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- W4386322458 abstract "To improve the current situation of insufficient metro capacity during peak hours in large cities, it is necessary to accurately forecast the subway passenger flow. To solve this problem, first, we carry out the data pre-processing after selecting the appropriate time granularity. Then, based on the PyTorch tool, we implement the LSTM network, employ a training scheme, and search the best parameters preferentially. Besides, we add an L2 regularization to the MSE loss function and utilize the NAG algorithm for optimization. After that, to enhance the model's anti-interference ability to abnormal data, we randomly inject the Gaussian noise into the training dataset. Finally, a set of experiments are carried out to evaluate the performance of our proposed method. The experimental results verify the feasibility and robustness of applying the LSTM model to subway passenger flow forecasts." @default.
- W4386322458 created "2023-09-01" @default.
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- W4386322458 date "2023-07-17" @default.
- W4386322458 modified "2023-10-16" @default.
- W4386322458 title "LSTM Model-based Subway Passenger Flow Forecast Solution" @default.
- W4386322458 cites W2965774650 @default.
- W4386322458 doi "https://doi.org/10.1109/icce-taiwan58799.2023.10226778" @default.
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