JP2005202546A - Population fluidity estimation method, device and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a population fluidity estimation method for further precisely estimating movement of people. <P>SOLUTION: A walk zone from a station is set (S1), and the walk zone is divided to a predetermined size of meshes (S2). With respect to each mesh, the total sum of capacity of buildings located within the mesh concerned is calculated (S3). The number of incoming and outgoing passengers of the station is assigned according to the building capacity of each mesh (S4). The shortest moving route between each mesh and the station is determined (S5). For each shortest moving route, the number of incoming and outgoing passengers assigned to the top mesh in the shortest moving route is assigned as the number of passing persons to each mesh on the shortest moving route and accumulatively added (S6 and S7). A map in which the number of passing persons is superimposed on a map is formed and displayed (S8). <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a population flow estimation method, apparatus, and program, and more specifically, to a method, apparatus, and program for estimating population flow around a station.

  In trade area analysis such as store opening plans and sales forecasts for convenience stores and supermarkets, it is necessary to accurately estimate the dynamics of the population.

  For example, when the number of passengers at a railway station is known, a model is adopted in which the number of passengers diffuses evenly from the station in the radial direction. In addition, in a shopping street or the like, there is also a method of directly counting human movements at important points.

  The former method is too inaccurate. For example, it is clear when considering the case where there is a building or a store where most of the passengers stop by near the station.

  In the latter method, a large number of personnel are required even in a small area, and it is difficult to measure the movement of a person in a large area in terms of cost and time.

  It is an object of the present invention to present a population flow estimation method, apparatus, and program capable of estimating a person's movement more accurately from widely known data.

  The population flow estimation method according to the present invention is a method for estimating movement of a person from / to a boarding / exiting point of a transportation means, a search area within a predetermined moving time from the boarding / exiting point, and a mesh of a predetermined size. A search area setting step for setting a search area divided into two, a person attraction coefficient setting step for setting a human attraction coefficient in each mesh in the search area, and the number of passengers at the boarding / exiting points for each mesh in the search area A step of assigning the number of passengers assigned to each mesh according to the person's attraction coefficient, a shortest moving route determining step for determining the shortest moving route between each mesh in the search area and the getting-on / off point, and each shortest moving route Assign the number of passengers assigned to the mesh at the tip of the shortest travel route as the number of passers to each mesh on the shortest travel route, and cumulatively add each mesh Characterized by comprising applying a steps.

  The population flow estimation device according to the present invention is a device that estimates the movement of a person from / to a boarding / exiting point of a transportation means. Search area setting means for setting a divided search area, human attraction coefficient setting means for setting a human attraction coefficient in each mesh in the search area, and the number of passengers at the boarding / exiting points for each mesh in the search area For each mesh assigned according to a person attraction coefficient, the number of passengers assigning means, the shortest moving route determining means for determining the shortest moving route between each mesh in the search area and the getting-on / off point, and each shortest moving route, The number of passengers assigned to the mesh at the tip of the shortest travel route is assigned to each mesh on the shortest travel route as the number of passers, and the number of passers is assigned for each mesh. Characterized by comprising an output means for outputting the number of passing's Interview.

  A population flow estimation program according to the present invention is a computer program for estimating movement of a person from / to a boarding / exiting point of a transportation means, and is a search area within a predetermined moving time from the boarding / exiting point, and has a predetermined size. A search area setting module for setting a search area divided into meshes, a human attraction coefficient setting module for setting a human attraction coefficient in each mesh in the search area, and the number of passengers at the boarding / exiting points. Decide the number of passengers assignment module assigned to each mesh according to the attraction coefficient of each mesh in the search area, and determine the shortest movement path to determine the shortest movement path between each mesh in the search area and the boarding / exiting point For each module and each shortest movement route, assign the number of passengers assigned to the mesh at the tip of the shortest movement route to each mesh on the shortest movement route as the number of passers-by. Passing toll allocation module cumulatively added for each Interview - characterized by comprising a Le.

  According to the present invention, the movement of a person from / to a boarding / exiting point of a transportation means can be estimated with higher accuracy and lower cost than in the past under a rational model in accordance with the actual situation.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

  FIG. 1 shows a schematic block diagram of an embodiment of the present invention, and FIG. 2 shows an operation flow of this embodiment. The embodiment shown in FIG. 1 is implemented as a program on a computer. A CPU 12, ROM 14, RAM 16, hard disk 18, monitor 20, keyboard 22 and mouse 24 are connected to the bus 10. The ROM 14 stores fixed data and programs used for the operation of the illustrated apparatus. The CPU 12 appropriately reads program software stored in the hard disk 18 into the RAM 16 and executes it.

  The hard disk 18 stores a station data file 30, a map data file 32, a road network file 34, and a building data file 36 as basic data.

  The station data file 30 contains data on the position, name, and number of passengers of the station, which is the starting point of movement of people in the area where artificial flow is estimated. The map data file 32 contains map data of the area including the search target area. The road network file 34 stores the main road data (position, width, intersection, etc.) of the area including the search target area. The building data file 36 stores the positions and building volumes of buildings of a certain size or more that exist in the area including the search target area.

  In addition to these basic data, the hard disk 18 stores program software for the CPU 12. Specifically, a walking area setting program (or module) 40 for setting a walking area on a map, a mesh dividing program 42 for dividing a map into meshes of a predetermined size (for example, 100 m × 100), each mesh According to the building volume calculation program 44 for totaling the building volume in the unit of the mesh, the walking time (for example, 15 minutes) set by the keyboard 22 etc., the number of passengers at the station is set for all meshes in the walking area. Passenger passenger number distribution program 46 that distributes according to the proportion of the building volume of the mesh, the shortest travel distance determination program 48 that determines the shortest distance from the station to each mesh, the station along the travel route of the determined shortest travel distance A cumulative addition program 50 that cumulatively adds the allocated population of each mesh across, and a map that displays the final number of people passing through each mesh numerically or by color on the map. There is an output map creation program 52 to create.

  With reference to FIG. 2, the operation of this embodiment will be described. Since the present embodiment is intended to infer which route the passengers at the station will travel, first, a walking area from the station is set (S1). For example, an appropriate walking time, for example, 15 minutes is input by the keyboard 22. The walking area setting program 40 refers to the road network 34 and sets the walking area from the target station from the input walking time and average walking speed. Since the walking area roughly limits the target area for estimating population flow, so much accuracy is not required. Then, according to the mesh size input from the keyboard 22, the mesh division program 42 divides the walking area into meshes M (i, j) (S2). Needless to say, a map divided into meshes in advance may be prepared, and a walking area may be set on the map.

  An example of the map after mesh division is shown in FIG. The main roads 64, 66, 68, 70, 72 extend from the station 60 from the station 62 on the track 60. The walking area is shown by a one-dot chain line 74. In FIG. 3, the walking area 74 is set around the station 62. However, when the illustrated area is divided on both sides of the track 60 and the entrance / exit of the station 62 is on one side, the walking area 74 is displayed on the entrance / exit. Set to the center.

  The building volume calculation program 44 calculates the building volume S (ij) of each mesh M (i, j) with reference to the building data 46 (S3). Specifically, a building belonging to the noticed mesh M (ij) is searched from the building data 46, and the volume of the found building is cumulatively added.

  When one building spans multiple meshes, the volume belonging to each mesh is allocated to each mesh, the volume of the entire building is allocated to each mesh by the floor area ratio of each mesh, and the entire volume is allocated to the mesh with the entrance / exit There is a method.

The passenger number distribution program 46 proportionally distributes passengers to each mesh M (ij) according to the building volume S (i, j) of each mesh M (i, j) (S4). For example, when the number of passengers is A, the assigned passenger number b (i, j) of each mesh M (i.j) is the walking area of the area S (i, j) of the target mesh M (i, j). A value obtained by multiplying the result of dividing by the sum ΣS (i, j) of the building volumes S (i, j) of all meshes,
b (i, j) = A × S (i, j) / ΣS (i, j)
It becomes. In other words, b (i, j) indicates the number of people who use the mesh M (i.j) as the destination (for passengers getting off) or the departure place (for passengers). Here, b (i.j) is substituted into a variable c (i.j) that holds the number of passing people. In the trade area survey, since the person (the number of staying people) who uses the corresponding mesh as the destination or departure point can be the target of the customer, these are also counted. When counting the number of people who pass purely, the number of people staying is not considered.

  Since the larger the building volume, the more humans can be accommodated, it can be considered that the building volume can be a numerical index for the action of attracting people. Of course, this may vary depending on whether it is a weekday or a public holiday, and the presence or absence of special events or events, but it is generally considered reasonable. For fluctuation factors, a weighting factor for correcting the building volume is introduced as appropriate. For example, a department store, a shopping street, a cinema complex in which a large number of movie theaters are accumulated, and the like are facilities that have a higher effect of attracting people than ordinary buildings. Therefore, for example, 2.0 is set for department stores, 1.2 to 1.5 for shopping districts, and 1.7 for cinema complexes. It is preferable to multiply the area of each building or each mesh in advance by a coefficient.

  The shortest movement route determination program 48 refers to the road network 34 to determine the shortest route between the station and each mesh (S5). Such a shortest path determination algorithm is well known in, for example, a car navigation system, and therefore detailed description thereof is omitted here. Basically, it is only necessary to extract a usable road between the target mesh and the station and find a route having the shortest distance among them. Although many will overlap, the shortest path is determined by the number of meshes within walking distance. FIG. 4 shows an example of movement in a mesh along the roads 66 and 68 of the map shown in FIG. The direction of movement of the person heading for the station 62 is indicated by an arrow.

  For each shortest route, the cumulative addition program 50 calculates the assigned passenger number of the mesh at the tip (the mesh farthest from the station) and the assigned passenger number b (i of each mesh M (i, j) that passes through the shortest route. , J) is cumulatively added (S6). In this cumulative addition, a variable c (i.j) is used. The passing number of people c (i.j) is cumulatively added for the purpose or starting point of the mesh (i.j) (S6). Step S6 is executed for all meshes (S7). As a result, the variable c (i.j) of each mesh M (i.j) indicates the number of persons passing the mesh M (i.j) (the number of persons having the mesh M (i.j) as the destination or starting point). Included).

  With reference to the model shown in FIG. Assume that the number of passengers assigned to meshes M (0) to M (10) is b (0) to b (10). Let c (0) -c (10) be the number of people passing through meshes M (0) -M (10) from other meshes (including those who will be destinations or departure points), and b (0) -b in advance. Assume that (10) is assigned.

A person who goes from the mesh M (10) to the station passes through the mesh M (9), M (8), M (3), M (2), M (1), M (0). This
c (10) = b (10)
c (9) = b (9) + b (10)
c (8) = b (8) + b (10)
c (3) = b (3) + b (10)
c (2) = b (2) + b (10)
c (1) = b (1) + b (10)
c (0) = b (0) + b (10)
It becomes.

A person heading for the station from the mesh M (9) passes through M (8), M (3), M (2), M (1), and M (0). This
c (8) = b (8) + b (10) + b (9)
c (3) = b (3) + b (10) + b (9)
c (2) = b (2) + b (10) + b (9)
c (1) = b (1) + b (10) + b (9)
c (0) = b (0) + b (10) + b (9)
It becomes.

A person heading from the mesh M (8) to the station passes through M (3), M (2), M (1), and M (0). This
c (3) = b (3) + b (10) + b (9) + b (8)
c (2) = b (2) + b (10) + b (9) + b (8)
c (1) = b (1) + b (10) + b (9) + b (8)
c (0) = b (0) + b (10) + b (9) + b (8)
It becomes.

A person heading from the mesh M (7) to the station passes through M (6), M (3), M (2), M (1), and M (0). This
c (6) = b (6) + b (7)
c (3) = b (3) + b (10) + b (9) + b (8) + b (7)
c (2) = b (2) + b (10) + b (9) + b (8) + b (7)
c (1) = b (1) + b (10) + b (9) + b (8) + b (7)
c (0) = b (0) + b (10) + b (9) + b (8) + b (7)
It becomes.

A person heading for the station from the mesh M (6) passes through M (3), M (2), M (1), and M (0). This
c (3) = b (3) + b (10) + b (9) + b (8) + b (7) + b (6)
c (2) = b (2) + b (10) + b (9) + b (8) + b (7) + b (6)
c (1) = b (1) + b (10) + b (9) + b (8) + b (7) + b (6)
c (0) = b (0) + b (10) + b (9) + b (8) + b (7) + b (6)
It becomes.

A person heading for the station from the mesh M (5) passes through M (4), M (3), M (2), M (1), and M (0). This
c (4) = b (4) + b (5)
c (3) = b (3) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5)
c (2) = b (2) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5)
c (1) = b (1) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5)
c (0) = b (0) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5)
It becomes.

A person heading for the station from the mesh M (4) passes through M (3), M (2), M (1), and M (0). This
c (3) = b (3) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5) + b (4)
c (2) = b (2) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5) + b (4)
c (1) = b (1) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5) + b (4)
c (0) = b (0) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5) + b (4)
It becomes.

A person heading from the mesh M (3) to the station passes through the meshes M (2), M (1), and M (0). This
c (2) = b (2) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5) + b (4) + b (3)
c (1) = b (1) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5) + b (4) + b (3)
c (0) = b (0) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5) + b (4) + b (3)
It becomes.

A person heading from the mesh M (2) to the station passes through the meshes M (1) and M (0). This
c (1) = b (1) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5) + b (4) + b (3) + b (2)
c (0) = b (0) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5) + b (4) + b (3) + b (2)
It becomes.

A person who goes from the mesh M (1) to the station passes through M (0). This
c (0) = b (0) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5) + b (4) + b (3) + b (2) + b (1)
It becomes.

As a result, the passing numbers c (0) to c (10) of the meshes M (0) to M (10)
c (10) = b (10)
c (9) = b (9) + b (10)
c (8) = b (8) + b (10) + b (9)
c (7) = b (7)
c (6) = b (6) + b (7)
c (5) = b (5)
c (4) = b (4) + b (5)
c (3) = b (3) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5) + b (4)
c (2) = b (2) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5) + b (4) + b (3)
c (1) = b (1) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5) + b (4) + b (3) + b (2)
c (0) = b (0) + b (10) + b (9) + b (8) + b (7) + b (6) + b (5) + b (4) + b (3) + b (2) + b (1)
It becomes.

  Thus, the number of passengers passing through each mesh can be counted by adding the number of passengers assigned to the mesh at the tip of each shortest route to the number of passengers assigned to each mesh on the shortest route. The total number of passengers assigned to the mesh at the tip of each shortest route is cumulatively added to each mesh on that shortest route, so that the number of people who pass purely, that is, the number of people who exclude each mesh as the starting point or destination, is excluded. Can be counted.

  FIG. 6 is a map shown in FIG. 3 and shows an example of the number of passengers assigned to the meshes along the roads 66 and 68. FIG. 7 shows the number of people passing through each mesh as a result.

  When the number of people passing through all the meshes is accumulated and tracked (S7), the output map creation program 52 creates a map that displays the number of people passing through each mesh c (ij) superimposed on the map of the map data 32 ( S8). The created map is displayed on the screen of the monitor 20. For example, it is easy to understand if each mesh is colored with a color corresponding to the number of passing people. Of course, the number of people passing through each mesh c (i.j) may be displayed as a numerical value as it is superimposed on each mesh M (i, j). If necessary, the estimation result is stored in the hard disk 18.

  In the above example, the building volume is used as an index of the action of attracting people. However, this is based on the assumption that people move in daily life. Because it does. Note that the building volume can be easily measured by the aerial survey. The floor area may be used instead of the building volume. When there are events, and when there are special factors that encourage people to move, such as amusement parks, museums, and museums such as public holidays, these factors are quantified and the incentive indicator or the above weight Use as a coefficient.

  As described above, in this embodiment, the flow of people can be reasonably estimated. Passengers move toward the station, and passengers move away from the station. By adding them together, you can estimate the number of people passing through the mesh of interest. If the passengers at the station can be measured in units of time, movement in units of hours can be estimated.

  When there are a plurality of stations, the above processing is performed for each station, and the results may be added for each mesh.

  The railway is exemplified as the transportation means, but it may be an airplane, a bus, or other transportation means. In the case of an airplane, the station 62 in this embodiment corresponds to an airfield. It is possible to equate a large-scale parking lot with the station 62 of the present embodiment. In that case, the transportation means includes a private car in addition to the bus. It is also possible to equate the interchange of the highway with the station 62 of this embodiment. In this case, the movement of the car entering the highway from the interchange and / or the car getting off the highway is Similar to the above-described embodiment, estimation can be made.

  Although the invention has been described with reference to specific illustrative embodiments, various modifications and alterations may be made to the above-described embodiments without departing from the scope of the invention as defined in the claims. This is obvious to an engineer in the field to which the present invention belongs, and such changes and modifications are also included in the technical scope of the present invention.

It is a schematic block diagram of one Example of this invention. It is an operation | movement flowchart of a present Example. It is an example of the map divided | segmented into the mesh. It is an example of a moving direction of the person who goes to the station 62. FIG. It is an explanatory model of the passing number calculation method of a present Example. The map shown in FIG. 3 shows an example of the number of passengers assigned to the mesh along the roads 66 and 68. The number of people passing through each mesh as a result of the numerical example shown in FIG.

Explanation of symbols

10: Bus 12: CPU
14: ROM
16: RAM
18: Hard disk 20: Monitor 22: Keyboard 24: Mouse 30: Station data file 32: Map data file 34: Road network file 36: Building data file 40: Walking area position setting Program 42: Mesh division program 44: Building volume calculation program 46: Number of passengers distribution program 48: Shortest travel distance determination program 50: Output map creation program 60: Track 62: Stations 64, 66, 68, 70, 72: Main road 74 : Walking distance

Claims (30)

A method of estimating the movement of a person from / to a boarding / exiting point of transportation,
A search area setting step (S1, S2) for setting a search area within a predetermined movement time from the getting-on / off point and divided into meshes of a predetermined size;
A human attraction coefficient setting step (S3) for setting a human attraction coefficient in each mesh in the search area;
Assigning the number of passengers at the boarding point to each mesh in accordance with the human attraction coefficient of each mesh in the search area (S4),
A shortest movement route determination step (S5) for determining a shortest movement route between each mesh in the search area and the getting-on / off point;
For each shortest movement route, assigning the number of passengers assigned to the mesh at the tip of the shortest movement route is assigned to each mesh on the shortest movement route as the number of passers, and the number of passers is assigned for each mesh (S6, S6). S7)
And a population flow estimation method characterized by comprising: The search area setting step includes
A step (S1) of setting a search area within a predetermined travel time from the getting-on / off point;
Dividing the search area into meshes of a predetermined size (S2)
The population flow estimation method according to claim 1, further comprising: The search area setting step includes
Preparing a map that includes the search area and is divided into meshes of a predetermined size;
The population flow estimation method according to claim 1, further comprising a step of setting a search area within a predetermined travel time from the getting-on / off point in the map.   The population flow estimation method according to any one of claims 1 to 3, wherein the number of passing persons includes the number of passengers assigned to each mesh.   The population flow estimation method according to any one of claims 1 to 4, further comprising a step (S8) of creating an output map in which the number of passing persons is synthesized on the map.   The population flow estimation method according to claim 5, further comprising a step (S8) of displaying the output map.   The population flow estimation method according to any one of claims 1 to 6, wherein the transportation means is a railway and the boarding / alighting point is a station of the railway.   The population flow estimation method according to claim 7, wherein the search area is a walking area.   The population flow estimation method according to any one of claims 1 to 8, wherein the attraction coefficient is one of a sum of building volumes and a sum of building floor areas in each mesh.   The population flow estimation method according to any one of claims 1 to 8, wherein the human attraction coefficient is corrected by a weighting coefficient set in consideration of the human attraction force of the facility in the mesh. A device for estimating the movement of a person from / to a boarding / exiting point of transportation,
Search area setting means (40, 42) for setting a search area within a predetermined travel time from the getting-on / off point and divided into meshes of a predetermined size;
Human attraction coefficient setting means (44) for setting a human attraction coefficient in each mesh in the search area;
Passenger number assigning means (46) for assigning the number of passengers at the boarding / exiting points to each mesh in accordance with the attraction coefficient of each mesh in the search area;
Shortest movement route determining means (48) for determining the shortest movement route between each mesh in the search area and the getting-on / off point;
Passer number assigning means (50) for assigning the number of assigned passengers of the mesh at the tip of the shortest travel route as the number of passers to each mesh on the shortest travel route for each shortest travel route, and cumulatively adding for each mesh When,
Output means for outputting the number of passers of each mesh (52)
And a population flow estimation device. The search area setting means is
Search area setting means (40) for setting a search area within a predetermined travel time from the getting-on / off point;
Mesh dividing means for dividing the search area into meshes of a predetermined size (42)
The population flow estimation apparatus according to claim 11, comprising: The search area setting means is
A map including the search area and divided into meshes of a predetermined size;
The population flow estimation apparatus according to claim 11, further comprising means for setting a search area within a predetermined travel time from the boarding / alighting point in the map.   The population flow estimation device according to any one of claims 11 to 13, wherein the number of passing persons includes the number of passengers assigned to each mesh.   15. The population flow estimation according to any one of claims 11 to 14, wherein the output means is map creation means (52) for creating an output map obtained by combining the number of passing persons on the map. apparatus.   16. The population flow estimation apparatus according to claim 15, wherein the output means displays the output map on a monitor screen.   The population flow estimation device according to any one of claims 11 to 16, wherein the transportation means is a railway and the boarding / alighting point is a station of the railway.   The population flow estimation device according to claim 17, wherein the search area is a walking area.   The population flow estimation apparatus according to any one of claims 11 to 18, wherein the attraction coefficient is one of a sum of building volumes and a sum of building floor areas in each mesh.   The population flow estimation device according to any one of claims 11 to 19, wherein the person attraction coefficient is corrected by a weighting coefficient set in consideration of the attraction force of the facility in the mesh. A computer program for estimating a person's movement from / to a point of transportation
A search area setting module (S1, S2) for setting a search area within a predetermined movement time from the getting-on / off point and divided into meshes of a predetermined size;
A human attraction coefficient setting module (S3) for setting a human attraction coefficient in each mesh in the search area;
A passenger number assignment module (S4) for assigning the number of passengers at the boarding / alighting point to each mesh in accordance with the human attraction coefficient of each mesh in the search area;
A shortest movement path determination module (S5) for determining the shortest movement path between each mesh in the search area and the boarding / exiting point;
For each shortest movement route, the number of passers-by assigners of the mesh at the tip of the shortest movement route is assigned as the number of passers to each mesh on the shortest movement route, and the number-of-passers allocation module ( S6, S7)
A population flow estimation program characterized by comprising: The search area setting module is
A module (S1) for setting a search area within a predetermined travel time from the getting-on / off point;
Module for dividing the search area into meshes of a predetermined size (S2)
The population flow estimation program according to claim 21, further comprising: The search area setting module is
A module for preparing a map including the search area and divided into meshes of a predetermined size;
The population flow estimation program according to claim 21, further comprising a module for setting a search area within a predetermined travel time from the boarding / exiting point in the map.   The population flow estimation program according to any one of claims 21 to 23, wherein the number of passing persons includes the number of passengers assigned to each mesh.   25. The population flow estimation program according to any one of claims 21 to 24, further comprising a module (S8) for creating an output map in which the number of passing persons is synthesized on the map.   26. The population flow estimation program according to claim 25, further comprising a module (S8) for displaying the output map.   27. The population flow estimation program according to any one of claims 21 to 26, wherein the transportation means is a railway and the boarding / alighting point is a station of the railway.   28. The population flow estimation program according to claim 27, wherein the search area is a walking area.   29. The population flow estimation program according to any one of claims 21 to 28, wherein the attraction coefficient is one of a sum of building volumes and a sum of building floor areas in each mesh.   The population flow estimation program according to any one of claims 21 to 29, wherein the human attraction coefficient is corrected with a weighting coefficient set in consideration of the human attraction of facilities in the mesh.

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