Spatial-temporal graph convolutional networks for traffic flow prediction considering multiple traffic parameters

Su, Ziyi; Liu, Tong; Hao, Xiatong; Hu, Xiaojian

doi:10.1007/s11227-023-05383-0

Cited by 4 publications

(1 citation statement)

References 48 publications

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“…The construction of graph structures mainly consists of two types, namely static graphs and dynamic graphs. There are various methods for constructing static graph structures [13,14,17,[36][37][38][39][40][41][42][43][44], including those based on road topology, geographical distance and time series similarity. Representative modeling methods for static graph structures include STGCN [13], GRU [43], DCRNN [14] and STFGNN [17].…”

Section: Traffic Flow Predictionmentioning

confidence: 99%

An Integrated Static and Dynamic Graph Fusion Approach for Traffic Flow Prediction

Che,

Xiong,

Zhang

et al. 2024

Preprint

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The major challenge in accurate traffic flow prediction lies in effectively capturing the dynamic spatiotemporal correlations within the traffic system. In this paper, we propose a novel traffic flow prediction method based on the fusion of static and dynamic graphs. Firstly, a predefined graph structure is used as the initial static graph. Secondly, a temporal graph convolution module constructed in a data-driven manner is designed, further implementing a dynamic graph structure that varies with the input data, thoroughly constructing the spatial relations between traffic flow sequence data. Finally, specific spatial and temporal relations are modeled from the perspective of graphs, effectively merging static and dynamic spatial relations. The performance of our method was validated using two real public datasets, PEMS04 and PEMS08. Experimental results demonstrate that our model outperforms existing traffic flow prediction models by 9.32% in terms of prediction error when compared to 18 benchmark methods.

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Section: Traffic Flow Predictionmentioning

confidence: 99%

An Integrated Static and Dynamic Graph Fusion Approach for Traffic Flow Prediction

Che,

Xiong,

Zhang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Origin-destination demand prediction of public transit using graph convolutional neural network

Shanthappa,

Mulangi,

Manjunath

2024

Case Studies on Transport Policy

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Spatial–temporal combination and multi-head flow-attention network for traffic flow prediction

Yu,

Liu,

et al. 2024

Sci Rep

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Traffic flow prediction based on spatial–temporal data plays a vital role in traffic management. However, it still faces serious challenges due to the complex spatial–temporal correlation in nonlinear spatial–temporal data. Some previous methods have limited ability to capture spatial–temporal correlation, and ignore the quadratic complexity problem in the traditional attention mechanism. To this end, we propose a novel spatial–temporal combination and multi-head flow-attention network (STCMFA) to model the spatial–temporal correlation in road networks. Firstly, we design a temporal sequence multi-head flow attention (TS-MFA), in which the unique source competition mechanism and sink allocation mechanism make the model avoid attention degradation without being affected by inductive biases. Secondly, we use GRU instead of the linear layer in traditional attention to map the input sequence, which further enhances the temporal modeling ability of the model. Finally, we combine the GCN with the TS-MFA module to capture the spatial–temporal correlation, and introduce residual mechanism and feature aggregation strategy to further improve the performance of STCMFA. Extensive experiments on four real-world traffic datasets show that our model has excellent performance and is always significantly better than other baselines.

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Spatial-temporal graph convolutional networks for traffic flow prediction considering multiple traffic parameters

Cited by 4 publications

References 48 publications

An Integrated Static and Dynamic Graph Fusion Approach for Traffic Flow Prediction

An Integrated Static and Dynamic Graph Fusion Approach for Traffic Flow Prediction

Origin-destination demand prediction of public transit using graph convolutional neural network

Spatial–temporal combination and multi-head flow-attention network for traffic flow prediction

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