I have met some problems when implements the RNN-LSTM model, the error is very huge when the part of known trips is short, for instance, only the arrival time of the first stop is known, the predictions of the next a few stops are very terrible and then becomes better. I think that's why the final MAE is 408. The solution to this problem might be due to the lack of iteration for the beginning steps, so I increase the time of epochs. Another problem is the initial weight before training which may affect the performance of the model according to the paper. By the way, I found when the point is mapped to the nearest stop, there are some stops share the same point which means the arrival time is the same at different stops which is not reasonable.
I have met some problems when implements the RNN-LSTM model, the error is very huge when the part of known trips is short, for instance, only the arrival time of the first stop is known, the predictions of the next a few stops are very terrible and then becomes better. I think that's why the final MAE is 408. The solution to this problem might be due to the lack of iteration for the beginning steps, so I increase the time of epochs. Another problem is the initial weight before training which may affect the performance of the model according to the paper. By the way, I found when the point is mapped to the nearest stop, there are some stops share the same point which means the arrival time is the same at different stops which is not reasonable.