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Apr 11, 2022 · Attention mechanism allows the model to have better memory ability, so the model can concentrate on those important data regardless of distance.
In this work, we first introduce attention mechanisms into the traffic matrix prediction field by proposing an attention-based deep learning model for traffic ...
To alleviate it, if network traffic can be predicted, it is possible to efficiently allocate network bandwidth and identify unusual traffic in the network [3] .
This paper proposes several TM prediction methods based on Neural Networks (NN) and predicts TM from three perspectives: predict the Overall TM directly, ...
To address these limitations, this paper proposed an attention-based recurrent neural network architecture for multi-step traffic flow prediction. Experimental ...
To address these limitations, this paper proposed an attention-based recurrent neural network architecture for multi-step traffic flow prediction. Experimental ...
Missing: Matrix | Show results with:Matrix
To improve the prediction accuracy of the dynamic network traffic in the long term, we propose an Attention-based Spatial–Temporal Graph Network (ASTGN) model ...
Missing: Matrix | Show results with:Matrix
This paper proposes a LSTM RNN framework for predicting short and long term Traffic Matrix (TM) in large networks and validates the framework on real-world ...
Dec 9, 2023 · In this paper, we propose a multi-modal attention neural network for traffic flow prediction by capturing long-short term sequence correlation ( ...
In this proposed model, we use attention mechanism to incorporate network traffic state data into urban vehicle trajectory prediction. The model is evaluated by ...
Missing: Matrix | Show results with:Matrix