An improved traffic flow prediction model: Spatial-Temporal network based on Wavelet and LSTM

Short term traffic prediction is a common Spatial-Temporal sequence training and learning prediction. This paper presents a new prediction model STWLSTM. Firstly, the model is decomposed and reconstructed by wavelet analysis, and then LSTM is trained. The LSTM prediction results are decomposed by wavelet, and the wavelet high-frequency coefficients of historical time data and first-order neighborhood spatial data are fused before reconstruction to obtain the final results. The experimental results show that the prediction results of STWLSTM model conform to the inherent attributes of traffic prediction: periodic stability and mutation.