Traffic intersection congestion prediction method based on machine learning

A traffic intersection and machine learning technology, applied in the field of machine learning, can solve the problems of short prediction time span, few applicable scenarios, and low scalability, and achieve the effects of reducing deviation and variance, low equipment demand, and fast speed

Inactive Publication Date: 2020-07-28
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
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AI Technical Summary

Problems solved by technology

In the complex environment, the method of the prior art has few applicable scenarios due to high equipment requirements, low scalability, short prediction time span and low accuracy

Method used

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  • Traffic intersection congestion prediction method based on machine learning
  • Traffic intersection congestion prediction method based on machine learning
  • Traffic intersection congestion prediction method based on machine learning

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Embodiment Construction

[0053] The present invention will be further described below in conjunction with examples.

[0054] The present embodiment provides a method for predicting congestion at traffic intersections based on machine learning, comprising the following steps:

[0055] Step 1. Collect the traffic flow data at the intersection, and divide the congestion level of the intersection to build a data set D;

[0056] Step 1-1. Carry out traffic flow statistics at traffic intersections with cameras;

[0057] The Gaussian mixture model is used to establish the background model, and then the background difference method is used to extract the foreground, and the moving vehicles are obtained through morphological processing. Finally, the multi-instance learning method is used to track the target, and the traffic flow of a specific intersection is counted by combining opencv;

[0058] Step 1-2. Select features;

[0059] Feature f1 is the time period. The degree of congestion at intersections in di...

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Abstract

The invention discloses a traffic intersection congestion prediction method based on machine learning, and belongs to the technical field of machine learning. According to the method, the congestion degree of a corresponding traffic intersection is predicted through multiple structural features, the requirement for equipment is low, and the speed is high; meanwhile, the degree of intersection congestion within a long time can be predicted, and an unstable model is adopted as a base learner for training; and finally, the deviation and variance of the model are reduced through integrated learning and model fusion, so that the generalization ability of a prediction result of the model in an actual scene is ensured.

Description

technical field [0001] The invention belongs to the technical field of machine learning, and in particular relates to a machine learning-based prediction method for traffic intersection congestion. Background technique [0002] With the continuous development of society and the increasing demand of people, the number of motor vehicles in cities has shown explosive growth. The growth of motor vehicles has brought enormous pressure to urban traffic, and also affected people's daily travel. For example, the traffic flow during morning and evening peaks is huge, which leads to abnormal congestion at some intersections. Traffic congestion seriously affects the safety of the city and people's travel costs, and brings more uncertainty to people's travel. [0003] Accurate prediction of traffic jams at corresponding intersections can relieve the pressure of urban traffic scheduling, and at the same time give people more choices for travel and accelerate social efficiency. The dete...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01G06K9/62
CPCG08G1/0104G08G1/0145G06F18/2411
Inventor 许文波廖志州胡四泉罗欣冷庚
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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