Nov 16, 2024 · This study introduces a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network for predicting traffic flow.
Nov 16, 2024 · Traffic predictions benefit from having a large and diverse dataset. Bi-LSTM model can capture temporal patterns more effectively than the LSTM.
Nov 18, 2024 · The results demonstrated the model's strong competitiveness in the field of traffic flow prediction. Specifically, on the PeMSD4 dataset, our ...
Nov 21, 2024 · This study aims to find the optimal deep bidirectional long short-term memory (LSTM) neural network to predict the short-term traffic flow under ...
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Abstract—In this paper, the expressway traffic flow prediction model based on the Bi-LSTM is designed, and four sections of expressway are applied to the ...
The aim of this study is to develop a complex framework for forecasting short-term traffic flow with the purpose of improving prediction accuracy.
Canonical long short-term memory (LSTM) networks are easily drawn to focus on min-to-min fluctuations rather than the long term dependencies of the traffic flow ...
The results show that the proposed Bi-LSTM model exhibits higher prediction performance. Afterwards, this paper makes a comparative analysis of the predicted ...
Nov 29, 2023 · The evaluation metrics demonstrate that Bi-LSTM outperforms LSTM by a wide margin, with a coefficient of determination value of 0.99 against ...
Dec 25, 2023 · On the basis of the BiLSTM model, the attention mechanism is introduced to calculate different weights for the output vectors of the BiLSTM ...