Multiple Information Spatial–Temporal Attention based Graph Convolution Network for traffic prediction

Tao, Shiming; Zhang, Huyin; Yang, Fei; Wu, Yonghao; Li, Cong

doi:10.1016/j.asoc.2023.110052

Cited by 16 publications

(6 citation statements)

References 17 publications

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“…Zhao et al [30] combined a GCN with a GRU to capture the spatial-temporal correlation of urban road network traffic. Tao et al [31] proposed a graph convolutional network based on multi-information spatialtemporal attention (MISTAGCN), which uses the graph convolutional network to mine the potential information of multi-input features and effectively analyzes the spatial-temporal correlation information between different nodes. Based on the analysis of the above literature, it is found that the GCN can learn the correlation information between nodes.…”

Section: Related Workmentioning

confidence: 99%

Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network

Zhang,

Li,

Cheng

et al. 2024

Energies

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High-precision spatial-temporal wind power prediction technology is of great significance for ensuring the safe and stable operation of power grids. The development of artificial intelligence technology provides a new scheme for modeling with strong spatial-temporal correlation. In addition, the existing prediction models are mostly ‘black box’ models, lacking interpretability, which may lead to a lack of trust in the model by power grid dispatchers. Therefore, improving the model to obtain interpretability has become an important challenge. In this paper, an interpretable short-term wind power prediction model based on ensemble deep graph neural network is designed. Firstly, the graph network model (GNN) with an attention mechanism is applied to the aggregate and the spatial-temporal features of wind power data are extracted, and the interpretable ability is obtained. Then, the long short-term memory (LSTM) method is used to process the extracted features and establish a wind power prediction model. Finally, the random sampling algorithm is used to optimize the hyperparameters to improve the learning rate and performance of the model. Through multiple comparative experiments and a case analysis, the results show that the proposed model has a higher prediction accuracy than other traditional models and obtains reasonable interpretability in time and space dimensions.

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Section: Related Workmentioning

confidence: 99%

Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network

Zhang,

Li,

Cheng

et al. 2024

Energies

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“…The attention-based spatial-temporal graph convolution network (AST-GCN) takes it a step further by integrating attention mechanisms with GCNs, facilitating the model to effectively learn latent spatial dynamics and time-dependent relationships in transportation (Guo et al 2019). The multiple information spatial-temporal attentionbased GCN (MISTAGCN) assimilates spatial and temporal attention separately into a K-order GCN layer incorporating numerous hidden variables to rectify information imbalances inherent in multi-sourced traffic datasets (Tao et al 2023). While GCN-based methods have demonstrated remarkable performance in general traffic predictions, their application to commuting forecasts remains limited.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting the commuting generation using metropolis-informed GCN and the topological commuter portrait

Chen,

Zhao,

Chen

2024

Transportation

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Understanding commuter traffic in transportation networks is crucial for sustainable urban planning with commuting generation forecasts operating as a pivotal stage in commuter traffic modeling. Overcoming challenges posed by the intricacy of commuting networks and the uncertainty of commuter behaviors, we propose MetroGCN, a metropolis-informed graph convolutional network designed for commuting forecasts in metropolitan areas. MetroGCN introduces dimensions of metropolitan indicators to comprehensively construct commuting networks with diverse socioeconomic features. This model also innovatively embeds topological commuter portraits in spatial interaction through a multi-graph representation approach capturing the semantic spatial correlations based on individual characteristics. By incorporating graph convolution and temporal convolution with a spatial–temporal attention module, MetroGCN adeptly handles high-dimensional dependencies in large commuting networks. Quantitative experiments on the Shenzhen metropolitan area datasets validate the superior performance of MetroGCN compared to state-of-the-art methods. Notably, the results highlight the significance of commuter age and income in forecasting commuting generations. Statistical significance analysis further underscores the importance of anthropic indicators for commuting production forecasts and environmental indicators for commuting attraction forecasts. This research contributes to technical advancement and valuable insights into the critical factors influencing commuting generation forecasts.

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“…However, when temporal correlations are present, a standard GNN approach does not offer significant advantages. Forecasts or estimates considering spatial and temporal dependencies have been a challenge not only in streamflow but in other domains as well, such as traffic forecasting (Li et al, 2023;Tao et al, 2023) or traffic speed forecasts (Xu et al, 2021;Zhang et al, 2021). Recently, many deeplearning approaches have been proposed to overcome similar challenges, and models that are based on graph convolutional neural networks with additional components to capture a better understanding of complex spatial and temporal relationships have gained lots of attention (Jiang and Luo, 2022;Wu et al, 2020).…”

Section: Attention Based Spatial-temporal Graph Convolutional Network...mentioning

confidence: 99%

Spatial Downscaling of Streamflow Data with Attention Based Spatio-Temporal Graph Convolutional Networks

Sit¹,

Demiray²,

Demir³

2023

Preprint

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Accurate streamflow data is vital for various climate modeling applications, including flood forecasting. However, many streams lack sufficient monitoring due to the high operational costs involved. To address this issue and promote enhanced disaster preparedness, management, and response, our study introduces a neural network-based method for estimating historical hourly streamflow in two spatial downscaling scenarios. The method targets two types of ungauged locations: (1) those without sensors in sparsely gauged river networks, and (2) those that previously had a streamflow sensor, but the gauge is no longer available. For both cases, we propose the ScaleGNN, a graph neural network based on Attention-Based Spatio-Temporal Graph Convolutional Networks (ASTGCN). We evaluate the performance of ScaleGNN against a Long Short-Term Memory (LSTM) baseline and spatial persistence in estimating discharge values over a 36-hour period. Our findings indicate that ScaleGNN surpasses spatial persistence in the first scenario, while both neural network approaches demonstrate their effectiveness compared to spatial persistence in the second scenario.

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Multiple Information Spatial–Temporal Attention based Graph Convolution Network for traffic prediction

Cited by 16 publications

References 17 publications

Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network

Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network

Forecasting the commuting generation using metropolis-informed GCN and the topological commuter portrait

Spatial Downscaling of Streamflow Data with Attention Based Spatio-Temporal Graph Convolutional Networks

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