird e-scooter positions. To access to the API we followed the istructions available here: https://github.com/ubahnverleih/WoBike/blob/master/Bird.md

Section 2: pre-process the data.

This section is specific for Turin: if you want to test our model in another city, you should find the following data available for your city:

• Geolocated crashes involving bikes;
• Population density divided (at least) by neighborhood.

Before running these notebooks, please download these data sets and put it in the "data" folder:

• Crashes in Turin, you can download the 2019 data: http://aperto.comune.torino.it/dataset/elenco-incidenti-nell-anno-2019-nella-citta-di-torino; and/or the 2020 data: http://aperto.comune.torino.it/dataset/elenco-incidenti-2020
• Shapefiles of statistical areas of Turin, available here: http://aperto.comune.torino.it/en/dataset/popolazione-per-sesso-e-zona-statistica-2020
• Number of residents in Turin (2020) by statistical areas, available here: http://geoportale.comune.torino.it/geodati/zip/zonestat_popolazione_residente_geo.zip

Then:

• Run the notebook "2a_crashes-data.ipynb" to process the crashes data and obtain a data set with the geolocation of crashes involving at least 1 bike.
• Run the notebook "2b_generate-ODdata.ipynb" to generate Origin-Destination data set from the e-scooter positions collected in "1b_collect-escooter-data.ipynb".
• Run the notebook "2c_population-density.ipynb" to generate a data set with the population density of Turin (2020) divided by statistical area.

Section 3: Run the model and analyze the results.

• Run the notebook "3a_prepare-networks.ipynb" to download from OpenStreetMap the car and bicycle networks. In the folder "data/turin" we provided the data of Turin, downloaded on 2021-07-12, but if you want to use the last updated networks (or if you want to try the model on another city) you should run this notebook. This notebook is developed by M. Szell, you can find it also here: https://github.com/mszell/bikenwgrowth.
• Run the notebook "3b_seed-points.ipynb" to set the seed points. This notebook is based on the notebook of M. Szell "02_prepare_pois.ipynb" available here: https://github.com/mszell/bikenwgrowth.
• Run the notebook "3c_crash&trips-per-link.ipynb" to compute the number of crashes and trips passing through each link.
• Run the notebook "3d_network-growth.ipynb" to compute the network growth and analyze the results. This notebook is based on the notebooks of M. Szell "03_poi_based_generation.ipynb" and "04_analyze_results.ipynb" available here: https://github.com/mszell/bikenwgrowth.
• Run the notebook "3e_plots.ipynb" to visualize the results, creating figures equivalent to the figures we showed in our paper.

The .py files "functions.py", "path.py", "setup.py" and "parameters.py" are based on the code of M. Szell available here: https://github.com/mszell/bikenwgrowth.

The file "function_index.txt" contains the name of the functions in "functions.py" sorted in alphabetical order. For each function the number of the line is reported to find it in the document.

Setting up code environment

conda create --override-channels -c conda-forge -n OSMNX python=3 osmnx=0.16.2 python-igraph watermark haversine rasterio tqdm geojson

conda activate OSMNX

conda install -c conda-forge ipywidgets

pip install opencv-python

conda install -c anaconda gdal

pip install --user ipykernel

python -m ipykernel install --user --name=OSMNX

Run Jupyter Notebook with kernel OSMNX (Kernel > Change Kernel > OSMNX)