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Traffic jam condition prediction method and system based on two-stage spectral clustering

A technology of traffic congestion and forecasting method, applied in forecasting, data processing applications, instruments, etc., can solve problems such as long congestion time, traffic congestion, slow dredging, etc., and achieve strong generalization ability, fast speed, and strong error correction ability Effect

Active Publication Date: 2021-09-03
SHANDONG NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But as more and more vehicles are on the street, the problem that comes with it is traffic jams. If the congestion lasts for a long time, the dredging will be slow.

Method used

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  • Traffic jam condition prediction method and system based on two-stage spectral clustering
  • Traffic jam condition prediction method and system based on two-stage spectral clustering
  • Traffic jam condition prediction method and system based on two-stage spectral clustering

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0049] like figure 1 and figure 2 As shown, the traffic jam prediction method based on two-stage spectral clustering of the present embodiment includes the following steps:

[0050] Step 1. Obtain street information and meteorological information of various periods in several historical periods of the city;

[0051] The street information includes the street position, the traffic flow of each street in each time period, and the traffic flow direction of each intersection in each time period;

[0052] In the present embodiment, a cycle T is divided into multiple time periods t, and the street traffic flow, street vehicle migration matrix and meteorological information of each period in the past 7 days are obtained, that is, 7 cycles (days), and each day is divided into 4 Three time periods: peak hours (07:00-11:00) on weekdays and weekends, daytime hours (11:00-16:00), evening peak hours (16:00-21:00) and late night hours ( 21:00-24:00; 00:00-09:00);

[0053] The meteorolo...

Embodiment 2

[0095] The present embodiment provides a traffic jam prediction system based on two-stage spectral clustering, including:

[0096] The data acquisition module is used to acquire street information and weather information of the city at various times;

[0097] The clustering module is used for clustering according to the street information of each time period, and obtains the clusters to which the streets belong in each time period;

[0098] The first prediction module is used to predict the street traffic flow in the next cycle using the gradient enhanced regression tree model according to the clusters and meteorological information to which the street belongs in each time period;

[0099]The second prediction module is used to predict the traffic flow of each street in different time periods in the next cycle using a multi-similarity reasoning model according to the clusters and meteorological information to which the streets belong in each period;

[0100] The street conges...

Embodiment 3

[0104] This embodiment provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions are suitable for being loaded by a processor of a terminal device and executing the above-mentioned method for predicting traffic congestion based on two-level spectral clustering .

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Abstract

The invention provides a traffic jam condition prediction method and system based on two-stage spectral clustering, and the method comprises the steps: firstly obtaining the time information, street information and weather information of urban streets; secondly, based on two-stage spectral clustering, clustering the streets, and obtaining class clusters to which the streets belong in all time periods; using a gradient enhanced regression tree model and a multi-similarity reasoning model to predict the street traffic flow of the next period, the street traffic flow of each time period in the next period and a street vehicle migration matrix; and finally, comprehensively analyzing the street congestion condition according to the predicted street traffic flow of the next period, the street traffic flow of each time period in the next period and the street vehicle migration matrix, and dredging the vehicles.

Description

technical field [0001] The invention belongs to the field of traffic jam prediction, in particular to a traffic jam prediction method and system based on two-level spectrum clustering. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] With the development of the economy, there are more and more ways to travel, and the motor vehicle modes that people can choose to travel, such as private cars, trains, and public transportation, are gradually increasing. To a certain extent, motor vehicle travel modes The appearance of the utility model facilitates people's life, saves time and cost for people's going out. However, as more and more vehicles are on the street, the ensuing problem is that there will be traffic jams, and the congestion will be slow for a long time. Solving the problem of traffic congestion can reduce carbon dioxide emissions an...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26G06K9/62
CPCG06Q10/04G06Q50/26G06F18/23
Inventor 贾伟宽孟虎王志芬贾艺鸣赵艳娜
Owner SHANDONG NORMAL UNIV