The official code for "Spatial temporal transformer networks for traffic flow forecasting"
Traffic forecasting has emerged as a core component of intelligent transportation systems. However, timely accurate traffic forecasting, especially long-term forecasting, still remains an open challenge due to the highly nonlinear and dynamic spatial-temporal dependencies of traffic flows. In this paper, we propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) that leverages dynamical directed spatial dependencies and long-range temporal dependencies to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies with self-attention mechanism to capture realtime traffic conditions as well as the directionality of traffic flows. Furthermore, different spatial dependency patterns can be jointly modeled with multi-heads attention mechanism to consider diverse relationships related to different factors (e.g. similarity, connectivity and covariance). On the other hand, the temporal transformer is utilized to model long-range bidirectional temporal dependencies across multiple time steps. Finally, they are composed as a block to jointly model the spatial-temporal dependencies for accurate traffic prediction. Compared to existing works, the proposed model enables fast and scalable training over a long range spatial-temporal dependencies. Experimental results demonstrate that the proposed model achieves competitive results compared with the state-of-the-arts, especially in forecasting long-term traffic flows on real-world PeMS-Bay and PeMSD7(M) datasets.
Our code is based on Python3.6, a few depended libraries as as follows:
- Tensorflow>=1.4.0
- NumPy (>= 1.15)
- SciPy (>= 1.1.0)
- Pandas (>= 0.24)
We adopted the same dataset as "Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting" and "Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting". Please refer to STGCN for the description and preprocessing of the dataset PeMSD7 and DCRNN for that of the dataset PeMS-bay.
If you find this repository, e.g., the code and the datasets, useful in your research, please cite the following paper:
@article{xu2020spatial,title={Spatial-temporal transformer networks for traffic flow forecasting},
author={Xu, Mingxing and Dai, Wenrui and Liu, Chunmiao and Gao, Xing and Lin, Weiyao and Qi, Guo-Jun and Xiong, Hongkai},
journal={arXiv preprint arXiv:2001.02908},
year={2020}
}