Short-term traffic flow prediction based on ACBiGRU model

Aiming at the traditional short-term traffic flow prediction methods only focused on the temporal characteristics of traffic flow without considering the spatial characteristics，a combined prediction model of convolutional neural network and bidirectional gated circulation unit (ACBiGRU) model with attention mechanism was proposed．The combined model used attention mechanism of convolution neural network to mine spatial correlation of adjacent road traffic flow，and embeded the attention mechanism in the convolution neural network． The results of the convolution layer with different weight were effective to extract the spatial characteristics of traffic flow，and then the BiGRU model was used to extract the traffic flow time series characteristics．Finally，the extracted temporal and spatial features were fused to complete the short-term traffic flow prediction．Experimental results show that the ACBiGRU model is better than other models in the real dataset，and the RMSE (root mean square error) of the prediction results is reduced by 8% on average compared with that of the traditional time series model，verifying the effectiveness and superiority of the short-term traffic flow prediction combining with the spatio-temporal characteristics. © 2023 Huazhong University of Science and Technology. All rights reserved.