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