New York Congestion Tax Simulation

• Social Networking course final project
• model used: USTGCN
• project details: presentation report

USTGCN: Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network

Unified Spatio-Temporal Graph Convolutional Network, USTGCN. The unified spatio-temporal adjacency matrix, A_ST showcases the cross space-time connections among nodes from different timestamps which consists of three types of submatrix: A as diagonal submatrix, Ã as lower submatrix and 0 as upper submatrix. A_ST, a lower triangular matrix, facilitates traffic feature propagation from neighboring nodes only from the previous timestamps. The input features of different timestamps at convolution layer l are stacked into X^l_self which is element-wise multiplied with broadcasted temporal weight parameter W^l_Temp indicating the importance of the feature at the different timestamp. Afterwards, graph convolution is performed followed by weighted combination of self representation, X^l_self and spatio-temporal aggregated vector, X^l_ST to compute the representation X^l+1_self that is used as input features at next layer, l+1 or fed into the regression task.

Model Architecture

To learn both daily and current-day traffic pattern, for each node we stack the traffic speeds of the last seven days (traffic pattern during 09:30 AM - 10:30 AM for the last week depicted with green color) along with the current-day traffic pattern for the past hour (traffic speed during 9:05 AM - 10:00 AM on current day i.e. Tuesrday depicted with red color) into the corresponding feature vector. We feed the feature matrix stacked for N nodes in the traffic network across T = 12 timestamps to the USTGCN model of K convolution layers to compute spatio-temporal embedding. Finally, the regression module predicts future traffic intensities by utilizing the spatio-temporal embeddings.

Comarison with Baselines

Envirnoment Set-Up

Clone the git project:

$ git clone https://github.com/AmitRoy7781/USTGCN

Create a new conda Environment and install required packages (Commands are for ubuntu 16.04)

$ conda create -n TrafficEnv python=3.7
$ conda activate TrafficEnv
$ pip install -r requirements.txt

Basic Usage:

Main Parameters:

--dataset           The input traffic dataset(default:PeMSD7)
--GNN_layers        Number of layers in GNN(default:3)
--num_timestamps    Number of timestamps in Historical and current model(default:12)
--pred_len          Traffic Prediction after how many timestamps(default:3)
--epochs            Number of epochs during training(default:200)
--seed              Random seed. (default: 42)
--cuda              Use GPU if declared
--save_model        Save model if declared
--trained_model     Run pretrained model if declaired

Example Usage

Train Model Using:

$ python3 USTGCN.py --cuda --dataset PeMSD7 --pred_len 3 --save_model

Cite

If you find our paper or repo useful then please cite our paper:

@inproceedings{roy2021unified,
  title={Unified spatio-temporal modeling for traffic forecasting using graph neural network},
  author={Roy, Amit and Roy, Kashob Kumar and Ali, Amin Ahsan and Amin, M Ashraful and Rahman, AKM Mahbubur},
  booktitle={2021 International Joint Conference on Neural Networks (IJCNN)},
  pages={1--8},
  year={2021},
  organization={IEEE}
}