As the core of coordinating urban traffic,intelligent transportation sys-tem(ITS)is developing rapidly,as well as,the traffic flow prediction is an important part of ITS,which is regarded as the key factor for successful deployment of ITS.Because of the complex spatial topological structure of the traffic network,the traffic flow shows higher-order nonlinearity and dynamic spatial-temporal complexity.In or-der to better predict the traffic grid data,this paper proposes a new spatial-temporal network model DCSTGCN,which has the following characteristics:1)The Chebyshev polynomial(Ch)is applied to the graph convolutional neural network,and the tradi-tional fixed traffic topology is converted with self-diffused convolution to make it more random and dynamic;2)The spatial Transformer model is added.While considering the data heterogeneity,the multi head self attention mechanism is used to consider the multi attribute problems of nodes,local neighbors,and non local nodes,and the hidden feature information between nodes is considered from the high-dimensional subspace;3)Combining the temporal transformer with a 1 × 1 2D convolutional neu-ral network(Conv2d).Multiple weights are assigned to the traffic flow time series information to obtain important time features,and the Conv2d network is used to predict the output.The experimental verification shows that the method model is better than a variety of comparative baseline models.
关键词
交通流预测/切比雪夫多项式/图卷积/Transformer/随机游走
Key words
Traffic flow prediction/Chebyshev polynomial/graph convolution/trans-former/random walk
维普
万方数据