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Resident trip chain generation method and a ride-sharing query method based on multi-source data fusion

A multi-source data and travel chain technology, applied in the traffic control system of road vehicles, traffic flow detection, instruments, etc., can solve the problems of single information type and insufficient model accuracy, achieve rich information, expand application scenarios and The effect of adaptation

Active Publication Date: 2021-08-13
SHENZHEN URBAN TRANSPORT PLANNING CENT
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Problems solved by technology

[0003] The problem solved by the present invention is that the existing travel chain model is only established using mobile phone signaling data, the information type is single, and the accuracy of the model is insufficient

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  • Resident trip chain generation method and a ride-sharing query method based on multi-source data fusion
  • Resident trip chain generation method and a ride-sharing query method based on multi-source data fusion
  • Resident trip chain generation method and a ride-sharing query method based on multi-source data fusion

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Embodiment Construction

[0067] In order to make the above objects, features and advantages of the present invention more comprehensible, specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0068] In order to facilitate the understanding of the present invention, the problems existing in the prior art are briefly introduced first.

[0069] 1) Establishing a travel chain model based only on mobile phone signaling data has relatively large limitations. The mobile phone signaling data only has the trajectory, and the analysis of the travel mode, vehicle, and specific departure and destination is insufficient. Data such as job-housing distribution and remote sensing can only provide macro data, and can only guide and assist specific travel chains. Therefore, using job-resident distribution, remote sensing data combined with mobile phone signaling data to establish a travel chain model, the travel mode, vehicle, and specific depar...

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Abstract

The invention discloses a resident trip chain generation method and a ride-sharing query method based on multi-source data fusion. The resident trip chain generation method comprises the following steps: performing related processing on mobile phone signaling data to obtain trip information of a user; cutting a user travel based on the travel information; carrying out travel mode matching on each travel of the user; performing space-time matching on the bus travel route, the bus route track and the bus number information to generate a first matching result; performing space-time matching on the track traffic travel route, the track traffic line track and the track traffic train number information to generate a second matching result; performing space-time matching on the car travel journey and cars in a preset database to generate a third matching result; and according to a matching result, obtaining a riding train number of each travel of the user, and generating a travel chain of the user by combining the travel origin and destination of each travel of the user. According to the invention, during construction of the trip chain, joint modeling of people and public transport vehicles is carried out, and a more accurate trip analysis result is obtained.

Description

technical field [0001] The invention relates to the technical field of traffic data processing, in particular to a method for generating travel chains of residents based on multi-source data fusion and a method for carpooling query. Background technique [0002] Early research on travel chain models was based on travel surveys. With the maturity of the transportation big data platform, a large number of researches on the travel of urban residents based on mobile phone signaling have emerged. In 2017, Dai Yuxin of Dalian University of Technology conducted research on mobile positioning algorithms based on LTE signaling data. In 2018, Dr. Cai Zhengyi of Zhejiang University processed mobile phone signaling data in Hangzhou and based on its Individual data are modeled to more accurately analyze traffic conditions in each region. In the same year, Lu Taiyu of Southwest Jiaotong University analyzed the identification sensitivity of different modes of transportation based on mobi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01G08G1/123
CPCG08G1/0125G08G1/0137G08G1/123
Inventor 张晓春陈振武梁晨张稷彭逸洲周勇邢锦江吴宗翔吴若乾刘维怡王卓高彦
Owner SHENZHEN URBAN TRANSPORT PLANNING CENT
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