Traffic flow prediction method based on Transform space-time diagram convolutional network

Abstract

The invention relates to a traffic flow prediction method based on a Transform space-time diagram convolutional network, and belongs to the technical field of traffic flow prediction, and the method comprises the following steps: constructing a static adjacency matrix according to detectors deployed in a road network and the connectivity and Euclidean distance between the detectors; merging the traffic flow original data collected by the detector according to a specified time interval; performing normalization processing on the data set by adopting a maximum-minimum method, constructing a traffic flow space-time diagram, and dividing the data set into a training set and a test set; a convolutional network prediction model based on the Transform space-time diagram is constructed; training a prediction model by taking the training set data as input; and performing traffic flow prediction on the test set by using the trained space-time diagram convolutional network prediction model, and performing evaluation analysis on a prediction error according to a prediction result and actual traffic data. Compared with a traditional method, the method has the advantages that the spatial-temporal correlation in the traffic flow data can be effectively extracted, information in the traffic flow data can be more fully mined, and the prediction precision is improved.

Description

technical field [0001] The invention belongs to the technical field of traffic flow prediction, and relates to a traffic flow prediction method based on Transformer space-time graph convolution network. Background technique [0002] With the advancement of urbanization, the number of motor vehicles in my country continues to rise. As of December 2021, the number of motor vehicles will reach 393 million, ranking first in the world. Traffic congestion has gradually become one of the major social problems. The application and development of intelligent transportation system can help alleviate traffic congestion, and traffic flow prediction is one of the keys to ensure the effectiveness of intelligent transportation system. Accurate traffic flow forecasting, especially long-term forecasting, can help travelers obtain future traffic information in advance and make better travel plans; on the other hand, it can help managers formulate reasonable traffic guidance plans, improve tr...

AI Technical Summary

Problems solved by technology

But stacked convolutional layers still degrade the performance of the model

Therefore, existing methods also have certain difficulties for long-term forecasting

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