# GCN_predict-Pytorch

**Repository Path**: shine2010/GCN_predict-Pytorch

## Basic Information

- **Project Name**: GCN_predict-Pytorch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2021-09-28
- **Last Updated**: 2021-09-28

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# GCN_predict-Pytorch
Traffic flow predict. Implementation of  graph convolutional network（GCN,GAT,Chebnet） with PyTorch

Requirements：

​	- Pytorch

​	- Numpy

​	- Pandas

​	- Matplotlib



Example Dataset：

​	The datasets are collected by the Caltrans Performance Measurement System (PEMS-04) 

​	Numbers:307 detectors

​	Date:Jan to Feb in 2018 (2018.1.1——2018.2.28)

​	Features:flow, occupy, speed.



Exploring data analysis:

​	1.there is three features:flow,occupy and speed.First, we conduct a visual analysis of data distribution 

​	2.run code: python data_view.py	

​	3.Every node(detector) has three fetures,but two features data distribution are basically stationary, so we only take the first dimension features.



Read dataset:

​	In the traffic_dataset.py file,the get_adjacent_matrix and get_flow_data functions are to read adjacent matrix and flow data.



Model training:

​	In the traffic_preditcion.py,there are three graph convolution neural network models:GCN,ChenNET and GAT.Correspondingly, you only need to modify the 45th line of code in this file, and then observe the different results of model training.

​	python traffic_preditcion.py