FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph

[ECML PKDD 2023]This is a Pytorch and DGL implementation of the following paper : "FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph".

Data

Please download the Manhattan data from https://drive.google.com/file/d/1TxVluhAEU3oFhlzoXq6FxmP7TTJjOuZG/view?usp=sharing and unzip it in ./data folder

Directory

The Root is described as below

ROOT
+-- data
|   +-- manhattan_train.dgl
|   +-- manhattan_val.dgl
|   +-- ...
+-- outputs
|   +-- ...
+-- model.py
+-- test.py
+-- train.py
+-- utils.py

• data the dataset folder. Here it contains Manhattan dataset.
• outputs contains the training log and model file. Each time run train.py to launch a new training process, it will automatically create a folder to store the training log and model file.
• train.py python script of training FDTI .
• test.py python script of testing the model.
• utils.py some useful function
• model.py contains the GNN module.

For training

python train.py --setting manhattan 

After finish training, a process for evaluation will be automatically created.

For evaluation

python test.py --setting manhattan --test_setting manhattan --test_folder $TRAIN_FOLDER

$TRAIN_FOLDER is the result folder name in ./outputs/manhattan/ which is created based on the time that training process begins.