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Traffic characteristic prediction method and system and storage medium

A technology of traffic characteristics and prediction methods, applied in the field of intelligent transportation, can solve the problems of reducing the occupancy rate of video memory, low accuracy, and short training time.

Active Publication Date: 2020-04-21
SUN YAT SEN UNIV
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  • Claims
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AI Technical Summary

Problems solved by technology

[0005] The present invention provides a traffic feature prediction method aimed at the technical features of large amount of calculation and low accuracy existing in the existing traffic feature prediction method. , and using the pure convolution method takes less time to train, and at the same time predicts that it can achieve higher accuracy

Method used

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  • Traffic characteristic prediction method and system and storage medium
  • Traffic characteristic prediction method and system and storage medium
  • Traffic characteristic prediction method and system and storage medium

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Embodiment 1

[0079] like figure 1 as shown, figure 1 It is a schematic diagram of the structure of the deep learning model GA-GCN.

[0080] The present invention describes some steps in the method in detail in conjunction with specific examples.

[0081] Step 1: Each road obtains an instant speed every 5 minutes, so each road has 288 instant speeds a day.

[0082] Step 2: Preprocess the data. If there are missing values ​​in the input data, use the linear interpolation method to complete them, and divide the data into training set and test set in proportion. In the experimental data set, the California data uses 34 days as the training set, 5 days as the verification set, and 5 days as the test set; the Los Angeles data uses 5 days as the training set, 1 day as the verification set, and 1 day as the test set.

[0083] Step 3: Divide the data set, and the length of the historical time window is 60 minutes, that is, 12 known instant speeds are used to predict the speed of the next 15 minu...

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Abstract

The invention relates to a traffic feature prediction method, which is realized based on a deep learning model GA-GCN, and comprises the following steps: obtaining a historical traffic feature data set; preprocessing the historical traffic characteristic data set; dividing the historical traffic characteristic data set according to a fixed time interval; training a deep learning model GA-GCN by using each divided historical traffic feature data set; and ending the training, and predicting the traffic features in the test set by using the trained deep learning model GA-GCN to obtain a prediction result.

Description

technical field [0001] The present invention relates to the technical field of intelligent transportation, and more specifically, to a traffic characteristic prediction method, system and storage medium. Background technique [0002] Urban public transportation system is an important part of modern urban transportation. Timely and accurate traffic forecast is becoming more and more important in the control and guidance of urban traffic, and it is an indispensable part of today's social life. The traditional methods of traffic forecasting cannot meet the medium-term and long-term forecasting tasks, and the traditional methods do not consider some dependencies of time and space, so it is difficult to predict accurately. According to the survey, in 2015, the average American spent 48 minutes a day on the road. Therefore, real-time traffic forecasting is necessary for both citizens and governments. [0003] In order to solve the problem of traffic forecasting, the best effect ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26G06N3/04G06N3/08
CPCG06Q10/04G06Q50/26G06N3/084G06N3/048G06N3/045Y02T10/40
Inventor 刘玉葆黄荣洲
Owner SUN YAT SEN UNIV
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