Traffic flow prediction method fusing space-time attention neural network and traffic model

Abstract

The invention provides a traffic flow prediction method fusing a space-time attention neural network and a traffic model, and the method comprises the steps: dividing feature data according to time slices, carrying out the GAT operation of the data on each time slice, and obtaining a new representation of a flow feature and a speed feature, the new representation of the speed characteristics is input into a traffic simulation model Greenshifts parabola model for transformation to obtain another new representation of the flow characteristics, then the two new representations of the flow characteristics are respectively subjected to gated loop unit network GRU processing, and then the two obtained flow representations are spliced to obtain input of a full connection layer; and performing full connection layer processing on the spliced feature data to obtain a final prediction result, and finally training a neural network model based on a deep learning theory. And obtaining a prediction result on the test set by using the trained network model. According to the method, the traffic flow prediction in the future time period can be realized under the condition that the traffic network and the flow characteristic and speed characteristic data thereof are known.

Description

technical field [0001] The invention relates to the fields of deep learning, spatio-temporal sequence prediction and traffic simulation, in particular to a traffic flow forecasting method integrating spatio-temporal attention neural network and traffic model. Background technique [0002] With the development of intelligent transportation systems, the urban traffic data that can be collected is constantly enriched, and traffic forecasting has attracted more and more attention. It is a key part of advanced traffic management system and an important part of realizing traffic planning, traffic management and traffic control. Traffic forecasting is the process of analyzing urban road traffic conditions (including flow, speed and density, etc.), mining traffic patterns, and predicting road traffic trends. Traffic forecasting can not only provide a scientific basis for traffic managers to perceive traffic congestion in advance and restrict vehicles, but also provide guarantees fo...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is: the current traffic flow prediction problem cannot simultaneously capture the temporal correlation and spatial correlation of data, and the pure speed-flow model ignores the technical problem of the spatial structure of the road network

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