Traffic volume estimation device and traffic volume estimation method

Abstract

A traffic volume estimation device (1) is provided with: an extraction unit (12) that extracts at least one related area related to a target area; and an estimation unit (13) that estimates the volume of traffic pertaining to the number of people passing through the target area on the basis of the size of each at least one related area and the number of people estimated to be present in each at least one related area. The extraction unit (12) may extract at least one related area each including a part of the target area. The extraction unit (12) may extract at least one related area on the basis of position information observed in the target area.

Description

[Technical Field]

[0001] One aspect of this disclosure relates to a traffic volume estimation device and a traffic volume estimation method for estimating traffic volume related to the number of people passing through a target area. [Background technology]

[0002] Patent Document 1 below discloses an information distribution system that identifies the amount of pedestrian traffic in a collection area according to the time of day. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2017-142749 [Overview of the Initiative] [Problems that the invention aims to solve]

[0004] The above information distribution system cannot estimate traffic volume within a collection area from perspectives other than the collection area itself. For example, it cannot estimate traffic volume within a collection area based on areas related to the collection area. Therefore, it is desirable to be able to estimate traffic volume within the target area based on areas related to the target area. [Means for solving the problem]

[0005] A traffic volume estimation device relating to one aspect of this disclosure includes an extraction unit that extracts at least one related area related to a target area, and an estimation unit that estimates the traffic volume relating to the number of people passing through the target area based on the size of at least one related area and the estimated number of people present in at least one related area.

[0006] In this aspect, the traffic volume of the target area is estimated based on at least one or more related areas associated with the target area. That is, the traffic volume of the target area can be estimated based on the areas related to the target area. Effects of the Invention

[0007] According to one aspect of the present disclosure, the traffic volume of the target area can be estimated based on the areas related to the target area. Brief Description of the Drawings

[0008] [Figure 1] It is a diagram showing an example of the system configuration of a traffic volume estimation system including the traffic volume estimation device according to the embodiment. [Figure 2] It is a diagram showing an example of the functional configuration of the traffic volume estimation device according to the embodiment. [Figure 3] It is a diagram showing an example of an advertisement viewable area in Ginza. [Figure 4] It is a diagram showing an example of related areas extracted based on the advertisement viewable area in Figure 3. [Figure 5] It is a diagram showing the width and height of the related area in Figure 4. [Figure 6] It is a diagram showing an example of an advertisement viewable area in Kanamecho. [Figure 7] It is a diagram showing an example of related areas extracted based on the advertisement viewable area in Figure 6. [Figure 8] It is a diagram showing the width and height of the related area in Figure 7. [Figure 9] It is a diagram showing an example of an advertisement viewable area in Shibuya. [Figure 10] It is a diagram showing an example of related areas extracted based on the advertisement viewable area in Figure 9. [Figure 11] It is a diagram showing the width and height of the related area in Figure 10. [Figure 12] It is a flowchart showing an example of processing executed by the traffic volume estimation device according to the embodiment. [Figure 13]This figure shows an example of a mesh information table. [Figure 14] This figure shows an example of a table containing population information within an area. [Figure 15] This figure shows an example of a GPS data table. [Figure 16] This figure shows an example of a table of movement information. [Figure 17] This figure shows an example of location points indicated by GPS data observed in the ad viewing area shown in Figure 3. [Figure 18] This figure shows an example of the results of clustering the points in Figure 17. [Figure 19] This figure shows an example of the results of principal component analysis on the clustered point cloud shown in Figure 18. [Figure 20] This figure shows an example of the result of rotating the point cloud in Figure 19. [Figure 21] This figure shows an example of a mesh extracted based on the ad viewing area shown in Figure 3. [Figure 22] This figure shows an example of the hardware configuration of a computer used in the traffic volume estimation device according to this embodiment. [Modes for carrying out the invention]

[0009] The embodiments of this disclosure will be described in detail below with reference to the drawings. In the description of the drawings, the same elements will be denoted by the same reference numerals, and redundant descriptions will be omitted. Furthermore, the embodiments of this disclosure described below are specific examples of the present invention and are not limited to these embodiments unless otherwise stated to limit the present invention.

[0010] Figure 1 shows an example of the system configuration of a traffic volume estimation system 3 including a traffic volume estimation device 1 according to an embodiment. As shown in Figure 1, the traffic volume estimation system 3 is composed of a traffic volume estimation device 1 and at least one terminal 2. The traffic volume estimation device 1 and each terminal 2 are connected to each other by a network such as a mobile communication network, and can send and receive information from each other.

[0011] Traffic volume estimation device 1 is a computer device that estimates traffic volume related to the number of people passing through a target area (region, area, section).

[0012] An area is, for example, represented by a predetermined region on a two-dimensional map. The target area is the area on which traffic volume is estimated. The target area may also be an advertising viewing area, which is an area where outdoor advertisements can be viewed (visible). The target area may also be a collection of meshes (small areas) where outdoor advertisements can be viewed.

[0013] In this embodiment, the traffic volume is N a,t Represented by: Traffic volume N a,t `t` represents the number of people per hour passing through the advertising viewing area at location `a` of the outdoor advertisement and estimated time `t`. Location `a` of the outdoor advertisement is the location where the outdoor advertisement is installed (e.g., Kanamecho, Shibuya, Ginza, etc.). For example, location `a` ∈ {Kanamecho, Shibuya, Ginza, ...}. Estimated time `t` is the aggregated value of the time when the traffic volume was estimated (e.g., when t=2, it is 2:00 to 3:00). For example, estimated time `t` ∈ {0, 1, 2, 3, ..., 23}.

[0014] Details of the traffic volume estimation device 1 will be described later.

[0015] Terminal 2 is a mobile communication terminal or a computer device such as a laptop computer that performs mobile communication. In this embodiment, terminal 2 is assumed to be a smartphone, but is not limited to this. Terminal 2 is carried by the user of terminal 2. Terminal 2 is equipped with GPS (Global Positioning System) and uses GPS to acquire GPS data, which is the current location information of terminal 2 (date and time, latitude, longitude, etc.). The location information of terminal 2 can also be said to be the location information of the user of terminal 2. Note that terminal 2 may acquire its current location information based on base station information without using GPS. In this embodiment, for convenience, location information is referred to as GPS data. Terminal 2 may acquire GPS data as appropriate and transmit the acquired GPS data to the traffic volume estimation device 1 as appropriate. When terminal 2 transmits GPS data to the traffic volume estimation device 1, it may also transmit it together with identification information that identifies its own terminal. Terminal 2 may be equipped with other sensors or functions that are generally found in smartphones.

[0016] Figure 2 shows an example of the functional configuration of the traffic volume estimation device 1 according to an embodiment. As shown in Figure 2, the traffic volume estimation device 1 is composed of a storage unit 10, an acquisition unit 11, an extraction unit 12 (extraction unit), and an estimation unit 13 (estimation unit).

[0017] Each functional block of the traffic volume estimation device 1 is intended to function within the traffic volume estimation device 1, but is not limited to this. For example, some of the functional blocks of the traffic volume estimation device 1 may function within a computer device separate from the traffic volume estimation device 1, connected to the network with the traffic volume estimation device 1, while appropriately sending and receiving information with the traffic volume estimation device 1. Furthermore, some functional blocks of the traffic volume estimation device 1 may be omitted, multiple functional blocks may be integrated into a single functional block, or a single functional block may be decomposed into multiple functional blocks.

[0018] The following describes each function of the traffic volume estimation device 1 shown in Figure 2.

[0019] The storage unit 10 stores arbitrary information used in calculations by the traffic volume estimation device 1, as well as the results of calculations by the traffic volume estimation device 1. The information stored in the storage unit 10 may be referenced as appropriate by each function of the traffic volume estimation device 1.

[0020] The acquisition unit 11 may acquire (receive, input) information via the communication device 1004 or input device 1005 described later, and may store the acquired information in the storage unit 10, or output it to the extraction unit 12 or estimation unit 13.

[0021] The acquisition unit 11 acquires GPS data from at least one terminal 2 and stores it in the storage unit 10. The acquisition unit 11 may also acquire identification information along with the GPS data from the terminal 2 and store the GPS data for each terminal 2 based on the identification information. In this embodiment, to simplify the explanation, the explanation of the processing for each terminal 2 will be omitted as appropriate and referred to as the processing for terminal 2.

[0022] The acquisition unit 11 acquires information that identifies the target area, information that identifies the location a of the outdoor advertisement, and information that identifies the estimated time t. The acquired information may be stored in the storage unit 10, or it may be output to the extraction unit 12 or the estimation unit 13. The extraction unit 12 and the estimation unit 13 perform subsequent processing based on this information as appropriate.

[0023] The extraction unit 12 extracts at least one related area that is related to the target area. The extraction unit 12 may also extract at least one related area that each contains a part of the target area. The extraction unit 12 may store the related area information for the extracted at least one related area in the storage unit 10, or output it to the estimation unit 13.

[0024] Figure 3 shows an example of an advertising viewing area in Ginza. In Figure 3, the map of Ginza shows multiple meshes, which are small squares with patterns, mainly along the roads. The collection of these meshes represents the advertising viewing area (target area).

[0025] FIG. 4 is a diagram illustrating an example of related areas extracted based on the advertisement viewable areas in FIG. 3. The areas indicated by the two ellipses shown in FIG. 4 represent two related areas related to the advertisement viewable areas in FIG. 3, which are extracted by an extracting unit 12. Details of the extraction method performed by the extracting unit 12 will be described later.

[0026] FIG. 5 is a diagram illustrating the horizontal width and vertical width of the related areas in FIG. 4. More specifically, the horizontal width and vertical width of one of the two related areas are respectively d a,w,1 and d a,h,1 , and the horizontal width and vertical width of the other of the two related areas are respectively d a,w,2 and d a,h,2 .

[0027] FIG. 6 is a diagram illustrating an example of an advertisement viewable area in Kaname-cho. Similar to FIG. 3, in FIG. 6, on a map showing Kaname-cho, a plurality of meshes are provided mainly along roads, and a set of these meshes constitutes the advertisement viewable area (target area).

[0028] FIG. 7 is a diagram illustrating an example of related areas extracted based on the advertisement viewable area in FIG. 6. Similar to FIG. 4, the areas indicated by the two ellipses shown in FIG. 7 represent two related areas related to the advertisement viewable area in FIG. 6, which are extracted by the extracting unit 12.

[0029] FIG. 8 is a diagram illustrating the horizontal width and vertical width of the related areas in FIG. 7. More specifically, the horizontal width and vertical width of one of the two related areas are respectively d a,w,1 and d a,h,1 , and the horizontal width and vertical width of the other of the two related areas are respectively d a,w,2 and d a,h,2 .

[0030] FIG. 9 is a diagram illustrating an example of an advertisement viewable area in Shibuya. Similar to FIG. 3, in FIG. 9, on a map showing Shibuya, a plurality of meshes are provided mainly along roads, and a set of these meshes constitutes the advertisement viewable area (target area).

[0031] Figure 10 shows an example of a related area extracted based on the ad-viewable area in Figure 9. Similar to Figure 4, the area shown by the single ellipse in Figure 10 represents a single related area extracted by the extraction unit 12 that is associated with the ad-viewable area in Figure 9.

[0032] Figure 11 shows the width and height of the related area in Figure 10. More specifically, the width and height of the related area are d a,w,1 and d a,h,1 That is the case.

[0033] The extraction unit 12 may extract at least one related area based on GPS data (location information) observed in the target area. The extraction unit 12 may extract at least one related area, each containing a portion of the locations indicated by the GPS data observed in the target area. The extraction unit 12 may extract at least one related area based on clustering of the locations indicated by the GPS data observed in the target area. Details of the extraction method by the extraction unit 12 will be described later.

[0034] The estimation unit 13 estimates the traffic volume related to the number of people passing through the target area based on the size of at least one related area and the estimated number of people present in at least one related area. The size of a related area may be based on the maximum width of that related area. The estimated number of people present in a related area may be based on the estimated number of people present in the target area, GPS data observed in the target area, and GPS data observed in the related area. The estimation unit 13 may also estimate the number of viewers of outdoor advertisements in the target area based on traffic volume. Details of the estimation method by the estimation unit 13 will be described later.

[0035] The estimation unit 13 may output the estimation result. More specifically, the estimation unit 13 may output (transmit) the estimation result to another device via the communication device 1004 described later, or it may output (display, audio output) via the output device 1006 described later.

[0036] Next, we will explain an example of the process performed by the traffic volume estimation device 1, referring to Figures 12 to 20.

[0037] Figure 12 is a flowchart showing an example of the process (traffic volume estimation method) performed by the traffic volume estimation device 1. First, the extraction unit 12 extracts the population n within the area from the GPS data. a,t ,

number

number

number

number

[0038] Population within the area n a,t This represents the number of people within the advertising viewing area at location a of the outdoor advertisement and at the estimated time t.

[0039] The extraction unit 12 may, for example, identify the advertising viewing area based on mesh information pre-stored by the storage unit 10. Figure 13 shows an example of a mesh information table. As shown in Figure 13, the mesh information associates an advertising name that identifies an outdoor advertisement with a mesh number that identifies the mesh. The mesh information may also be further associated with a set of latitudes and longitudes (for example, the latitude and longitude of each of the four vertices of a square) to identify the range and location of the mesh.

[0040] The extraction unit 12 is used to extract data from the population n within the area. a,t This is calculated using existing technologies, such as those disclosed in the references listed below. References: International Publication No. 2020 / 095480

[0041] Figure 14 shows an example of a table of area population information. As shown in Figure 14, the area population information associates the advertisement name, the mesh number, a holiday flag indicating whether the day of observation was a holiday or not, the time of observation, and the population within the mesh area.

[0042]

number

number

number

number

[0043] As described above, the GPS data may be acquired by the acquisition unit 11 or it may be stored in advance by the storage unit 10. Figure 15 shows an example of a GPS data table. As shown in Figure 15, the GPS data associates the date and time of observation with the observed latitude and longitude. The GPS data shown in Figure 15 may be observed within skm (kilometers) of the outdoor advertisement location a.

[0044] The extraction unit 12 may calculate movement information, including movement speed (m / s), based on GPS data. Figure 16 shows an example of a movement information table. As shown in Figure 16, the movement information is associated with the date and time when the GPS data was observed, the latitude and longitude at which the GPS data was observed, the time difference (seconds) from the previous record, the previous record latitude, the previous record longitude, the distance traveled (m), which is the distance between coordinates determined by the latitude and longitude of the previous record, and the movement speed (m / s). For each record of the movement information (one record), the extraction unit 12 calculates the time difference, distance traveled, and movement speed based on a comparison with the previous record.

[0045] The extraction unit 12, based on the calculated movement speed,

number

number

number

number

[0046] Returning to Figure 12, following step S1, the extraction unit 12 extracts data from one month's worth of GPS data that were within the ad viewing area (step S2). The extraction unit 12 performs this extraction based on latitude and longitude.

[0047] Figure 17 shows an example of a point indicating the location of GPS data observed in the ad viewing area shown in Figure 3. The points shown in Figure 17 correspond to the GPS data observed in the ad viewing area, which were extracted by the extraction unit 12 in step S2.

[0048] Returning to Figure 12, following step S2, the extraction unit 12 clusters the GPS data using the Dirichlet Process GMM (step S3). The number of classes may be estimated. The extraction unit 12 may determine the number of related areas based on the clustering results. Figure 18 shows an example of the results of clustering the points in Figure 17. As shown in Figure 18, the points in Figure 17 are clustered into two point groups, an upper half and a lower half. The Dirichlet Process GMM is represented, for example, by the following formula.

number

[0049] Returning to Figure 12, following step S3, the extraction unit 12 sets the value of the repeating control variable i to 1 in order to perform the subsequent loop of steps S5 to S7 for each clustered class (class 1, class 2, class 3, ...). Note that i is also an ID that identifies the related area.

[0050] Following step S4, the extraction unit 12 performs principal component analysis on the data included in class i (step S5). Figure 19 shows an example of the results of principal component analysis on the clustered point cloud in Figure 18. As shown in Figure 19, eigenvector 1 and eigenvector 2 are extracted as a result of principal component analysis on the point cloud of the upper half of the class in Figure 18.

[0051] Returning to Figure 12, following step S4, the extraction unit 12 calculates the distance between the rotated point clouds, specifically the value between the points with the maximum width on the vertical and horizontal axes, d w d h (Step S6). Figure 20 shows an example of the result of rotating the point cloud in Figure 19. As shown in Figure 20, the extraction unit 12 selects the maximum width value (value between points) from the result of rotating the point cloud in Figure 19 to the left by about 45 degrees. a,w,i We estimate that the maximum vertical width value (value between points) is d a,h,i This is an estimate.

[0052] Returning to Figure 12, following step S6, the extraction unit 12 apportions the population within the area according to the number of observed clustered points (step S7). For example, the population within the area of ​​the ad viewing area n a,t If n is "50000", the total number of GPS data observed within the ad viewing area is "31000", and the number of GPS data included in class i to be estimated is "12000", then the extraction unit 12 takes the result of the formula "50000*(12000 / 31000)", which is "19354.839", and the population n within the area of ​​the related area (class i) a,t,i It is calculated (estimated) as follows.

[0053] Following step S7, the extraction unit 12 determines whether i is greater than the number of GPS observations within the ad viewing area (step S8). If it is determined in step S8 that i is not greater (S8: NO), the extraction unit 12 increments the value of i by 1 and returns to step S5.

[0054] On the other hand, if it is determined to be large in step S8 (S8: YES), the clustering will not be performed to a number of classes greater than the observed data, so the loop of processing in steps S5 to S7 is terminated, and the estimation unit 13 calculates the traffic volume (step S9). The details of step S9 will be explained below.

[0055] The estimation unit 13 calculates the traffic volume using the following formula.

number

[0056] D a,i This is shown by the following formula. D a,i =max(d a,w,i d a,h,i ) D a,i This represents the cross-sectional distance of the relevant area, and since it is the distance across the relevant area, the maximum value is used.

[0057] θ slow θ is a low-speed parameter.fast θ is a parameter for high speed. slow and θ fast For example, machine learning may be used to set a coefficient that minimizes the mean squared error with respect to the ground truth data. The ground truth data may be values ​​obtained by counting the number of people who actually passed through the area where the advertisement could be viewed, through a pedestrian traffic survey. θ slow and θ fast This parameter does not change depending on the location and estimated time of the outdoor advertisement.

[0058] The above N a,t Of the following equations,

number

number

number

number

[0059] The estimation unit 13 may further calculate (estimate) the number of viewers of outdoor advertisements in the target area based on the traffic volume calculated (estimated) in step S9.

[0060] The estimation unit 13 first calculates IMP, which is the amount of foot traffic that moves within the ad viewing area during the ad delivery time, based on the following formula. IMP = Area population + (Traffic volume - Area population) / 3600 * Distribution time (s)

[0061] The above formula is explained below. The population within the area is the population that, on average, resides within the ad-viewable area during a given time period (the population that is consistently present within the ad-viewable area). The reasoning behind defining IMP for the population within the area is that if an ad is delivered for even one second, the population within the ad-viewable area will be greater than or equal to the population within the ad-viewable area. Traffic volume is the amount of traffic that moves through the ad-viewable area in one hour. The reasoning behind defining IMP for traffic volume is that if an ad is delivered for one hour, the IMP will be equal to the traffic volume. In the above formula, "(traffic volume - population within the area) / 3600" represents the population that newly enters the area per second.

[0062] The estimation unit 13 then calculates V-imp, which is the number of people who view the advertisement within the advertisement's delivery time, based on the following formula. V-imp=IMP*α Here, α is a parameter that indicates the number of people who view the advertisement relative to the number of people passing through the area where the advertisement can be viewed. One way to determine this parameter is to conduct a survey of passersby and calculate the proportion of passersby who view the advertisement (for example, α = 0.44).

[0063] As described above, the estimation unit 13 estimates IMP or V-imp based on two data points: the population within the area and traffic volume.

[0064] The following sections will explain alternative methods for calculating traffic volume. For example, traffic volume can be calculated using the following formula.

number

[0065] D a This is shown by the following formula. D a =(d a,w +d a,h ) / 2 D a This is the cross-sectional distance of the ad viewable area. a For example, the east-west distance of the ad viewing area (for example, d above) a,w) and the distance from north to south (for example, the distance from north to south as described above d a,h This is the average value.

[0066] The above N a,t Of the following equations,

number

number

[0067] The above N a,t This explains the problem of calculation using the formula. When outdoor advertisements are placed, the viewable area of ​​the advertisement takes on various shapes depending on the location of the outdoor advertisement. For example, the shapes of the viewable area of ​​the advertisement shown in Figures 3, 6, and 9 can be cited. For example, if the viewable area of ​​the advertisement shown in Figure 3 includes an intersection, it is thought that it is possible to divide it into two (extract two related areas), but the above N a,t The formula does not take this into account. Figure 21 shows an example of a mesh extracted based on the ad viewing area in Figure 3. Above N a,t In the formula, the crossing distance of a person passing through the advertising viewing area in Figure 21 is approximated by the mesh shown in Figure 21 (using the average value of the length and width). To improve the estimation accuracy, it is necessary to measure the crossing distance more precisely. That is, the above N a,t The formula presents a problem in calculating the cross-sectional distance and cannot take into account the complex shapes of the ad viewing area.

[0068] On the other hand, in traffic volume estimation by traffic volume estimation device 1, as described above, the accuracy of traffic volume and viewer number estimation is improved by accurately determining the crossing distance. For example, as shown in Figure 4, by considering the case where two roads intersect and dividing the area into two (extracting two related areas), the calculation of the crossing distance is made more precise. More specifically, in traffic volume estimation by traffic volume estimation device 1, the crossing distance is determined more accurately by approximating with the maximum length of two (not limited to two, but one or more) related areas.

[0069] Next, the effects and benefits of the traffic volume estimation device 1 according to this embodiment will be explained.

[0070] The traffic volume estimation device 1 includes an extraction unit 12 that extracts at least one related area associated with the target area, and an estimation unit 13 that estimates the traffic volume related to the number of people passing through the target area based on the size of at least one related area and the estimated number of people present in at least one related area. With this configuration, the traffic volume of the target area is estimated based on at least one related area associated with the target area. In other words, the traffic volume of the target area can be estimated based on areas associated with the target area.

[0071] The extraction unit 12 of the traffic volume estimation device 1 may extract at least one related area, each containing a portion of the target area. This configuration allows for a more accurate estimation of traffic volume in the target area based on at least one related area, each containing a portion of the target area.

[0072] The extraction unit 12 of the traffic volume estimation device 1 may extract at least one related area based on GPS data (location information) observed in the target area. This configuration allows for a more accurate estimation of traffic volume in the target area based on at least one related area derived from GPS data observed in the target area.

[0073] The extraction unit 12 of the traffic volume estimation device 1 may extract at least one related area, each containing a portion of the locations indicated by the GPS data observed in the target area. This configuration allows for a more accurate estimation of traffic volume in the target area based on at least one related area, each containing a portion of the locations indicated by the GPS data observed in the target area.

[0074] The extraction unit 12 of the traffic volume estimation device 1 may extract at least one related area based on the clustering of locations indicated by GPS data observed in the target area. This configuration allows for a more accurate estimation of traffic volume in the target area based on at least one related area based on the clustering of locations indicated by GPS data observed in the target area.

[0075] The size of the relevant area may be based on the maximum width of that area. This configuration allows for a more accurate estimation of traffic volume in the target area based on the maximum width of the relevant area.

[0076] The estimated number of people in a related area may be based on the estimated number of people in the target area, GPS data observed in the target area, and GPS data observed in the related area. This configuration allows for a more accurate estimation of traffic volume in the target area based on the estimated number of people in the target area, GPS data observed in the target area, and GPS data observed in the related area.

[0077] The target area may also be an area where outdoor advertisements can be viewed (an advertising-viewable area). This configuration makes it possible to estimate the traffic volume in the advertising-viewable area.

[0078] The estimation unit 13 of the traffic volume estimation device 1 may estimate the number of viewers of outdoor advertisements in the target area based on traffic volume. This configuration makes it possible to estimate the number of viewers of outdoor advertisements in the target area.

[0079] Traffic volume estimation device 1 relates to area division (extraction of relevant areas) for estimating the number of advertising viewers. Traffic volume estimation device 1 can estimate the number of viewers (IMP) of outdoor advertising in situations where accurate demographic data is unavailable. Traffic volume estimation device 1 performs traffic volume estimation by dividing the advertising viewable area (extracting relevant areas). Traffic volume estimation device 1 uses a logic to divide the advertising viewable area (extract relevant areas) for traffic volume estimation. Traffic volume estimation device 1 divides the advertising viewable area (extracts relevant areas), calculates the traffic volume for each divided area (relevant area), and calculates (estimates) IMP or V-imp.

[0080] The traffic volume estimation device 1 of this disclosure may have the following configuration.

[0081] [1] An extraction unit that extracts at least one related area related to the target area, The traffic volume relating to the number of people passing through the aforementioned target area is The size of each of the at least one related area, The estimated number of people present in each of the aforementioned at least one related area and An estimation unit that estimates based on, A traffic volume estimation device equipped with the following features.

[0082] [2] The extraction unit extracts at least one related area, each containing a portion of the target area. Traffic volume estimation device as described in [1].

[0083] [3] The extraction unit extracts at least one related area based on the location information observed in the target area. Traffic volume estimation device as described in [1] or [2].

[0084] [4] The extraction unit extracts at least one related area, each containing a portion of the location indicated by the location information observed in the target area. A traffic volume estimation device as described in any one of items [1] to [3].

[0085] [5] The extraction unit extracts at least one related area based on the clustering of locations indicated by the location information observed in the target area. A traffic volume estimation device as described in any one of items [1] to [4].

[0086] [6] The size of the aforementioned related area is based on the maximum width of the said related area. A traffic volume estimation device as described in any one of items [1] to [5].

[0087] [7] The number of people estimated to be present in the aforementioned related area is based on the number of people estimated to be present in the aforementioned target area, the location information observed in the target area, and the location information observed in the aforementioned related area. A traffic volume estimation device as described in any one of items [1] to [6].

[0088] [8] The aforementioned target area is an area where outdoor advertisements can be viewed. A traffic volume estimation device as described in any one of items [1] to [7].

[0089] [9] The estimation unit estimates the number of viewers of outdoor advertisements in the target area based on the traffic volume. A traffic volume estimation device as described in any one of items [1] to [8].

[0090] The block diagrams used in the description of the above embodiments show functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or it may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wired or wireless connections). A functional block may also be realized by combining the above one device or the above multiple devices with software.

[0091] Functions include, but are not limited to, judgment, decision, judgment, calculation, calculation, processing, derivation, investigation, exploration, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, assumption, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating (mapping), and assigning. For example, a functional block (configuration part) that enables transmission is called a transmitting unit or transmitter. As mentioned above, the method of implementation is not particularly limited.

[0092] For example, the traffic volume estimation device 1 in one embodiment of the present disclosure may function as a computer that processes the traffic volume estimation method of the present disclosure. Figure 22 is a diagram showing an example of the hardware configuration of the traffic volume estimation device 1 according to one embodiment of the present disclosure. The traffic volume estimation device 1 described above may be physically configured as a computer device including a processor 1001, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, bus 1007, etc.

[0093] In the following explanation, the term "device" can be replaced with "circuit," "device," "unit," etc. The hardware configuration of the traffic volume estimation device 1 may include one or more of the devices shown in the figure, or it may be configured without some of the devices.

[0094] Each function in the traffic volume estimation device 1 is realized by loading predetermined software (programs) onto hardware such as the processor 1001 and memory 1002, which allows the processor 1001 to perform calculations, control communication by the communication device 1004, and control at least one of the reading and writing of data in the memory 1002 and storage 1003.

[0095] The processor 1001 controls the entire computer, for example, by running the operating system. The processor 1001 may be composed of a central processing unit (CPU) that includes interfaces with peripheral devices, control devices, arithmetic units, registers, etc. For example, the acquisition unit 11, extraction unit 12, and estimation unit 13 described above may be implemented by the processor 1001.

[0096] Furthermore, the processor 1001 reads programs (program code), software modules, data, etc., from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes accordingly. The program used is one that causes the computer to execute at least a part of the operations described in the above embodiment. For example, the acquisition unit 11, the extraction unit 12, and the estimation unit 13 may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and other functional blocks may be implemented similarly. The above-described various processes have been explained as being executed by one processor 1001, but they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The program may also be transmitted from a network via a telecommunications line.

[0097] Memory 1002 is a computer-readable recording medium and may consist of at least one of the following: ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), etc. Memory 1002 may also be called a register, cache, main memory, etc. Memory 1002 can store executable programs (program code), software modules, etc., for carrying out a wireless communication method according to one embodiment of the present disclosure.

[0098] Storage 1003 is a computer-readable recording medium and may consist of at least one of the following: an optical disc such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disc, a digital multipurpose disc, a Blu-ray® disc), a smart card, flash memory (e.g., a card, a stick, a key drive), a floppy® disk, a magnetic strip, etc. Storage 1003 may also be called an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, server, or other suitable medium including at least one of memory 1002 and storage 1003.

[0099] The communication device 1004 is hardware (transceiver / receiver device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, network controller, network card, communication module, etc. The communication device 1004 may be configured to include high-frequency switches, duplexers, filters, frequency synthesizers, etc., in order to implement at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the acquisition unit 11, extraction unit 12, and estimation unit 13 described above may be implemented by the communication device 1004.

[0100] The input device 1005 is an input device that accepts input from an external source (e.g., a keyboard, mouse, microphone, switch, button, sensor, etc.). The output device 1006 is an output device that outputs to an external source (e.g., a display, speaker, LED lamp, etc.). The input device 1005 and the output device 1006 may be configured as an integrated unit (e.g., a touch panel).

[0101] Furthermore, each device, such as the processor 1001 and memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or different buses may be configured for each device.

[0102] Furthermore, the traffic volume estimation device 1 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), or an FPGA (Field Programmable Gate Array), and some or all of each functional block may be realized by such hardware. For example, the processor 1001 may be implemented using at least one of these hardware components.

[0103] The notification of information is not limited to the manner / embodiments described herein and may be carried out by other means.

[0104] Each aspect / embodiment described in this disclosure may be applied to at least one of the following systems: LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), FRA (Future Radio Access), NR (new Radio), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, UWB (Ultra-WideBand), Bluetooth (registered trademark), and other appropriate systems, as well as next-generation systems extended based thereon. Furthermore, multiple systems may be applied in combination (for example, a combination of at least one of LTE and LTE-A with 5G).

[0105] The processing procedures, sequences, flowcharts, etc., of each aspect / embodiment described herein may be reordered, provided they are consistent with each other. For example, the methods described herein present various step elements in an exemplary order and are not limited to that specific order.

[0106] Input and output information may be stored in a specific location (e.g., memory) or managed using a management table. Input and output information may be overwritten, updated, or appended to. Output information may be deleted. Input information may be transmitted to other devices.

[0107] The determination may be made by a value represented by 1 bit (0 or 1), by a boolean value (true or false), or by a numerical comparison (for example, a comparison with a predetermined value).

[0108] Each aspect / embodiment described herein may be used individually, in combination, or switched between as needed during implementation. Furthermore, notification of specific information (e.g., notification that "X is") is not limited to explicit notification, but may also be implicit (e.g., by not providing such notification).

[0109] Although the present disclosure has been described in detail above, it will be clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the intent and scope of the present disclosure as defined by the claims. Therefore, the descriptions in the present disclosure are illustrative and not intended to be restrictive in any way.

[0110] Software should be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, and so on, whether they are called software, firmware, middleware, microcode, hardware description languages, or by any other name.

[0111] Furthermore, software, instructions, information, etc., may be transmitted and received via a transmission medium. For example, if software is transmitted from a website, server, or other remote source using at least one of wired technology (such as coaxial cable, fiber optic cable, twisted pair, or digital subscriber line (DSL)) and wireless technology (such as infrared or microwave), then at least one of these wired and wireless technologies is included in the definition of a transmission medium.

[0112] The information, signals, etc. described in this disclosure may be represented using any of the various different techniques. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0113] In addition, terms used in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meaning.

[0114] The terms “system” and “network” as used in this disclosure are interchangeable.

[0115] Furthermore, the information, parameters, etc., described in this disclosure may be expressed using absolute values, relative values ​​from a predetermined value, or corresponding other information.

[0116] The names used for the parameters described above are not restrictive in any way. Furthermore, the formulas and other expressions using these parameters may differ from those expressly disclosed in this disclosure.

[0117] As used in this disclosure, the terms “determining” and “determining” may encompass a wide variety of actions. “Determining” may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiry (e.g., searching in a table, database, or other data structure), and ascertaining. “Determining” may also include, for example, receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, and accessing (e.g., accessing data in memory). Furthermore, "judgment" and "decision" can include considering something as having been "judged" or "decided" after resolving, selecting, choosing, establishing, comparing, etc. In other words, "judgment" and "decision" can include considering something as having been "judged" or "decided" after some action. Also, "judgment (decision)" can be reinterpreted as "assuming," "expecting," or "considering."

[0118] The terms “connected,” “coupled,” or any variation thereof, mean any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are “connected” or “coupled” with each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, “connection” may be reinterpreted as “access.” As used in this disclosure, two elements may be considered to be “connected” or “coupled” with each other using at least one of one or more wires, cables, and printed electrical connections, and, in some non-limiting and non-exclusive examples, electromagnetic energy having wavelengths in the radio frequency domain, microwave domain, and optical (both visible and invisible) domain.

[0119] In this disclosure, the phrase "based on" does not mean "based solely on" unless otherwise specified. In other words, the phrase "based on" means both "based solely on" and "based at least on."

[0120] Any reference to elements using the designations “first,” “second,” etc., as used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Accordingly, references to the first and second elements do not imply that only two elements may be employed, or that the first element must precede the second element in any way.

[0121] In the configuration of each of the above devices, "means" may be replaced with "part," "circuit," "device," etc.

[0122] Where the terms “include,” “including,” and variations thereof are used in this disclosure, these terms are intended to be inclusive, as is the term “comprising.” Furthermore, the term “or” as used in this disclosure is not intended to mean exclusive OR.

[0123] In this disclosure, if articles are added by translation, such as a, an, and the in English, this disclosure may include the fact that the noun following these articles is plural.

[0124] In this disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "combine" may be interpreted similarly to "different." [Explanation of Symbols]

[0125] 1...Traffic volume estimation device, 2...Terminal, 3...Traffic volume estimation system, 10...Storage unit, 11...Acquisition unit, 12...Extraction unit, 13...Estimation unit, 1001...Processor, 1002...Memory, 1003...Storage, 1004...Communication device, 1005...Input device, 1006...Output device, 1007...Bus.

Claims

1. An extraction unit that extracts at least one related area related to the target area, The traffic volume relating to the number of people passing through the aforementioned target area is The size of each of the at least one related area, The estimated number of people present in each of the aforementioned at least one related area and An estimation unit that estimates based on, Equipped with, The number of people estimated to be present in the aforementioned related area is based on the number of people estimated to be present in the aforementioned target area, the number of location data points observed in the target area, and the number of location data points observed in the aforementioned related area. Traffic volume estimation device.

2. The extraction unit extracts at least one related area, each containing a portion of the target area. Traffic volume estimation device according to claim 1.

3. The extraction unit extracts at least one related area based on the location information observed in the target area. Traffic volume estimation device according to claim 1.

4. The extraction unit extracts at least one related area, each containing a portion of the location indicated by the location information observed in the target area. Traffic volume estimation device according to claim 1.

5. The extraction unit extracts at least one related area based on the clustering of locations indicated by the location information observed in the target area. Traffic volume estimation device according to claim 1.

6. The size of the aforementioned related area is based on the maximum width of the said related area. Traffic volume estimation device according to claim 1.

7. The aforementioned target area is an area where outdoor advertisements can be viewed. Traffic volume estimation device according to claim 1.

8. The estimation unit estimates the number of viewers of outdoor advertisements in the target area based on the traffic volume. Traffic volume estimation device according to claim 1.

9. An extraction step in which a computer extracts at least one related area related to the target area, The computer determines the traffic volume related to the number of people passing through the target area. The size of each of the at least one related area, The estimated number of people present in each of the aforementioned at least one related area and An estimation step based on, Includes, The number of people estimated to be present in the aforementioned related area is based on the number of people estimated to be present in the aforementioned target area, the number of location data points observed in the target area, and the number of location data points observed in the aforementioned related area. Traffic volume estimation method.

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