GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction

1. Quang-Huy Tran
Network Science Lab
Dept. of Artificial Intelligence
The Catholic University of Korea
E-mail: huytran1126@gmail.com
2024-03-18
GSTNet: Global Spatial-Temporal Network
for Traffic Flow Prediction
Shen Fang et al.
IJCAI ’19: 2019 International Joint Conference on Artificial Intelligence

2. 2
OUTLINE
• MOTIVATION
• INTRODUCTION
• METHODOLOGY
• EXPERIMENT & RESULT
• ADVANTAGES & DISADVANTAGES
• CONCLUSION

3. 3
MOTIVATION
• The complicated and dynamic traffic in urban have been increasing.
o Traffic congestion is a major problem in many cities around the world.
• Traffic flow is complex and depends on many factors, including:
o Past traffic patterns.
o Current weather conditions.
o Special events.
o Accidents.
PROBLEM OF TRAFFIC FLOW
Source: Google

4. 4
MOTIVATION
• Traffic Flow Prediction becomes important task.
o measures the number of vehicles entering or leaving a particular road over a specific time interval.
• However, traditional methods for traffic flow prediction are not always accurate.
o Not preserve or consider relationship between space and time domain.
CHALLENGE OF TRAFFIC FLOW PREDICTION

5. 5
INTRODUCTION
• Global Spatial-Temporal Network (GSTNet) is a deep learning model, which
consists of several layers of spatial-temporal blocks.
• GSTNet preserve space information: both local and non-local.
o Also, consider the temporal dependency: short-term and the long-term.

6. 6
INTRODUCTION
• Given a graph 𝐺 = (𝑉, 𝐸). 𝑉 set of nodes, e𝑖,𝑗 ∈ 𝐸 is accessible routes between
nodes.
• There are 𝑀 types of traffic flow data and the time axis length 𝑇.
• A historical traffic data flow is 𝑋.
• The objective is learning a mapping function 𝑓𝜃 from 𝑋 and 𝐺.
o Predict the traffic flow of all nodes at the next time:
OBJECTIVE
𝑋 = 𝑓𝜃(𝑋, 𝐺)

7. 7
METHODOLOGY
Model Architecture
GSTNet Architecture

8. 8
METHODOLOGY
Multi-Resolution Temporal Module
• Captures short & long-term temporal patterns simultaneously.
o Develop causal convolution to preserve the chronological order of data
• Output feature
• Uses dilated causal convolutions with exponentially increasing dilation rates
• Long receptive field to model periodicities (daily/weekly)

9. 9
METHODOLOGY
Global Correlated Spatial Module
• Combines localized graph convolutions (model local correlations)
• Consider the signal with adjacent matrix of the graph, using the Chebyshev
polynomial to capture the convolutional result.
o Calculate Laplacian matrix
• Output features of localized spatial

10. 10
METHODOLOGY
Global Correlated Spatial Module
• With non-local correlated mechanism (model non-local correlations), calculate the
global spatial feature
• Exploits graph topology to learn meaningful spatial dependencies

11. 11
METHODOLOGY
Model Summarization
• Output layer: attention mechanism on temporal domain, which automatically
selects the relevant historical traffic data.
• Mean Square Error (MSE) Loss Function is adopted to train model:
𝑊
𝑜: learnable parameter
𝜓: Frobenius inner product of two matrices

12. 12
EXPERIMENT AND RESULT
• Dataset: Beijing Subway with Bus system and taxi GPS trajectories in Beijing.
EXPERIMENT
• Task: Predict flow in the next time.

13. 13
• Algorithm:
o Historic Average (HA).
o Long Short-Term Memory.
o Stacked Auto Encoder (SAE)[1].
o ChebNet[2]: Graph CNN with Chebyshev polynomial approximation.
o GCRNN-GCN[3]: Graph Convolution Recurrent Neural Network with GCN.
o STGCN-Action[4]: Spatial-temporal graph convolutional networks for skeleton-based human action
recognition.
EXPERIMENT AND RESULT
EXPERIMENT
[1] Zhongjian Lv, Jiajie Xu, Kai Zheng, Hongzhi Yin, Pengpeng Zhao, and Xiaofang Zhou. LCRNN: A deep learning model for traffic speed prediction. In IJCAI, pages 3470–3476, 2018.
[2] Michael Defferrard, Xavier Bres- ¨ son, and Pierre Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. In NIPS, pages 3844–3852, 2016.
[3] Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In ICLR, pages 1– 16, 2018.
[4] Sijie Yan, Yuanjun Xiong, and Dahua Lin. Spatial temporal graph convolutional networks for skeleton-based action recognition. In AAAI, pages 7444– 7452, 2018.
• Measurement:
o Mean Absolute Error (MAE).
o Mean Absolute Percentage Error (MAPE).

14. 14
EXPERIMENT AND RESULT
EXPERIMENT

15. 15
EXPERIMENT AND RESULT
EXPERIMENT

16. 16
EXPERIMENT AND RESULT
EXPERIMENT
Training time

17. 17
ADVANTAGE AND DISADVANTAGE
ADVANTAGE
• Captures both short-term neighboring and long-term periodic dependencies in
traffic flow prediction.
• Considers both local and non-local spatial correlations.
• Employs a multi-resolution temporal module and a global correlated spatial
module.
o Enable the extraction of dynamic temporal dependencies and global spatial
correlations simultaneously

18. 18
ADVANTAGE AND DISADVANTAGE
DISADVANTAGE
• Their assumption is simple and only apply in linear information.
• Long training time.
• Apply only in one dataset (Beijing).

19. 19
CONCLUSION
• Proposed novel deep learning model for predicting traffic flow on traffic networks.
• Integrating a multi resolution temporal module and a global correlated spatial
module.
• Their model perform well in predicting the next time.
• Future direction could be:
o Multi next time step prediction.
o Long-term prediction.