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Method for identifying individual subway bus taking and waiting behaviors by using mobile phone signaling data

A mobile phone signaling and subway technology, applied in the field of rail transit data analysis, can solve the problem of inability to refine the identification and analysis of individual subway travel behavior.

Pending Publication Date: 2022-04-22
SUN YAT SEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem that the traditional rail transit data analysis method cannot be refined to realize the identification and analysis of individual subway travel behaviors, the present invention proposes a method of using mobile phone signaling data to identify individual subway ride behaviors, and analyzes individual behaviors in a fine-grained and refined manner. Metro travel behavior to support accurate identification, precise traceability and precise management of subway network operations

Method used

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  • Method for identifying individual subway bus taking and waiting behaviors by using mobile phone signaling data
  • Method for identifying individual subway bus taking and waiting behaviors by using mobile phone signaling data
  • Method for identifying individual subway bus taking and waiting behaviors by using mobile phone signaling data

Examples

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Embodiment 1

[0047] At present, in the construction of the subway, the section of the subway network is not traceable, and the timeliness is low. The future subway lines will develop in a network trend, and more routes can be selected between the same OD. However, the granularity of traditional rail transit data analysis is only to the traffic starting and ending point OD, which is insufficient. To support the individual-level travel behavior analysis in the subway, it is impossible to analyze the real travel route selection and waiting behavior of the individual, and accurately identify the individual travel route, which is not conducive to the management and control of the subway operation and the accurate analysis of passenger flow. Based on this, On the basis of traditional rail transit data analysis, a method of using mobile phone signaling data to identify individual subway ride behavior is proposed in Embodiment 1. For the flow chart, see figure 1 , including the following steps:

...

Embodiment 2

[0069] Based on the MeanShift clustering performed according to the arrival time of all users passing through the research site described in Embodiment 1, in this embodiment, the specific process of identifying the group of fellow passengers on the line where the research site is located is described.

[0070] In the traditional K-Means algorithm, the final clustering effect is affected by the initial clustering center. The proposal of the K-Means++ algorithm provides a basis for selecting a better initial clustering center, but in the algorithm, the clustering The number of categories k still needs to be determined in advance. For data sets whose number of categories is unknown in advance, K-Means and K-Means++ will be difficult to accurately solve them.

[0071] In the application scenario of identification of fellow passengers in the subway, the number of subway operating shifts of the day cannot be known in advance. For this, this embodiment applies the improved clustering ...

Embodiment 3

[0081] In this example, based on the methods proposed in Example 1 and Example 2, on the basis of individual subway travel chain reconstruction and waiting behavior identification, the Guangzhou line network is analyzed from three levels: point, line, and plane. in,

[0082] Point: Calculate the number of waiting times at the starting station / the number of waiting times at the transfer station, analyze the proportion of waiting times at the station, and use it as the basis for judging the congestion situation at the station.

[0083] Line: identify the congestion section of the line network, collect statistics on the passenger flow of the section by time period, and extract the congestion peak period and the congestion section. Carry out congestion warning and look for alternative travel plans in congested sections.

[0084] Aspects: 1) Traceability of station control in congested sections, trace the origin of passenger stations in congested sections, and control passenger fl...

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Abstract

The invention provides a method for identifying individual subway taking and waiting behaviors by using mobile phone signaling data, and relates to the technical field of rail transit data analys.The method comprises the steps that firstly, individual subway trip chain reconstruction is conducted on subway trip passengers, the taking and waiting behaviors of the passengers are restored, and meanwhile the passenger flow conditions of subway stations, sections and lines are reflected; then, by utilizing a clustering method, according to mobile phone signaling data of users with the same subway travel starting station on a certain day, identifying the co-riding user as a starting point, analyzing individual subway travel behaviors in a fine-grained and refined manner, including identification and analysis of riding behaviors and waiting behaviors, as a judgment basis for the congestion condition of the subway station; according to congestion conditions and travel time of different lines, reasonable travel induction is performed, so that accurate identification, accurate traceability and accurate treatment of subway line network operation are supported.

Description

technical field [0001] The invention relates to the technical field of rail transit data analysis, and more specifically, to a method for identifying individual subway ride behaviors by using mobile phone signaling data. Background technique [0002] Mobile phone signaling data is a kind of data generated by the transmission and reception of mobile phone signals. It usually refers to the data collected and recovered by communication operators to maintain the normal operation of mobile communications. It is a new type of big data source. It is different from other types of data. Compared with data, it has advantages that other data sources do not have, such as real-time, completeness, and full coverage of travel time and space. In recent years, with the widespread popularization of personal mobile phones and the development of ICT technology, mobile phone signaling data has become more and more abundant. Its sample size is large, multi-dimensional, fine-grained, dynamic conti...

Claims

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Application Information

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IPC IPC(8): H04W4/20G06K9/62
CPCH04W4/20G06F18/23213Y02D30/70
Inventor 朱蓓媚何兆成
Owner SUN YAT SEN UNIV
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