FedAGAT: Real-time traffic flow prediction based on federated community and adaptive graph attention network

Al-Huthaifi, Rasha; Li, Tianrui; Al-Huda, Zaid; Li, Chongshou

doi:10.1016/j.ins.2024.120482

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2024

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Cited by 2 publications

References 38 publications

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Spatial-Temporal Selective State Space (St-Mamba) Model for Traffic Flow Prediction

Zhiiqi,

Bell,

Wang

et al. 2024

Preprint

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Spatial-Temporal Selective State Space (St-Mamba) Model for Traffic Flow Prediction

Zhiiqi,

Bell,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

Multi-Granularity Temporal Embedding Transformer Network for Traffic Flow Forecasting

Huang,

Yan,

Chen

et al. 2024

Sensors

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Traffic flow forecasting is integral to transportation to avoid traffic accidents and congestion. Due to the heterogeneous and nonlinear nature of the data, traffic flow prediction is facing challenges. Existing models only utilize plain historical data for prediction. Inadequate use of temporal information has become a key problem in current forecasting. To address the problem, we must effectively analyze the influence of time periods while integrating the distinct characteristics of traffic flow across various time granularities. This paper proposed a multi-granularity temporal embedding Transformer network, namely MGTEFormer. An embedding input adeptly merges complex temporal embeddings, a temporal encoder to consolidate rich temporal information, and a spatial encoder to discern inherent spatial characteristics between different sensors. The combined embeddings are fed into the attention mechanism’s encoder, culminating in prediction results obtained through a linear regression layer. Temporal embedding can help by fussing the period and various temporal granularities into plain historical traffic flow that can be learned adequately, reducing the loss of time information. Experimental analyses and ablation studies conducted on real traffic datasets consistently attest to the superior performance of the MGTEFormer. Our approach reduces the mean absolute error of the original models by less than 1.7%. Extensive experiments demonstrate the superiority of the proposed MGTEFormer over existing benchmarks.

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FedAGAT: Real-time traffic flow prediction based on federated community and adaptive graph attention network

Cited by 2 publications

References 38 publications

Spatial-Temporal Selective State Space (St-Mamba) Model for Traffic Flow Prediction

Spatial-Temporal Selective State Space (St-Mamba) Model for Traffic Flow Prediction

Multi-Granularity Temporal Embedding Transformer Network for Traffic Flow Forecasting

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