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- W4386821345 abstract "Traffic state forecasting plays a vital role in the Intelligent Transportation System, a critical topic in transportation. Researchers have recently begun combining correlated information, such as spatial dependency, with this issue. A Bayesian optimized spatial-temporal attention long short-term memory neural network is proposed to improve the prediction performance. Considering the latent influence of spatial position and temporal period, a hybrid model based on long short-term memory utilizes convolutional modules and attention mechanisms to dig out the spatial and temporal features. Then, to reduce the influence of hyperparameters in the models, Bayesian optimization is utilized to search out the most suitable hyperparameters to make the forecasting performance the best. The RMSE and MAE of BO-STLSTM perform best in the real-world dataset comparison experiments at 25.77 and 18.98, respectively. Furthermore, the experiments also proved that the hybrid model has the best forecasting performance when the number of training epochs is 400." @default.
- W4386821345 created "2023-09-19" @default.
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- W4386821345 date "2023-07-24" @default.
- W4386821345 modified "2023-09-26" @default.
- W4386821345 title "A Bayesian Optimized Spatial-Temporal LSTM for Traffic Flow Predition" @default.
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- W4386821345 doi "https://doi.org/10.23919/ccc58697.2023.10240511" @default.
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