City-wide Origin-Destination Matrix Generation via Graph Denoising Diffusion.

This repo contains the codes and data for our submitted KDD'23 research track paper under review.

The models are trained on RTX3090-24G.

Environment

• python == 3.8.13
• torch == 1.12.1+cu113

Files

• code # python scripts
  • utils # tool codes
    • metrics.py # all metrics
    • MyLogger.py # A logger for logging experimental information
    • procedure.py # pipeline function
    • tool.py # simple tool functions
  • data_load.py # load data
  • eval.py #
  • main.py # entry
  • model.py # models
  • train.py # training scripts
• data # datasets
• exp # experimental information
  • config # configurations
  • logs # losses and evaluation results
  • results # generations
  • running #
  • runs # for tensorboard
  • weightes # trained model parameters

Usage

• The experimental configuration can be adapted in exp/config/xxx.json
• In the config file, adjust the exp_name to record meta information for different experiments.
• In the config file, adjust src_cities and tar_cities to select the cities for training and testing. The names of the cities need to be consistent with the names of the subdirectories in the data directory
• In main.py, modify the selected config file.
• Trained models have been saved in exp/weights. Adjust exp_name to load them.

training

• set topo_train to 1 means training the topology diffusion model
• set flow_train to 1 means training the flow diffusion model
• set T_mode to INIT means training the topology diffusion model from scratch
• set F_mode to INIT means training the flow diffusion model from scratch
• Extra setting
  • set teacher_force to 1 means training the flow diffusion models in collaborative mode
  • set mem_need to check GPU memory, at leat 23000

testing

• set topo_train and flow_train to 0 to skip the training process
• set T_mode and F_mode to LOAD to load existing trained models