For the short-term traffic flow prediction problem based on deep learning,the essence of spatiotemporal correlation modeling was revealed,the multi-scale spatiotemporal characteristics,heterogeneity,dynamics,nonlinearity,and other characteristics involved in the modeling process were analyzed,the core challenges were clarified,and the external information integration,multi-step prediction and single-step prediction,as well as individual prediction and integrated prediction were elaborated.The latest research directions were reviewed with two organization methods of traffic flow data:grid and topology.Research results indicate that for gridded traffic flow data,current research mainly includes three prediction modeling methods:2D image convolutional neural network,2D image convolutional neural network combined with recurrent neural network,and 3D image convolutional neural network.For topological traffic flow data,current research mainly includes four prediction modeling methods:1D causal image convolution combined with convolutional graph neural network,recurrent neural network combined with convolutional graph neural network,self-attention combined with convolutional graph neural network,and spatiotemporal synchronous learning of convolutional graph neural network.Overall,the short-term traffic flow prediction based on deep learning methods significantly improves the prediction accuracy compared to time series method and classical machine learning method.In the future,the combination of physics theory,knowledge graphs,and deep learning,the construction of large-scale models for multi-temporal and spatial data mining,as well as the lightweight,interpretability,and automated model structure search,will become important research directions.1 tab,8 figs,81 refs.
维普
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