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Uncertainty-aware Network-level traffic speed, flow, and demand prediction

This is the source code of the uncertainty-aware Network-level traffic speed, flow, and demand prediction model. This model extends the Dynamic Graph Convolution (DGC) module proposed initially by Li et al. (2021).

  • Visit the DiTTlab demo page below to visualize how the model works and how the predictions are interpreted (may not working before 27-08-2023):

Dittlab Online Demo (click)

  • The paper manuscript is still under review.

Requirement

  • Python = 3.9
  • PyTorch ≥ 2.0.1
  • shapely = 1.8.5
  • zarr = 2.16.0

Data Preparation

  • The used Dutch highway dataset is provided by National Data Warehouse NDW.
  • Data examples can be obtained from the DittLab application page: tools-dittlab.
  • Run python get_data.py to get the speed and flow data from the NDW server.
  • Processed data will be in the datasets folder.
  • If there is any difficulty in preparing the dataset, please send us an email: G.Li-5@tudelft.nl. We will share the fully-processed data that is ready to use.

Model Training

  • Run the TrainingModels.ipynb to train the model.
  • Detailed instructions are provided in the notebook.