Pytorch-基于GCN-GAT-Cebnet图神经网络的交通流预测实现-图神经网络交通流预测.zip

# 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