Short-Time Traffic Flow Prediction Based on a Combined Model of EMD and LSTM

In traffic management, accurate forecasting of short-term traffic patterns is of utmost importance to achieve optimal performance and efficiency of road networks. This research proposes a prediction technique for short-term traffic flow, which utilizes empirical modal decomposition (EMD) and long short-term memory neural networks (LSTM). Firstly, the traffic flow sequence is decomposed into a series of relatively stable subseries using EMD, minimizing the impact of various trend data interactions. Secondly, to improve model training efficiency, normalization is applied separately to each subseries. Subsequently, an LSTM-based time-series prediction model is built for each subseries, which enhances the model's predictive accuracy. Finally, the forecasted values of short-term traffic flow are obtained by aggregating the prediction outcomes of each subseries. The simulation results demonstrate that the proposed method more accurately predicts the traffic flow change trend and achieves higher stability than conventional prediction techniques.