https://github.com/taehooie/cgodme
This is an experiment version of calibrating origin-destination matrix estimation using link traffic counts
https://github.com/taehooie/cgodme
automatic-differentiation computational-graphs origin-destination-estimation tensorflow
Last synced: 10 months ago
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This is an experiment version of calibrating origin-destination matrix estimation using link traffic counts
- Host: GitHub
- URL: https://github.com/taehooie/cgodme
- Owner: Taehooie
- Created: 2024-02-20T05:17:49.000Z (over 2 years ago)
- Default Branch: develop
- Last Pushed: 2024-11-20T06:53:30.000Z (over 1 year ago)
- Last Synced: 2025-08-19T15:26:02.288Z (11 months ago)
- Topics: automatic-differentiation, computational-graphs, origin-destination-estimation, tensorflow
- Language: Python
- Homepage:
- Size: 546 KB
- Stars: 3
- Watchers: 1
- Forks: 6
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
# CGODME
This experimental version aims to find optimal path flows by systematically mapping variables (zonal totals, origin-destination demand, and link flows).
## Quick Start
Users can find [Jupyter notebook](https://github.com/Taehooie/CGODME/blob/develop/tutorial/tutorial.ipynb) that provides step-by-step instructions for utilization.
## Installation
cgodme has been published on [PyPI](https://pypi.org/project/cgodme/) and can be installed using
```
$ pip install cgodme
```
## How to Cite
- Journal article: [Kim, T., et al. Computational graph-based mathematical programming reformulation for integrated demand and supply models](https://www.sciencedirect.com/science/article/abs/pii/S0968090X2400192X)