Traffic volume estimation device and traffic volume estimation method

Abstract

A traffic volume estimation device (1) is provided with an estimation unit (12) that estimates traffic volume relating to the number of persons traversing a target area, such estimation performed on the basis of a plurality of transport means presumed to be in use during traversal. The estimation unit (12) may make estimations on the basis of the respective velocities of the plurality of transport means. The estimation unit (12) may make estimations on the basis of a continuous distribution based on the respective velocities of the plurality of transport means. The estimation unit (12) may make estimations on the basis of a distribution based on the respective velocities of the plurality of transport means and a distribution of the velocities of persons observed in the target area. The estimation unit (12) may make estimations on the basis of a convolution computation of the distribution based on the respective velocities of the plurality of transport means and the distribution of the velocities of persons observed in the target area.

Description

Traffic volume estimation device and traffic volume estimation method

[0001] One aspect of the present 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.

[0002] The following Patent Document 1 discloses an information distribution system that identifies the traffic volume of pedestrians passing through a collection area according to the time of day.

[0003] JP 2017-142749 A

[0004] In the above information distribution system, it is not possible to estimate the traffic volume in a collection area from a viewpoint other than walking. For example, it is not possible to estimate the traffic volume in a collection area based on multiple modes of transportation. Therefore, it is desirable to estimate the traffic volume in a target area based on multiple modes of transportation.

[0005] A traffic volume estimation device according to an aspect of the present disclosure includes an estimation unit that estimates traffic volume related to the number of people passing through a target area based on multiple modes of transportation that are expected to be used when passing through.

[0006] In this aspect, the traffic volume in the target area is estimated based on a plurality of means of transportation that are expected to be used when passing through the target area. That is, the traffic volume in the target area can be estimated based on a plurality of means of transportation.

[0007] According to one aspect of the present disclosure, traffic volume in a target area can be estimated based on multiple modes of transportation.

[0008] FIG. 1 is a diagram showing an example of the system configuration of a traffic volume estimation system including a traffic volume estimation device according to an embodiment. FIG. 2 is a diagram showing an example of an image of transportation means used when passing through an advertisement viewing area. FIG. 3 is a diagram showing an example of a probability distribution of speeds for each transportation means. FIG. 4 is a diagram showing an example of the functional configuration of a traffic volume estimation device according to an embodiment. FIG. 5 is a flowchart showing an example of processing executed by a traffic volume estimation device according to an embodiment. FIG. 6 is a diagram showing an example of a table of outdoor advertisement information. FIG. 7 is a diagram showing an example of a table of GPS data. FIG. 8 is a diagram showing an example of a table of movement information. FIG. 9 is a diagram showing an example of an observation point. FIG. 10 is a diagram showing an example of a graph of a probability distribution related to observed speeds. pｓ It is a figure which shows the example of the graph of g. It is a figure which shows the example of the graph of z. It is a figure which shows the example of the hardware configuration of the computer which is used in the traffic volume estimation device based on the embodiment.

[0009] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the description of the drawings, the same elements are designated by the same reference numerals, and duplicate explanations will be omitted. Furthermore, the embodiments of the present disclosure in the following description are specific examples of the present invention, and the present invention is not limited to these embodiments unless otherwise specified to limit the present invention.

[0010] Fig. 1 is a diagram showing 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 Fig. 1, the traffic volume estimation system 3 includes the traffic volume estimation device 1 and at least one or more terminals 2. The traffic volume estimation device 1 and each terminal 2 are communicatively connected to each other via a network such as a mobile communication network, and can transmit and receive information to and from each other.

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

[0012] The area is indicated by, for example, a predetermined region on a two-dimensional map. The target area is an area for which traffic volume is to be estimated. The target area may be an advertising viewable (visible) area, which is an area where outdoor advertisements can be viewed (seen). 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 is expressed as traffic volume N ａ，ｔ is the number of people passing through the advertisement viewable area per hour at outdoor advertisement location a and estimated time t. Outdoor advertisement location a is the location where the outdoor advertisement is installed (e.g., Kanamecho, Shibuya, Ginza, etc.). For example, outdoor advertisement location a∈{Kanamecho, Shibuya, Ginza, ...}. Estimated time t is the aggregate value for the time when traffic volume is estimated (e.g., 2:00 to 3:00 when t=2). For example, estimated time t∈{0, 1, 2, 3, ..., 23}.

[0014] The traffic volume estimation device 1 performs estimation based on the premise that the traffic volume (or advertisement viewing rate) in an advertisement viewing area is related to the means of transportation (mobility) used when passing through the advertisement viewing area. FIG. 2 is a diagram showing an example of the means of transportation used when passing through the advertisement viewing area. As shown in FIG. 2, in the advertisement viewing area, which is a target area where outdoor advertisements can be viewed, people passing by foot and people passing by car (vehicle) are shown as means of transportation. Here, people passing by foot move relatively slowly, and therefore it is thought that they take a long time to pass through the advertisement viewing area. On the other hand, people in cars move relatively quickly, and therefore it is thought that they take a short time to pass through the advertisement viewing area.

[0015] Since traffic lights are often separated for pedestrians and automobiles, it is also possible to separate people traveling on foot from those traveling by other means of transportation. For example, in order to separate people traveling on foot from those traveling other than walking, it is possible to calculate traffic volume by classifying people into two categories: faster and slower than a certain speed k. Note that the speed k may be determined in advance based on a typical walking speed of people (e.g., 1.9 m / s).

[0016] However, in reality, there are various traffic conditions around the world, and it is necessary to classify into multiple categories. However, it is difficult to set the number of categories for each country or region individually. For example, there are countries where motorcycles are widely used as a means of transportation, and there are countries where there are no traffic lights. Figure 3 shows an example of the probability distribution of speeds by transportation means. Figure 3 shows an example of the probability distribution of speeds for walking, bicycle (vehicle), motorcycle (vehicle), bus (vehicle), car (vehicle), and train (vehicle). For example, the observed speed of a bicycle is an average of m ｂ , variance σ ｂ As mentioned above, the problem is that there are differences in the means of transportation and speeds used depending on the country or region.

[0017] To solve the above problem, the traffic volume estimation device 1 performs calculations (estimations) using a continuous distribution (for example, one for each country or region) rather than calculations (estimations) using a speed threshold k. More specifically, the traffic volume estimation device 1 performs calculations using a convolution operation to calculate the speed distribution related to transportation means against the distribution of speed and number of people. This allows the traffic volume estimation device 1 to perform calculations without providing the type of transportation means used in each country or region in advance. Details of the traffic volume estimation device 1 will be described later.

[0018] The terminal 2 is a computer device such as a mobile communication terminal or a laptop computer that performs mobile communication. In this embodiment, the terminal 2 is assumed to be a smartphone, but is not limited to this. The terminal 2 is carried by the user of the terminal 2. The terminal 2 is equipped with a Global Positioning System (GPS) and uses the GPS to acquire GPS data, which is current location information (date, time, latitude, longitude, etc.) of the terminal 2. The location information of the terminal 2 can also be considered location information of the user of the terminal 2. Note that the terminal 2 may acquire current location information based on base station information without using GPS. In this embodiment, for convenience, location information is referred to as GPS data. The terminal 2 may acquire GPS data as appropriate and transmit the acquired GPS data to the traffic volume estimation device 1 as appropriate. When transmitting the GPS data to the traffic volume estimation device 1, the terminal 2 may transmit identification information that identifies the terminal itself. The terminal 2 may also have other sensors or functions that are included in a typical smartphone.

[0019] 4 is a diagram illustrating an example of a functional configuration of the traffic volume estimation device 1 according to the embodiment. As illustrated in FIG. 4, the traffic volume estimation device 1 includes a storage unit 10, an acquisition unit 11, and an estimation unit 12 (estimation unit).

[0020] Although it is assumed that each functional block of the traffic volume estimation device 1 functions within the traffic volume estimation device 1, this is not a limitation. For example, some of the functional blocks of the traffic volume estimation device 1 may function within a computer device different from the traffic volume estimation device 1 and connected to the traffic volume estimation device 1 via a network, while appropriately transmitting 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 one functional block, or one functional block may be separated into multiple functional blocks.

[0021] Hereinafter, each function of the traffic volume estimation device 1 shown in FIG. 4 will be described.

[0022] The storage unit 10 stores any information used in calculations in the traffic volume estimation device 1 and the results of calculations in the traffic volume estimation device 1. The information stored by the storage unit 10 may be referenced by each function of the traffic volume estimation device 1 as needed.

[0023] The acquisition unit 11 acquires (receives, inputs) information via the communication device 1004 or the input device 1005 described below, and may store the acquired information in the storage unit 10 or output it to the estimation unit 12.

[0024] The acquisition unit 11 acquires GPS data from at least one or more terminals 2, and stores the data in the storage unit 10. The acquisition unit 11 may acquire identification information along with the GPS data from the terminals 2, and store the GPS data for each terminal 2 based on the identification information. In this embodiment, to simplify the explanation, hereinafter, explanations regarding processing for each terminal 2 will be omitted as processing for terminal 2, as appropriate.

[0025] The acquisition unit 11 acquires information specifying the target area, information specifying the location a of the outdoor advertisement, and information specifying the estimated time t, and may store the acquired information in the storage unit 10 or output the acquired information to the estimation unit 12. The estimation unit 12 may perform subsequent processing based on this information as appropriate.

[0026] The acquisition unit 11 may acquire information identifying a country or region, and store the acquired information in the storage unit 10, or output the acquired information to the estimation unit 12. The estimation unit 12 may perform subsequent processing appropriately based on the information identifying a country or region, for example, by using a continuous distribution corresponding to the information identifying a country or region. Note that the traffic volume estimation device 1 may perform subsequent processing in a state where the country or region has been determined in advance.

[0027] The estimation unit 12 estimates traffic volume, which is related to the number of people passing through the target area, based on multiple means of transportation that are expected to be used when passing through. The estimation unit 12 may make the estimation based on the speed of each of the multiple means of transportation. The estimation unit 12 may make the estimation based on a continuous distribution based on the speed of each of the multiple means of transportation. The estimation unit 12 may make the estimation based on a distribution based on the speed of each of the multiple means of transportation and a distribution of people's speeds observed in the target area. The estimation unit 12 may make the estimation based on a convolution operation between the distribution based on the speed of each of the multiple means of transportation and the distribution of people's speeds observed in the target area. The estimation unit 12 may estimate the number of viewers of outdoor advertisements in the target area based on traffic volume. Details of the estimation method used by the estimation unit 12 will be described later.

[0028] The plurality of transportation means may be either walking or vehicles. The plurality of transportation means may be, for example, walking, bicycle, motorcycle, bus, car, or train, as shown in FIG.

[0029] The estimation unit 12 may output the estimation result. More specifically, the estimation unit 12 may output (transmit) the estimation result to another device via a communication device 1004 described later, or may output (display, output as sound) the estimation result via an output device 1006 described later.

[0030] Next, an example of the process (traffic volume estimation method) executed by the traffic volume estimation device 1 will be described with reference to FIGS.

[0031] 5 is a flowchart showing an example of a process (traffic volume estimation method) executed by the traffic volume estimation device 1. First, the estimation unit 12 acquires one month's worth of GPS data, extracts GPS data within n km (kilometers) from the advertisement, and calculates the speed (step S1). Details of step S1 will be described below.

[0032] The estimation unit 12 determines the location of an advertisement based on outdoor advertising information, which is information related to outdoor advertisements. Fig. 6 is a diagram showing an example of a table of outdoor advertising information. As shown in Fig. 6, the outdoor advertising information associates an advertisement name that identifies an advertisement with the latitude and longitude of the location where the advertisement is installed.

[0033] As described above, the GPS data may be acquired by the acquisition unit 11, or may be stored in advance by the storage unit 10. Fig. 7 is a diagram showing an example of a table of GPS data. As shown in Fig. 7, the GPS data associates the observation date and time, the observation latitude, and the observation longitude. The GPS data shown in Fig. 7 may be observed within s km (kilometers) around location a of the outdoor advertisement.

[0034] The estimation unit 12 calculates movement information including movement speed (m / s) based on the GPS data. FIG. 8 is a diagram showing an example of a movement information table. As shown in FIG. 8, the movement information corresponds to the date and time when the GPS data was observed, the latitude when the GPS data was observed, the longitude when the GPS data was observed, the time difference (seconds) from the previous record, the previous record latitude which is the latitude of the previous record, the previous record longitude which is the longitude of the previous record, the movement distance (m) which is the distance between the coordinates determined by the latitude and longitude of the previous record, and the movement speed (m / s). For each record (one record) of the movement information, the estimation unit 12 calculates the time difference, movement distance, and movement speed based on a comparison with the previous record.

[0035] The estimation unit 12 estimates the observation point m based on the movement information. ａ，ｔ，ｉ(m / s) is calculated. Here, a is the advertisement position, t is the time, and i is the index of the observation point. The observation point is the speed (observed speed) calculated based on GPS data. There is one observation point per record. The total number of observation points is K ａ，ｔ FIG. 9 is a diagram showing an example of observation points.

[0036] Returning to FIG. 5, following step S1, the estimation unit 12 calculates from the calculation result of step S1 of (Step S2) Details of step S2 will be explained below.

[0037] The estimation unit 12 determines m ａ，ｔ，ｉ is generated from a certain probability distribution, Let's say. is called the probability distribution over the observed velocities. are the parameters of the probability distribution, and the set of observed data The estimation unit 12 calculates the probability distribution of the observed speed using a Poisson process or the like. FIG. 10 is a graph showing an example of the probability distribution of the observed speed. FIG. 11 is a graph showing the probability distribution of the observed speed using a Poisson process or the like. ｓ FIG. 10 is a diagram showing an example of a graph of

[0038] This will be explained in more detail. ｓ is the probability distribution for the observed velocity, as mentioned above. ａ，ｔ，ｉ is the probability distribution p ｓ It is a probability distribution that can be assumed to be randomly drawn from (·). ｓ When a velocity m is substituted into p, it returns the probability that the velocity m will be sampled. This probability distribution can be expressed, for example, by the Poisson distribution or a neural network. ｓ When is a Poisson distribution, the only parameter that determines the shape of the Poisson distribution is the mean. ｓ If is a Poisson distribution, the observed data m ａ，ｔ，ｉ The average of and the Poisson distribution is calculated.

[0039] Also, is the probability part pｓ Specifically, p ｓ If is a Poisson distribution, the parameter that determines the shape of the Poisson distribution is the mean. is p ｓ If is a Poisson distribution, the observed data m ａ，ｔ，ｉ The following formula is an example of how to calculate it: Here, K ａ，ｔ indicates the total number of records.

[0040] 5, after step S2, the estimation unit 12 performs a convolution operation on the distribution of speeds and the number of people with the distribution of speeds related to mobility (transportation means) (step S3). Details of step S3 will be described below.

[0041] The estimation unit 12 performs a convolution calculation on the speed distribution related to the means of transportation with the speed and number of people distribution using the following formula. Here, g, more specifically g(m;θ ｇ ) is the distribution of vehicle speeds. g(m;θ ｇ ) is calculated by the estimation unit 12 using a Gaussian distribution or the like. ｇ is a parameter of the probability distribution, such as the variance. For example, g(m;θ ｇ ) is expressed by the following formula: FIG. 12 is a diagram showing an example of a graph of g.

[0042] A more detailed explanation follows. g is a window function used in the convolution operation. By performing calculations using g, it is possible to extract features from the function before convolution. Specifically, it is a function expressed by a Gaussian distribution or a neural network. When g is a Gaussian distribution, the parameter θ of the Gaussian distribution is ｇ is calculated by using the mean variance of the probability distribution of speeds by means of transportation as a parameter of the Gaussian distribution.

[0043] Also, θ ｇ is a parameter of the window function g used in the convolution operation. Specifically, when the window function g is a Gaussian distribution, it is the variance of the distribution. ｇ In the case of a Gaussian distribution, the parameter θ of the Gaussian distribution ｇθ may be calculated by using the average variance of the probability distribution of the speed of each mode of transportation as a parameter of the Gaussian distribution. ｇ An example of how to calculate θ is shown in the following formula. ｇ = (σ 歩行者 +σ バス +σ 自動車 +σ 鉄道 ) / 4 where σ 歩行者 denotes the variance of pedestrian speed. σ バス denotes the variance of the bus speed. σ 自動車 denotes the variance of the car's speed. σ 鉄道 denotes the variance of railway speed.

[0044] Also, k ａ，ｔ is the probability distribution p ｓ Specifically, the features are extracted from k using a window function. ａ，ｔ is (p ｓ *g) is a composite function calculated by calculating (m). ａ，ｔ teeth, and g(m;θ ｇ ) is calculated by convolution.

[0045] 5, following step S3, the estimation unit 12 converts the result of the convolution operation in step S3 into a probability distribution (step S4). Step S4 will be described in detail below.

[0046] The estimation unit 12 calculates k by the following formula: ａ，ｔ (m) is converted into a probability distribution.

[0047] This will be explained in more detail. is k ａ，ｔ is converted into a probability distribution. is a probability distribution ａ，ｔ (m)dm=1. Therefore, k ａ，ｔ (m) to ∫k ａ，ｔ (m) It is calculated by dividing by dm.

[0048] Following step S4, the estimation unit 12 calculates the traffic volume (step S5). Step S5 will be described in detail below.

[0049] The estimation unit 12 calculates the traffic volume N ａ，ｔ Calculate. Here, n ａ，ｔ is the population in the area, and is the number of people in the advertisement viewable area at the outdoor advertising location a and the estimated time t. ａ，ｔ is calculated by existing techniques such as those disclosed in the following reference documents: Reference: WO 2020 / 095480 D ａ is the crossing distance of the ad viewable area. ａ is, for example, the east-west distance of the advertisement viewable area (for example, d ａ，ｗ ) and north-south distance (e.g., d ａ，ｈ ) may be calculated using the following formula: ａ = (d ａ，ｗ +d ａ，ｈ ) / 2 z (m; θ ｚ ) is a continuous function of the parameter θ ｚ is a function z(m;θ ｚ ) is a parameter that determines the shape of the

[0050] FIG. 13 is a diagram showing an example of a graph of z (m; θ ｚ ) does not change depending on the location of the outdoor advertisement and the estimated time. ｚ ) may be set to a coefficient that minimizes the mean square error with the correct answer data when machine learning is performed. The correct answer data may be a value counted through a traffic survey of people who actually passed through the advertisement viewing area. The function z(m;θ ｚ ) is of the form Alternatively, it may be estimated using machine learning such as a neural network. ｚ ) is the θ ｓｌｏｗ and θ ｆａｓｔ is the parameter corresponding to

[0051] θ ｚ may be calculated using the following formula: where: indicates the correct data.

[0052] To explain in more detail, z is a function that returns the value of the corresponding parameter when a certain velocity m is substituted. The parameter that determines the shape of z is θ ｚ θ ｚ is based on accurate data collected by counting people who actually passed through areas where ads can be viewed through traffic surveys, etc. Or it is calculated using machine learning such as neural networks.

[0053] Also, θ ｚ is a continuous function z(m;θ ｚ ) are parameters of the continuous function z(m;θ ｚ ) determines the shape of the

[0054] The estimation unit 12 may further calculate (estimate) the number of viewers of the outdoor advertisement in the target area based on the traffic volume calculated (estimated) in step S5.

[0055] The estimation unit 12 first calculates IMP, which is the amount of traffic coming and going within the advertisement viewable area during the advertisement distribution time, based on the following formula: IMP = population within area + (traffic volume - population within area) / 3600 * distribution time (s)

[0056] A supplement to the above formula: The population within an area is the population that exists within the ad-viewable area on average during a time period (the population that exists within the ad-viewable area at a steady rate). When defining the IMP of the area's population, one way of thinking is that if an advertisement is delivered for even one second, there will be more people than the population within the ad-viewable area. Traffic volume is the amount of people traveling within the ad-viewable area in one hour. When defining the IMP of traffic volume, one way of thinking is that if an advertisement is delivered for one hour, the IMP will be equal to the traffic volume. In the above formula, "(traffic volume - population within area) / 3600" indicates the population that newly flows into the area per second.

[0057] The estimation unit 12 then calculates V-imp, which is the number of people who will view the advertisement within the advertisement distribution time, based on the following formula: V-imp=IMP*α where α is a parameter that indicates the number of people who will view the advertisement relative to the number of people passing through the advertisement viewable area. One method for determining this parameter is to conduct a questionnaire survey of passersby and calculate and determine the ratio of ad viewers to passersby (for example, α=0.44).

[0058] As described above, the estimation unit 12 estimates IMP or V-imp based on two pieces of data: the population in the area and the traffic volume.

[0059] Another method for calculating traffic volume will be described below. For example, traffic volume can be calculated using the following formula:

[0060] The above N ａ，ｔ Among the formulas, is the proportion of the population slower than a certain speed k within the advertising viewable area at outdoor advertising location a and estimated time t. is the average speed (m / s) of people slower than a certain speed k within the advertisement viewable area at the outdoor advertising location a and estimated time t. ｓｌｏｗ is a slow parameter. is the proportion of the population moving faster than a certain speed k within the advertising viewable area at outdoor advertising location a and estimated time t. is the average speed (m / s) of people faster than a certain speed k within the advertisement viewable area at the outdoor advertising location a and estimated time t. ｆａｓｔ is a high-speed parameter. ｓｌｏｗ and θ ｆａｓｔ For example, machine learning may be performed to set a coefficient that minimizes the mean square error with the correct answer data. The correct answer data may be a value obtained by counting people who actually passed through the advertisement viewable area through a traffic survey. ｓｌｏｗ and θ ｆａｓｔ is a parameter that does not change depending on the location of the outdoor advertisement and the estimated time.

[0061] The above N ａ，ｔ Among the formulas, indicates the population of slow-moving people in the advertisement viewing area. ａ，ｔ Among the formulas, corresponds to the calculation of the number of slow people changing places. ａ，ｔ Among the formulas, indicates the population of people in the area where the advertisement can be viewed. ａ，ｔ Among the formulas, corresponds to the calculation of the number of high-speed people changes.

[0062] Another method for calculating traffic volume is the above N ａ，ｔ The problem of calculation using the formula above will be explained. ａ，ｔ In the formula, for example, in order to separate walking from non-walking, traffic volume is calculated by classifying traffic into two categories: faster or slower than a certain speed k. In other words, the classes are divided discretely. However, as mentioned above, in reality, there are various traffic conditions, and it is necessary to classify into multiple categories. It is difficult to set the number of these categories individually for each country or region.

[0063] On the other hand, in estimating the traffic volume by the traffic volume estimation device 1, as described above, calculations are performed using a continuous distribution, rather than calculations using a speed threshold value. In other words, traffic volume is treated continuously.

[0064] Next, the effects of the traffic volume estimation device 1 according to the embodiment will be described.

[0065] The traffic volume estimation device 1 includes an estimation unit 12 that estimates traffic volume related to the number of people passing through a target area based on multiple means of transportation that are expected to be used when passing through. With this configuration, traffic volume in the target area is estimated based on multiple means of transportation that are expected to be used when passing through. In other words, traffic volume in the target area can be estimated based on multiple means of transportation.

[0066] The estimation unit 12 of the traffic volume estimation device 1 may estimate the traffic volume based on the speeds of each of the multiple means of transportation. With this configuration, the traffic volume in the target area can be more reliably estimated based on the speeds of each of the multiple means of transportation.

[0067] The estimation unit 12 of the traffic volume estimation device 1 may estimate based on a continuous distribution based on the speeds of each of a plurality of transportation modes. With this configuration, the traffic volume in the target area can be more reliably estimated based on the continuous distribution based on the speeds of each of a plurality of transportation modes.

[0068] The estimation unit 12 of the traffic volume estimation device 1 may estimate the traffic volume in the target area based on the distribution based on the speeds of each of the multiple means of transportation and the distribution of the speeds of people observed in the target area. With this configuration, the traffic volume in the target area can be more reliably estimated based on the distribution based on the speeds of each of the multiple means of transportation and the distribution of the speeds of people observed in the target area.

[0069] The estimation unit 12 of the traffic volume estimation device 1 may estimate the traffic volume based on a convolution operation between the distribution based on the speeds of each of a plurality of transportation means and the distribution of the speeds of people observed in the target area. With this configuration, the traffic volume in the target area can be more reliably estimated based on a convolution operation between the distribution based on the speeds of each of a plurality of transportation means and the distribution of the speeds of people observed in the target area.

[0070] The plurality of transportation modes may be either walking or vehicles. With this configuration, it is possible to more reliably estimate the traffic volume in the target area based on the plurality of transportation modes, either walking or vehicles.

[0071] The plurality of transportation modes may be any of walking, bicycle, motorcycle, bus, automobile, and train. With this configuration, it is possible to more reliably estimate the traffic volume in the target area based on the plurality of transportation modes, which are walking, bicycle, motorcycle, bus, automobile, and train.

[0072] The target area may be an area where outdoor advertisements can be viewed (advertisement viewable area). With this configuration, it is possible to estimate the traffic volume in the advertisement viewable area.

[0073] The estimation unit 12 of the traffic volume estimation device 1 may estimate the number of viewers of an outdoor advertisement in a target area based on traffic volume. With this configuration, it is possible to estimate the number of viewers of an outdoor advertisement in a target area. The traffic volume estimation device 1 estimates the traffic volume of the advertisement viewable area based on the means of transportation used by people when they pass through the advertisement viewable area.

[0074] The traffic volume estimation device 1 estimates the number of viewers of an advertisement corresponding to various modes of transportation.

[0075] The traffic volume estimation device 1 of the present disclosure may have the following configuration.

[0076] [1] A traffic volume estimation device including an estimation unit that estimates traffic volume related to the number of people passing through a target area based on multiple means of transportation that are expected to be used when passing through.

[0077] [2] The traffic volume estimation device according to [1], wherein the estimation unit estimates based on the speed of each of the plurality of means of transportation.

[0078] [3] The traffic volume estimation device according to [1] or [2], wherein the estimation unit performs estimation based on a continuous distribution based on the speeds of the respective plurality of transportation means.

[0079] [4] The traffic volume estimation device according to any one of [1] to [3], wherein the estimation unit estimates based on a distribution based on the speeds of the plurality of transportation means and a distribution of the speeds of people observed in the target area.

[0080] [5] The traffic volume estimation device according to any one of [1] to [4], wherein the estimation unit estimates based on a convolution operation of a distribution based on the speeds of the plurality of transportation means and a distribution of the speeds of people observed in the target area.

[0081] [6] The traffic volume estimation device according to any one of [1] to [5], wherein the plurality of means of transportation are either walking or vehicles.

[0082] [7] The traffic volume estimation device according to any one of [1] to [6], wherein the plurality of means of transportation are any of walking, bicycle, motorcycle, bus, automobile, and train.

[0083] [8] The traffic volume estimation device according to any one of [1] to [7], wherein the target area is an area where outdoor advertisements can be viewed.

[0084] [9] The traffic volume estimation device according to any one of [1] to [8], wherein the estimation unit estimates the number of viewers of the outdoor advertisement in the target area based on the traffic volume.

[0085] The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of hardware and / or software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are directly or indirectly connected (e.g., wired, wireless, etc.) and these multiple devices. The functional block may also be realized by combining software with the single device or multiple devices.

[0086] Functions include, but are not limited to, judgment, determination, assessment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0087] For example, the traffic volume estimation device 1 according to an embodiment of the present disclosure may function as a computer that performs processing of the traffic volume estimation method of the present disclosure. Fig. 14 is a diagram illustrating an example of the hardware configuration of the traffic volume estimation device 1 according to an embodiment of the present disclosure. The traffic volume estimation device 1 described above may be physically configured as a computer including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc.

[0088] In the following description, the term "apparatus" may be interpreted as a circuit, a device, a unit, etc. The hardware configuration of the traffic volume estimation apparatus 1 may be configured to include one or more of the devices shown in the drawings, or may be configured to exclude some of the devices.

[0089] Each function of the traffic volume estimation device 1 is realized by loading specified software (programs) onto hardware such as a processor 1001 and a memory 1002, causing the processor 1001 to perform calculations, control communication via a communication device 1004, and control at least one of reading and writing data in the memory 1002 and the storage 1003.

[0090] The processor 1001 controls the entire computer by running, for example, an operating system. The processor 1001 may be configured by a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, the acquisition unit 11 and the estimation unit 12 described above may be realized by the processor 1001.

[0091] The processor 1001 also reads programs (program codes), 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 in accordance with these programs. The programs used are those that cause a computer to execute at least some of the operations described in the above-described embodiments. For example, the acquisition unit 11 and the estimation unit 12 may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and similar implementations may be made for other functional blocks. While the above-described various processes have been described as being executed by one processor 1001, they may also be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The programs may also be transmitted from a network via a telecommunications line.

[0092] The memory 1002 is a computer-readable recording medium and may be configured by, for example, at least one of a read-only memory (ROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a random access memory (RAM), etc. The memory 1002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 1002 can store executable programs (program codes), software modules, etc. for implementing a wireless communication method according to an embodiment of the present disclosure.

[0093] Storage 1003 is a computer-readable recording medium, and may be composed of at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other appropriate medium including at least one of memory 1002 and storage 1003.

[0094] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as, for example, a network device, a network controller, a network card, a communication module, etc. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the above-mentioned acquisition unit 11 and estimation unit 12, etc. may be realized by the communication device 1004.

[0095] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that receives input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that outputs to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0096] Furthermore, each device, such as the processor 1001 and the memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0097] The traffic volume estimation device 1 may also be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0098] Notification of information is not limited to the aspects / embodiments described in this disclosure, and may be performed using other methods.

[0099] Each aspect / embodiment described in the present disclosure may be applied to at least one of systems using LTE (Long Term Evolution), LTE-Advanced (LTE-A), 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-Wide Band), Bluetooth (registered trademark), or other suitable systems, and next-generation systems enhanced based on these. Furthermore, a combination of multiple systems (e.g., a combination of at least one of LTE and LTE-A with 5G, etc.) may also be applied.

[0100] The order of the procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0101] Input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be transmitted to another device.

[0102] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0103] The aspects / embodiments described in this disclosure may be used alone, in combination, or switched depending on the implementation. Notification of predetermined information (e.g., notification that "X is true") is not limited to explicit notification, but may be implicit (e.g., not notifying the predetermined information).

[0104] Although the present disclosure has been described in detail above, it is 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 spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0105] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0106] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then these wired and / or wireless technologies are included within the definition of transmission media.

[0107] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0108] In addition, terms explained in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings.

[0109] As used in this disclosure, the terms "system" and "network" are used interchangeably.

[0110] Furthermore, the information, parameters, etc. described in this disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information.

[0111] The names used for the above parameters are not limiting in any way, and furthermore, the mathematical formulas etc. using these parameters may differ from those explicitly disclosed in this disclosure.

[0112] As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like, all of which are considered to be "determining." "Determining" and "determining" may also include resolving, selecting, choosing, establishing, comparing, and the like, all of which are considered to be "determining." In other words, "judgment" and "decision" can include regarding some action as having been "judged" or "decided." Also, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

[0113] The terms "connected," "coupled," or any variation thereof, refer to 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" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using one or more wires, cables, and / or printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

[0114] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0115] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0116] The "means" in the configuration of each of the above devices may be replaced with "part," "circuit," "device," etc.

[0117] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, when the term "or" is used in this disclosure, it is not intended to be an exclusive or.

[0118] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0119] In the present 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 "coupled" may also be interpreted in the same way as "different."

[0120] 1...traffic volume estimation device, 2...terminal, 3...traffic volume estimation system, 10...storage unit, 11...acquisition unit, 12...estimation unit, 1001...processor, 1002...memory, 1003...storage, 1004...communication device, 1005...input device, 1006...output device, 1007...bus.

Claims

1. A traffic volume estimation device comprising an estimation unit that estimates a traffic volume regarding the number of people passing through a target area based on a plurality of means of movement that are assumed to be used when passing through.

2. The traffic volume estimation device according to claim 1, wherein the estimation unit estimates based on the speed of each of the plurality of means of movement.

3. The traffic volume estimation device according to claim 1, wherein the estimation unit estimates based on a continuous distribution based on the speed of each of the plurality of means of movement.

4. The traffic volume estimation device according to claim 1, wherein the estimation unit estimates based on a distribution based on the speed of each of the plurality of means of movement and a distribution of the speed of people observed in the target area.

5. The traffic volume estimation device according to claim 1, wherein the estimation unit estimates based on a convolution operation between a distribution based on the speed of each of the plurality of means of movement and a distribution of the speed of people observed in the target area.

6. The traffic volume estimation device according to claim 1, wherein the plurality of means of movement are either walking or a vehicle.

7. The traffic volume estimation device according to claim 1, wherein the plurality of means of movement are any one of walking, a bicycle, a motorcycle, a bus, a car, or a train.

8. The traffic volume estimation device according to claim 1, wherein the target area is an area where outdoor advertisements can be viewed.

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

10. A traffic volume estimation method in which a computer includes an estimation step of estimating a traffic volume regarding the number of people passing through a target area based on a plurality of means of movement that are assumed to be used when passing through.

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