GAMCN

This is the Tensorflow Keras implementation of GAMCN in the following paper:
Jianzhong Qi, Zhuowei Zhao, Egemen Tanin, Tingru Cui, Neema Nassir, and Majid Sarvi. "A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction"

Data

Adjacent matrix files and traffic speed files are included in the data folder. The datasets are from Caltrans Performance Measurement System (PeMS) (https://pems.dot.ca.gov/). The road networks are collected from OpenStreetMap (https://planet.osm.org).

Model

Requirements

Python 3.7
TensorFlow 2.9.1
Numpy

Run Demo

python demo.py

The default dataset is PEMS04, if you would like to test other datasets, you can change the dir at line 5 and line 35

Prepare the raw data

The traffic data is preprocessed into a numpy array whose size is (T,N), where T is the time slot number and N is the node number. The adjacent file is a text file, and for each line, it consists of node1 Id, node2 Id and their normalized distance.

Citation

If you find this repository useful in your research, please cite the following paper:

@inproceedings{qi2022gamcn,
  title={A Graph and Attentive Multi-Path Convolutional
Network for Traffic Prediction},
  author={Jianzhong Qi, Zhuowei Zhao, Egemen Tanin, Tingru Cui, Neema Nassir, and Majid Sarvi},
  booktitle={IEEE Transactions on Knowledge and Data Engineering (TKDE)},
  year={2022}
}