People flow route estimation apparatus and people flow route information estimation system

Abstract

To propose a mechanism which estimates people flow route information in a specific time zone in a predetermined region on the basis of demographic information of the predetermined region.SOLUTION: An information processing system 1000 includes a people flow route estimation unit 2102 which estimates people flow route information indicating movement of people in each region at a predetermined time, in accordance with distances between regions of one group and distances that the one group can move based on traffic route information, on the basis of predetermined demographic information 2302 including information on the number of people located in a predetermined region at least at the predetermined time, and the traffic route information 2304 related to transportation means included in a region of the one group.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a pedestrian flow path information estimation device and a pedestrian flow path information estimation system that estimate pedestrian flow paths moving using transportation means in a specified area. [Background technology]

[0002] Conventionally, there are services that provide demographic information created using the mechanisms of mobile phone networks (see, for example, Non-Patent Document 1).

[0003] 20 to 22 are diagrams showing an overview of a service that provides such demographic information.

[0004] 20 to 22, "Mobile Spatial Statistics (registered trademark)" provided by NTT DoCoMo, Inc. is described as an example.

[0005] Such "Mobile Spatial Statistics (registered trademark)" is explained in, for example, Non-Patent Document 2.

[0006] "Mobile Spatial Statistics (registered trademark)" is demographic information created using the mechanisms of mobile phone networks. This spatial statistics is demographic information that shows only the number of people in a group, and so has the characteristic that it cannot identify individual mobile phone or smartphone users. In this respect, it differs from the treatment of so-called "personal information," and it is possible to provide third parties with demographic information for a certain area at a certain time without obtaining the consent of the person to whom the information belongs.

[0007] Here, this spatial statistics makes it possible to grasp the population distribution for every hour of Japan, 24 hours a day, 365 days a year. Moreover, this spatial statistics makes it possible to grasp the population structure by gender, age group, and residential area, making it possible to conduct analyses tailored to the purpose. Since the service started in October 2013, it has been widely used by the Japan Tourism Agency, local governments, private companies, etc.

[0008] More specifically, mobile phone networks periodically track the mobile phones located in each base station area so that users can make calls and send emails anytime, anywhere. Using this system, the number of mobile phones can be tallied and the population can be estimated by taking into account the penetration rate of mobile phone carriers. This is Mobile Spatial Statistics (registered trademark).

[0009] Demonstration experiments were carried out in the public sector, such as urban development and disaster prevention planning, and it was confirmed that this spatial statistics could be widely used in society. As mentioned above, the service was launched in October 2013 so that it can be used not only in the public sector but also in academic and industrial fields.

[0010] This spatial statistics makes it possible to analyze data by gender, age group, and residential area (domestic residents), and by country and region (foreign visitors to Japan), and by time period according to purpose.

[0011] As shown in Figure 20(a), the statistical target area is the nationwide DoCoMo service area, and the population distribution throughout the country can be grasped every hour. Therefore, the population distribution can be grasped every hour, 24 hours a day, 365 days a year.

[0012] As shown in Figure 20(b), this demographics also allows us to grasp the population structure by gender and age group, covering people aged 15 to 79.

[0013] Furthermore, as shown in Figure 21, this spatial statistics makes it possible to grasp the population by residential area in a specific time period (every hour) in a specific area, which, as mentioned above, makes it possible to grasp the population distribution by residential area based on registered mobile phone information.

[0014] Furthermore, assuming that spatial statistical information is available, technology has been proposed for understanding and predicting "people flow" using demographic information as input data (see, for example, non-patent document 3).

[0015] According to Non-Patent Document 3, "demographic data" can be classified into "trajectory data" and "aggregated data." "Trajectory data" is data related to moving objects such as people, and is often acquired by portable terminals such as smartphones and car navigation systems.

[0016] On the other hand, "aggregated data" is data associated with a location, and is collected by traffic volume sensors installed on roads, wireless base stations, and so on. Because data is acquired by equipment installed in specific locations, once the equipment is installed, data can be collected fairly comprehensively. In addition, because the data is not tied to individual subjects, there are fewer privacy concerns, and it is highly likely that data measured once can be used for a variety of purposes. On the other hand, because information across locations cannot generally be obtained, there are limitations to its use when understanding the flow of people in a city.

[0017] One type of aggregate data is "demographic information," which is data that records the population by location and time in a city. Most demographic information records population information by area and time. When targeting information over a wide area, mobile phone information is often used in consideration of comprehensiveness, and the number of terminals in each area is counted based on terminal connection information at mobile phone base stations and information such as CDR (Call Data Record), and the actual population is estimated while taking into account the utilization rate by attribute, and this is used as demographic information.

[0018] The "spatial statistics" described in the above-mentioned Non-Patent Documents 1 and 2 correspond to "demographic information" in this sense.

[0019] FIG. 22 is a diagram showing a data aggregation procedure in "Mobile Spatial Statistics (registered trademark)" disclosed in Non-Patent Document 2.

[0020] Referring to FIG. 22, in this "spatial statistics", the following processing is carried out to ensure safety by eliminating information that can identify individuals.

[0021] As mentioned above, in order to strictly protect customer privacy, this spatial statistic is demographic information that represents only the number of people in a group, and therefore cannot identify individual users.

[0022] The spatial statistics are created through the following steps: i) De-identification: The process of removing identifying information, such as name, telephone number, date of birth, etc. from operational data. ii) Aggregation processing: Processing to derive statistical "group-related information" by making statistical inferences from de-identified information. iii) Confidentiality processing: Processing to prevent figures from small population areas from being included in the tabulation results

[0023] In addition, Non-Patent Document 3 discloses a technology for performing people flow analysis in order to more effectively utilize demographic information, in which input is demographic information corresponding to multiple time periods, and based on that, it executes a task of outputting the amount of movement between cells between times.

[0024] On the other hand, when using two-dimensional information, there is a technology for a system that distributes demographic data of large meshes to small meshes based on estimated information of building area (see, for example, Patent Document 1).

[0025] Alternatively, Patent Document 2 discloses an information processing device capable of automating and improving the accuracy of store sales forecasts using regional mesh statistics, station statistics, and geographic information. This discloses a configuration in which a regional mesh statistics and geographic information acquisition unit acquires regional mesh statistics, station statistics, and geographic information by inputting the longitude and latitude of a store, and a plurality of interpolation points are set within the store location point and the surrounding regional mesh or station mesh, statistical values ​​are assigned to the interpolation points, and the statistical values ​​assigned to the interpolation points within the aggregation area are automatically aggregated while taking into account competing stores. Patent Document 3 discloses a system that predicts information such as the number of people at a specific time in the future at a prediction target point with high accuracy.

[0026] FIG. 23 shows an overview of the "Basic Survey on Social Life" (Non-Patent Document 4) issued by the Statistics Bureau of the Ministry of Internal Affairs and Communications.

[0027] As shown in Figure 23, the Basic Survey on Social Life surveys collects lifestyle information (behavioral classification) in 15-minute increments by region, age, and sex. [Prior art documents] [Patent documents]

[0028] [Patent Document 1] Patent No. 6226493 [Patent Document 2] Patent No. 7037223 [Patent Document 3] JP 2022-38482 A [Non-patent literature]

[0029] [Non-Patent Document 1] https: / / mobaku.jp / [Non-Patent Document 2] https: / / www.bcm.co.jp / bcm / wp-content / uploads / 2018 / 03 / ntt-docomo-2017-10-01.pdf [Non-Patent Document 3] Hiroyuki Toda, author of "Understanding and Predicting People Flow in Cities" https: / / www.jstage.jst.go.jp / article / oubutsu / 90 / 8 / 90_481 / _pdf / -char / ja [Non-Patent Document 4] https: / / www.stat.go.jp / data / shakai / 2021 / gaiyou.html Summary of the Invention [Problem to be solved by the invention]

[0030] However, the techniques described in Non-Patent Document 1 and Non-Patent Document 2 merely provide statistics such as the age (generation), sex, etc. of people present in a certain area on an hourly basis.

[0031] In addition, the technology described in Non-Patent Document 3 estimates "people flow" (the number of people moving) from "demographic data," but does not go so far as to estimate the movement of people in a given area at a given time based on demographic information.

[0032] The information in Non-Patent Document 4 merely indicates what activities people in different occupations are engaged in at certain times.

[0033] Furthermore, the technology described in Patent Document 1 merely discloses a technology for estimating the number of people in a building based on demographic data, and the technology described in Patent Document 2 merely discloses a technology for interpolating statistical information of a regional mesh. Patent Document 3 does not disclose the use of aggregated data.

[0034] For this reason, firstly, even if demographic data is accumulated, it is difficult to estimate the movement of people in a given area based on demographic information at intervals shorter than the time interval at which this demographic data is accumulated. Furthermore, when estimating such movement of people, there is a problem in that it is difficult to analyze and use information that classifies the behavior of people in a given area (hereinafter referred to as "lifestyle information").

[0035] The present invention has been made to solve the problems described above, and its object is to provide a pedestrian flow path information estimation device and a pedestrian flow path information estimation system that are capable of estimating the movements of people in a specified area based on demographic information of the specified area.

[0036] More specifically, the present invention provides a pedestrian flow path information estimation device and a pedestrian flow path information estimation system that are capable of estimating the movement of people by transportation means in a specified area based on demographic information and transportation route information of the specified area. [Means for solving the problem]

[0037] In order to achieve the above object, according to one aspect of the present invention, a pedestrian flow path information estimation device includes a storage device that stores, in a time series, predetermined demographic information including information on the number of people present in a group of areas and traffic route information related to the means of transportation included in the group of areas, and a calculation device that executes a process of estimating pedestrian flow path information based on the demographic information and the traffic route information, wherein the calculation device performs the functions of a pedestrian flow path estimation means that estimates pedestrian flow path information showing the movements of people in each area at a predetermined time in accordance with the distance between each of the group of areas and a movable distance based on the traffic route information, and an image generation means that generates the estimation result of the pedestrian flow path as image information.

[0038] Preferably, the demographic information is demographic information as aggregate data about mobile phone users collected by a mobile phone network, and the transportation route information includes information on the type of transportation and the route of the transportation.

[0039] Preferably, the storage device stores demographic information, traffic route information, lifestyle information, and area location information as data acquired from an external server via a network, the demographic information being aggregated data of demographic data for each first time interval, and being information on the number of people by age and sex present in each predetermined area mesh at the first time interval, the lifestyle information being information for each predetermined time interval, the predetermined time interval being a second time interval shorter than the first time interval, and the lifestyle information being capable of associating a person's age, sex, and a predetermined classification of a person's lifestyle and behavior with a person's occupation. The area location information is location information for each specified section, and includes information on the total floor area of ​​facilities within the specified area and the occupations within the facilities. The area life information estimation means further comprises an area life information estimation means for estimating the life information for each specified section, and the area life information estimation means i) estimates the number of employed people by occupation type located in the specified area for each second time interval based on the demographic information and the life information, and ii) apportions the number of employed people by occupation type to each facility based on the total floor area and the area allocated to the occupation type of each facility, and estimates aggregate data of the number of people in an estimated area mesh smaller than the specified area mesh of the demographic information.

[0040] Preferably, the pedestrian flow path estimation means estimates the pedestrian flow path information at least for each specified area at a specified time based on aggregated data on the number of people in the estimation area mesh and a movable distance based on traffic route information.

[0041] Preferably, the lifestyle information includes predetermined work information by occupation, and the people flow trajectory estimation means outputs the people flow trajectory information based on the work information by occupation.

[0042] Preferably, the image generating means includes map output means for outputting people flow path information indicating the estimated movement of people in each area at a predetermined time on a map in association with each area.

[0043] Preferably, the area location information includes information identifying an area of ​​the specified location, and the pedestrian flow path estimation means includes area setting means for fitting an estimated area mesh to the area based on boundary information of the specified location, and estimates pedestrian flow path information for the area.

[0044] Preferably, the estimated area mesh has a size in which each side of the predetermined area mesh is divided by a power of two, and corresponds to the distance that a human can travel on foot within a predetermined time.

[0045] Preferably, the pedestrian flow path estimation means estimates pedestrian flow path information over a series of time series included in a predetermined period of time, based on predetermined demographic information.

[0046] Preferably, the people flow path estimation means includes a configuration information output means for estimating and outputting information constituting a town including the area based on a series of predetermined demographic information over a time series for the area to be estimated.

[0047] Preferably, the area life information output means outputs the area life information based on predetermined area place information set in the past or future.

[0048] According to another aspect of the present invention, there is provided a people flow path information estimation system, comprising: a first data providing server providing demographic information including information on the number of people present in a group of areas in a time series at a first predetermined time interval; a second data providing server providing life information at a second predetermined time interval including information on people's lives and actions, the second time interval being shorter than the first time interval; a third data providing server providing area place information including information on places of predetermined sections present in the predetermined area; a fourth data providing server providing traffic route information including information on types of transportation and routes of the transportation; and a people flow path information estimation device, wherein the people flow path information estimation device comprises a storage device receiving and storing the demographic information, life information, area place information and traffic route information from the first, second, third and fourth data providing servers; and a calculation device that executes a process of estimating people flow path information based on location information and traffic route information, wherein the calculation device executes the function of area life information estimation means that estimates life information for each predetermined section, and the area life information estimation means further executes the functions of people flow path estimation means that i) estimates the number of employed people by occupation type located in the predetermined area for each second time interval based on the demographic information and the life information, and ii) apportions the number of employed people by occupation type to each facility based on the total floor area and the area allocated to the occupation type of each facility, estimates aggregate data of the number of people in an estimated area mesh that is smaller than the predetermined area mesh of the demographic information, and estimates people flow path information showing the movement of people in each area at a predetermined time based on the aggregate data of the number of people in the estimated area mesh, the distance between each group of areas, and the movable distance based on the traffic route information, and an image generation means that generates the estimation result of the people flow path as image information. Effect of the Invention

[0049] According to the present invention, the movements of people in a given area at a given time can be estimated based on demographic information, so that information on estimated people flow paths can be utilized in various ways by business operators, researchers, government officials, and others.

[0050] Furthermore, according to the present invention, it is possible to estimate the movement of people in a given area based on demographic information (for example, by age group 15-20 / 20-25, male / female, for example, the crowding situation based on the crowding situation in a mesh including Hankyu Umeda Station, a mesh including JR Osaka Station, and a mesh including an anime event venue). More specifically, it is possible to estimate the "attraction force" that causes the flow of people at a given time in each area not only during normal times but also during non-normal times such as disasters. [Brief description of the drawings]

[0051] [Figure 1] FIG. 1 is a diagram showing an overview of a lifestyle information estimating system according to a first embodiment of the present invention. [Diagram 2] FIG. 2 is a functional block diagram showing an outline of the system of a life information service providing server 2000. [Diagram 3] 2 is a diagram for explaining the hardware configuration of a life information service providing server 2000. FIG. [Figure 4] FIG. 1 is a diagram for explaining the contents of data provided from data providing servers 5000.1 to 5000.M. [Diagram 5] FIG. 11 is a first diagram illustrating a processing flow of the life information service providing server 2000. [Figure 6] FIG. 2 is a second diagram illustrating the processing flow of the life information service providing server 2000. [Figure 7] FIG. 2 is a diagram showing an outline of data output from a calculation device 2100. [Figure 8] FIG. 13 is a conceptual diagram showing data generated when facility occupancy estimation is performed from behavior-specific number of people estimation at a first time point. [Figure 9] FIG. 13 is a conceptual diagram showing data generated when facility occupancy estimation is performed from behavior-specific number of people estimation at a second time point. [Figure 10] FIG. 13 is a diagram showing the concept of the process of improving the accuracy of spatial resolution in the behavior-specific number of people estimation process (S120, S210). [Figure 11]13 is a diagram showing another example of the map image and the superimposed image of the number of people distribution output by the map output unit 2104. FIG. [Figure 12] 13 is a diagram for explaining a process flow of estimating people flow paths by the life information service providing server 2000. FIG. [Figure 13] FIG. 13 is a diagram illustrating the concept of the process of estimating an increase or decrease in the number of people in a predetermined movement area in association with traffic movement in step S160 of FIG. [Figure 14] FIG. 11 is a diagram showing an example of estimated pedestrian flow path information. [Figure 15] FIG. 1 is a conceptual diagram showing a hierarchy of data and a hierarchy of data processing used in the lifestyle information estimation system. [Figure 16] FIG. 2 is a conceptual diagram showing an overview of the flow of data processing. [Figure 17] FIG. 1 is a diagram showing an overview of services realized by using a lifestyle information estimating system 1000. [Figure 18] FIG. 18 is a conceptual diagram showing an example of the service in FIG. 17 provided via a smartphone. [Figure 19] FIG. 13 is a diagram showing another example of data visualization by integrated analysis. [Figure 20] FIG. 1 is a diagram showing an overview (1) of mobile spatial statistics of the prior art. [Figure 21] FIG. 1 is a diagram showing an overview (2) of mobile spatial statistics of the prior art. [Figure 22] FIG. 1 is a diagram showing an overview (3) of mobile spatial statistics according to the prior art. [Figure 23] FIG. 1 is a diagram showing the contents of the Basic Survey on Social Life as a background art. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0052] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0053] In the following, we will explain the configuration of an information processing system for estimating people flow path information (hereinafter referred to as the "people flow path information estimation system") and a people flow path information estimation device for estimating lifestyle information in the people flow path information estimation system.

[0054] As described below, the people flow path information estimation device in this embodiment is a device that estimates people's movements in a specified area during a specified period of time based on demographic information of the area, more specifically, people's movements by transportation means.

[0055] FIG. 1 is a diagram showing an overview of a people flow path information estimation system according to a first embodiment of the present invention.

[0056] Referring to FIG. 1, a people flow path information estimation system 1000 according to a first embodiment of the present invention includes a life information service providing server 2000 that operates as a people flow path information estimation device, a life information user terminal 3000 of a user that receives services from the life information service providing server 2000, data providing servers 5000.1 to 5000.M (M: natural number) that provide the life information service providing server 2000 with data necessary for estimating life information as described below, and a mobile phone or smartphone 200 that is used by a user 100 and receives the provision of services as described below.

[0057] FIG. 2 is a functional block diagram showing an outline of the system of the life information service providing server 2000. As shown in FIG.

[0058] 4 is a diagram for explaining the contents of data provided from the data providing servers 5000.1 to 5000.M. Note that hereinafter, when the data providing servers 5000.1 to 5000.M are collectively referred to, they will simply be referred to as "data providing server 5000."

[0059] With reference to FIG. 2, the life information service providing server 2000 includes a computing device 2100 and a storage device 2300 .

[0060] The storage device 2300 first stores predetermined "demographic information 2302." Here, the "demographic information 2302" includes at least information on the number of people present in a predetermined area at a predetermined time.

[0061] Here, the "predetermined time" refers to, for example, a first predetermined time interval, for example, every hour, and the "predetermined area" refers to, for example, an area in a specific region, for example, a first area mesh unit, for example, a 500m mesh (a square of 500m on each side). The "predetermined demographic information" is "demographic data" and means "aggregated data", and corresponds to, for example, the above-mentioned "Mobile Spatial Statistics Information (registered trademark)" as shown in FIG. 4.

[0062] Furthermore, the storage device 2300 stores a predetermined "lifestyle information 2306". The "lifestyle information 2306" includes at least information related to a person's life. Here, the predetermined "lifestyle information 2306" is, more specifically, information on a classification of a predetermined behavior by region, age, and sex, in a second time interval that is shorter than a first predetermined time interval and is preferably a divisor of the first time interval. Here, the "lifestyle information" corresponds to information in a database based on the "Basic Survey on Social Life" by the Ministry of Internal Affairs and Communications, which classifies behaviors into 20 predetermined types by region, age, and sex, and in 15-minute increments, as shown in FIG. 4. Here, the "classification of behavior" corresponds to classifications such as "sleep", "commuting", "school", "work", and "academic work".

[0063] The storage device 2300 also stores "occupation-specific employment information 2308." Here, the "occupation-specific employment information 2308" is data showing the employment ratio of each occupation by age and sex, and corresponds to information in the Ministry of Internal Affairs and Communications' Labor Force Survey database, for example, as shown in FIG.

[0064] In addition, the "predetermined living information" may include such "occupation-specific employment information."

[0065] Therefore, the life information estimation unit 2110 can estimate the number of people by behavior (hereinafter, referred to as "number of people by behavior") who are present in the "predetermined area" set by the area setting unit 2106 in response to an instruction from a user, during the "second time interval", based on the "demographic information 2302" and the "predetermined life information 2306". Furthermore, by applying the "employment information by occupation" to the "number of people by behavior" estimated in this way for people with the behavior classification of working or studying, the life information estimation unit 2110 can calculate the classification data of the "occupation" of people who are present in the "predetermined area" during the "second time interval". Note that the "occupation" may not only be the so-called "type of occupation", but may also be any "task" such as "study" that is not a business, but in which a person is obviously engaged in the same behavior for a certain period of time or more in that place (facility / building).

[0066] The storage device 2300 further stores a predetermined "area location information 2310". Here, the "predetermined area location information" refers to "information on a predetermined place existing in the above-mentioned predetermined area (including information on the type of job at that place)". For example, the "area location information" preferably includes information on buildings and facilities in the area of ​​the second area mesh unit, which is smaller than the above-mentioned first area mesh unit and preferably has a divisor of each side when the first area mesh unit is a rectangle, and information on the types of jobs performed in the buildings and facilities. For example, as shown in FIG. 4, the "area location information 2310" corresponds to map data including information on buildings and facilities in a town, information on the types of jobs performed in the buildings and facilities, and information on roads and water areas in 62.5m mesh units. Some of this "map data" is provided by private companies.

[0067] Based on such area location information 2310, the area information estimation unit 2108 estimates the area corresponding to each job type in the total floor area of ​​facilities and buildings located in the "specified location" of the "specified area" set by the area setting unit 2106.

[0068] Therefore, the lifestyle information estimation unit 2110 of the calculation device 2100 of the lifestyle information service providing server 2000 calculates and outputs information on the work of people located at a specified location in a specified area at the second time interval (hereinafter referred to as "people's area lifestyle information") by expanding (for example, proportionally allocating) the "number of people per occupation" into the "area corresponding to each occupation" of the "specified location."

[0069] For example, when calculating such "human area life information," the classification of "tasks" that people are engaged in at a certain location (facility / building) within the target area is calculated according to the method described above.

[0070] As a relationship between a certain life information and a certain area location information, for example, during a certain time period, it is possible to assume that doctors and nurses work in hospital facilities, farmers work on farmland, fishermen work in fishing grounds, housewives raise children, and pensioners live in houses. Also, during commuting hours, a certain percentage of people will have the attribute "movement." The specific processing will be described later.

[0071] Furthermore, the map output unit 2104 generates and outputs a screen that displays the thus estimated "personal area life information" in association with each "predetermined place" in the "predetermined area" (for example, superimposed on the map information). By generating such an output, the user can visually grasp the life information of people in the "predetermined area" that he or she has specified.

[0072] As described above, since the "number of people by behavior" or the "number of people corresponding to people's area life information" is calculated for each area in the second area mesh unit and at the second time interval, the people flow path information estimation unit 2102 can be configured to estimate and output people flow path information (the route along which people moved) by tracking changes in the number of people distribution for the area in the second area mesh unit and its surrounding areas along the time axis and integrating this with "information on routes along which people can move" (for example, roads, railways, etc.) on the map data. The output result of the people flow path information output unit 2102 is stored in the people flow path information 2312 of the storage device 2300.

[0073] The storage device 2300 further stores "traffic route information 2304." Here, the "traffic route information 2304" includes the following data located in a predetermined area: Route information on road maps, information on transportation route nodes such as the location of highway interchanges (ICs) - Average travel speed on a road at a given time on a given day based on historical road congestion information Location of public transport stations and stops (railroads, buses, etc.) Public transport (train, bus, etc.) routes on maps Public transport timetables (average travel speed can also be calculated based on timetable and route information)

[0074] However, as long as the information is related to transportation, the data included in the transportation route information 2304 is not limited to these.

[0075] The traffic route information 2304 stores information as described below. By relating pedestrian flow information to traffic route information, the pedestrian flow path information estimation unit 2102 can estimate information on the amount of people moving along each traffic route, and stores the estimation results in the storage device 2300 as pedestrian flow path information 2312.

[0076] FIG. 3 is a diagram for explaining the hardware configuration of the life information service providing server 2000. As shown in FIG.

[0077] 3, the life information service providing server 2000 may be configured such that a calculation device (CPU: Central Processing Unit) in its own housing executes calculation processing, or a part of the program processing may be further executed on another server. In the following, the description will be given assuming that the calculation device in its own housing executes the calculation processing.

[0078] Referring to FIG. 3, server 2000 includes a computer device 2010, a network communication unit 2012 for communicating with a network, a recording medium (e.g., a memory card) 2210 for recording data from the outside and providing the data to computer device 2010, a keyboard 2400 as an input device, and a display 2420 as a display device.

[0079] For example, a USB memory, a memory card, or an external storage device can be used as the recording medium 2210. Also, for example, a communication function of a wired LAN or a wireless LAN can be used as the network communication unit 2012. The network communication unit 2012 and the input / output interface 2090 configure a communication interface.

[0080] As shown in FIG. 3, the computer main body constituting the computer device 2010 includes, in addition to the disk drive 2030 and the memory drive 2020, a CPU (Central Processing Unit) 2100 connected to a bus 2050, a memory 2200 including a ROM (Read Only Memory) 2200.1 and a RAM (Random Access Memory) 2200.2, a non-volatile rewritable non-volatile storage device 2300, and an input / output interface 2090 for communicating via a network and sending and receiving data with the outside. For example, a HDD (Hard Disc Drive) or an SSD (Solid State Drive) is used as the non-volatile storage device 2300. In the following description, it is assumed to be an SSD. An optical disk can be attached to the disk drive 2030. A memory card 2210 can be attached to the memory drive 2020.

[0081] When the programs of the computer device 2010 operate, the data and programs that store the information that is the basis for the operation of the computer are stored in the SSD 2300.

[0082] 3, the medium capable of recording information such as a program to be installed in the computer main body may be, for example, a DVD-ROM (Digital Versatile Disc), a memory card, a USB memory, etc. In order to accommodate such cases, the computer main body is provided with a drive device (memory drive 2020, disk drive 2030) capable of reading such media.

[0083] The main components of the computer device 2010 are computer hardware and software executed by the CPU 2100. Generally, such software is stored in a storage medium and distributed or distributed via a network, and is acquired via the disk drive 2030 or the network communication unit 2012 and temporarily stored in the SSD 2300. The software is then read from the SSD 2300 to the RAM 2200.2 in the memory and executed by the CPU 2100. Note that, when connected to a network, the software may be directly loaded into the RAM and executed without being stored in the SSD 2300.

[0084] When a program for functioning as computer device 2010 is distributed, the program does not necessarily need to include an operating system (OS) that causes computer main body 2010 to execute functions of an information processing device, etc. The program only needs to include an instruction portion that calls appropriate functions (modules) in a controlled manner and produces desired results. How computer system 2010 operates is well known, and a detailed description will be omitted.

[0085] Furthermore, the CPU 2100 may be a one-core processor or a multiple-core processor, i.e., a single-core processor or a multi-core processor. The server 2000 may also be configured with multiple servers to execute distributed processing.

[0086] The hardware configurations of the lifestyle information user terminal 3000 and the data providing servers 5000.i (1≦i≦M) are basically the same, and therefore the description thereof will not be repeated. (Estimation process of life information within a specified area)

[0087] In the following, as a premise for the process of estimating people flow path information by the life information service providing server 2000, the process of estimating the number of people by behavior and the number of people in a facility based on the life information of people present in a given area during a given time period will be described.

[0088] Fig. 5 is a first diagram illustrating the processing flow of the number of people estimation by behavior and the number of people in a facility estimation by the life information service providing server 2000. Fig. 6 is a second diagram illustrating the processing flow of the number of people estimation by behavior and the number of people in a facility estimation by the life information service providing server 2000.

[0089] FIG. 7 is a diagram showing an outline of data output from the arithmetic device 2100. As shown in FIG.

[0090] FIG. 8 is a conceptual diagram showing data generated when facility population estimation is performed from behavior-specific population estimation at a first time point.

[0091] FIG. 9 is a conceptual diagram showing data generated when facility population estimation is performed from behavior-specific number of people estimation at a second time point.

[0092] 5 and 6, when the processing of the behavior-specific number of people estimation and facility number of people estimation starts, the calculation device 2100 acquires spatial statistical data via the network (S110, S200).

[0093] As mentioned above, to obtain spatial statistical data, it is possible to use "Mobile Spatial Statistics (registered trademark)" provided by NTT DoCoMo. Spatial statistical data is, for example, data on the number of people by age and sex in a given time unit (for example, 1 hour) within a mesh of a given width (for example, 500m square).

[0094] Next, for a "predetermined area" specified by the area setting unit 2106 based on the user's designation, the lifestyle information estimation unit 2110 classifies people (whose age and gender are obtained from the spatial statistical data) who are staying in a specific area (for example, the Umeda area of ​​Osaka) during a specific time period (for example, a certain hour) into states (behavior classification information) such as work and movement (for example, commuting) in more detailed time units (for example, 15 minutes) within this specific time period, referring to the Basic Survey of Social Life database, and estimates the number of people in each category (attributes of age and gender) (S120, S210).

[0095] The lower part of Figure 7(a) shows an overview of such estimation of the number of people by behavior.

[0096] The lower diagram in Figure 8 shows the estimated number of people by activity in a specific area at 2 a.m. Given the time of day, most people's activities are classified as "sleeping."

[0097] The lower graph in Figure 9 shows the estimated number of people by activity in a specific area at 3:00 p.m. (15:00). Judging from the time of day, most people are classified as "working" or "studying."

[0098] Returning to FIG. 5 and FIG. 6 , further, the lifestyle information estimation unit 2110 estimates the number of employees (age and gender attributes) by occupation type for every 15 minutes for the above-mentioned specific area based on the estimated number of people (age and gender attributes) for each behavior category, for example, based on the Ministry of Internal Affairs and Communications Labor Force Survey database (S130, S220).

[0099] Next, the lifestyle information estimation unit 2110 expands (for example, proportionally apportions) the estimated number of employees (attributes of age and gender) by occupation for every 15 minutes to each building (facility) based on the total floor area of ​​the facilities and buildings on the map estimated by the area location information estimation unit 2108 and the area allocated to the occupation of each facility and building, and estimates aggregated data of the number of people in a mesh (for example, a 62.5 m mesh) that is smaller than the unit mesh of the spatial statistical data (S140, S230).

[0100] The upper part of Fig. 7(a) shows an overview of such facility occupancy estimation.

[0101] The upper diagram in Figure 8 shows the results of estimating the number of people in facilities in a specific area at 2 a.m. Judging from the time of day, most people are located in "residences" or "hotels and inns."

[0102] The upper diagram in Figure 9 shows the results of facility population estimation in a specific area at 3:00 p.m. (15:00). Based on the time of day, it can be seen that most people are assigned to the "occupation" or "task" corresponding to the facility or building in each facility.

[0103] 5 and 6, the map output unit 2104 further superimposes the number of people deployed for each facility / building on the map and outputs it by color-coding, for example, using a heat map method (S140, S240). Here, when displaying the heat map, a method of displaying a chart showing the ratio of the number of people doing the work at that location by color coding may be used.

[0104] FIG. 7(b) is a diagram showing an example of an output that visualizes such a distribution of the number of people.

[0105] FIG. 10 is a diagram showing the concept of the process of improving the accuracy of spatial resolution in the behavior-specific number of people estimation process (S120, S210).

[0106] As shown in Figure 10, when conducting analysis using various information such as spatial statistical data (or people flow data) and map information, some data may have a spatial resolution of 500m, other data may have a spatial resolution of 100m, and still other data may have a spatial resolution of land lot number (address unit).

[0107] Because the units of the data do not match, analysis usually ends up being performed based on data with larger units.

[0108] However, as described above, in the life information service providing server 2000, people flow data (including number distribution data at a specific point in time), building data, facility data, land data (including information on roads, railways, roads and water bodies), etc. are all decomposed and converted into the same unit (area mesh) and stored.

[0109] Hereafter, such a database stored in the same unit will be referred to as a "data lake." Therefore, for example, it is possible to perform real-time analysis in the same unit (area mesh) at any point in Japan.

[0110] As described above, the same area mesh has a side length of 62.5 m, as shown in FIG.

[0111] If the resolution is 62.5 m, since Mobile Spatial Statistics (registered trademark) is a mesh with sides of 500 m, it is possible to create an area mesh with sides of 1 / 8 (1 / 8) of this 500 m, which is approximately the distance a person walks in one minute. When using other spatial statistics, it is possible to unify the area mesh with sides equivalent to the distance a person walks in one minute in the same way. By using the distance a person walks in one minute, it can be said that the resolution is suitable for analyzing people flow, including people who are moving by foot, for example.

[0112] However, it is possible to divide it into units smaller than 62.5 m (for example, the smallest unit of 1 m). However, this would increase the amount of data to be analyzed and the amount of calculation, so from the viewpoint of cost-effectiveness, it is preferable to use, for example, 62.5 m.

[0113] FIG. 11 is a diagram showing another example of the map image and the superimposed image of the number of people distribution output by the map output unit 2104. In FIG.

[0114] As shown in FIG. 11, it is also possible to designate an area of ​​a certain size as the specified region, and furthermore, to allow the user to specify a portion of that area as the "specified region" in which the estimation process will be performed. (Estimation processing of people flow information)

[0115] The following describes the process of estimating people flow path information of people located in a specific area during a specific time period, performed by the life information service providing server 2000.

[0116] FIG. 12 is a diagram for explaining the flow of the process of estimating people flow paths performed by the life information service providing server 2000. In FIG.

[0117] In addition, in FIG. 12, the process from steps S110 to S140 is similar to the process of estimating life information described in FIG. 5, and therefore description thereof will not be repeated.

[0118] In practice, for example, the data calculated in the processing from steps S110 to S140 is temporarily stored as common data within the data lake, and can be configured to be shared in the processing for estimating the number of people by behavior and estimating the number of people in the facility described in Figure 5, and the processing for estimating people flow paths described in Figure 12.

[0119] Referring to FIG. 12, following the processing of steps S110 to S140, the calculation device 2100 refers to the transportation route information 2304 as described in FIG. 4, and estimates an increase or decrease in the number of people associated with movement by transportation for the movement area every 15 minutes, based on the route information of the transportation and the (average) movement speed (S160).

[0120] Then, the arithmetic device 2100 estimates "people flow paths" as vectors on the traffic route on the map (S170).

[0121] FIG. 13 is a diagram for explaining the concept of the process of estimating an increase or decrease in the number of people in a predetermined movement area in association with traffic movement in step S160 of FIG.

[0122] In FIG. 13, an area mesh is illustrated by a dotted line.

[0123] With reference to FIG. 13, for example, on a railway line, station 1 is located in area R1, station 6 is located in area R6, and stations 2 to 5 are located therebetween.

[0124] i) Assume that the number of people present at station 1 has decreased by ΔNP1 between time t1 and time (t1+Δt), where Δt is the time unit (for example, 15 minutes) estimated in processing steps S110 to S140.

[0125] It is assumed that, from time t1 to time (t1+Δt), a train departs from station 1 to station 2 based on the timetable information. Also, the average travel speed of the train is also acquired from the traffic route information.

[0126] ii) At time (t1 + 2 · Δt), if Station 2 is located in region R2 that contains the train's predicted position based on the train's speed, an estimate is made of the increase in the number of people in this region R2 from a time Δt earlier.

[0127] In addition, since the increase in the number of people in region R2 during this time Δt may be due to factors other than the increase in the number of people caused by train movement, a certain correction process is performed.

[0128] For example, the increase in the number of people between 2·Δt and Δt ago is unrelated to the arrival of a train, and a similar increase in the number of people between time (t1+Δt) and time (t1+2·Δt) can be treated as being unrelated to the arrival of a train, and a correction can be made by subtracting this from the increase in the number of people mentioned above.

[0129] iii) Thereafter, by similarly estimating the increase in the number of people in areas R3, R4, R5, and R6 (Station 6) as the train moves, it is possible to estimate the number of people who moved from Station 1 to Station 6 as the train moved.

[0130] Therefore, by estimating the number of people present in a group of areas for a given area mesh as described above based on spatial statistical information and the like, using predetermined demographic information including information on the number of people present in a group of areas in a time series, and also using traffic route information related to the means of transportation included in the group of areas, it is possible to estimate people flow path information showing the movement of people in each area at a given time by utilizing the relationship between the distance between each group of areas and the travelable distance based on the traffic route information. Here, the "relationship with the travelable distance" refers to, for example, information on whether or not it is possible to travel between two specific areas by available means of transportation, and more specifically, it means whether the estimated area exists in the area that can be traveled in the time unit for estimating the number of people based on the average travel speed of the means of transportation.

[0131] In addition, since the "people flow path information" has information on the number and direction of people flowing into or out of the area in a given time unit, it can be expressed as a vector. The results of people flow path estimation can also be displayed as such vectors on a map.

[0132] In that sense, if the magnitude of a person who is in a certain area moving into or out of a certain area over time is understood as a vector with a direction and magnitude, this corresponds to an "absorption vector." In other words, as something that exerts an attractive (repulsive) force on such a person, it is also possible to figuratively speak of that area as a "magnetic field" (or "electric field") on the person.

[0133] As a method for estimating people flow based on a predetermined area mesh "aggregated data", for example, the method disclosed in the following document can be used. Note that the area mesh may be "an area mesh (estimated area mesh) smaller than the area mesh of the aggregated data used for estimation" as described in this specification. Publicly known literature 1: Shun Kojima, Fujio Toriumi, Kunio Arakawa, "Estimating People Flow Using Point Density," Japanese Society for Artificial Intelligence, Second-class research materials, Business Informatics Research Group SIG-B1 0202-19 Publicly known literature 2: Hiroyuki Toda, "Understanding and predicting pedestrian flow in cities" https: / / www.jstage.jst.go.jp / article / oubutsu / 90 / 8 / 90_481 / _pdf / -char / ja

[0134] FIG. 14 is a diagram showing an example of people flow path information estimated in this manner.

[0135] FIG. 14 shows an example of people flow route information for the inner loop of the JR Loop Line in Osaka City.

[0136] As shown in Figure 14, by analyzing data on the increase or decrease in people (people flow data) within the area mesh estimated between steps S110 and S140 in Figure 12 based on route information and city data, it is possible to estimate people flow path information that indicates the flow of people using transportation means.

[0137] The data lake of the life information service providing server 2000 accumulates data on "bus stops," "bus routes," "railroad stations," "railroad routes," "airports," and "airline routes" as transportation route information. By combining and analyzing such public transportation-related data with various data accumulated in the data lake, information on people traveling via routes can be obtained.

[0138] Furthermore, in addition to analyzing the state of the areas along the line and people flow patterns, it will be possible to estimate CO2 emissions based on information on CO2 emissions from transportation methods used, and visualize various information in real time, such as estimated population along the line and which stations have the most users.

[0139] Furthermore, in steps S110 to S140 in FIG. 12, it is also possible to estimate the attributes (age, sex, place of residence, . . . ) of people within each area mesh.

[0140] Therefore, the people flow path information estimation unit 2102 can estimate people flow path information for each specific attribute for each location included in each area at least at a predetermined time based on predetermined life information including at least information related to people's lives (for example, life information by region, age / gender unit, and 15-minute unit (for example, classified into 20 types of activities according to the Ministry of Internal Affairs and Communications' Basic Survey on Social Life)) and predetermined area location information including information on predetermined locations located in a group of areas (for example, 62.5 m mesh unit, town information, building information, road / water area information, etc.).

[0141] For example, the predetermined life information includes predetermined employment information by occupation (for example, age and gender unit, employment ratio by occupation). In this case, the people flow path information estimation unit 2102 can estimate people flow path information by occupation based on the employment information by occupation.

[0142] As a result, more precise estimations can be made about employment, which is an important part of people's lives, based on the employment information by occupation.

[0143] Furthermore, the map output unit 2104 outputs people flow path information indicating the movement of people in each area at a given time estimated in this way, in association with each area, onto a map. Therefore, the estimated people flow path information can be associated with each area on a map and displayed in a manner that allows the user to easily understand.

[0144] In the above description, the inflow and outflow of people is estimated for each estimated area mesh unit. However, the people flow trajectory information estimation unit 2102 can also integrate estimated area meshes based on data registered in advance as area location information 2310, and estimate people flow trajectory information for the integrated area.

[0145] That is, the people flow path information estimation unit 2102 has a function of estimating the boundary line of a predetermined place and fitting and setting the area based on the data registered in advance as the area location information 2310. For example, by fitting the area of ​​the estimated area mesh so as to follow the boundary line of a venue such as a fireworks display, it is possible to perform a more detailed analysis of people flow path information that meets the needs.

[0146] As mentioned above, the estimated area mesh is, for example, a 62.5 m mesh, which is an area large enough that a person can walk through in one minute. Therefore, the people flow path information estimation unit 2102 can output people flow path information of people located in a specific area based on the specified area location information for each specific area, making it possible to make accurate estimations even in areas where people movement is mainly by walking.

[0147] In addition, the people flow path information estimation unit 2102 estimates people flow path information for a series of times based on predetermined demographic information over a series of times included in a predetermined period. Therefore, it is possible to estimate fluctuations in people's movements in a predetermined period by time series analysis and present them to the user. Therefore, for example, it is possible to visualize the movement of people from Kobe to Osaka during the morning commute by public transportation.

[0148] Furthermore, by using the estimation results by the people flow path information estimation unit 2102, it is also possible to estimate town composition information for the area to be estimated based on predetermined demographic information over a series of time periods (for example, information on the number of people by age, sex, residence, within the prefecture, outside the prefecture, etc.). Examples of areas to be estimated include residential areas, commercial areas, farmland, universities, theme parks, event venues, etc. By outputting in this way, it is possible to estimate the composition of people's movement in the town of the area from the time series movement of the demographics of the area.

[0149] The people flow path information estimation unit 2102 can also estimate or predict people flow path information by using predetermined area location information set in the past or future.

[0150] FIG. 15 is a conceptual diagram showing the hierarchy of data and the hierarchy of data processing used in the people flow path information estimation system.

[0151] FIG. 16 is a conceptual diagram showing an outline of the flow of data processing.

[0152] As shown in Figure 15, the "public domain," which is a public and open domain, includes, for example, the "urban OS" as "software that supports urban infrastructure" and the data integration platform.

[0153] In Japan, for example, the following Cabinet Office documents are publicly available: Publicly known documents: https: / / www8.cao.go.jp / cstp / stmain / a-whitepaper3_200331.pdf

[0154] Here, there are three challenges to realizing smart cities in Japan: i) reuse and horizontal deployment of services, ii) utilization of data across fields, and iii) low scalability.

[0155] i) In terms of reusing and expanding services, traditionally systems were specialized for each field or organization, which made it difficult to reuse or expand services to other regions.

[0156] ii) When it comes to utilizing data across sectors, traditional services have separate data for each sector and organization, making it difficult to build new services that span sectors.

[0157] iii) Low scalability: Conventional specialized systems have the problem that expanding functions requires large costs and effort, making it difficult to continuously and easily evolve services.

[0158] In order to address the challenges in realizing these smart cities in Japan, the City OS will be designed to have the following features: i) interoperability (connection), ii) data distribution (flow), and iii) ease of expansion (sustainability).

[0159] The "people flow path information estimation system area" of this embodiment is conceptually configured to exist on such a foundation.

[0160] As shown in FIG. 15, in addition to "DOCOMO Spatial Statistics (registered trademark)" as shown in FIG. 4, the people flow path information estimation system 100 is also expected to utilize weather data for a specific time period in a specific area obtained from the Japan Meteorological Agency server, "disaster risk information" obtained from the National Research Institute for Earth Science and Disaster Prevention, and the like.

[0161] When this type of data is stored in the above-mentioned "data lake," it is first stored as general-purpose raw data, and then finalized for a certain time period and stored as a "unified format for the people flow path information estimation system (for example, unifying the unit area mesh)" as mentioned above.

[0162] As shown in Figures 15 and 16, data in a general-purpose data format provided to the "Data Lake" may be posted from a smartphone via a smartphone application, for example. The posted data may be subjective evaluation data on the congestion situation at a specific facility in a specific location, or may be photographs or videos of the congestion situation.

[0163] Furthermore, as shown in Figures 15 and 16, above the "data lake" there is a data analysis layer, which corresponds to the data integration and analysis processing performed by the above-mentioned life information service providing server 2000.

[0164] The system is structured so that a section that adds some sort of evaluation to the analysis results, or collaborates with other companies on the results, and finally visualizes and presents them, exists at a higher data display level.

[0165] In particular, while spatial statistics are based on an hourly basis, the above-mentioned processing makes it possible to perform data analysis and visualization in 15-minute increments, which is almost real-time.

[0166] As shown in Figure 16, after carrying out the above-mentioned integrated analysis process, facilities in the town are evaluated based on the information, and people are allocated to each facility, so the people flow distribution is created from independent information (people flow → behavior → town information), and therefore the people flow distribution changes when the "town information (general map information)" is updated. Therefore, it is possible to visualize not only current real-time information, but also people flow distribution in towns that differ from the present in the past and future.

[0167] For example, by using city information from the Edo period, we can estimate the distribution of people at that time and visualize the life of the city at that time, and by using city information from virtual worlds such as movies, anime, and games, we can estimate the distribution of people in the virtual world. Of course, it is also possible to visualize the life of a specific city in the future (e.g. Osaka City).

[0168] These analytical results can be used for urban planning, environmental optimization, energy optimization, real estate value assessment, logistics optimization, etc. For example, when assessing real estate value, it is possible to analyze and evaluate what kind of changes in population distribution may occur if a certain facility is hypothetically constructed in a certain location in the future, and this information can be added to the real estate value assessment.

[0169] One possible use case for data integration and analysis would be to distribute admission tickets to product promotional events (whether at a physical or virtual venue) via smartphone based on users' behavioral history, etc.

[0170] FIG. 17 is a diagram showing an overview of services realized by using the people flow path information estimation system 1000.

[0171] FIG. 18 is a conceptual diagram showing an example of the service shown in FIG. 17, which is provided via a smartphone.

[0172] As shown in Figure 17, maps, people flows, various sensor data, weather data, satellite data, real estate data, and data posted via smartphones stored in the data lake can be analyzed using an integrated analysis platform to analyze the condition of each region (artificial intelligence prediction), calculate a score for each region for specified evaluation items, and map and visualize each region.

[0173] In this case, the output of the integrated analysis platform will be provided with interfaces suitable for residents, local businesses, and local governments.

[0174] For example, residents could be encouraged to make behavioral changes through a smartphone application to improve the score for their neighborhood or area of ​​residence.

[0175] For example, as shown in FIG. 18, in the real world, it is possible to imagine a moving person (or the smartphone carried by the person) as a kind of sensor for accumulating data in a data lake via a smartphone application. In the example described in this specification, information from a user moving by means of transportation represents data on the flow of people along a transportation route. By using city information posted by smartphone users (which may include not only crowding but also data on weather, accidents, crime, etc.), it is possible to provide a location analysis platform as a service in the digital space, such as the distribution of people within walking distance, or even the distribution of people in the surrounding area, and even statistical data on the state of the city in a larger living area.

[0176] With this configuration, it is possible to collect information about the town's attractions directly from local residents, and it is also possible to encourage behavioral changes among people who can share information via smartphone (such as visiting the town).

[0177] Furthermore, by using detailed analysis reports and town simulation tools to base policy decisions and implementation effectively and efficiently on objective evidence, the government will be able to keep the cycle of town development for everyone going.

[0178] Local businesses will be able to carry out area marketing such as store development and sales promotion based on the scores obtained from the integrated analysis platform.

[0179] FIG. 19 is a diagram showing another example of data visualization by integrated analysis.

[0180] We have explained above how the current state of a city can be displayed in real time by using an integrated analysis platform.

[0181] In this case, the types of data that can be superimposed on the map information include "distribution of the number of people (including people flow. People flow can also be shown, for example, using vectors)" and "elevation," as well as the type of use of each location within the area, such as "commercial use," "residential use," or "office use," the spatial utilization rate, and even the distribution of road maintenance conditions.

[0182] As described above, by utilizing the integrated analysis by the life information service providing server 2000, more precise estimation can be made regarding employment, which is an important part of a person's life, based on employment information by occupation.

[0183] Furthermore, by utilizing the integrated analysis by the life information service providing server 2000, the estimated people flow information can be provided to the user in a form that is easily understandable to the user by visualizing the information in a chart or the like.

[0184] Furthermore, by utilizing the integrated analysis by the life information service providing server 2000, the user can easily understand the estimated people flow path information by relating it to locations on a map.

[0185] Furthermore, by utilizing the integrated analysis by the life information service providing server 2000, accurate estimation can be performed for each area (62.5 m mesh) in which a person can move in one minute, for example.

[0186] Furthermore, by utilizing the integrated analysis by the life information service providing server 2000, it is possible to perform a time series analysis for a predetermined period of time.

[0187] Furthermore, by utilizing the integrated analysis by the life information service providing server 2000, it is possible to simulate life information in the past or future.

[0188] Furthermore, by utilizing the integrated analysis by the life information service providing server 2000, when a predetermined area location information cannot be obtained as statistical information or the like, it can be supplemented by estimation.

[0189] Furthermore, by utilizing the integrated analysis by the life information service providing server 2000, when predetermined people flow path information cannot be obtained as statistical information or the like, it is possible to supplement it with estimation.

[0190] Furthermore, by utilizing the integrated analysis by the lifestyle information service providing server 2000, it is possible to estimate pedestrian flow information for an area that includes at least information related to pedestrian flow based on demographic information that includes at least information related to the origin of movement (e.g., people commuting from home, people returning home from a restaurant, etc.).

[0191] Although one embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and modifications, improvements, etc. within the scope that can achieve the object of the present invention are included in the present invention.

[0192] Furthermore, for example, the series of processes described above can be executed by hardware or software.

[0193] In other words, the functional configurations in FIGS. 1 to 19 are merely examples and are not particularly limited.

[0194] That is, it is sufficient that the information processing system is provided with a function capable of executing the above-mentioned series of processes as a whole, and the type of functional block used to realize this function is not limited to the examples in Figures 1 to 19. Furthermore, the locations of the functional blocks and databases are not particularly limited to those in Figures 1 to 19, and may be arbitrary. For example, at least a part of the functional blocks and databases required for executing various processes may be transferred to a user terminal or the like. Conversely, the functional blocks and databases of the user terminal may be transferred to a server or the like.

[0195] Furthermore, one functional block may be configured as a single piece of hardware, a single piece of software, or a combination of both.

[0196] When the series of processes is executed by software, the program constituting the software is installed into a computer or the like from a network or a recording medium.

[0197] The computer may be a computer implemented with dedicated hardware.

[0198] Furthermore, the computer may be a computer capable of executing various functions by installing various programs thereon, such as a server, a general-purpose smartphone, or a personal computer.

[0199] A recording medium containing such a program may be composed not only of a removable medium (not shown) that is distributed separately from the device main body in order to provide the program to a user, etc., but also of a recording medium that is provided to a user, etc. in a state where it is pre-installed in the device main body.

[0200] In this specification, the steps of describing a program to be recorded on a recording medium include not only processes that are performed chronologically according to the order, but also processes that are not necessarily performed chronologically but are executed in parallel or individually.

[0201] In addition, in this specification, the term "system" refers to an overall device that is composed of a plurality of devices, a plurality of means, etc.

[0202] The embodiments disclosed herein are merely examples of configurations for specifically implementing the present invention, and do not limit the technical scope of the present invention. The technical scope of the present invention is indicated by the claims, not by the description of the embodiments, and is intended to include modifications within the literal scope of the claims and within the scope of equivalent meanings. [Explanation of symbols]

[0203] 2 network, 100 user, 200 smartphone, 1000 lifestyle information estimation system, 2000 lifestyle information service providing server, 2100 computing device, 2102 people flow path information estimation unit, 2104 map output unit, 2106 area setting unit, 2102 area location information estimation unit, 2110 lifestyle information estimation unit, 2300 storage device, 2302 demographic information, 2304 traffic route information, 2306 lifestyle information, 2308 employment information by occupation, 2310 area location information, 2312 people flow path information, 3000 lifestyle information user terminal, 5000.1 to 5000.M data providing server.

Claims

1. A people flow path information estimation device, A storage device for storing, in a time series, predetermined demographic information including information on the number of people present in a group of areas, and transportation route information regarding the transportation means included in the group of areas; a calculation device that executes an estimation process of people flow path information based on the demographic information and the traffic route information, The computing device includes: a pedestrian flow path estimation means for estimating pedestrian flow path information indicating the movement of people in each area at a predetermined time according to the distance between each area of ​​the group and a movable distance based on the traffic route information; and an image generating means for generating the estimation result of the pedestrian flow path as image information.

2. the demographic information is aggregated demographic information about mobile phone users collected by a mobile phone network; The pedestrian flow path information estimation device according to claim 1 , wherein the traffic route information includes information on a type of transportation means and a route of the transportation means.

3. the storage device stores the demographic information, the traffic route information, the lifestyle information, and the area location information as data acquired from an external server via a network; The demographic information is aggregated data of demographic data for each first time interval, and is information on the number of people by age and sex present in each predetermined area mesh in the predetermined area during the first time interval; the life information is information for each predetermined time unit, the predetermined time unit being a second time interval shorter than the first time interval, and the life information is information capable of associating the age, sex, and a predetermined classification of the person's life and behavior with the occupation of the person; The area location information is location information for each predetermined section, and includes information on the total floor area of ​​a facility within the predetermined area and the type of job within the facility, The area life information estimation means further includes a region life information estimation means for estimating the life information for each of the predetermined sections, the region life information estimation means including: i) estimating the number of people employed by occupation type in the specified area for each second time interval based on the demographic information and the lifestyle information; ii) apportioning the number of employees by occupation type to each of the facilities based on the total floor area and the area allocated to the occupation type of each of the facilities, and estimating aggregate data of the number of people in an estimated area mesh smaller than the specified area mesh of the demographic information.

4. 4. The pedestrian flow path information estimation device according to claim 3, wherein the pedestrian flow path estimation means estimates the pedestrian flow path information for at least each predetermined area at a predetermined time based on aggregated data on the number of people in the estimation area mesh and a movable distance based on the traffic route information.

5. The pedestrian flow path information estimation device according to claim 4 , wherein the lifestyle information includes predetermined work information by occupation, and the pedestrian flow path estimation means outputs the pedestrian flow path information based on the work information by occupation.

6. 2. The pedestrian flow path information estimation device according to claim 1, wherein the image generation means includes a map output means for outputting pedestrian flow path information indicating the estimated movement of people in each area at the specified time on a map in association with each area.

7. The area location information includes information identifying an area of ​​a predetermined location, 4. The pedestrian flow trajectory information estimation device according to claim 3, wherein the pedestrian flow trajectory estimation means includes an area setting means for fitting the estimated area mesh to the area based on information on a boundary line of the predetermined location, and estimates the pedestrian flow trajectory information for the area.

8. The pedestrian flow path information estimation device according to claim 3 , wherein the estimated area mesh has a size in which each side of the specified area mesh is divided by a power of two, and corresponds to a distance that a person can travel on foot within a specified time.

9. The pedestrian flow path information estimation device according to claim 1 , wherein the pedestrian flow path estimation means estimates the pedestrian flow path information over a series of time series included in a predetermined period based on the predetermined demographic information.

10. The pedestrian flow path information estimation device according to claim 3, wherein the pedestrian flow path estimation means includes a configuration information output means for estimating and outputting information constituting a town including an area to be estimated based on the predetermined demographic information over the series of time series for the area to be estimated.

11. 4. The people flow path information estimation device according to claim 3, wherein the area life information output means outputs the area life information based on predetermined area place information set in the past or future.

12. a first data providing server that provides demographic information including information on the number of people located in a group of areas in a time series at a first predetermined time interval; a second data providing server that provides life information at a predetermined second time interval including information on the life and behavior of a person, the second time interval being shorter than the first time interval; A third data providing server that provides area location information including location information for each predetermined section located in the predetermined area; a fourth data providing server that provides transportation route information including information on types of transportation and routes of the transportation; A pedestrian flow path information estimation device is provided, a storage device that receives and stores the demographic information, the lifestyle information, the area location information, and the traffic route information from the first, second, third, and fourth data providing servers; a computing device that executes an estimation process of people flow path information based on the demographic information, the lifestyle information, the area location information, and the traffic route information, The computing device includes: The function of the area life information estimation means for estimating the life information for each of the predetermined sections is executed, and the area life information estimation means i) estimating the number of people employed by occupation type in the specified area for each second time interval based on the demographic information and the lifestyle information; ii) apportioning the number of employees by occupation type to each of the facilities based on the total floor area and the area allocated to the occupation type of each of the facilities, and estimating aggregate data of the number of people in an estimated area mesh smaller than the specified area mesh of the demographic information; a pedestrian flow path estimation means for estimating pedestrian flow path information indicating the movement of people in each area at a predetermined time according to aggregate data of the number of people in the estimated area mesh, the distance between each area of ​​the group, and a movable distance based on the traffic route information; and an image generating means for generating the estimation result of the pedestrian flow path as image information.

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