Method for expanding traffic volume using mobile communication data

Abstract

The present invention discloses a method for converting traffic volume based on mobile communication data. The method for converting traffic volume based on mobile communication data includes: collecting resident registration population data corresponding to a first year; generating telecommunication company residence data, telecommunication company gender data, and telecommunication company age group data corresponding to the first year based on telecommunication company customer data; calculating a first conversion coefficient based on the resident registration population data, telecommunication company residence data, telecommunication company gender data, and telecommunication company age group data; extracting traffic data in which the travel origin data of a traffic chain database is identified as a residence; and generating converted traffic volume by applying the first conversion coefficient to the traffic volume of the traffic data.

Description

Technology Field

[0001] The present invention relates to a method for converting traffic volume based on mobile communication data. Specifically, the present invention relates to a method for converting traffic volume based on mobile communication data that converts traffic volume using data possessing population attributes from a telecommunications carrier's mobile communication data. Background Technology

[0003] The content described in this section merely provides background information regarding the present embodiment and does not constitute prior art.

[0004] The raw data of the travel chain database includes customer identification information, virtual base stations, start and end times of stay, age groups, and gender.

[0005] Telecommunication companies perform totalization adjustments on traffic volume based on their market share. For example, a telecommunications company calculates a totalization factor by back-calculating based on the number of users and market share, and then totalizes mobile communication data-based traffic volume based on this factor. In this process, the totalized traffic volume is calculated as the product of the totalization factor and the traffic volume.

[0006] However, total correction using existing market share results in lower accuracy because it excludes detailed market shares by gender, age, and region, and there are limitations in processing and utilizing the data.

[0007] In addition, data aggregated based on the totalization method using market share has the problem of being overestimated or underestimated depending on the number of telecommunications carrier users in terms of gender, age, and region.

[0008] Therefore, there is a need to improve data accuracy by additionally applying a resident population coefficient that includes detailed attributes such as gender, age, and region to the total correction using existing market share. The problem to be solved

[0010] The objective of the present invention is to provide a method for quantifying traffic volume with high accuracy by reflecting detailed attributes including gender, age, and region.

[0011] In addition, the objective of the present invention is to provide a traffic volume quantification method that calculates traffic volume by human attribute based on traffic quantification.

[0012] The objects of the present invention are not limited to those mentioned above, and other unmentioned objects and advantages of the present invention may be understood from the following description and will be more clearly understood by the embodiments of the present invention. Furthermore, it will be readily apparent that the objects and advantages of the present invention can be realized by the means and combinations thereof set forth in the claims. means of solving the problem

[0014] A method for transcribing traffic volume based on mobile communication data according to some embodiments of the present invention comprises: collecting resident registration population data corresponding to a first year; generating telecommunication company residence data, telecommunication company gender data, and telecommunication company age group data corresponding to the first year based on telecommunication company customer data; calculating a first transcription coefficient based on the resident registration population data, telecommunication company residence data, telecommunication company gender data, and telecommunication company age group data; extracting traffic data in which the travel origin data of a traffic chain database is identified as a residence; and generating transcribed traffic volume by applying the first transcription coefficient to the traffic volume of the traffic data.

[0015] Additionally, the step of calculating the first totalization coefficient includes the step of calculating a seconda totalization coefficient based on the ratio of the resident registration population data and the telecommunications company residence data, the step of calculating a secondb totalization coefficient based on the ratio of the resident registration population data and the telecommunications company gender data, and the step of calculating a secondc totalization coefficient based on the ratio of the resident registration population data and the telecommunications company age group data, and the second totalization coefficient may include the seconda totalization coefficient, the secondb totalization coefficient, and the secondc totalization coefficient.

[0016] Additionally, the step of calculating the first totalization coefficient may include: a step of extracting a first totalization coefficient (1a) corresponding to the first residence data of the travel data from the second totalization coefficient (2a); a step of extracting a first totalization coefficient (1b) corresponding to the first gender data of the travel data from the second totalization coefficient (2b); a step of extracting a first totalization coefficient (1c) corresponding to the first age group data of the travel data from the second totalization coefficient (2c); and a step of calculating the first totalization coefficient corresponding to the travel data based on the first totalization coefficient (1a), the first totalization coefficient (1b), and the first totalization coefficient (1c).

[0017] Additionally, the method may further include the step of re-aggregating the traffic data in the traffic chain database into units corresponding to the grid unit of the first distance for the traffic volume, and the step of calculating a fourth totalization coefficient of the re-aggregated traffic data using the second totalization coefficient.

[0018] Additionally, the step of calculating the fourth totalization coefficient may include: a step of extracting a fourth totalization coefficient corresponding to the second residence data of the re-aggregated travel data from the second totalization coefficient of the second a, a step of extracting a fourth totalization coefficient corresponding to the second gender data of the re-aggregated travel data from the second totalization coefficient of the second b, a step of extracting a fourth totalization coefficient corresponding to the second age group data of the re-aggregated travel data from the second totalization coefficient of the second c, and a step of calculating the fourth totalization coefficient corresponding to the re-aggregated travel data based on the fourth totalization coefficient of the second a, the fourth totalization coefficient of the second b, and the fourth totalization coefficient of the second c.

[0019] Additionally, the method may further include a step of reflecting the fourth totalization coefficient into the traffic volume of the re-aggregated traffic data and summing the reflected traffic volumes to calculate the total traffic volume.

[0020] Additionally, the method may further include a step of grouping the re-aggregated travel data based on at least one of the reference date, departure, arrival, gender, age group, and means of travel.

[0021] Additionally, the step of grouping the re-aggregated travel data may include the step of extracting a first re-aggregated travel data and a second re-aggregated travel data in which at least one of the reference date data, departure data, arrival data, gender data, age group data, and means of transport data of the re-aggregated travel data matches, and the step of generating a third re-aggregated travel data by integrating the first re-aggregated travel data and the second re-aggregated travel data.

[0022] Additionally, the step of generating the third re-aggregated travel data may include the step of calculating the average travel time of the first travel time of the first re-aggregated travel data and the second travel time of the second re-aggregated travel data.

[0023] Additionally, the step of generating the third re-aggregated traffic data may include the step of calculating the third traffic volume reflecting the fullness of the third re-aggregated traffic data by adding the first traffic volume reflecting the fullness of the first re-aggregated traffic data and the second traffic volume reflecting the fullness of the second re-aggregated traffic data. Effects of the invention

[0025] The mobile communication data-based traffic volume digitization method of the present invention can improve the accuracy of traffic digitization by reflecting detailed attributes of a person, including gender, age, and region.

[0026] In addition, the traffic volume totalization method can improve the precision of traffic volume data corresponding to human detailed attributes by generating traffic volume data that considers human detailed attributes.

[0027] In addition to the above, the specific effects of the present invention are described together with the specific details for implementing the invention below. Brief explanation of the drawing

[0029] FIG. 1 is a flowchart for explaining a method for converting traffic volume based on mobile communication data according to the first embodiment of the present invention based on the resident population. FIGS. 2 to 4 are flowcharts for explaining a method of converting traffic volume based on a mobile communication data-based traffic volume conversion method according to a second embodiment of the present invention based on the origin. Figure 3 is a diagram illustrating the travel chain database and the origin of the travel. FIG. 4 is a diagram illustrating the step S120 of calculating the first full-transformation coefficient of FIG. 2 and the step S130 of updating the traffic volume using the first full-transformation coefficient. FIG. 5 is a flowchart illustrating a method for transcribing traffic volume based on residential area according to a mobile communication data-based traffic volume transcription method according to a third embodiment of the present invention. FIG. 6 is a diagram for explaining the 2a total hydration coefficient, 2b total hydration coefficient, and 2c total hydration coefficient of FIG. 5. FIG. 7 is a flowchart for explaining step S250 of updating the traffic volume of the second traffic data of FIG. 5. FIGS. 8 to 10 are drawings for explaining a method of digitizing using a residential area and an aggregated population according to a fourth embodiment of the present invention. FIG. 11 is a diagram illustrating a hardware implementation of a server providing a mobile communication data-based communication transfer method according to some embodiments of the present invention. Specific details for implementing the invention

[0030] Terms and words used in this specification and claims shall not be interpreted as being limited to their general or dictionary meanings. In accordance with the principle that an inventor may define the concept of a term or word to best describe their invention, they shall be interpreted in a meaning and concept consistent with the technical spirit of the invention. Furthermore, since the embodiments described in this specification and the configurations illustrated in the drawings are merely one embodiment of the invention and do not represent the entire technical spirit of the invention, it should be understood that various equivalents, modifications, and applicable examples capable of replacing them may exist at the time of filing this application.

[0031] The terms first, second, A, B, etc., as used in this specification and claims may be used to describe various components, but said components should not be limited by said terms. These terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component. The term "and / or" includes a combination of a plurality of related described items or any of a plurality of related described items.

[0032] The terms used in this specification and claims are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" should be understood as not precluding the existence or addition of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification.

[0033] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which this invention pertains.

[0034] Terms such as those defined in commonly used dictionaries should be interpreted as having meanings consistent with their meanings in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0035] In addition, each component, process, procedure, or method included in each embodiment of the present invention may be shared within a scope that is not technically contradictory to one another.

[0036] Hereinafter, with reference to FIGS. 1 to 11, a method for converting traffic volume based on mobile communication data according to several embodiments of the present invention will be described in detail.

[0038] FIG. 1 is a flowchart for explaining a method for converting traffic volume based on mobile communication data according to the first embodiment of the present invention based on the resident population.

[0039] Referring to FIG. 1, a server performing a traffic volume digitization method can generate resident population data reflecting a preset digitization coefficient from the log data of a telecommunications carrier customer (S10). For example, the resident population data may include telecommunications carrier regional data, telecommunications carrier virtual base station data, telecommunications carrier age group data, telecommunications carrier gender data, and telecommunications carrier lodging population data. The telecommunications carrier regional data may be data aggregated from the number of subscribers for each region of the telecommunications carrier customer. The telecommunications carrier virtual base station data may be data regarding virtual base stations used by the telecommunications carrier customer. The telecommunications carrier age group data may be data aggregated from the number of subscribers for each age group based on the age of the telecommunications carrier customer. The telecommunications carrier gender data may be data aggregated from the number of subscribers according to the gender of the telecommunications carrier customer. The telecommunications carrier lodging population data may be data aggregated from the number of subscribers based on whether the telecommunications carrier customer is lodging or staying. However, this description is merely illustrative and the embodiments of the present invention are not limited thereto.

[0040] The preset totalization factor may be a preset totalization factor based on the resident registration population.

[0041] Next, the server can combine the resident population data with the travel chain database (S20). For example, the travel chain database may include data on the travel of each person using transportation. The travel chain database may include reference date data, origin data, arrival data, gender data, age group data, mode of transportation data, total traffic volume data, and average travel time. The travel chain database will be described later with reference to FIG. 3.

[0042] Next, with reference to FIGS. 2 to 4, a method for a server to convert travel data based on the origin is described.

[0044] FIGS. 2 to 4 are flowcharts for explaining a method for converting traffic volume based on a mobile communication data-based traffic volume conversion method according to a second embodiment of the present invention based on a starting point. FIG. 3 is a diagram for explaining a traffic chain database and a starting point of traffic. FIG. 4 is a diagram for explaining the step S120 of calculating the first conversion coefficient of FIG. 2 and the step S130 of updating the traffic volume using the first conversion coefficient.

[0045] First, of Fig. 3 <a11>Referring to the above, the travel data (p) may include travel departure data, travel arrival data, gender data, age group data, total travel volume, average travel time, and travel date for travel moving from a origin to a destination. For example, the first travel data (p1) may be data generated by departing from a first region (s1) and arriving at a second region (s2). The second travel data (p2) may be data generated by departing from a second region (s2) and arriving at a third region (s3). However, this description is merely illustrative and the embodiments of the present invention are not limited thereto.

[0046] In addition, the starting point may include various locations such as residence, school, and workplace. For the sake of convenience of explanation, an example where the starting point is a residence will be described below. This description is merely illustrative and does not mean that the starting point is limited to a residence.

[0047] Referring to FIGS. 2 and FIGS. 3, the server is, of FIG. 3 <a12>In the travel chain database described above, first travel data can be extracted in which the travel origin data is identified as a residence (S110). For example, the server can extract at least one travel data in which the type of travel origin data is identified as a residence from the travel chain database. Here, although it is explained that the "first travel data" is identified as a residence, it is understood that it may be a different origin other than a residence. For convenience of explanation, the extracted travel data is described below by way of an example of an embodiment including first travel data (p1), second travel data (p2), and third travel data (p3). This description is merely illustrative and the embodiments of the present invention are not limited thereto.

[0048] Next, the server can calculate a first totalization coefficient for residence, gender, and age group based on the first resident registration population data, which aggregates the number of residents in each region (S120). For example, of FIG. 4 <a13>By referring to the above together, the server can calculate a first totalization coefficient (c1) corresponding to each traffic data extracted using the first ratio of the population by residence with the first resident registration population data, the second ratio of the number of genders with the first resident registration population data, and the third ratio of the population by age group with the first resident registration population data. The first totalization coefficient (c1) can be calculated by the four arithmetic operations of the first ratio, the second ratio, and the third ratio.

[0049] Next, Fig. 4 <a14>By referring to the above together, the server can update each traffic data in the traffic chain database based on the traffic volume of each traffic data and the first totalization coefficient (c1) (S130). For example, the server can generate a total traffic volume (tvc1) by multiplying the total traffic volume (tv1) of each traffic data by the first totalization coefficient (c1). Specifically, the server can calculate a first total traffic volume (tvc1) by multiplying the total traffic volume (tv1) of the first traffic data (p1) by the first totalization coefficient (c1) corresponding to the first traffic data (p1). The generated total traffic volume may be a total traffic volume.

[0050] Figure 3 <a12>When referring together with the above, the updated first travel data (p1') includes the travel date, travel departure data, travel arrival data, gender data, age group data, total travel volume (tv1), and average travel time of the first travel data (p1), and may further include the first totalization coefficient (c1) and the totalized total travel volume (tvc1).

[0051] The server can calculate a totalization factor for travel data regarding locations other than the place of origin, by utilizing the traveler's place of residence, gender, and age group, or by utilizing information from the travel chain database on previous travels where the place of origin is the place of residence. That is, the server can calculate a totalization factor based on the place of residence, gender, and age group of the user corresponding to each travel data. For example, for each travel data, the server may calculate a first totalization factor for the travel data by utilizing first resident registration population data that includes the place of residence, gender, and age group of the user of each travel data. Accordingly, the travel volume of each travel data can be totalized by multiplying it by the calculated first totalization factor.

[0052] Next, with reference to FIGS. 5 to 7, a method for a server to collect traffic volume based on residential areas is described.

[0054] FIG. 5 is a flowchart illustrating a method for converting traffic volume based on residential area according to a third embodiment of the present invention. FIG. 6 is a diagram illustrating the 2a conversion coefficient, 2b conversion coefficient, and 2c conversion coefficient of FIG. 5. FIG. 7 is a flowchart illustrating step S250 of updating the traffic volume of the second traffic data of FIG. 5.

[0055] Referring to FIG. 5, the server can collect second resident registration population data corresponding to the first year (S210). For example, the server can generate second resident registration population data by extracting the resident registration population by administrative district corresponding to the first year. In addition, the server may receive or collect second resident registration population data from an external system.

[0056] Next, the server can aggregate telecommunications company residence data, telecommunications company gender data, and telecommunications company age group data corresponding to the first year based on telecommunications company customer data (S220). For example, telecommunications company residence data may be data aggregated by the number of subscribers in each region based on the residence of customers subscribed to the telecommunications company. Telecommunications company gender data may be data in which the gender ratio of customers subscribed to the telecommunications company is calculated, or data aggregated by the number of subscribers according to the gender of customers subscribed to the telecommunications company. Telecommunications company age group data may be data in which the age ratio of customers subscribed to the telecommunications company is calculated, or data aggregated by the number of subscribers in each age group based on the age of customers subscribed to the telecommunications company.

[0057] Subsequently, the server can calculate a second totalization coefficient based on the second resident registration population data, telecommunications carrier residence data, telecommunications carrier gender data, and telecommunications carrier age group data (S230). For example, the server can calculate a seconda totalization coefficient based on the ratio of the second resident registration population data and the telecommunications carrier residence data (S230a). Fig. 6 <b11>Referring to [the source], the server can generate the 2a totalization factor by calculating the ratio of the 1st year telecommunications carrier residence data to the 1st year resident registration population data. That is, the 2a totalization factor may be the ratio of the 1st year telecommunications carrier residence data to the 1st year resident registration population data.

[0058] In addition, the server can calculate the 2b totalization coefficient based on the ratio of the 2nd resident registration population data and the telecommunications carrier gender data (S230b). Fig. 6 <b12>Referring to [the source], the server can generate the 2b totalization coefficient by calculating the ratio of the first year's telecommunications carrier gender data to the first year's resident registration population data. The 2b totalization coefficient may be the ratio of the first year's telecommunications carrier gender data to the first year's resident registration population data.

[0059] The server can calculate the 2c totalization coefficient based on the ratio of the 2nd resident registration population data and the telecommunications carrier age group data (S230c). Fig. 6 <b13>Referring to [this], the server can generate the 2c totalization coefficient by calculating the ratio of the 1st year telecommunication carrier age group data to the 1st year resident registration population data.

[0060] The second hydration coefficient may include a seconda hydration coefficient, a secondb hydration coefficient, and a secondc hydration coefficient. Additionally, the second hydration coefficient may be the product of the seconda hydration coefficient, the secondb hydration coefficient, and the secondc hydration coefficient. However, this description is merely illustrative and the embodiments of the present invention are not limited thereto.

[0061] Next, the server can extract each second travel data from the travel chain database (S240). Next, the server can update the traffic volume of the second travel data based on a second digitization coefficient including a second digitization coefficient, a second digitization coefficient, and a second digitization coefficient (S250). The updated traffic volume may be a digitized traffic volume obtained by multiplying the traffic volume of the second travel data by the second digitization coefficient.

[0062] For example, with reference to FIG. 7, the server can extract a thirda complete coefficient corresponding to the residence identification value of the second travel data from the seconda complete coefficient (S251). The thirda complete coefficient may be a complete coefficient for a region corresponding to the residence of a traveler (hereinafter, person) corresponding to the second travel data in the seconda complete coefficient.

[0063] Additionally, the server can extract a third-b totalization coefficient corresponding to the gender identification value of the second traffic data from the second-b totalization coefficient (S253). That is, the server can extract a third-b totalization coefficient corresponding to the gender identification value of the second traffic data from the second-b totalization coefficient by using the gender identification value of the person corresponding to the second traffic data.

[0064] The server can extract a third-c totalization coefficient corresponding to the age group identification value of the second travel data from the second-c totalization coefficient (S255). For example, the server can extract a third-c totalization coefficient corresponding to the age or age group of the second travel data from the second-c totalization coefficient by using the age or age group of the person corresponding to the second travel data.

[0065] Subsequently, the server can calculate a third digitization coefficient corresponding to the second travel data based on the thirda digitization coefficient, the thirdb digitization coefficient, and the thirdc digitization coefficient (S257). For example, the server can calculate the third digitization coefficient by multiplying the thirda digitization coefficient, the thirdb digitization coefficient, and the thirdc digitization coefficient. That is, the server can calculate each third digitization coefficient corresponding to each combination of the case of residence, gender, and age group. The third digitization coefficient may be stored in advance corresponding to each combination of the case of residence, gender, and age group, but may also be calculated whenever travel data is extracted as merely an example.

[0066] The server can update the traffic volume of the second traffic data by multiplying the third decryption coefficient and the traffic volume of the second traffic data (S259). For example, the server can generate the decrypted traffic volume of the second traffic data by multiplying the traffic volume of the second traffic data and the third decryption coefficient.

[0067] Next, with reference to FIGS. 8 to 10, a method for a server to collect traffic volume based on residential areas is described.

[0069] FIGS. 8 to 10 are drawings for explaining a method of totalizing data based on a residence and an aggregated population according to a fourth embodiment of the present invention. FIG. 8 is a drawing for explaining a method of totalizing data using a residence and an aggregated population according to a fourth embodiment of the present invention. FIG. 9 is a drawing illustrating the re-aggregated individual travel data of FIG. 8. FIG. 10 is a drawing for explaining a method of grouping the re-aggregated individual travel data.

[0070] Referring to FIG. 8, the server can re-aggregate the traffic data of the traffic chain database so that the traffic volume corresponds to a grid unit of the first distance (S310). For example, referring together with FIG. 9, the server can re-aggregate the data for the traffic so that the traffic volume of each traffic in the traffic chain database corresponds to a grid unit of the first distance. The first distance may be a basic unit for quantifying the traffic volume. The first distance may be a distance corresponding to a value of 500m. However, this description is merely illustrative and the embodiments of the present invention are not limited thereto. That is, the server can re-aggregate the traffic data of the traffic chain database based on the traffic volume corresponding to the first distance.

[0071] Referring again to FIG. 8, the server can calculate a fourth totalization coefficient for the residence data, gender data, and age group data of the re-aggregated travel data using the second totalization coefficient (S320). For example, the server can generate the fourth totalization coefficient by extracting the second totalization coefficient corresponding to the residence data, gender data, and age group data of the re-aggregated travel data, respectively. The fourth totalization coefficient can be generated based on the seconda totalization coefficient corresponding to the residence data of the re-aggregated travel data, the secondb totalization coefficient corresponding to the gender data of the re-aggregated travel data, and the secondc totalization coefficient corresponding to the age group data of the re-aggregated travel data.

[0072] Next, the server can calculate the total traffic volume by reflecting the fourth totalization coefficient of the re-aggregated traffic data into the traffic volume of the re-aggregated traffic data and summing the reflected traffic volumes (S330). For example, the server can calculate the total traffic volume by summing the traffic volumes reflecting the totalization of each re-aggregated traffic data. The traffic volume reflecting the fourth totalization coefficient can be calculated by multiplying the traffic volume of the re-aggregated traffic data by the fourth totalization coefficient.

[0073] Additionally, the server can group and integrate traffic data re-aggregated by the first criterion (c). For example, referring together with FIG. 10, the server can generate third re-aggregated traffic data (pp3) by merging the first re-aggregated traffic data (pp1) and the second re-aggregated traffic data (pp2), which have the same first criterion (c).

[0074] The first criterion (c) may include at least one of a reference date for travel, departure, arrival, gender, age group, and means of travel. However, this description is merely illustrative and the embodiments of the present invention are not limited thereto. The first criterion (c) may be set by data entered by a user for statistics or by a pre-stored reference value.

[0075] The server can calculate the average value of the average travel time (at) of the first re-aggregated travel data and the average travel time (at) of the second re-aggregated travel data as the average travel time of the third re-aggregated travel data.

[0076] Additionally, the server may calculate the total traffic volume (tv2') of the third re-aggregated traffic data (pp3) by summing the total traffic volume (tv2') of the first re-aggregated traffic data (pp1) and the total traffic volume (tv2') of the second re-aggregated traffic data (pp2). However, this description is merely illustrative and the embodiments of the present invention are not limited thereto.

[0077] Next, referring to Fig. 11, a hardware implementation of a server that provides a method for converting traffic volume using the registered resident population of a residential area will be described.

[0079] FIG. 11 is a diagram illustrating a hardware implementation of a server that provides a method for digitizing using residential areas and aggregated populations according to some embodiments of the present invention.

[0080] Referring to FIG. 11, a server that collects traffic volume using residential resident registration population according to some embodiments of the present invention may be implemented as an electronic device (1000). The electronic device (1000) may include a processor (1010), an input / output device (1020, I / O), a memory (1030, memory), an interface (1040), a storage (1050, storage), and a bus (1060, bus). The processor (1010), the input / output device (1020), the memory (1030), the interface (1040), and / or the storage (1050) may be combined with each other through the bus (1060). The bus (1060) corresponds to a path through which data moves.

[0081] Specifically, the processor (1010) may include at least one of a CPU (Central Processing Unit), MPU (Micro Processor Unit), MCU (Micro Controller Unit), GPU (Graphic Processing Unit), microprocessor, digital signal processor, microcontroller, application processor (AP), and logic elements capable of performing similar functions.

[0082] The input / output device (1020) may include at least one of a keypad, a keyboard, a touchscreen, and a display device.

[0083] The memory (1030) can load data and / or programs, etc. At this time, the memory (1030) is an operating memory for enhancing the operation of the processor (1010) and may include high-speed DRAM and / or SRAM, etc. The memory (1030) may include one or more volatile memory devices such as DDR SDRAM (Double Data Rate Static DRAM) and SDR SDRAM (Single Data Rate SDRAM) and / or one or more non-volatile memory devices such as EEPROM (Electrical Erasable Programmable ROM) and flash memory.

[0084] The interface (1040) can perform the function of transmitting data to a communication network or receiving data from a communication network. The interface (1040) may be in a wired or wireless form. For example, the interface (1040) may include an antenna or a wired / wireless transceiver.

[0085] Storage (1050) can store and retain data and / or programs, etc. Storage (1050) may include one or more non-volatile memory devices such as a solid-state drive (SSD), a hard drive, and a flash memory. In the present invention, storage (1050) can store a computer program consisting of instructions for converting the aforementioned traffic volume.

[0086] Additionally, the server can transmit data to a user terminal through a network. The network may include a network based on wired internet technology, wireless internet technology, and local area communication technology. Wired internet technology may include, for example, at least one of a local area network (LAN) and a wide area network (WAN).

[0087] Wireless internet technology may include, for example, at least one of Wireless LAN (WLAN), DMNA (Digital Living Network Alliance), WiBro (Wireless Broadband), WiMAX (World Interoperability for Microwave Access), HSDPA (High Speed ​​Downlink Packet Access), HSUPA (High Speed ​​Uplink Packet Access), IEEE 802.16, Long Term Evolution (LTE), LTE-A (Long Term Evolution-Advanced), Wireless Mobile Broadband Service (WMBS), and 5G NR (New Radio) technology. However, the present embodiment is not limited thereto.

[0088] Short-range communication technology may include, for example, at least one of Bluetooth, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), Ultra-Wideband (UWB), ZigBee, Near Field Communication (NFC), Ultra Sound Communication (USC), Visible Light Communication (VLC), Wi-Fi, Wi-Fi Direct, and 5G NR (New Radio). However, the present embodiment is not limited thereto.

[0089] A server communicating through a network may comply with technical standards and standard communication methods for mobile communication. For example, a standard communication method may include at least one of GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), CDMA2000 (Code Division Multi Access 2000), EV-DO (Enhanced Voice-Data Optimized or Enhanced Voice-Data Only), WCDMA (Wideband CDMA), HSDPA (High Speed ​​Downlink Packet Access), HSUPA (High Speed ​​Uplink Packet Access), LTE (Long Term Evolution), LTEA (Long Term Evolution-Advanced), and 5G NR (New Radio). However, the present embodiment is not limited thereto.

[0090] Through this, the mobile communication data-based traffic volume digitization method of the present invention can improve the accuracy of traffic digitization by reflecting detailed attributes of a person, including gender, age, and region.

[0091] In addition, the traffic volume totalization method can improve the precision of traffic volume data corresponding to human detailed attributes by generating traffic volume data that considers human detailed attributes.

[0092] The above description is merely an illustrative explanation of the technical concept of the present embodiment, and a person skilled in the art to which the present embodiment belongs would be able to make various modifications and variations within the scope of the essential characteristics of the present embodiment. Accordingly, the present embodiments are intended to explain, not limit, the technical concept of the present embodiment, and the scope of the technical concept of the present embodiment is not limited by these embodiments. The scope of protection of the present embodiment shall be interpreted by the claims below, and all technical concepts within an equivalent scope shall be interpreted as being included within the scope of rights of the present embodiment.

Claims

Claim 1 A method for transcribing travel data, comprising: a step of collecting resident registration population data corresponding to a first year; a step of generating telecommunications company residence data, telecommunications company gender data, and telecommunications company age group data corresponding to the first year based on telecommunications company customer data; a step of calculating a first transcription coefficient based on the resident registration population data, telecommunications company residence data, telecommunications company gender data, and telecommunications company age group data; a step of extracting travel data from a travel chain database for traffic; and a step of generating transcribed travel volume by applying the first transcription coefficient to the travel volume of the travel data. Claim 2 A method for transcribing traffic data according to claim 1, wherein the step of calculating the first transcription coefficient comprises: a step of calculating a 2a transcription coefficient based on the ratio of the resident registration population data and the telecommunications company residence data; a step of calculating a 2b transcription coefficient based on the ratio of the resident registration population data and the telecommunications company gender data; and a step of calculating a 2c transcription coefficient based on the ratio of the resident registration population data and the telecommunications company age group data, wherein the second transcription coefficient comprises the 2a transcription coefficient, the 2b transcription coefficient, and the 2c transcription coefficient. Claim 3 A method for transcribing traffic data according to claim 2, wherein the step of calculating the first transcription coefficient comprises: a step of extracting a first transcription coefficient of 1a corresponding to the first residence data of the traffic data from the second transcription coefficient of 2a; a step of extracting a first transcription coefficient of 1b corresponding to the first gender data of the traffic data from the second transcription coefficient of 2b; a step of extracting a first transcription coefficient of 1c corresponding to the first age group data of the traffic data from the second transcription coefficient of 2c; and a step of calculating the first transcription coefficient corresponding to the traffic data based on the first transcription coefficient of 1a, the first transcription coefficient of 1b, and the first transcription coefficient of 1c. Claim 4 A method for transcribing travel data according to claim 2, further comprising: a step of re-aggregating travel data in the travel chain database into units corresponding to a grid unit of a first distance for the travel volume; and a step of calculating a fourth transcription coefficient of the re-aggregated travel data using the second transcription coefficient. Claim 5 A method for transcribing traffic data according to claim 4, wherein the step of calculating the fourth transcription coefficient comprises: a step of extracting a 4a transcription coefficient corresponding to the second residence data of the re-aggregated traffic data from the 2a transcription coefficient; a step of extracting a 4b transcription coefficient corresponding to the second gender data of the re-aggregated traffic data from the 2b transcription coefficient; a step of extracting a 4c transcription coefficient corresponding to the second age group data of the re-aggregated traffic data from the 2c transcription coefficient; and a step of calculating the fourth transcription coefficient corresponding to the re-aggregated traffic data based on the 4a transcription coefficient, the 4b transcription coefficient, and the 4c ​​transcription coefficient. Claim 6 A method for converting traffic data, wherein, in claim 4, the fourth conversion coefficient is reflected in the traffic volume of the re-aggregated traffic data, and the reflected traffic volume is summed to calculate the total traffic volume. Claim 7 A method for transcribing travel data according to claim 4, further comprising the step of grouping the re-aggregated travel data based on at least one of a reference date, departure, arrival, gender, age group, and means of travel. Claim 8 A method for transcribing travel data according to claim 7, wherein the step of grouping the re-aggregated travel data comprises: a step of extracting a first re-aggregated travel data and a second re-aggregated travel data in which at least one of the reference date data, departure data, arrival data, gender data, age group data, and means of travel data of the re-aggregated travel data matches; and a step of integrating the first re-aggregated travel data and the second re-aggregated travel data to generate a third re-aggregated travel data. Claim 9 A method for fully collecting travel data according to claim 8, wherein the step of generating the third re-aggregated travel data includes the step of calculating the average travel time of the first travel time of the first re-aggregated travel data and the second travel time of the second re-aggregated travel data. Claim 10 A method for transcribing traffic data according to claim 8, wherein the step of generating the third re-aggregated traffic data comprises the step of calculating the third traffic volume reflecting the transcribing of the third re-aggregated traffic data by adding the first traffic volume reflecting the transcribing of the first re-aggregated traffic data and the second traffic volume reflecting the transcribing of the second re-aggregated traffic data.

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