Graph neural network traffic flow prediction method and system based on attention mechanism

Abstract

The invention discloses a graph neural network traffic flow prediction method and system based on an attention mechanism. The method comprises the steps of obtaining to-be-predicted urban traffic flowdata; constructing a road network map according to the road connection relationship; preprocessing the urban traffic flow data to be predicted; and inputting the road network map and the preprocessedresult into a pre-trained neural network based on an attention mechanism, and finally outputting a prediction result of the urban traffic flow. Roads and checkpoints are encoded according to the roadnetwork information, a road network graph structure is established according to the upstream and downstream relationship of the roads, vehicle passing data of the checkpoints is counted under different time dimensions nd summarized to form a road network traffic flow data table; a graph neural network formed by stacking multiple layers of attention modules is constructed, a time sequence attention mechanism and the graph attention network are used for modeling the traffic flow in the whole road network, and the future traffic flow condition of a specified checkpoint is predicted.

Description

technical field [0001] The present disclosure relates to the technical field of intelligent transportation, in particular to a graph neural network traffic flow prediction method and system based on an attention mechanism. Background technique [0002] The statements in this section merely mention background art related to the present disclosure and do not necessarily constitute prior art. [0003] With the rapid development of network information technology, various dynamic and static data related to the transportation system, such as people, vehicles, roads and the environment, have been collected in large quantities. These massive heterogeneous data provide new means and data for research in the field of transportation. support. The field of transportation, especially the field of intelligent transportation, has become one of the most typical and active fields for the application of artificial intelligence technology. Combining artificial intelligence technology and big ...

AI Technical Summary

Problems solved by technology

[0006] 1. Models based on statistical principles cannot capture the spatial correlation and local characteristics of geographic location information in traffic flow changes;

[0007] 2. Recurrent neural network RNN ​​or LSTM based on recursive calculation has low computational efficiency, and the structural characteristics of the model must require that the input of the current model is the output of the previous time point, and parallel training cannot be performed on traffic prediction tasks in large-scale areas;

[0008] 3. Neither the statistical model nor the traditional neural network model pays attention to the influence of road network graph structure on traffic flow

Images