基于D2STGNN的双向高效多尺度交通流预测OA

Due to the complexity of traffic flow and the insufficient extraction of spatio-temporal features,it is difficult for D2STGNN to capture the dynamic changes of traffic networks,which limits the improvement of prediction accuracy.In this paper,a Bi-EMHGRU model combining an efficient multi-head self-attention mechanism(EMHSA)and a bidirectional gated recurrent unit(BiGRU)is proposed.This model captures the sequential dependencies of both forward and backward timings through BiGRU and dynamically allocates weights to each time step by using the multi-head self-attention mechanism to focus on key sequential features.Meanwhile,a multi-scale time feature extraction module is introduced,which enhances the modeling ability for short-term fluctuations and long-term trends and improves the modeling effect of complex spatio-temporal dynamics.The experimental results show that Bi-EMHGRU performs excellently on the PEMS04 and PEMS08 datasets.The root mean square error value has decreased by approximately 0.55～1.55,the mean absolute error has decreased by approximately 0.89～1.40,and the mean absolute percentage error has decreased by approximately 0.86～1.77 percentage points.It can still maintain stable prediction performance when the prediction step length increases and has strong generalization ability.Compared with the existing benchmark models,Bi-EMHGRU can capture the dynamic spatio-temporal features of traffic flow more effectively and significantly improves the prediction accuracy and robustness.