Traffic station flow prediction method based on space-time multi-graph convolutional network

Abstract

The invention provides a traffic station flow prediction method based on space-time multi-graph convolution. The traffic station flow prediction method is used for solving the problems that in the prior art, the feature capture capacity and prediction precision of traffic station flow prediction are not high. The traffic station flow prediction method comprises the following steps: firstly, constructing a neighbor graph and a circulation flow graph, respectively constructing convolution components and capturing spatial and temporal feature output of station flow , mapping the spatial and temporal feature output into flow values with the same shape as a to-be-predicted result, and fusing the two components to obtain a spatial and temporal multi-graph convolution network model based on context gating; constructing training and testing data according to the station access flow data, obtaining a mature space-time multi-graph convolutional network model, and completing station flow prediction is completed. According to the method, multi-graph convolution is applied to deep mining of traffic station flow data, spatial-temporal characteristics of traffic station flow are fully captured from spatial dimensions and time dimensions, various factors used for predicting traffic station in-out flow are comprehensively considered, and traffic station flow prediction precision is improved.

Description

technical field [0001] The invention belongs to the field of intelligent transportation, and in particular relates to a traffic station flow prediction method based on spatio-temporal multi-image convolution. Background technique [0002] With the continuous development of cities, transportation is becoming more and more intelligent. Traffic flow prediction is an important part of intelligent transportation system. There are many kinds of traffic stations in life, such as urban subway stations, highway toll stations, civil aviation airports, etc. The traffic congestion at the stations is closely related to the normal operation of the entire transportation network, and will also affect the normal travel of passengers. If the inbound and outbound flow of traffic stations can be effectively predicted, it will help ensure the normal operation of the entire transportation network, take measures in advance to avoid traffic congestion, reduce safety hazards, facilitate passengers ...

AI Technical Summary

Problems solved by technology

Although the above method can effectively capture the spatio-temporal correlation between flows in various regions, it can only deal with Euclidean structure data and is not suitable for non-Euclidean structure data.

There is also a deep learning prediction method, which uses graph convolution and gated convolution to capture the space-time dependence of vehicle speed on each section of the expressway, and constructs a spatio-temporal g...

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