Attention-based Conv-LSTM and Bi-LSTM networks for large-scale traffic speed prediction

Hu, Xiaojian; Liu, Tong; Hao, Xiatong; Lin, Chenxi

doi:10.1007/s11227-022-04386-7

Cited by 26 publications

(18 citation statements)

References 39 publications

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“…Based on this, the prediction model established by the 1DCNN-LSTM network can accurately predict the improvement direction of the mapping technology under different parameters, which fully proves that the prediction model has strong applicability and high prediction accuracy. According to the above results, it can be concluded that the model meets the requirements of accurate prediction [28].…”

Section: Dcnn-lstm Model Prediction and Results Analysismentioning

confidence: 67%

Application of surveying and mapping technology based on deep learning model in petroleum geological exploration

Sun¹,

Shu²

2023

3C Tecnología

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Surveying and mapping technology is one of the key technologies used in petroleum geological exploration and has made significant contributions to geological exploration. However, with the development of science and technology, traditional surveying and mapping technology has low work efficiency and poor information accuracy, which limits its application. This study proposes a surveying and mapping technology based on the 1DCNN-LSTM deep learning model. Through feature selection and feature optimization, the important features extracted by 1DCNN are predicted through LSTM, and the development direction of surveying and mapping technology is optimized and predicted to promote the development of new surveying and mapping technologies. application. By using the orthogonal test to optimize the input factors, determine the relative order of the influence of the factors, and use the 1DCNN-LSTM and BP neural network to train and verify the input factors respectively. The research results show that 1DCNN-LSTM has higher prediction accuracy, and the prediction accuracy is The results show that the 1DCNN-LSTM deep learning model used in the optimization of petroleum geological exploration and mapping technology in this study has strong practical significance.

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Section: Dcnn-lstm Model Prediction and Results Analysismentioning

confidence: 67%

Application of surveying and mapping technology based on deep learning model in petroleum geological exploration

Sun¹,

Shu²

2023

3C Tecnología

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“…The correlations between G t s and H t s directly determine the attention score, which can be interpreted as an alignment operation between them. The higher the score, the more critical the impact of the current time series on the final prediction results ( 29 ). Thus, we can allocate enough attention to the key information and highlight the impact of important information, thereby improving the model’s performance.…”

Section: Methodsmentioning

confidence: 99%

Urban Road Traffic Flow Prediction with Attention-Based Convolutional Bidirectional Long Short-Term Memory Networks

Liu

Ding

2023

Transportation Research Record: Journal of the Transportation R

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Accurate traffic prediction is critical for industry practitioners and researchers in intervening and dredging future traffic in advance to avoid traffic congestion. Considering that most prediction models fail to effectively capture the complex nonlinearity of traffic data and thus cannot obtain satisfactory prediction results, we propose a novel deep-learning architecture for traffic flow prediction, called AC-BLSTM (attention-based convolutional bidirectional long short-term memory). The proposed model captures traffic information through multilayer network architectures composed of convolutional bidirectional long short-term memory (conv-BiLSTM) network and attention mechanism. The spatiotemporal features of traffic flow are extracted by convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) network. Then attention mechanism combines the outputs of CNN and BiLSTM to assign corresponding weights to the features extracted at different times. In addition, we employ a parallel sub-module structure to model three temporal properties of traffic flow, that is, weekly, daily, and recent dependencies. Finally, the results of these three parts are fused to predict the traffic flow values through the fully connected (FC) layers. Experiment results using a real-world urban road traffic dataset demonstrate that compared with other competing models, the proposed model has better prediction performance.

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“…where: C denotes the state of the memory cell, σ denotes the function with output between 0 and 1, tanh is the hyperbolic tangent function with output between −1 and 1, W is the weight matrix between the gates, and b is the bias term for each gate. BiLSTM, is a particular RNN for capturing the input sequence's forward and backward contextual information [24]. The main components include two LSTM, the forward LSTM, and the backward LSTM, respectively.…”

Section: Bilstm Networkmentioning

confidence: 99%

A lifetime prediction model based on two-path convolution with attention mechanism and bidirectional long short-term memory network

Sun,

Dong,

Bai

et al. 2024

Meas. Sci. Technol.

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With the continuous progress of technology, the structure of modern industrial equipment is becoming increasingly complex. Complex industrial processes often have multivariable, nonlinear, variable operating conditions and intense noise, making it a challenging research direction to establish accurate residual service life prediction models.This paper constructs a life prediction model based on two-way convolution, attention mechanisms, and a two-way short-term memory network. The front end of the model uses a two-way convolution scale and attention module to mine critical fault information of bearings, improve the anti-noise ability of the model, and use adaptive batch normalization (AdaBN) and Meta-Aconc activation function to adaptively adjust neurons to enhance the generalization ability of the model, At the back end of the model, the bidirectional long-term and short-term memory network is used to memorize the degradation information of the bearing and the residual service life of the bearing is predicted.Finally, it has a high prediction accuracy in noise interference and condition migration scenarios using the root mean square error (RMSE), average absolute error (MAE), and other prediction indicators.This model provides a method reference for predicting the lifespan of rotating machinery under intense noise and variable operating conditions.

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Attention-based Conv-LSTM and Bi-LSTM networks for large-scale traffic speed prediction

Cited by 26 publications

References 39 publications

Application of surveying and mapping technology based on deep learning model in petroleum geological exploration

Application of surveying and mapping technology based on deep learning model in petroleum geological exploration

Urban Road Traffic Flow Prediction with Attention-Based Convolutional Bidirectional Long Short-Term Memory Networks

A lifetime prediction model based on two-path convolution with attention mechanism and bidirectional long short-term memory network

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