Vehicle flow prediction method combining attention mechanism and dynamic space-time convolution model

Abstract

The invention relates to a vehicle flow prediction method in combination with an attention mechanism and a dynamic space-time convolution model, and provides a road-level flow transfer embedding module to learn tensor representation of a flow transfer time sequence between roads in order to solve the problem that an existing model cannot effectively utilize a vehicle flow transfer relation between the roads; aiming at the problem that the existing method cannot capture the spatial dependence of the dynamic change, a dynamic space attention module is provided and is used for calculating a spatial dependence matrix of the dynamic change; aiming at the problem that an existing model cannot effectively model time sequence importance, a dynamic time attention module is provided and is used for calculating a time sequence attention weight. Through the components, the model can effectively utilize road flow transfer data to capture the space-time dependency relationship of dynamic change between roads, so that the accuracy of road electric bicycle flow prediction is improved.

Description

technical field [0001] The invention relates to a vehicle flow forecasting method combining an attention mechanism and a dynamic spatiotemporal convolution model, specifically a road electric bicycle traffic forecasting method based on an attention mechanism and a dynamic spatiotemporal graph convolution model, belonging to the field of spatiotemporal data mining and traffic forecasting . Background technique [0002] In recent years, with the large-scale application of the Global Positioning System (GPS), GPS sensors have been deployed on a large scale in various vehicles, resulting in massive trajectory data. The sales and ownership of electric bicycles in my country are constantly increasing. Many cities realize the comprehensive management of electric bicycles in urban areas by installing GPS sensors on electric bicycles. Massive electric bicycle trajectory data can be used for a variety of research, such as mining the travel patterns of urban residents, analyzing urban...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem that the existing method based on the graph convolutional neural network model cannot effectively utilize the transfer relationship of road traffic flow and cannot capture the spatial dependence of dynamic changes, the present invention proposes a road that combines the attention mechanism and the dynamic spatiotemporal graph convolution model Electric Bicycle Flow Prediction Method

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