This course provides a basic introduction on the use of Python for transportation planning and modeling. It includes a brief review of the fundamentals of writing code in Python, as well as modules on tabular data analysis, visualizations, and geographic analysis. The course assumes that students are already somewhat familiar with the mathematical tools of transportation modeling in general, and focuses exclusively on the how these models are constructed and implemented within Python.
The first version of this course was developed with funding provided by the Florida Department of Transportation.
This version of the course is hosted on Github, and thus can be run from inside Binder, a free online server for Jupyter and Python. The resources available on this service are limited, and you likely will not find them satisfactory for production-level transportation planning and analysis work for nearly any purpose. However, they are sufficient to run the code demonstrated in these training exercises, and you will not need to install anything on your local machine beyond a standard web browser, which you undoubtedly already have.
Click here to open these tutorials online in Binder:
conda env create -f conda-environments/arboretum_linux.yml
conda init bash
source /home/codespace/.bashrc
conda activate arboretum
conda env export --name dscc > dscc.yml
conda env export --name arboretum > conda-environments/arboretum_linux.yml
conda env export --name arboretum --no-builds > conda-environments/arboretum_codespaces.yml
- create a 'tmp' folder under 'course-content/choice-modeling'
mkdir course-content/choice-modeling/tmp
raise ImportError("larch cannot be installed with pip, try installing using conda-forge instead.\nSee https://larch.newman.me/v5.7.0/intro.html for instructions.")
- pandas >=1.2,<1.5
conda install -c conda-forge larch==5.3
conda install -c conda-forge larch==5.7.0
pip install kaleido==0.2.1
https://anaconda.org/conda-forge/larch/files?version=5.3.0&page=4
git config --global user.email "aaa"
git config --global user.email "@qq.com"
conda install gh --channel conda-forge
- Github CLI login
-
gh auth login
import sys
import os
sys.path.insert(0, os.path.abspath('../../example-package/'))
import transportation_tutorials as tt
pip install osmnx
https://pypi.org/project/contextily/
contextily
/root/.vscode-server/extensions/grapecity.gc-excelviewer-4.2.57