Spatial-temporal attention-based seq2seq framework for short-term travel time prediction

Zhang, Ningqing; Wang, Fei; Chen, Xiong; Zhao, Tong; Kang, Qi

doi:10.1504/ijbic.2022.126277

Cited by 2 publications

(1 citation statement)

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“…LSTM adds three separate gating units on the basis of RNN to control information transmission, which makes it have stronger learning ability for long time series data, and effectively solves the problem of gradient disappearance and gradient explosion. [31][32][33] LSTM is good at processing strong coupling and highly time correlation data, which has many successful application cases in processing time series data in different fields, including power prediction, video data analysis, traffic flow prediction and so on. [34][35][36] LSTM network structure is shown in Figure 5.…”

Section: Long Short-term Memorymentioning

confidence: 99%

Fault diagnosis for wind turbines based on LSTM and feature optimization strategies

Feng

et al. 2023

Concurrency and Computation

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SummaryHigh penetration of renewable energy is the development trend of the future power system. As one of the clean energy sources, wind power generation has an increasing share in the energy market. However, due to the harsh working environment, the high fault rate and poor accessibility of the wind farms, resulting in the difficult maintenance process and high cost. This article proposes a fault diagnosis (FD) method based on long short‐term memory (LSTM) and feature optimization strategies for wind turbines (WTs), thus reducing the operation and maintenance costs of WTs. First, Pearson correlation coefficient analysis is performed on the collected data features to remove redundant features, and wavelet transform is adopted to remove the redundant data, so as to optimize the fault features and fault data. Then the selected features samples are used to train LSTM‐based FD model. Finally, the actual production data is adopted to verify the proposed method. The proposed method can effectively locate the faults, and provide data support for wind farms, thus improving the reliability, safety, and economic benefits of wind farms.

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Section: Long Short-term Memorymentioning

confidence: 99%

Fault diagnosis for wind turbines based on LSTM and feature optimization strategies

Feng

et al. 2023

Concurrency and Computation

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Travel Time Prediction Using Machine Learning Algorithms: Focusing on k-NN, LSTM, and Transformer

Jang

2024

TOTJ

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Background Travel time prediction is fundamentally important in advanced traveler information systems. Particularly, in the Dedicated Short-Range Communications (DSRC) traffic information system targeted by this study, travel times are obtained after vehicles terminate trips on the route, indicating that a time-lag phenomenon is inevitable. Therefore, travel time prediction is even more emphasized in DSRC systems. With advances in artificial intelligence technologies, many new machine learning algorithms have recently been introduced. Methods In this study, three machine learning algorithms—k-Nearest Neighbor (k-NN), Long Short-Term Memory (LSTM), and Transformer—were applied to predict travel times collected on a DSRC-equipped national highway. Travel time outliers caused by entry and exit maneuvers at intersections were filtered using a robust outlier treatment algorithm. Results From applying the prediction algorithms, errors of 12.4%, 11.7%, and 10.3% were revealed for k-NN, LSTM, and Transformer, respectively. To identify the statistical significance of the differences in prediction performance, paired t-tests were performed. Consequently, all the differences between the algorithms were proven to be statistically significant. The differences in performance were found to be generally more significant under congested conditions compared to uncongested conditions. Conclusion The enhanced reliability of real-time travel time information can increase the effectiveness of DSRC traffic information systems by encouraging drivers to divert from congested routes or adjust their trip schedules to less congested periods.

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Spatial-temporal attention-based seq2seq framework for short-term travel time prediction

Cited by 2 publications

References 0 publications

Fault diagnosis for wind turbines based on LSTM and feature optimization strategies

Fault diagnosis for wind turbines based on LSTM and feature optimization strategies

Travel Time Prediction Using Machine Learning Algorithms: Focusing on k-NN, LSTM, and Transformer

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