Data-Driven Strategies for Accelerating the Transition to Sustainable Logistics: Evaluating Cargo Bike Efficiency in Urban Micro-Regions

Using this Repository

Dependencies and Environment

This repository uses PDM to manage dependencies and environment. Please install PDM and run

pdm install to install dependencies

Data

The work relies on several data sources, some of which is contained in the repo. To fetch the remainng data, please run

1. pdm run scripts/get_city_osm.py ./config/paper.yaml
   1. Fetches the tag data and city boundary data from OpenStreetMap
2. pdm run scripts/get_almrcc.py ./config/paper.yaml
   1. Downloads the Amazon Last Mile Routing Challenge data
3. pdm run scripts/make_amazon_table.py ./config/paper.yaml
   1. Creates a table from the Amazon Last Mile Routing Challenge data

Figure Directory

The figures in the paper are generated by the following notebooks:

• Tables 2, 3: notebooks/statistical_analysis/amazon_round_analysis.ipynb
• Table 4: notebooks/statistical_analysis/service_time_tables.ipynb
• Table 5: notebooks/statistical_analysis/parking_distance.ipynb
• Table 6: notebooks/statistical_analysis/parking_distance.ipynb

• Figure 1: notebooks/city_analysis/p_service_time.ipynb
• Figure 2: notebooks/city_analysis/analyse_cities.ipynb ?
• Figures 3,4: notebooks/clustering/cluster_service_time.ipynb
• Tables 8,9: notebooks/regression/per_city.ipynb

Model Info

The tags used in the embedding model are located here