Traffic flow prediction method and system for urban road network

Abstract

The invention discloses a traffic flow prediction method and system for an urban road network, and relates to the technical field of intelligent transportation, and the method comprises the steps: building a training data set comprising an adjacent matrix and a time sequence vector matrix; establishing a graph wavelet attention gating recurrent neural network model; wherein the model comprises a graph convolutional neural network based on wavelet transform and a gating recurrent neural network connected with the graph convolutional neural network; the gating recurrent neural network comprisesgating recurrent units which are in one-to-one correspondence with the time periods and are integrated into the attention mechanism; training and optimizing the graph wavelet attention gating recurrent neural network model by using the training data set; and inputting the adjacency matrix of the urban road network to be predicted and the time sequence vector matrix into the trained and optimized graph wavelet attention gating recurrent neural network model to obtain the traffic flow of each road section in a future time period. The method and the system disclosed by the invention can accurately predict the traffic flow.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a method and system for predicting traffic flow of an urban road network. Background technique [0002] Traffic flow forecasting is an important part of intelligent transportation systems. Timely and accurate traffic flow forecasting can help realize real-time dynamic traffic light control and urban road planning, alleviate huge traffic congestion problems, and improve the safety and convenience of public transportation. However, accurate real-time traffic flow prediction has been challenging due to the complex spatial and temporal dependencies of traffic data. [0003] Spatial dependence means that the change of traffic flow is limited by the topological structure of the urban road network, which is not only reflected in the transmission effect of the traffic state of the upstream section on the downstream section, and the retroactive influence of the traffic ...

AI Technical Summary

Problems solved by technology

Some researchers can extract local features in the graph by improving the graph convolutional network, but lack of flexibility

Since the graph convolutional neural network cannot extract the local spatial features in the graph very well, the spatial-temporal graph convolutional neural network, diffusion convolutional recurrent neural network, and graph convolutional recurrent neural network proposed based on the spectral graph theory for traffic prediction In these graph convolution based models, the lack of flexibility in the local feature extraction process is still a big problem, so that the traffic flow cannot be accurately predicted.

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