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A Multi-behavioral Data Mining Method

A technology of data mining and behavior, which is applied in the direction of instruments, traffic flow detection, traffic control system of road vehicles, etc., can solve problems such as inability to achieve effectively, poor prediction effect, etc., and achieve the effect of accurate blocking probability

Active Publication Date: 2022-08-02
BEIJING TESTOR TECH +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a multivariate behavioral data mining method. The technical problem to be solved by the present invention is: high-speed congestion has become normal in some areas, and the reasons for the congestion are often diversified , as personal driving behavior data, it has high variables, which leads to the current traditional traffic volume prediction model only considering the time characteristics of traffic volume, which leads to poor prediction effect and cannot effectively achieve the effect of early decision-making.

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Effect test

Embodiment 1

[0037] A multi-behavioral data mining method, including the following methods:

[0038] S1. Establish a data service cluster based on the entrance and exit toll stations of the entire length of the expressway, and obtain information on the number of vehicles entering and leaving each toll station and the type of vehicles.

[0039] S2. Detect the status information of the whole section of the expressway, obtain the visibility, ground humidity and weather information and upload it to the data service cluster.

[0040] S3. Based on the lower layer of the data service cluster, a real-time monitoring network for the traffic state of the whole road section is established, and data is collected for the passing speed of vehicles and the density of vehicles.

[0041] S4. Estimate the safe traffic speed between each area of ​​the entire road segment through the weather and road section information obtained in S2, and simultaneously predict the best traffic capacity of the expressway in ...

Embodiment 2

[0065] A multi-behavioral data mining method, including the following methods:

[0066] S1. Establish a data service cluster based on the entrance and exit toll stations of the entire length of the expressway, and obtain information on the number of vehicles entering and leaving each toll station and the type of vehicles.

[0067] S2. Detect the status information of the whole section of the expressway, obtain the visibility, ground humidity and weather information and upload it to the data service cluster.

[0068] S3. Based on the lower layer of the data service cluster, a real-time monitoring network for the traffic state of the whole road section is established, and data is collected for the passing speed of vehicles and the density of vehicles.

[0069] S4. Estimate the safe traffic speed between each area of ​​the entire road segment through the weather and road section information obtained in S2, and simultaneously predict the best traffic capacity of the expressway in ...

Embodiment 3

[0076] A multi-behavioral data mining method, including the following methods:

[0077] S1. Establish a data service cluster based on the entrance and exit toll stations of the entire length of the expressway, and obtain information on the number of vehicles entering and leaving each toll station and the type of vehicles.

[0078] S2. Detect the status information of the whole section of the expressway, obtain the visibility, ground humidity and weather information and upload it to the data service cluster.

[0079] S3. Based on the lower layer of the data service cluster, a real-time monitoring network for the traffic state of the whole road section is established, and data is collected for the passing speed of vehicles and the density of vehicles.

[0080] S4. Estimate the safe traffic speed between each area of ​​the entire road segment through the weather and road section information obtained in S2, and simultaneously predict the best traffic capacity of the expressway in ...

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Abstract

The invention discloses a multi-behavior data mining method, which specifically relates to the technical field of road administration. The data service clusters established by each toll station are used to integrate diversified data and the behavior data of drivers. The road conditions and human factors, combined with the time scale, enable it to predict the traffic flow status of different road sections for a period of time in the future. The time scale prediction can be obtained by the average value of historical date traffic in different time periods, which can more accurately predict the occurrence of The probability of blockage, synchronizing with the information of the above detection, retrieving the corresponding corresponding historical data characteristics, and obtaining the processing method, integrating the efficient disposal measures in the historical plan, enabling early warning and sorting out corresponding suggested measures to solve the problem. Compared with the traditional traffic volume prediction model, it only considers the time characteristics of traffic volume, which leads to the problem of poor prediction effect.

Description

technical field [0001] The present invention relates to the technical field of road information, and more particularly, the present invention relates to a multi-behavior data mining method. Background technique [0002] With the rapid development of information technology, more and more people's behaviors are recorded in related computer systems. For example, securities trading system, credit card consumption system and medical security system and so on. Due to people's day-to-day life rhythm, data recording of many computer systems is also carried out day after day, so this daily behavior data of each person forms a behavior data set. By mining behavior data sets, it is possible to reveal various behavior patterns, phenomena and laws of people. [0003] Expressways belong to high-grade highways, and expressways "can adapt to roads with an average annual day and night passenger car traffic volume of more than 25,000 vehicles, dedicated vehicles for high-speed driving in se...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/065G08G1/01G08G1/048
Inventor 江何周鑫李忱陈忠国门殿春孟繁荣姚志强
Owner BEIJING TESTOR TECH