An encoder-decoder multi-step traffic flow prediction model based on long short-time memory network
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Abstract:
Most of the traffic flow sequences are single-step prediction. To realize multi-step prediction of traffic flow sequence, a long short-term memory (LSTM) model based on encoder-decoder (ED) framework was proposed. To verify the proposed encoder-decoder LSTM multi-step traffic flow prediction model (ED LSTM), autoregressive moving average, support vector regression machine, XGBOOST, recurrent neural network, convolutional neural network and LSTM were used as control groups for the experiment. Experimental results show that when the prediction time step increased, ED framework could slow down the decline of model performance, and LSTM could fully mine the nonlinear relationship in time series. In addition, under the condition of univariate input, the root mean squard error (RMSE) and mean absolute error (MAE) of ED LSTM model decreased by about 0.210-5.422 and 0.061-0.192, respectively, on PEMS-04 dataset with 12 time steps from t+1 to t+12. Compared with single-factor input, the ED LSTM model with multi-factor input decreased RMSE and MAE by about 0.840 and 0.136 respectively under 12 prediction time steps, demonstrating that ED LSTM model can be effectively applied to multi-step and single-factor and multi-factor forecasting of traffic flow series.
