Full traffic prediction method based on dual graph framework

Abstract

The invention discloses a full traffic prediction method based on a dual graph framework, and the method comprises the steps: (1) expressing a road network structure as a topological graph, taking anintersection as a node, and taking a road segment connected with the intersection as an edge; preparing historical edge and node data and future edge and node data; (2) constructing a historical information encoder, inputting historical data into the encoder, realizing information transmission between edges and nodes through multi-layer dual mapping, and splicing outputs of the multi-layer dual mapping into a historical feature tensor; (3) constructing a future prediction decoder, decoding the historical feature tensor into future spatial-temporal features, and outputting a future prediction result; (4) taking an error between the prediction result and the actual data as a loss function to perform model training until the loss function converges; and (5) carrying out model test by using the trained model, and carrying out application after the test is finished. According to the invention, the prediction result can obtain the total complete description of the future traffic condition, and the prediction accuracy is high.

Description

technical field [0001] The invention belongs to the field of artificial intelligence and time series forecasting, and in particular relates to a full traffic forecasting method based on a dual graph framework. Background technique [0002] The traffic forecasting task refers to predicting the traffic data of a future period based on the traffic data of a period of history within the range of a given regional road network, including traffic flow, average speed, and travel time prediction. Traffic prediction is the core of Intelligent Transportation System (ITS), and it is widely used in traffic efficiency optimization, traffic risk control, route planning and navigation, etc. Traffic forecasting is a very challenging problem. The difficulty lies in how to model the complex spatiotemporal dependencies between different locations in the road network. [0003] Traffic forecasting can be classified as a time series forecasting problem with road network structure, which has been ...

AI Technical Summary

Problems solved by technology

[0006] The present invention provides a full-volume traffic prediction method based on the dual graph framework, which well solves the problem of missing edge prediction results in existing traffic prediction methods; even if only node prediction is made, because the edge historical data, the present invention can also obtain better prediction accuracy

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