Transferability improvement in short-term traffic prediction using stacked LSTM network

Accurate and timely short-term traffic prediction is important for Intelligent Transportation System (ITS) to solve the traffic problem. This paper presents a hybrid model called SpAE-LSTM. This model considers the temporal and spatial features of traffic flow and it consists of sparse autoencoder and long short-term memory (LSTM) network based on memory units. Sparse autoencoder extracts the spatial features within the spatial-temporal matrix via full connected layers. It cooperates with the LSTM network to capture the spatial-temporal features of traffic flow evolution and make prediction. To validate the performance of the SpAE-LSTM, we implement it on the real-world traffic data from Qingyang District of Chengdu city, China, and compare it with advanced traffic prediction models, such as models only based on LSTM or SAE. The results demonstrate that the proposed model reduces the mean absolute percent error by more than 15%. The robustness of the proposed model is also validated and the mean absolute percent error on more than 86% road segments is below 20%. This research provides strong evidence suggesting that the proposed SpAE-LSTM effectively captures the spatial-temporal features of the traffic flow and achieves promising results.

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Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽

10.3390/en14113030 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3030

Author(s):

Simon Liebermann ◽

Jung-Sup Um ◽

YoungSeok Hwang ◽

Stephan Schlüter

Keyword(s):

Neural Network ◽

Goodness Of Fit ◽

Renewable Energy Sources ◽

Weather Conditions ◽

Solar Irradiation ◽

Weather Data ◽

Short Term ◽

Climate Conditions ◽

Lstm Network ◽

The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

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Short-term Traffic Prediction with Deep Neural Networks: A Survey

IEEE Access ◽

10.1109/access.2021.3071174 ◽

2021 ◽

pp. 1-1

Author(s):

Kyungeun Lee ◽

Moonjung Eo ◽

Euna Jung ◽

Yoonjin Yoon ◽

Wonjong Rhee

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Traffic Prediction ◽

Short Term

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A Combined Method for MEMS Gyroscope Error Compensation Using a Long Short-Term Memory Network and Kalman Filter in Random Vibration Environments

Sensors ◽

10.3390/s21041181 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1181

Author(s):

Chenhao Zhu ◽

Sheng Cai ◽

Yifan Yang ◽

Wei Xu ◽

Honghai Shen ◽

...

Keyword(s):

Kalman Filter ◽

Standard Deviation ◽

Error Compensation ◽

Random Vibration ◽

Short Term Memory ◽

Combined Method ◽

Short Term ◽

Mems Gyroscope ◽

Long Short Term Memory ◽

Lstm Network

In applications such as carrier attitude control and mobile device navigation, a micro-electro-mechanical-system (MEMS) gyroscope will inevitably be affected by random vibration, which significantly affects the performance of the MEMS gyroscope. In order to solve the degradation of MEMS gyroscope performance in random vibration environments, in this paper, a combined method of a long short-term memory (LSTM) network and Kalman filter (KF) is proposed for error compensation, where Kalman filter parameters are iteratively optimized using the Kalman smoother and expectation-maximization (EM) algorithm. In order to verify the effectiveness of the proposed method, we performed a linear random vibration test to acquire MEMS gyroscope data. Subsequently, an analysis of the effects of input data step size and network topology on gyroscope error compensation performance is presented. Furthermore, the autoregressive moving average-Kalman filter (ARMA-KF) model, which is commonly used in gyroscope error compensation, was also combined with the LSTM network as a comparison method. The results show that, for the x-axis data, the proposed combined method reduces the standard deviation (STD) by 51.58% and 31.92% compared to the bidirectional LSTM (BiLSTM) network, and EM-KF method, respectively. For the z-axis data, the proposed combined method reduces the standard deviation by 29.19% and 12.75% compared to the BiLSTM network and EM-KF method, respectively. Furthermore, for x-axis data and z-axis data, the proposed combined method reduces the standard deviation by 46.54% and 22.30% compared to the BiLSTM-ARMA-KF method, respectively, and the output is smoother, proving the effectiveness of the proposed method.

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Short-term Traffic Prediction Based on Genetic Improved Wavelet Neural Network

2021 International Symposium on Electrical, Electronics and Information Engineering ◽

10.1145/3459104.3459183 ◽

2021 ◽

Author(s):

Tianzi Ma ◽

Hao Chen

Keyword(s):

Neural Network ◽

Wavelet Neural Network ◽

Traffic Prediction ◽

Short Term

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Extraction of local and global features by a convolutional neural network–long short-term memory network for diagnosing bearing faults

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/09544062211016505 ◽

2021 ◽

pp. 095440622110165

Author(s):

Zhang Chao ◽

Wang Wei-zhi ◽

Zhang Chen ◽

Fan Bin ◽

Wang Jian-guo ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Condition Monitoring ◽

Short Term Memory ◽

Vibration Signal ◽

Short Term ◽

Global Features ◽

Term Memory ◽

Long Short Term Memory ◽

Lstm Network

Accurate and reliable fault diagnosis is one of the key and difficult issues in mechanical condition monitoring. In recent years, Convolutional Neural Network (CNN) has been widely used in mechanical condition monitoring, which is also a great breakthrough in the field of bearing fault diagnosis. However, CNN can only extract local features of signals. The model accuracy and generalization of the original vibration signals are very low in the process of vibration signal processing only by CNN. Based on the above problems, this paper improves the traditional convolution layer of CNN, and builds the learning module (local feature learning block, LFLB) of the local characteristics. At the same time, the Long Short-Term Memory (LSTM) is introduced into the network, which is used to extract the global features. This paper proposes the new neural network—improved CNN-LSTM network. The extracted deep feature is used for fault classification. The improved CNN-LSTM network is applied to the processing of the vibration signal of the faulty bearing collected by the bearing failure laboratory of Inner Mongolia University of science and technology. The results show that the accuracy of the improved CNN-LSTM network on the same batch test set is 98.75%, which is about 24% higher than that of the traditional CNN. The proposed network is applied to the bearing data collection of Western Reserve University under the condition that the network parameters remain unchanged. The experiment shows that the improved CNN-LSTM network has better generalization than the traditional CNN.

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