Short-Term Traffic Flow Prediction Based on the Optimization Study of Initial Weights of the Attention Mechanism

Lan, Tianhe; Zhang, Xiaojing; Qu, Dayi; Yang, Yue; Chen, Yi-Cheng

doi:10.3390/su15021374

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…Lan Tianhe et al proposed a prediction model (GWO Attention LSTM) based on the combination of the optimized attention mechanism and LSTM. The results indicate that the GWO attention LSTM model has good model performance and can provide effective assistance for traffic management control and traffic flow theory research [24]. Symmetrybased Bi-LSTM networks can overcome the drawback of one-way LSTM networks of being only able to learn unidirectional information [25,26].…”

Section: Literature Reviewmentioning

confidence: 90%

Prediction of High-Speed Traffic Flow around City Based on BO-XGBoost Model

2023

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The prediction of high-speed traffic flow around the city is affected by multiple factors, which have certain particularity and difficulty. This study devised an asymmetric Bayesian optimization extreme gradient boosting (BO-XGBoost) model based on Bayesian optimization for the spatiotemporal and multigranularity prediction of high-speed traffic flow around a city. First, a traffic flow dataset for a ring expressway was constructed, and the data features were processed based on the original data. The data were then visualized, and their spatiotemporal distribution exhibited characteristics such as randomness, continuity, periodicity, and rising fluctuations. Secondly, a feature matrix was constructed monthly for the dataset, and the BO-XGBoost model was used for traffic flow prediction. The proposed model BO-XGBoost was compared with the symmetric model bidirectional long short-term memory and integrated models (random forest, extreme gradient boosting, and categorical boosting) that directly input temporal data. The R-squared (R2) of the BO XGBoost model for predicting TF and PCU reached 0.90 and 0.87, respectively, with an average absolute percentage error of 2.88% and 3.12%, respectively. Thus, the proposed model achieved an accurate prediction of high-speed traffic flow around the province, providing a theoretical basis and data support for the development of central-city planning.

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Section: Literature Reviewmentioning

confidence: 90%

Prediction of High-Speed Traffic Flow around City Based on BO-XGBoost Model

2023

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“…Three gating units with different functions determine when the information flow in the LSTM module flows in, out, and is forgotten, respectively. As equation (21) shows, the forget gate takes x t and h t−1 as inputs and discards the information using the sigmoid function.…”

Section: Long Short Term Memory Networkmentioning

confidence: 99%

“…On the basis of this observation, Zhang et al [20] modified the genetic algorithm in order to accelerate the convergence of the optimized LSTM. The attention mechanism was added to the LSTM by Lan et al [21]. The Gray Wolf Optimization (GWO) method was used to change the initial weight values of the attention mechanism in order to increase the prediction model's attention to important information.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Traffic Flow Prediction Based on VMD and IDBO-LSTM

Zhao,

Guo,

Sun

et al. 2023

IEEE Access

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To improve the accuracy of short term traffic flow prediction and to solve the problems of nonlinearity of short term traffic flow, more noise in the data, and more difficult to determine the parametes of long short term memory networks, a combined traffic flow prediction model based on variational modal decomposition (VMD) and improved dung beetle optimization-long short term memory network (IDBO-LSTM) is proposed. First, to extract various modal components, the historical traffic flow data are smoothed using variational modal decomposition (VMD). Second, the LSTM prediction model is built for each individual subsequence, and the parameters of the LSTM are optimized using the IDBO algorithm which combines Singer chaos mapping, variable spiral search strategy, and Levy flight strategy. Finally, to acquire the final prediction results, the predicted values of various subsequences are added up and reassembled. Experiments were conducted using data collected from eight sensors along an interstate highway in California, and taking the straight road morning peak (S-M) data as an example, compared with LSTM and VMD-LSTM, the MAE of VMD-IDBO-LSTM is reduced by 26.69 and 7.5108, MAPE is reduced by 8.08059% and 2.27569%, and RMSE is reduced by 33.6912 and 8.7657. According to the findings, the VMD-IDBO-LSTM model that was proposed is capable of significantly improving the accuracy of short-term traffic flow prediction while also effectively addressing nonlinearity, data noise, and the difficulty of identifying the LSTM parameters.INDEX TERMS Short-time traffic flow prediction, variational modal decomposition, dung beetle optimization algorithm, long short term memory.

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“…The attention mechanism in the Transformer model 28 was introduced in this paper to promote the capture of relationship features between temporal signals. This mechanism can assign a weight to the input by itself, thereby enabling the model to focus on the essential information of rolling bearing faults and improving the efficiency of feature extraction.…”

Section: Siamese Cnn-bilstm Model-based Quantitative Diagnosis Of Rol...mentioning

confidence: 99%

A Siamese CNN-BiLSTM-based method for unbalance few-shot fault diagnosis of rolling bearings

Liu,

Chen,

Wang

et al. 2023

Measurement and Control

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Small and imbalanced fault samples have a profound impact on the diagnostic performance of a model in the process of locating and quantifying the rolling bearing damage of aeroengines in practice. Therefore, a Siamese Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model was proposed in this paper. Random selection and cross combination methods were used to augment and balance sample sizes at first. Then, two weight-sharing CNN-BiLSTM models were used for adaptive extraction and distance measurement of weak fault features. Finally, the fault classification was performed based on feature distance. Model performance was verified using simulated fault test data of rolling bearings. The results showed that the Siamese CNN-BiLSTM model could achieve an accuracy of up to 96.0% for quantitative diagnosis and 98.0% for location diagnosis. This model was also capable of solving the imbalanced classification of samples and made it possible to transfer between different rotating speeds and working conditions.

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Short-Term Traffic Flow Prediction Based on the Optimization Study of Initial Weights of the Attention Mechanism

Cited by 7 publications

References 20 publications

Prediction of High-Speed Traffic Flow around City Based on BO-XGBoost Model

Prediction of High-Speed Traffic Flow around City Based on BO-XGBoost Model

Short-Term Traffic Flow Prediction Based on VMD and IDBO-LSTM

A Siamese CNN-BiLSTM-based method for unbalance few-shot fault diagnosis of rolling bearings

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