Abstract: Traffic flow prediction has important and practical significance in the field of intelligent transportation. Because traffic flow data is affected by many factors, leads to poor stability, strong randomness, and presents a highly non-linear characteristic, it is extremely difficult to predict traffic flow. Aiming at the requirements of the accuracy of short-term traffic flow prediction, this paper proposes a short-term traffic flow prediction method based on CEEMD(Complete Ensemble Empirical Mode Decomposition, CEEMD), combined with CNN(Convolutional Neural Networks, CNN) and LSTM(Long Short-Term Memory, LSTM). The model uses CEEMD signal decomposition to reduce the impact of noise on traffic flow data prediction, CNN and LSTM are used to fully mine the temporal and spatial characteristics of the data, so that the model can make more accurate judgments and improve the learning efficiency of the neural network. Experimental verification on real traffic flow data shows that the model proposed in this paper can effectively improve the accuracy of traffic flow prediction.

Key words: short-term traffic flow forecast, complete ensemble empirical mode decomposition, convolutional neural network, long short-term memory network, learning efficiency