Urban traffic prediction based on densely connected spatial-temporal graph attention network of GRU
Due to the complexity topology of urban traffic network,the real-time change of traffic flow and external envi-ronmental factors,there are huge difficulties in traffic prediction.In view of the inadequacy of existing methods in mining the spatio-temporal features of road network and the insufficient consideration of external factors,a spatial-temporal network of dense graph attention network based on gated recurrent unit(GRU)(DG-GRU)is proposed.The function of gated recurrent unit is used to capture the dynamic changes of road network data.Densely connect-ed graph attention network(GAT)is used to extract the complex spatial structure characteristics of the road net-work.They can establish the dependence of urban traffic network data on time and space.Considering the influ-ence of external factors,the one-hot encoding is used to model the traffic events that occur in urban sections to en-hance the information attributes of transportation network.Taking Shenhua Road and its surrounding sections in Hangzhou as an example to verify the predictive ability and feasibility of the network.The experimental results il-lustrate that the prediction accuracy of the method is up to 81.64％.Compared with traditional mathematical model and mainstream neural network model,the prediction accuracy of DG-GRU is 35.42％higher than that of ARIMA.Compared with graph attention network(GAT)and GRU neural networks,its prediction accuracy is improved by 17.45％and 3.02％,respectively.Experimental results show that the model in this paper can adapt to complex traffic flow and carry out long-term traffic forecasting tasks.Meanwhile,it can enhance traffic management ability and reduce the costs traffic congestion.
万方数据
维普
