STGSA: A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

Wei, Zebing; Zhao, Hongxia; Li, Zhishuai; Bu, Xiaojie; Chen, Yuanyuan; Zhang, Xiqiao; Lv, Yisheng; Wang, Fei‐Yue

doi:10.1109/jas.2023.123033

Cited by 26 publications

(10 citation statements)

References 41 publications

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“…Traffic flow data is a kind of spatio-temporal data that exhibits strong dynamic correlation in both spatial and temporal dimensions, so the prediction of traffic flow has been a challenging and meaningful task [8,10,11]. After years of continuous research, researchers have achieved rich results in the field of traffic flow prediction, mainly including statistical methods [12][13][14], traditional machine learning methods [15][16][17][18][19], and deep learning methods [20,24,25,28,29,[32][33][34][35][36]39,40].…”

Section: Related Workmentioning

confidence: 99%

“…Ge et al [34] designed the global spatial-temporal graph convolutional network (GSTGCN) for urban traffic prediction, in which temporal features are extracted using 1D CNN, and residual connectivity and spatial features are extracted using GCN, considering the influence of external factors. Wei et al [35] proposed the novel spatial-temporal graph synchronous aggregation model (STGSA), which constructs the time dependency in time series as a graph with reference to the spatial graph and aggregates it with the spatial graph to extract spatio-temporal features. However, features may be lost in the process of graph construction and aggregation.…”

Section: Related Workmentioning

confidence: 99%

“…The methods using convolutional neural networks (CNN) are able to capture spatial features but are only effective for grid structures [27][28][29]. The methods using advanced techniques such as graph neural networks (GNN) or attention mechanisms can effectively capture the spatial features of the road network, but they often apply only one or two separate techniques and thus have a slight lack of ability to extract spatio-temporal features [30][31][32][33][34][35][36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

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Gated Recurrent Graph Convolutional Attention Network for Traffic Flow Prediction

Feng

Chen

et al. 2023

Sustainability

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Traffic flow prediction is an important function of intelligent transportation systems. Accurate prediction results facilitate traffic management to issue early congestion warnings so that drivers can avoid congested roads, thus directly reducing the average driving time of vehicles, which means less greenhouse gas emissions. However, traffic flow data has complex spatial and temporal correlations, which makes it challenging to predict traffic flow accurately. A Gated Recurrent Graph Convolutional Attention Network (GRGCAN) for traffic flow prediction is proposed to solve this problem. The model consists of three components with the same structure, each of which contains one temporal feature extractor and one spatial feature extractor. The temporal feature extractor first introduces a gated recurrent unit (GRU) and uses the hidden states of the GRU combined with an attention mechanism to adaptively assign weights to each time step. In the spatial feature extractor, a node attention mechanism is constructed to dynamically assigns weights to each sensor node, and it is fused with the graph convolution operation. In addition, a residual connection is introduced into the network to reduce the loss of features in the deep network. Experimental results of 1-h traffic flow prediction on two real-world datasets (PeMSD4 and PeMSD8) show that the mean absolute percentage error (MAPE) of the GRGCAN model is as low as 15.97% and 12.13%, and the prediction accuracy and computational efficiency are better than the baselines.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Gated Recurrent Graph Convolutional Attention Network for Traffic Flow Prediction

Feng

Chen

et al. 2023

Sustainability

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“…Some studies employ convolutional neural network (CNN) instead of RNN to learn temporal dynamics ( Wen et al, 2023 ; Ni & Zhang, 2022 ). To synchronize the extraction of spatial-temporal features, some work has designed graph structures that contain both spatial and temporal attributes ( Song et al, 2020 ; Li & Zhu, 2021 ; Jin et al, 2022 ; Wei et al, 2023 ). In spite of the pioneering advances in these studies, there is still a lack of sufficiently practical approaches in spatial and temporal synchronous learning owing to the complexity of traffic dynamics.…”

Section: Introductionmentioning

confidence: 99%

“…These studies took into consideration the influence of external factors, but they failed to accomplish spatial-temporal simultaneous modeling. In contrast, spatial-temporal synchronous graph convolutional networks (STSGCN) ( Song et al, 2020 ), spatial-temporal fusion graph neural networks (STFGNN) ( Li & Zhu, 2021 ), automated dilated spatio-temporal synchronous graph network (Auto-DSTSGN) ( Jin et al, 2022 ) and spatial-temporal graph synchronous aggregation model (STGSA) ( Wei et al, 2023 ) synchronously learned spatial and temporal dynamics. However, they only adopted traffic features as inputs to the model and ignored the impact of external factors.…”

Section: Introductionmentioning

confidence: 99%

Dynamic multiple-graph spatial-temporal synchronous aggregation framework for traffic prediction in intelligent transportation systems

Yu,

Bao,

Shi

2024

PeerJ Computer Science

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Accurate traffic prediction contributes significantly to the success of intelligent transportation systems (ITS), which enables ITS to rationally deploy road resources and enhance the utilization efficiency of road networks. Improvements in prediction performance are evident by utilizing synchronized rather than stepwise components to model spatial-temporal correlations. Some existing studies have designed graph structures containing spatial and temporal attributes to achieve spatial-temporal synchronous learning. However, two challenges remain due to the intricate dynamics: (a) Accounting for the impact of external factors in spatial-temporal synchronous modeling. (b) Multiple perspectives in constructing spatial-temporal synchronous graphs. To address the mentioned limitations, a novel model named dynamic multiple-graph spatial-temporal synchronous aggregation framework (DMSTSAF) for traffic prediction is proposed. Specifically, DMSTSAF utilizes a feature augmentation module (FAM) to adaptively incorporate traffic data with external factors and generate fused features as inputs to subsequent modules. Moreover, DMSTSAF introduces diverse spatial and temporal graphs according to different spatial-temporal relationships. Based on this, two types of spatial-temporal synchronous graphs and the corresponding synchronous aggregation modules are designed to simultaneously extract hidden features from various aspects. Extensive experiments constructed on four real-world datasets indicate that our model improves by 3.68–8.54% compared to the state-of-the-art baseline.

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Attentive graph structure learning embedded in deep spatial-temporal graph neural network for traffic forecasting

Bikram,

Das,

Biswas

2024

Appl Intell

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STGSA: A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

Cited by 26 publications

References 41 publications

Gated Recurrent Graph Convolutional Attention Network for Traffic Flow Prediction

Gated Recurrent Graph Convolutional Attention Network for Traffic Flow Prediction

Dynamic multiple-graph spatial-temporal synchronous aggregation framework for traffic prediction in intelligent transportation systems

Attentive graph structure learning embedded in deep spatial-temporal graph neural network for traffic forecasting

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