Temporal-spatial data mining-based metro passenger classification method

A classification method and spatio-temporal data technology, applied in database models, relational databases, electrical digital data processing, etc.

Active Publication Date: 2014-04-02
深圳市北斗智能科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The existing classification of passengers is mainly to analyze the similarities and differences based on the types of smart cards, such as student cards, elderly cards, and ordinary cards, but does not cluster passengers based on their own riding characteristics

Method used

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  • Temporal-spatial data mining-based metro passenger classification method
  • Temporal-spatial data mining-based metro passenger classification method
  • Temporal-spatial data mining-based metro passenger classification method

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Embodiment Construction

[0048] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0049]The present invention provides a method for classifying subway passengers based on spatio-temporal data mining, such as figure 1 shown, including steps:

[0050] Step S1, basic data calculation, including smart card effective statistics and inter-site similarity calculation;

[0051] Step S2, data preprocessing, such as figure 2 Shown:

[0052] S21. Filter the transaction records with missing fields; for example, the transaction records with missing travel time fields, card number fields, station information, etc.

[0053] S22. Using the card number of the smart card as the unique identifier of the passenger, sort all transaction records of the smart card by time, wherein the transaction records include outbound and inbound information, and calculate each ride record in the smart card; that is Match the starting point and e...

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Abstract

The invention discloses a temporal-spatial data mining-based metro passenger classification method, which comprises the steps of 1, basic data calculation comprising intelligent card efficient-statistics and inter-site similarity calculation; 2, data preprocessing; 3, passenger classification. The method has the beneficial effects that a time and space-based user travel law algorithm is used for clustering passengers with similar characteristics to finally finish classifying the passengers into a first class of rarely traveling passengers, a second class of singly temporally regular passengers, a third class of singly spatially regular passengers, a fourth class of temporally and spatially regular passengers and a fifth class of temporally and spatially irregular passengers by analyzing travel characteristics of the passengers; the classification method is effective and accurate, and life characteristics of the passengers can be effectively known about by classifying the passengers.

Description

technical field [0001] The invention relates to the field of information data processing, in particular to a subway passenger classification method based on spatio-temporal data mining. Background technique [0002] At present, smart cards have been widely used in various fields such as banking, medical care, transportation, and campuses, and have the advantages of convenience and speed. As a means of paying for public transportation, smart cards have been widely used in urban transportation fields such as buses, subways, taxis, ferries, and parking lots. Due to the characteristics of saving money and convenience of transportation smart cards, the number of passengers using transportation smart cards as a payment method for public transportation is also increasing, and the passenger's ride transaction data is becoming more and more complete and accurate. [0003] As a means of payment, traffic smart cards, in the era of big data, how to effectively analyze the rich user tra...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/285
Inventor 赵娟娟张帆田臣须成忠白雪邹瑜斌罗俊
Owner 深圳市北斗智能科技有限公司
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