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Rail-like traffic driving prediction method based on big data

A rail transit and prediction method technology, applied in the field of rail transit-like travel prediction based on big data

Active Publication Date: 2022-08-05
八维通科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, there is still a lack of research on the prediction of the driving state of similar rail transit

Method used

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  • Rail-like traffic driving prediction method based on big data
  • Rail-like traffic driving prediction method based on big data
  • Rail-like traffic driving prediction method based on big data

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Embodiment Construction

[0045] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0046] figure 1 is a schematic flow diagram of the present invention; as figure 1 As shown, the big data-based rail transit driving prediction method includes the following steps:

[0047] Step S1, extracting the GPS track original data of the rail-like traffic, and performing preprocessing on the GPS track original data.

[0048] The original GPS track data, including the GPS data recording the GPS track of the train, the recording time interval of the GPS data of the same train is 30 seconds, the travel time of each train is 24 hours, and the start position and the end position of the train are the same.

[0049] Each GPS data contains seven fields, which are:

[0050] 1. Vehicle number: the number of a similar rail transit vehicle;

[0051] 2. Recording time: the time recorded by GPS;

[0052] 3. GPS latitude: use degree DDD as the coordinate unit;

...

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Abstract

A big data-based similar rail transit driving prediction method belongs to the technical field of data processing methods, and comprises the following steps: S1, extracting GPS track original data of similar rail transit, and carrying out pre-processing and data conversion on the GPS track original data to obtain GPS track data; converting each piece of GPS data in the original data of the GPS track into the following four fields: a driving interval, a continuous interval speed difference, a congestion symptom Boolean type and an instant speed; s2, data analysis is conducted on four fields in each piece of converted GPS data through a C5.0 decision tree, seven leaf nodes are obtained, seven decision rules are generated, and a driving model ten minutes before pre-congestion of the rail transit is built; S3, the accuracy of the driving model is verified through test data. According to the scheme, it is found that the speed difference in the continuous interval is lower than a certain degree, high probability of congestion occurs in ten minutes in the future under the cooperation with other conditions, and corresponding rules or suggestions can be provided for similar rail transit.

Description

technical field [0001] The invention belongs to the technical field of data processing methods, and particularly relates to a large data-based rail transit-like driving prediction method. Background technique [0002] With the development of science and technology, there are more and more related services with geographic location data as the core data, such as map services, navigation services, data analysis services, and even other high value-added personalized services based on data analysis services. Customized service. The role and importance of geographic location data in application services is self-evident, and how to properly obtain geographic location data, storage, and application requires relevant research. [0003] The application of existing geographic location data is mostly focused on passenger flow prediction. For example, the Chinese patent of CN108694463A discloses a method for predicting the passenger flow in and out of the urban rail transit station, an...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B61L25/02B61L25/04B61L27/10B61L27/40G06N5/00
CPCB61L25/021B61L25/023B61L25/025B61L25/04B61L27/10B61L27/40G06N5/01
Inventor 伊尚丰陈刚李守勤杨武武汪霞黄春雨胡洲洪
Owner 八维通科技有限公司