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

A technology of convolutional network and prediction method, which is applied in traffic flow detection, neural learning method, biological neural network model, etc., can solve the problems of long-term prediction difficulties and reduce model performance, and achieve the effect of improving prediction accuracy and improving accuracy

Pending Publication Date: 2022-04-12
CHONGQING UNIV
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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

Method used

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

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Experimental program
Comparison scheme
Effect test

Embodiment

[0192] 1) Select experimental data

[0193]The data collected in real time by the California Transportation Agency (CalTrans) Performance Measurement System (PeMS) every 30 seconds is selected as the data set. The raw traffic flow data is aggregated into 5-minute intervals, containing data from 170 detectors in Dstrict08 from July 1, 2016 to August 31, 2016. The data of the first 43 days is used as the training set for model parameter training. The data of the last 19 days is used as the test set for prediction.

[0194] 2) Parameter determination

[0195] In the process of building the model, the main parameters involved are: the number of encoder / decoder layers L, the amount of historical traffic flow data T p , Predicted traffic flow data volume T f , model dimension d model , maximum interception distance m, historical days d, historical weeks w, convolution kernel K, number of self-attention heads h, control activation function saturation rate parameter γ, each param...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08G08G1/01
Inventor 郑林江陈逸灵刘卫宁孙棣华
Owner CHONGQING UNIV
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