TY  - JOUR
AU  - Li, Xuejun
AU  - Yin, Xiaoqi
AU  - Gu, Chenbin
AU  - Yang, Yudong
AU  - Ali, Kharudin Bin
PY  - 2026
DA  - 2026/04/09
TI  - Traffic Flow Prediction Model Based on Variant Hybrid Multi-Hop Graph Convolution
JO  - ICCK Transactions on Intelligent Systematics
T2  - ICCK Transactions on Intelligent Systematics
JF  - ICCK Transactions on Intelligent Systematics
VL  - 3
IS  - 2
SP  - 81
EP  - 93
DO  - 10.62762/TIS.2025.954751
UR  - https://www.icck.org/article/abs/TIS.2025.954751
KW  - traffic flow prediction
KW  - deep learning
KW  - spatio-temporal convolutional layer
KW  - variant hybrid multi-hop graph convolution
AB  - Accurate traffic flow prediction is a crucial step in building an intelligent transportation system, and it is of great significance for alleviating urban traffic congestion and optimizing travel routes. Due to the complex spatial topology of the transportation network and the highly nonlinear temporal dynamic characteristics of the flow data, traditional prediction methods are difficult to fully capture the inherent spatio-temporal dependencies. Therefore, this paper proposes a traffic flow prediction model based on variant hybrid multi-hop graph convolution. Firstly, by introducing a multi-hop graph convolution operator, the model explicitly aggregates the spatial information of multiple-order neighborhoods in the traffic sensor network to capture long-distance spatial dependencies. Secondly, a variant hybrid graph convolution module is designed, which combines Chebyshev polynomial approximation and adaptive adjacency matrix, while balancing computational efficiency and enhancing the model's ability to mine implicit spatial correlations. In the temporal dimension, we adopt a method combining gated temporal convolution and attention mechanisms to dynamically capture the temporal change patterns of traffic flow. Evaluations across public datasets reveal that our method demonstrates consistent advantages when benchmarked against baseline approaches. The model achieves better performance in short-term and medium-term traffic flow prediction tasks, significantly reducing prediction errors and verifying the effectiveness and advancement of the proposed model.
SN  - 3068-5079
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Li2026Traffic,
  author = {Xuejun Li and Xiaoqi Yin and Chenbin Gu and Yudong Yang and Kharudin Bin Ali},
  title = {Traffic Flow Prediction Model Based on Variant Hybrid Multi-Hop Graph Convolution},
  journal = {ICCK Transactions on Intelligent Systematics},
  year = {2026},
  volume = {3},
  number = {2},
  pages = {81-93},
  doi = {10.62762/TIS.2025.954751},
  url = {https://www.icck.org/article/abs/TIS.2025.954751},
  abstract = {Accurate traffic flow prediction is a crucial step in building an intelligent transportation system, and it is of great significance for alleviating urban traffic congestion and optimizing travel routes. Due to the complex spatial topology of the transportation network and the highly nonlinear temporal dynamic characteristics of the flow data, traditional prediction methods are difficult to fully capture the inherent spatio-temporal dependencies. Therefore, this paper proposes a traffic flow prediction model based on variant hybrid multi-hop graph convolution. Firstly, by introducing a multi-hop graph convolution operator, the model explicitly aggregates the spatial information of multiple-order neighborhoods in the traffic sensor network to capture long-distance spatial dependencies. Secondly, a variant hybrid graph convolution module is designed, which combines Chebyshev polynomial approximation and adaptive adjacency matrix, while balancing computational efficiency and enhancing the model's ability to mine implicit spatial correlations. In the temporal dimension, we adopt a method combining gated temporal convolution and attention mechanisms to dynamically capture the temporal change patterns of traffic flow. Evaluations across public datasets reveal that our method demonstrates consistent advantages when benchmarked against baseline approaches. The model achieves better performance in short-term and medium-term traffic flow prediction tasks, significantly reducing prediction errors and verifying the effectiveness and advancement of the proposed model.},
  keywords = {traffic flow prediction, deep learning, spatio-temporal convolutional layer, variant hybrid multi-hop graph convolution},
  issn = {3068-5079},
  publisher = {Institute of Central Computation and Knowledge}
}