Key detection node identification method for expressways based on multi-scale temporal graph convolutional network model

ZHAO Yan; WANG Can; RUI Yi-kang; LU Wen-qi; RAN Bin

doi:10.19818/j.cnki.1671-1637.2025.04.019

Volume 25 Issue 4

Aug. 2025

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ZHAO Yan, WANG Can, RUI Yi-kang, LU Wen-qi, RAN Bin. Key detection node identification method for expressways based on multi-scale temporal graph convolutional network model[J]. Journal of Traffic and Transportation Engineering, 2025, 25(4): 267-280. doi: 10.19818/j.cnki.1671-1637.2025.04.019

Citation:

ZHAO Yan, WANG Can, RUI Yi-kang, LU Wen-qi, RAN Bin. Key detection node identification method for expressways based on multi-scale temporal graph convolutional network model[J]. Journal of Traffic and Transportation Engineering, 2025, 25(4): 267-280. doi: 10.19818/j.cnki.1671-1637.2025.04.019

Citation:

ZHAO Yan, WANG Can, RUI Yi-kang, LU Wen-qi, RAN Bin. Key detection node identification method for expressways based on multi-scale temporal graph convolutional network model[J]. Journal of Traffic and Transportation Engineering, 2025, 25(4): 267-280. doi: 10.19818/j.cnki.1671-1637.2025.04.019

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Key detection node identification method for expressways based on multi-scale temporal graph convolutional network model

doi: 10.19818/j.cnki.1671-1637.2025.04.019

ZHAO Yan^{1, 2, 3},
WANG Can^{1, 2, 3},
RUI Yi-kang^{1, 2, 3
,
,},
LU Wen-qi⁴,
RAN Bin^{1, 2, 3, 5}

1.
School of Transportation, Southeast University, Nanjing 211189, Jiangsu, China
2.
Institute on Internet of Mobility, Southeast University and University of Wisconsin-Madison, Southeast University, Nanjing 211189, Jiangsu, China
3.
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, Jiangsu, China
4.
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China
5.
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison WI 53706, Wisconsin, USA

Funds:

National Natural Science Foundation of China 41971342

National Key R&D Program of China 2022ZD0115600

Jiangsu Province Postgraduate Research and Practice Innovation Program SJCX24-0094

Innovation Capacity Enhancement Program for Doctoral Students of Southeast University CXJH-SEU 24185

More Information

Corresponding author: RUI Yi-kang (1983-), male, associate researcher, PhD, 101012189@seu.edu.cn
Received Date: 2024-07-13
Accepted Date: 2025-04-02
Rev Recd Date: 2025-02-01
Publish Date: 2025-08-28

Abstract

Abstract

To improve the quality of expressway traffic detection data and optimize the layout of detectors, a key node identification method based on Multi-scale Temporal Graph Convolutional Network (MT-GCN) was proposed; it integrated multi-scale temporal analysis with adaptive dilated convolution to enhance the capacity for learning both short-term fluctuations and long-term trends. It also enhanced the graph convolutional network to learn the topological structure of the traffic network, so as to capture the spatial interaction relationships among key nodes. Combining gradient importance analysis, the network screened out the most representative key detection nodes. Two sets of comparative experiments were designed for the verification of the method's effectiveness, and ablation experiments were performed for the analysis of the contributions of multi-scale temporal analysis and GCN-based spatial feature learning. The research results show that MT-GCN achieves the smallest error under all node coverage rates, and the combination of MT-GCN with Traffic Former performs the best. Under a 60% node coverage rate, the mean absolute error (MAE) is 2.08 km·h^-1, and the mean absolute percentage error (MAPE) is 6.25%. Under an 80% node coverage rate, the MAE is 1.42 km·h^-1and the MAPE is 4.91%. When the key node coverage rate is in the range of 60%-65%, the optimal balance between performance and resources can be achieved. The ablation experiments show that the performance of the complete MT-GCN is better than that of the models using only GCN or multi-scale temporal analysis. For example, when combined with Spatio-Temporal Graph Neural Network (ST-GNN) under an 80% node coverage rate, the MAE of MT-GCN is 1.59 km·h^-1, while the MAEs of the multi-scale temporal analysis model and the GCN model are 1.89 and 2.02 km·h^-1, respectively. MT-GCN performs better in representing overall traffic flow than other methods, and can maintain low error rates even when combined with estimation methods that have relatively weak performance.
- intelligent traffic,
- traffic detection,
- graph convolutional network,
- key detection node identification,
- multi-scale temporal analysis,
- adaptive dilated convolution

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References(38)

References

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Key detection node identification method for expressways based on multi-scale temporal graph convolutional network model

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