This codes includes our works on Traffic Flow Prediction by Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting.
The code consists of two parts. The first part is dynamic causal graph generation module, and the second part is multi-step prediction module. We supply the demo of transportation and FMRI dataset.
##For the dynamic causal graph generation module, The setting are shown in configuration document：./graph_generation/configurations/Tdrive.conf
- tensorflow
- scipy
- numpy
- matplotlib
- pandas
- math
- seaborn
- sklearn
- argparse
- configparser
- time
./graph_generation/Transportation or FMRI\main.py
Then we will get the parameter file after training, for example, Tdrive_normalization_parameter.npz. Run dynamic_graph_trans_.py to generate the dynamic transition matrix, such as dynamic_Tdrive_adj.npy file.
##For the second part, put the generative file from the first step to ./prediction/Transportation or FMRI/generated_adj (file directory)
- torch
- shutil
- numpy
- matplotlib
- pandas
- math
- tensorflow
- sklearn
- argparse
- csv
- time
./prediction-code/Transportation or FMRI\main.py
If you have any questions, please feel free to email me, [email protected]!