Project: GMNS Data Analysis Welcome to my-gmns-project, a repository for analyzing a large-scale transportation network dataset following the General Modeling Network Specification (GMNS). This README explains how to configure and run the analysis both locally and in the cloud.
- Overview Goal: Investigate congestion patterns, route choices, and time-dependent attributes in a GMNS-formatted dataset. Key Tools: Python 3.8+ (or R 4.x, if using R scripts) Libraries: pandas, numpy, geopandas, and matplotlib for visualization Optional AI Tools: Hugging Face Transformers (for data augmentation), GitHub Copilot (for code assistance) Main Scripts: data_cleaning.py: Cleans and merges the GMNS CSV files (node.csv, link.csv) traffic_assignment.py: Runs the core static/dynamic assignment methods
- Local Setup Install Python
Recommended: Anaconda Python 3.8+ for easy package management. 2. Create a Virtual Environment:
conda create -n gmns-env python=3.8
conda activate gmns-env- Install Required Packages
pip install -r requirements.txtwhere requirements.txt might include:
pandas==1.4.0
numpy==1.22.0
geopandas==0.10.2
matplotlib==3.5.0
# Optional AI Tools
transformers==4.26.0- Configure Data Paths Place all GMNS CSV files (node.csv, link.csv, etc.) inside data/. Update any file paths within data_cleaning.py or other scripts if you’ve moved or renamed directories.
- Run the Analysis
python code/data_cleaning.py
python code/traffic_assignment.py- Cloud Setup Option A: Google Colab
Upload Repository Zip and upload the entire repository to your Google Drive or clone it from GitHub within a Colab notebook. Create a Colab Notebook Install dependencies:
!pip install geopandas matplotlib transformers- Execute Scripts Run each Python script with:
!python code/data_cleaning.py
!python code/traffic_assignment.py- Clone Repo and Run
git clone [https://github.com/yourusername/my-gmns-project](https://github.com/Rising-Stars-by-Sunshine/Jiayang_Hong_PS1).git
cd my-gmns-project
python code/data_cleaning.py
python code/traffic_assignment.py