JP7650334B2 - People flow path information estimation device and people flow path information estimation system - Google Patents

Description

The present invention relates to a pedestrian flow path information estimation device and a pedestrian flow path information estimation system that estimates pedestrian flow paths moving using transportation means in a specified area.

Conventionally, there are services that provide population statistical information created using the mechanisms of mobile phone networks (for example, see non-patent document 1).

Figures 20 to 22 show an overview of a service that provides such demographic information.

Figures 20 to 22 explain "Mobile Spatial Statistics (registered trademark)" provided by NTT DoCoMo, Inc. as an example.

This type of "Mobile Spatial Statistics (registered trademark)" is explained, for example, in Non-Patent Document 2.

"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 unique feature 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 pertains.

Here, using this spatial statistics, it is possible to grasp the hourly population distribution across Japan, 24 hours a day, 365 days a year. Moreover, using this spatial statistics, it is possible to grasp the population structure by gender, age group, and residential area, making it possible to conduct analyses tailored to specific purposes. Since the service was launched in October 2013, it has been widely used by the Japan Tourism Agency, local governments, private companies, and others.

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

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 used widely throughout society. As mentioned above, the service was made available in October 2013 so that it could be used not only in the public sector, but also in academic and industrial fields.

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

As shown in FIG. 20(a), the statistical target area is the nationwide DoCoMo service area, and it is possible to grasp the population distribution throughout the country on an hourly basis. Therefore, it is possible to grasp the population distribution on an hourly basis 24 hours a day, 365 days a year.

As shown in Figure 20(b), this demographics makes it possible to grasp the population composition by gender and age group. In this case, the target age group is 15 to 79 years old.

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 the registered mobile phone information.

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).

According to Non-Patent Document 3, "demographic data" can be divided into "trajectory data" and "aggregated data." "Trajectory data" is data associated with moving objects such as people, and is often collected by portable devices such as smartphones and car navigation systems.

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 using equipment installed in specific locations, once the equipment is installed, data can be collected fairly comprehensively. In addition, because the data is not linked 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 that crosses locations cannot generally be obtained, there are limitations to its use when understanding the flow of people in cities.

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 to ensure comprehensiveness. 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 utilization rates by attribute, and this is used as demographic information.

The "spatial statistics" described in the above-mentioned non-patent documents 1 and 2 correspond to "demographic information" in this sense.

Figure 22 shows the data aggregation procedure in "Mobile Spatial Statistics (registered trademark)" disclosed in Non-Patent Document 2.

Referring to Figure 22, this "spatial statistics" performs the following processing to eliminate any personally identifiable information and ensure safety.

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

The spatial statistics are created through the following steps:
i) De-identification processing: Processing to remove identifying information such as names, telephone numbers, and dates of birth from operational data. ii) Aggregation processing: Processing to derive statistical "group-related information" by performing statistical estimation from de-identified information. iii) Concealment processing: Processing to prevent figures from small population areas from being included in the aggregation results.

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

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

Alternatively, Patent Document 2 discloses an information processing device that can automate and improve the accuracy of store sales forecasts using regional mesh statistics, station statistics, and geographic information. This discloses a configuration that includes a regional mesh statistics and geographic information acquisition unit that acquires regional mesh statistics, station statistics, and geographic information by inputting the longitude and latitude of a store, and that sets multiple interpolation points within the store location point and the surrounding regional mesh or station mesh, assigns statistical values to the interpolation points, and automatically tallies the statistical values assigned to the interpolation points within the aggregation area while taking into account competing stores. Patent Document 3 also discloses a system that predicts information such as the number of people at a specific time in the future at a prediction target location with high accuracy.

Figure 23 shows an overview of the "Basic Survey of Social Life" (Non-Patent Document 4) published by the Statistics Bureau of the Ministry of Internal Affairs and Communications.

As shown in Figure 23, the Basic Survey on Social Life surveys collects lifestyle information (behavioral classification) by region, age, and sex every 15 minutes.

Patent No. 6226493 Patent No. 7037223 JP 2022-38482 A

https://mobaku.jp/ https://www.bcm.co.jp/bcm/wp-content/uploads/2018/03/ntt-docomo-2017-10-01.pdf Hiroyuki Toda, author: "Understanding and predicting pedestrian flow in cities" https://www.jstage.jst.go.jp/article/oubutsu/90/8/90_481/_pdf/-char/ja https://www.stat.go.jp/data/shakai/2021/gaiyou.html

However, the technology described in Non-Patent Document 1 and Non-Patent Document 2 only provides statistics on the age (generation), gender, and other information of people in a certain area on an hourly basis.

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.

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

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, while 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.

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

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

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 for the specified area.

In order to achieve the above object, according to one aspect of the present invention, there is provided a people flow path information estimation device, comprising a storage device for storing, in a time series, predetermined demographic information including information on the number of people present in a predetermined area, transportation route information related to transportation means included in the predetermined area , lifestyle information, and area location information , wherein 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 the predetermined area for each predetermined area mesh in the first time interval, the lifestyle information is information for each predetermined time unit, and the predetermined time unit is a second time interval shorter than the first time interval, the lifestyle information is information capable of associating a person's age, sex, and a predetermined classification of the person's life and behavior with a person's occupation, and the area location information is location information for each predetermined section, and includes information on the total floor area of facilities in the predetermined area and the occupations within the facilities. and a calculation device that executes a process of estimating people flow path information based on demographic information and traffic route information, wherein the calculation device executes the function of an area life information estimation means that estimates life information for each predetermined section, and the area life information estimation means executes the functions of: i) estimating 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) apportioning 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, estimating 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 estimating people flow path information showing the movement of people in each area at a predetermined time based on the distance between a group of areas corresponding to the estimated area meshes respectively within the predetermined area 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 information as image information.

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 transportation route.

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 in the specified area and the types of jobs within the facilities, and further includes 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 job 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 job type to each facility based on the total floor area and the area allocated to the job type of each facility, and estimates aggregate data on the number of people in an estimated area mesh smaller than the specified area mesh of the demographic information.

Preferably, the pedestrian flow path estimation means estimates pedestrian flow path information for at least each group of areas at a given 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.

Preferably, the lifestyle information includes employment information by predetermined occupation type, and the people flow path estimation means outputs people flow path information based on the employment information by occupation type.

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

Preferably, the area location information includes information identifying an area of a specified location, and the pedestrian flow path estimation means includes an area setting means for fitting an estimated area mesh to the area based on boundary information of the specified location, and estimating pedestrian flow path information for the area.

Preferably, the estimated area mesh has a size where each side of the specified area mesh is a power of 2 divided by 1, which corresponds to the distance a human can travel on foot within a specified time.

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.

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

Preferably, the pedestrian flow trajectory estimation means outputs pedestrian flow trajectory information based on predetermined area location information set in the past or future.

According to another aspect of the present invention, a people flow path information estimation system includes a first data providing server that provides demographic information including information on the number of people present in a predetermined area in a time series, 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 in the predetermined area at the first time interval, and further includes a second data providing server that provides life information for a predetermined second time interval including information on the people's lives and behavior, the second time interval being shorter than the first time interval, the life information being information capable of associating a person's age, sex, and a predetermined classification of the person's life and behavior with a person's occupation, 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 traffic route information including information on types of transportation and routes of the transportation, and a people flow path information estimation device, and the people flow path information estimation device calculates the demographic information, the life information, the area location information, and the traffic route information in a first data providing server that calculates the demographic information, the life information, the area location information, and the traffic route information in a first data providing server that calculates the demographic information, the life information, the area location information, and the traffic route information in a second ... The data providing system includes a storage device that receives and stores data 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 demographic information, life information, area location information and traffic route information, and the calculation device executes a function as 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 a group of areas corresponding to the estimated area meshes within the predetermined area respectively and a movable distance based on the traffic route information, and an image generation means that generates the estimation result of the people flow path information as image information.

According to the present invention, the movement 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 used in a variety of ways by business operators, researchers, government officials, and others.

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 that includes Hankyu Umeda Station, a mesh that includes JR Osaka Station, and a mesh that includes an anime event venue). More specifically, it is possible to estimate the "attraction force" that causes people to flow at a given time in each area, not only during normal times but also during non-normal times such as disasters.

1 is a diagram showing an overview of a lifestyle information estimation system according to a first embodiment of the present invention. FIG. 2 is a functional block diagram showing a system overview of a life information service providing server 2000. 2 is a diagram for explaining the hardware configuration of a life information service providing server 2000. [0023] FIG. FIG. 10 is a diagram for explaining the contents of data provided from data providing servers 5000.1 to 5000.M. FIG. 11 is a first diagram illustrating a processing flow of the life information service providing server 2000. FIG. 2 is a second diagram illustrating the processing flow of the life information service providing server 2000. FIG. 2 is a diagram showing an overview of data output from a calculation device 2100. 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 instant; 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. FIG. 11A and 11B are diagrams illustrating a concept of a process for improving the accuracy of spatial resolution in the behavior-specific number of people estimation process (S120, S210). FIG. 13 is a diagram showing another example of a map image and a superimposed image of a number of people distribution output by the map output unit 2104. 13 is a diagram for explaining the process flow of estimating people flow paths of the life information service providing server 2000. FIG. 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. 12. FIG. 11 is a diagram showing an example of estimated pedestrian flow path information. FIG. 1 is a conceptual diagram showing a hierarchy of data and a hierarchy of data processing used in the lifestyle information estimation system. FIG. 2 is a conceptual diagram showing an overview of the flow of data processing. FIG. 1 is a diagram showing an overview of services realized using the lifestyle information estimating system 1000. FIG. 18 is a conceptual diagram showing an example of a service provided via a smartphone for the service of FIG. 17. FIG. 13 is a diagram showing another example of data visualization by integrated analysis. FIG. 1 is a diagram showing an overview (1) of mobile spatial statistics of the prior art. FIG. 1 is a diagram showing an overview (2) of mobile spatial statistics of the prior art. FIG. 1 is a diagram showing an overview (3) of mobile spatial statistics of the prior art. FIG. 1 is a diagram showing the contents of the Basic Survey on Social Life as a background art.

The following describes an embodiment of the present invention with reference to the drawings.

Below, we will explain the configuration of an information processing system for estimating pedestrian flow path information (hereinafter referred to as the "pedestrian flow path information estimation system") and a pedestrian flow path information estimation device for estimating lifestyle information in the pedestrian flow path information estimation system.

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

Figure 1 is a diagram showing an overview of a pedestrian flow path information estimation system according to embodiment 1 of the present invention.

Referring to FIG. 1, the people flow path information estimation system 1000 of the first embodiment of the present invention includes a lifestyle information service providing server 2000 that operates as a people flow path information estimation device, a lifestyle information user terminal 3000 of a user who receives services from the lifestyle information service providing server 2000, data providing servers 5000.1 to 5000.M (M: natural number) that provide the lifestyle information service providing server 2000 with data necessary for estimating lifestyle 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.

Figure 2 is a functional block diagram showing an overview of the system of the lifestyle information service providing server 2000.

Figure 4 is a diagram explaining the contents of the data provided by 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 servers 5000."

Referring to FIG. 2, the lifestyle information service providing server 2000 includes a computing device 2100 and a storage device 2300.

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.

Here, "a specified time" refers to, for example, a first specified time interval, for example, every hour, and "a specified area" refers to, for example, an area in a specific region, in a first area mesh unit, for example, a 500m mesh (a square of 500m on each side). "Specified demographic information" means "demographic data" and "aggregated data", and corresponds to, for example, the aforementioned "Mobile Spatial Statistics Information (registered trademark)", as shown in Figure 4.

Furthermore, the storage device 2300 stores 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 predetermined behaviors by region, age, and sex, at 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 Ministry of Internal Affairs and Communications' "Basic Survey on Social Life," which classifies behaviors into 20 predetermined types by region, age, and sex, and in 15-minute increments, as shown in FIG. 4. Here, the "behavior classification" corresponds to classifications such as "sleep," "commuting," "school," "work," and "academics."

The storage device 2300 also stores "employment information by occupation 2308." Here, "employment information by occupation 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 Figure 4.

In addition, the "prescribed lifestyle information" may include such "occupation-specific employment information."

Therefore, using the "demographic information 2302" and the "predetermined lifestyle information 2306", the lifestyle 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 at the user's instruction during the "second time interval". 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 lifestyle information estimation unit 2110 can calculate data on the "occupation" classification of people who are present in the "predetermined area" during the "second time interval". Note that "occupation" does not only refer to so-called "type of occupation", but also to "work" such as "study" that is not a business but in which a person is obviously engaged in the same behavior in that place (facility/building) for a certain period of time or more.

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 that exists 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 within the second area mesh unit area, which is smaller than the above-mentioned first area mesh unit and preferably has a side that is a divisor of the first area mesh unit when the first area mesh unit is a rectangle, and information on the types of jobs performed within the buildings and facilities. For example, as shown in FIG. 4, the "area location information 2310" corresponds to map data in 62.5 m mesh units that includes information on buildings and facilities in the city, information on the types of jobs performed within the buildings and facilities, and information on roads and water areas. Some of this "map data" is provided by private companies.

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.

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 during 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."

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.

As a relationship between specified lifestyle information and specified 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 of this will be described later.

Furthermore, the map output unit 2104 generates and outputs a screen that displays the "person's area life information" thus estimated in association with each "predetermined location" in the "predetermined area" (for example, superimposed on top of 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.

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" on the map data (for example, roads, railways, etc.). 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.

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 transport route nodes such as the locations of highway interchanges (ICs) ・Average travel speed on roads at a given time on a given day based on past road congestion information ・Locations of public transport stations and stops (railroads, buses, etc.) ・Public transport routes on maps (railroads, buses, etc.) ・Public transport timetables (average travel speed can also be calculated from timetable and route information)

However, if the information is about transportation, the data included in the transportation route information 2304 is not limited to these.

The traffic route information 2304 stores information as described below. By associating pedestrian flow information with 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.

Figure 3 is a diagram explaining the hardware configuration of the lifestyle information service providing server 2000.

Referring to FIG. 3, the life information service providing server 2000 may be configured so that a computing device (CPU: Central Processing Unit) within its own housing executes the arithmetic processing, or a part of the program processing may be executed on another server. In the following description, it is assumed that a computing device within its own housing executes the arithmetic processing.

Referring to FIG. 3, the 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 the computer device 2010, a keyboard 2400 as an input device, and a display 2420 as a display device.

For example, the recording medium 2210 may be a USB memory, a memory card, or an external storage device. The network communication unit 2012 may be a wired LAN or wireless LAN communication function. The network communication unit 2012 and the input/output interface 2090 together constitute a communication interface.

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 the 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 to and from 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 2030. A memory card 2210 can be inserted into the memory drive 2020.

When the computer device 2010's programs run, the data and programs that store the information that is the basis for the computer's operation will be described as being stored in the SSD 2300.

In FIG. 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, or a USB memory. In such cases, the computer main body is provided with a drive device (memory drive 2020, disk drive 2030) capable of reading these media.

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. It 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.

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 instructions that call appropriate functions (modules) in a controlled manner and produce the desired results. How computer system 2010 operates is well known, and a detailed explanation will be omitted.

Furthermore, the CPU 2100 may be a one-core processor or a multiple-core processor. That is, it may be a single-core processor or a multi-core processor. The server 2000 may also be configured with multiple servers and perform distributed processing.

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

Below, we will explain the processing of estimating the number of people by behavior and the number of people in a facility based on the lifestyle information of people present in a specific area during a specific time period, as a prerequisite for the processing of estimating people flow path information by the lifestyle information service providing server 2000.

Figure 5 is a first diagram explaining the processing flow of the life information service providing server 2000 for estimating the number of people by behavior and estimating the number of people in a facility. Figure 6 is a second diagram explaining the processing flow of the life information service providing server 2000 for estimating the number of people by behavior and estimating the number of people in a facility.

Figure 7 shows an overview of the data output from the computing device 2100.

Figure 8 is a conceptual diagram showing data generated when facility occupancy estimation is performed from behavior-specific occupancy estimation at a first time point.

Figure 9 is a conceptual diagram showing data generated when facility occupancy estimation is performed from behavior-specific occupancy estimation at a second time.

Referring to Figures 5 and 6, when the processing for estimating the number of people by behavior and estimating the number of people in a facility starts, the calculation device 2100 acquires spatial statistical data via the network (S110, S200).

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, 500 m square).

Next, for the "predetermined area" specified by the area setting unit 2106 at the user's request, the lifestyle information estimation unit 2110 classifies people (whose age and gender are obtained from the spatial statistical data) who are in a specific area (e.g., the Umeda area of Osaka) during a specific time period (e.g., a certain hour) based on the spatial statistical data, into work, movement (e.g., commuting) and other status (behavior classification information) for more detailed time periods (e.g., 15 minutes) within this specific time period, with reference to the Basic Survey of Social Life database, and estimates the number of people in each category (age and gender attributes) (S120, S210).

The lower part of Figure 7(a) shows an overview of this type of behavior-specific number of people estimation.

The lower graph 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."

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's activities are classified as "work" or "study."

Returning to Figures 5 and 6, the lifestyle information estimation unit 2110 further estimates the number of people (age and gender attributes) employed 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).

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

The upper part of Figure 7(a) shows an overview of such facility occupancy estimation.

The upper graph in Figure 8 shows the results of estimating the number of people in facilities in a specific area at 2:00 a.m. Judging from the time of day, most people are located in "residences" or "hotels and inns."

The upper graph in Figure 9 shows the results of estimating the number of people in a facility in a specific area at 3:00 p.m. (15:00). Based on the time of day, we can see that most people in each facility are assigned to the "occupation" or "task" that corresponds to the facility or building.

Returning to Figures 5 and 6, the map output unit 2104 further superimposes the number of people displayed for each facility/building on the map and outputs it, for example, color-coded using a heat map method (S140, S240). Here, when displaying the heat map, a color-coded chart showing the proportion of people performing tasks at that location may be used.

Figure 7 (b) shows an example of an output that visualizes such a population distribution.

Figure 10 shows the concept of the process for improving the accuracy of spatial resolution in the behavior-specific number of people estimation process (S120, S210).

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

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

However, as mentioned above, in the lifestyle 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 areas), etc. are all broken down and converted into the same unit (area mesh) and stored.

A database stored in this way in uniform units is referred to below as a "data lake." Therefore, for example, it is possible to perform real-time analysis in the same units (area mesh) at any point in Japan.

As mentioned above, the same area mesh has a side length of 62.5 m, as shown in Figure 10.

If we use 62.5 m, since Mobile Spatial Statistics (registered trademark) is a mesh with sides of 500 m, we can create an area mesh with sides of 1/8 (1/2) of 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 a similar manner. By using the distance a person walks in one minute, it can be said that this resolution is suitable for analyzing people flow, including people who are moving by foot, for example.

However, it is also possible to divide it into units smaller than 62.5 m (for example, a minimum of 1 m). However, this would increase the amount of data to be analyzed and the amount of calculations, so from a cost-effectiveness perspective, it is desirable to use, for example, 62.5 m.

Figure 11 shows another example of a map image and a superimposed image of the number of people distribution output by the map output unit 2104.

As shown in FIG. 11, a certain area is designated as the specified region, and further, the user can specify a portion of the "specified region" in which the estimation process will be performed.
(Estimation process of people flow path information)

The following describes the process by the lifestyle information service providing server 2000 to estimate people flow path information for people located in a specific area during a specific time period.

Figure 12 is a diagram explaining the process flow of estimating people flow paths by the lifestyle information service providing server 2000.

In FIG. 12, the processing from steps S110 to S140 is similar to the lifestyle information estimation processing described in FIG. 5, so the description will not be repeated.

In practice, for example, the data calculated in the processes from steps S110 to S140 is temporarily stored as common data in the data lake, and can be configured to be shared in the processes for estimating the number of people by behavior and estimating the number of people in a facility described in FIG. 5 and the process for estimating people flow paths described in FIG. 12.

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 the increase or decrease in the number of people associated with travel by transportation for the travel area every 15 minutes from the transportation route information and (average) travel speed (S160).

Then, the calculation device 2100 estimates the "people flow path" as a vector on the traffic route on the map (S170).

Figure 13 is a diagram explaining the concept of the process of estimating the increase or decrease in the number of people in a specified movement area in conjunction with traffic movement in step S160 of Figure 12.

In Figure 13, the area mesh is illustrated by a dotted line.

Referring to FIG. 13, for example, on a railway line, station 1 is located in region R1, station 6 is located in region R6, and stations 2 to 5 are located between them.

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

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

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 region R2 from the time Δt earlier.

Note that the increase in the number of people in area R2 during this time Δt may be due to factors other than the increase in the number of people caused by train movement, so a certain correction process is performed.

For example, the increase in the number of people between 2·Δt ago and Δt ago is unrelated to the arrival of the 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 the train, and a correction can be made by subtracting this from the increase in the number of people mentioned above.

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.

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 of the group of areas and the travelable distance based on the traffic route information. Here, "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 an estimated area exists in a travelable area in the time unit for estimating the number of people based on the average travel speed of the means of transportation.

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

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

As a method for estimating people flow based on a predetermined area mesh "aggregated data", for example, a method such as that disclosed in the following document can be used. Note that the area mesh may be "an area mesh (estimated area mesh) that is 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

Figure 14 shows an example of pedestrian flow path information estimated in this way.

Figure 14 shows an example of pedestrian flow route information for the inner loop of the JR Loop Line in Osaka City.

As shown in FIG. 14, by analyzing the data on the increase or decrease in people (people flow data) within the area mesh estimated between steps S110 and S140 in FIG. 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.

The data lake of the lifestyle information service providing server 2000 stores data on "bus stops," "bus routes," "train stations," "train routes," "airports," and "airline routes" as transportation route information. By combining and analyzing such public transportation-related data with various data stored in the data lake, it is possible to obtain information on people traveling via the routes.

Furthermore, in addition to analyzing the state of the local areas along the line and people flow patterns, it will also 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.

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

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 specified time based on predetermined lifestyle information including at least information related to people's lives (for example, lifestyle information by region, age/gender, and 15-minute intervals (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 units, town information, building information, road/water area information, etc.).

For example, the specified lifestyle information includes specified employment information by occupation (e.g., age and gender units, employment ratio by occupation). In this case, the people flow path information estimation unit 2102 can also estimate people flow path information by occupation based on the employment information by occupation.

As a result, more precise estimates can be made about employment, an important part of people's lives, based on occupation-specific employment information.

The map output unit 2104 also outputs people flow path information indicating the movement of people in each area at a specific time estimated in this manner, in association with each area, onto a map. Therefore, the estimated people flow path information can be associated with each area on the map and displayed in a manner that is easily understandable to the user.

In the above explanation, the inflow and outflow of people is estimated for each estimated area mesh unit. However, the people flow path 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 path information for this integrated area.

In other words, the people flow path information estimation unit 2102 has a function of estimating the boundary line of a specified location based on data registered in advance as area location information 2310, and fitting and setting the area. For example, by fitting the area of the estimated area mesh to follow the boundary line of a fireworks display venue, etc., it is possible to perform a more precise analysis of people flow path information that meets the needs of the user.

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 specific area location information for each specific area, making it possible to make accurate estimations even in areas where people mainly move by walking.

The people flow path information estimation unit 2102 also 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 using time series analysis and present them to the user. Therefore, for example, it is possible to visualize the movement of a peak in the influx of people from Kobe to Osaka during the morning commute by public transportation.

Furthermore, by using the estimation results from the people flow path information estimation unit 2102, it is also possible to estimate town composition information for the area being estimated based on specified demographic information over a series of time periods (e.g., information on the number of people by age, gender, residence, within the prefecture, outside the prefecture, etc.). Possible areas to be estimated include, for example, 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 moving around the town in that area from the time series movement of the demographics of that area.

In addition, the pedestrian flow path information estimation unit 2102 can estimate or predict pedestrian flow path information by using specific area location information set in the past or future.

Figure 15 is a conceptual diagram showing the hierarchy of data and data processing used in the pedestrian flow path information estimation system.

Figure 16 is a conceptual diagram showing an overview of the data processing flow.

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 sharing platform.

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

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.

i) When it comes to reusing and expanding services, traditionally systems have been specialized for each field or organization, which makes it difficult to reuse or expand services to other regions.

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.

iii) Low scalability: Conventional specialized systems have the problem that expanding functionality requires significant cost and effort, making it difficult to continuously and easily evolve services.

To address these issues in realizing Japan's smart cities, the City OS will be designed to have the following characteristics: i) interoperability (connection), ii) data distribution (flow), and iii) ease of expansion (sustainability).

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

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 use 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.

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

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.

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 process performed by the lifestyle information service providing server 2000 described above.

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

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.

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. Therefore, since the people flow distribution is created from independent information (people flow → behavior → town information), updating the "town information (general map information)" also changes the people flow distribution. Therefore, it is possible to visualize not only current real-time information, but also people flow distribution in a town that differs from the present in the past and future.

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

These analytical results can be used for urban planning, environmental optimization, energy optimization, real estate value assessment, logistics optimization, and so on. 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.

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

Figure 17 shows an overview of the services provided using the pedestrian flow path information estimation system 1000.

Figure 18 is a conceptual diagram showing an example of a service provided via a smartphone for the service in Figure 17.

As shown in Figure 17, the maps, people flow, various sensor data, weather data, satellite data, real estate data, and data posted via smartphones stored in the data lake can be used to analyze the condition of each region (artificial intelligence prediction), calculate the score of each region for specified evaluation items, and map and visualize each region.

In this case, the output of the integrated analysis platform will have interfaces suitable for residents, local businesses, and local governments.

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.

For example, as shown in FIG. 18, in the real world, it is possible to imagine a moving person (or the smartphone they carry) as a kind of sensor that accumulates data in a data lake via a smartphone application. In the example described in this specification, information from users moving by public transport represents data on the flow of people along the transport 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.

By configuring it in this way, it is possible to collect information about the town's attractions directly from local residents, and to encourage behavioral changes among people who can share information via smartphone (such as visiting the town).

In addition, 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.

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.

Figure 19 shows another example of data visualization using integrated analysis.

Above, we have explained how the current state of a city can be displayed in real time by using an integrated analysis platform.

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.

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

In addition, by utilizing the integrated analysis by the lifestyle information service providing server 2000, the estimated pedestrian flow information can be provided to users in a form that is easily understandable to them through visualized information such as charts.

In addition, by utilizing the integrated analysis by the lifestyle information service providing server 2000, users can easily understand the estimated pedestrian flow path information by relating it to locations on a map.

In addition, by utilizing the integrated analysis by the lifestyle 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.

In addition, by using the integrated analysis by the lifestyle information service providing server 2000, it is possible to perform time series analysis over a specified period of time.

In addition, by using the integrated analysis by the lifestyle information service providing server 2000, it is possible to simulate lifestyle information in the past or future.

In addition, by utilizing the integrated analysis by the lifestyle information service providing server 2000, it is possible to make up for specific area location information by estimation when it is not available as statistical information, etc.

In addition, by utilizing the integrated analysis by the lifestyle information service providing server 2000, when specific people flow path information cannot be obtained as statistical information, etc., it is possible to supplement it with estimation.

In addition, 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.).

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

For example, the above-mentioned series of processes can be executed by hardware or software.

In other words, the functional configurations shown in Figures 1 to 19 are merely examples and are not particularly limited.

In other words, it is sufficient that the information processing system is provided with the functionality to execute the above-mentioned series of processes as a whole, and the functional blocks used to realize this functionality are not limited to the examples of Figures 1 to 19. Furthermore, the locations of the functional blocks and databases are not particularly limited to those of Figures 1 to 19, and may be arbitrary. For example, at least a portion of the functional blocks and databases required to execute 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.

Furthermore, a single functional block may be configured as hardware alone, software alone, or a combination of both.

When a series of processes is executed by software, the programs that make up the software are installed onto a computer or other device from a network or recording medium.

The computer may be a computer built on dedicated hardware.

The computer may also be a computer capable of executing various functions by installing various programs, such as a server, a general-purpose smartphone, or a personal computer.

The recording medium containing such a program may be configured not only as a removable medium (not shown) that is distributed separately from the device body in order to provide the program to the user, but also as a recording medium that is provided to the user in a state where it is already installed in the device body.

In this specification, the steps of writing 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.

In addition, in this specification, the term "system" refers to an overall device that is composed of multiple devices, multiple means, etc.

The embodiments disclosed herein are illustrative 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.

2 Network, 100 User, 200 Smartphone, 1000 Lifestyle information estimation system, 2000 Lifestyle information service providing server, 2100 Calculation 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 Occupation-specific employment information, 2310 Area location information, 2312 People flow path information, 3000 Lifestyle information user terminal, 5000.1 to 5000. M Data providing server.

Claims (12)

A people flow path information estimation device,
A storage device is provided for storing, in a time series, predetermined demographic information including information on the number of people present in a predetermined area, transportation route information relating to a means of transportation included in the predetermined area , lifestyle information, and area location information ;
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,
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:
The function of a region life information estimation means for estimating the life information for each of the predetermined sections is executed, and the region 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 a distance between each of a group of areas corresponding to the estimation area meshes in the predetermined area and a movable distance based on the traffic route information;
and an image generating means for generating an estimation result of the pedestrian flow path information as image information. 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. 2. The pedestrian flow path information estimation device according to claim 1 , wherein 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. 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 of a group of the areas at a predetermined time based on aggregate data on the number of people in the estimation area mesh and a movable distance based on the traffic route information. The pedestrian flow path information estimation device according to claim 4, wherein the lifestyle information includes employment information by predetermined occupation type, and the pedestrian flow path estimation means outputs the pedestrian flow path information based on the employment information by occupation type. 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. 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. The pedestrian flow path information estimation device according to claim 3, wherein the estimated area mesh has a size where each side of the specified area mesh is a power of 2, and corresponds to a distance that a person can travel on foot within a specified time. 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. 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 about the configuration of 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. The pedestrian flow path information estimation device according to claim 3 , wherein the pedestrian flow path estimation means outputs the pedestrian flow path information based on predetermined area location information set in the past or future. A first data providing server provides demographic information including information on the number of people located in a predetermined area in a time series ;
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 mesh of the predetermined area during the first 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 the person, the second time interval being shorter than the first time interval, and the life information being information that can associate the age, sex, and a predetermined classification of the life and behavior of the person with the occupation of the person;
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, distances between a group of areas corresponding to the estimated area meshes in the specified area, and a movable distance based on the traffic route information;
and an image generating means for generating an estimation result of the pedestrian flow path information as image information.

Images