This is a PyTorch lightning implementation of Two-Level resolution Neural Network (TwoResNet) for traffic forecasting.
python3 -m venv envsource env/bin/activatepip install -r requirements.txtIn case error occurs, try to install PyTorch according to your local environment following the description here.
You can find tensorboard logs for pretrained models here.
python run.py --config=data/config/training.yaml --train --dataset=lapython run.py --config=data/config/training.yaml --train --dataset=baypython run.py --config=data/config/test.yaml --test --dataset=laResult
Horizon 1 (5 min) - MAE: 2.24, RMSE: 3.86, MAPE: 5.32
Horizon 2 (10 min) - MAE: 2.49, RMSE: 4.60, MAPE: 6.19
Horizon 3 (15 min) - MAE: 2.65, RMSE: 5.08, MAPE: 6.78
Horizon 4 (20 min) - MAE: 2.79, RMSE: 5.47, MAPE: 7.29
Horizon 5 (25 min) - MAE: 2.90, RMSE: 5.79, MAPE: 7.73
Horizon 6 (30 min) - MAE: 3.01, RMSE: 6.07, MAPE: 8.14
Horizon 7 (35 min) - MAE: 3.09, RMSE: 6.30, MAPE: 8.47
Horizon 8 (40 min) - MAE: 3.17, RMSE: 6.51, MAPE: 8.78
Horizon 9 (45 min) - MAE: 3.23, RMSE: 6.68, MAPE: 9.05
Horizon 10 (50 min) - MAE: 3.29, RMSE: 6.83, MAPE: 9.28
Horizon 11 (55 min) - MAE: 3.34, RMSE: 6.96, MAPE: 9.50
Horizon 12 (60 min) - MAE: 3.39, RMSE: 7.08, MAPE: 9.71
Aggregation - MAE: 2.97, RMSE: 6.01, MAPE: 8.02python run.py --config=data/config/test.yaml --test --dataset=bayResult
Horizon 1 (5 min) - MAE: 0.87, RMSE: 1.56, MAPE: 1.67
Horizon 2 (10 min) - MAE: 1.12, RMSE: 2.21, MAPE: 2.26
Horizon 3 (15 min) - MAE: 1.30, RMSE: 2.73, MAPE: 2.72
Horizon 4 (20 min) - MAE: 1.43, RMSE: 3.14, MAPE: 3.08
Horizon 5 (25 min) - MAE: 1.53, RMSE: 3.45, MAPE: 3.37
Horizon 6 (30 min) - MAE: 1.61, RMSE: 3.69, MAPE: 3.60
Horizon 7 (35 min) - MAE: 1.68, RMSE: 3.88, MAPE: 3.79
Horizon 8 (40 min) - MAE: 1.73, RMSE: 4.03, MAPE: 3.95
Horizon 9 (45 min) - MAE: 1.78, RMSE: 4.15, MAPE: 4.09
Horizon 10 (50 min) - MAE: 1.82, RMSE: 4.25, MAPE: 4.21
Horizon 11 (55 min) - MAE: 1.85, RMSE: 4.33, MAPE: 4.31
Horizon 12 (60 min) - MAE: 1.89, RMSE: 4.41, MAPE: 4.40
Aggregation - MAE: 1.55, RMSE: 3.59, MAPE: 3.45If you find this repository, e.g., the code and the datasets, useful in your research, please cite the following paper:
@inproceedings{Li2022tworesnet,
title = {TwoResNet: Two-level resolution neural network for traffic forecasting of freeway networks},
author = {Li, Danya and Kwak, Semin and Geroliminis, Nikolas},
year = {2022},
publisher={25th IEEE International Conference on Intelligent Transportation Systems (ITSC)},
venue = {Macau, China}, eventdate={2022-10-08/2022-10-12},
}