JP7060460B2 - Simulator, simulation method and simulation program - Google Patents

Description

The present invention relates to a simulator, a simulation method and a simulation program for predicting the movement of an individual in a predetermined space.

A simulator that predicts the behavior of the crowd is used to verify the design of the space where the crowd can move smoothly and the appropriate guidance plan of the crowd in the existing space.
For example, in Patent Document 1, when predicting the movement of a crowd in the event of a fire, it is assumed that the crowd will follow the evacuation direction instruction (evacuation guidance) even if it goes against intuition, and the force acting on the crowd. Assuming the guidance potential by evacuation guidance, a simulator that acts on the movement speed of the crowd is described.

Further, for example, in Patent Document 2, when predicting the behavior of an evacuee in the event of a fire, a self-evacuee may (1) use the exit closest to himself / herself if there is an exit in his / her space, (2). If there is a guide light in your space, the door indicated by the guide light closest to you, (3) In other cases, the door closest to you among the doors in your room, each short-term movement target An evacuation behavior prediction system that predicts the movement of self-evacuated evacuees is described according to the rules selected as.

Japanese Unexamined Patent Publication No. 58-186895 Japanese Unexamined Patent Publication No. 2006-107231

The prior art assumes that the attracted individual behaves uniformly accordingly. For this reason, it is not possible to predict the behavior of the community closer to the real world, including individual differences in the effects of attraction.
For example, when compliance with right-hand traffic is posted, some passers-by stubbornly protect right-hand traffic, while others flexibly use right-hand traffic and left-hand traffic. The behavior of the individual including the difference could not be predicted.
The present invention has been made in view of the above problems, and an object of the present invention is to realize a simulation that can predict the behavior of an individual when there is an individual difference in the influence of the attraction.

According to one embodiment of the present invention, a simulator for predicting the movement of an individual in a predetermined space is provided. The simulator is a spatial information storage means that stores spatial information including a movement target position set in the space, and an attraction area that stores an attraction area set to attract a person who moves to the movement target position. A storage means, an individual information storage means for storing individual information including at least the degree of attraction indicating the position of the individual in space and the degree of attraction of the individual to the attraction area, and an attraction area located around the individual. It is provided with an action determining means for setting a movement amount to bring an individual closer to both a movement target position and an individual information changing means for updating an individual's position according to the movement amount. The action determining means sets the amount of movement that brings the individual closer to the attracting region as the degree of attraction increases.

According to another embodiment of the present invention, a simulation method for predicting the movement of an individual in a predetermined space is provided. In the simulation method, the spatial information including the movement target position set in the space, the attraction area set to attract the person who moves to the movement target position, the position of the individual in the space, and the attraction area. A computer that stores at least individual information including the degree of attraction, which indicates the degree of ease of attraction of an individual, in a storage device so that the higher the degree of attraction, the closer the individual is to the attraction area located around the individual. To execute a process of setting the amount of movement to bring the individual closer to both the attraction area and the movement target position located around the individual, and a process of updating the position of the individual according to the amount of movement.

According to still another embodiment of the present invention, a simulation program is provided that causes a computer to predict the movement of an individual in a predetermined space. The simulation program provides spatial information including the movement target position set in the space, an attraction area set to attract a person moving to the movement target position, an individual position in the space, and an attraction area. A computer that stores at least individual information including the degree of attraction, which indicates the degree of ease of attraction of an individual, in a storage device so that the higher the degree of attraction, the closer the individual is to the attraction area located around the individual. To execute a process of setting the amount of movement to bring the individual closer to both the attraction area and the movement target position located around the individual, and a process of updating the position of the individual according to the amount of movement.

According to the present invention, it is possible to predict the behavior of an individual when there is an individual difference in the influence of the attraction.

It is a schematic block diagram of an example of the simulator which concerns on embodiment. It is a functional block diagram of an example of the simulator which concerns on embodiment. (A) is a diagram showing an example of setting a movable area in which an individual can move in the target space, and (b) is an intermediate target for forming a representative point of the movement target position and a representative path to the movement target position. It is a figure which shows the setting example of a point. It is a figure which shows an example of the attraction area information. (A) to (i) are diagrams showing an example of an attracting region set by the attracting region information of FIG. (A) is a diagram showing an example of time-independent individual information, and (b) is a diagram showing an example of time-dependent individual information. It is a figure which shows an example of the action area. (A) is an explanatory diagram of an example of a calculation method of the acceleration e _i (t) approaching the selected target position, and (b) is an explanatory diagram of an example of the calculation method of the acceleration _ai (t) approaching the attraction region. .. It is explanatory drawing of an example of the calculation method of the acceleration of an individual. It is a flowchart of an example of the simulation method which concerns on embodiment. It is a flowchart of an example of the attraction degree update process. It is a flowchart of an example of an action decision process.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. It should be noted that the embodiments of the present invention shown below exemplify devices and methods for embodying the technical idea of the present invention, and the technical idea of the present invention includes the structure, arrangement, etc. of components. Is not specified as the following. The technical idea of the present invention may be modified in various ways within the technical scope specified by the claims described in the claims.

As an embodiment of the present invention, an example of a simulator that predicts the behavior of each of a plurality of people to move to one of the contact points in a T-junction having three contact points of a west side contact point, an east side contact point, and a south side contact point. Is shown.
That is, in the present embodiment, the space (target space) to be simulated is a T-junction, the individuals constituting the crowd are humans, and the purpose of each individual is provided by a facility or the like at the end of the contact point. Services, etc.

Each individual has a contact port corresponding to the purpose of the individual as a movement target position, and is trying to achieve the purpose by means of movement toward the movement target position.
Further, in this embodiment, a state in which a simulation performer (hereinafter referred to as a user) predicts the behavior of a crowd when a measure for operating on the right side of a T-junction is applied by using a simulator. The explanation will be given using an example.

The T-junction described in the present specification is merely an example of the target space, and the subject of the present invention is not limited thereto. For example, it may be a more complicated space such as a space composed of a plurality of rooms or passages. In that case, the movable area in which the individual can move in the target space has a complicated shape excluding the areas such as walls and fixtures.

(Constitution)
FIG. 1 is a schematic configuration diagram of an example of a simulator according to an embodiment.
The simulator 1 includes an operation input unit 2, a file input / output unit 3, a storage unit 4, a control unit 5, and a display unit 6. Of these, the file input / output unit 3, the storage unit 4, and the control unit 5 can be realized by a so-called computer, and the operation input unit 2 and the display unit 6 can be realized as peripheral devices of the computer.

The operation input unit 2 is a user interface such as a keyboard and a mouse, and is operated by a user to be used for data input and the like. The operation input unit 2 is connected to the control unit 5, converts the user's operation into an operation signal, and outputs the operation to the control unit 5.

The file input / output unit 3 is a DVD (Digital Versatile Disc) drive, a USB (Universal Serial Bus) interface, a network interface, etc., one of which is connected to an external device (not shown), a recording medium, a network, etc., and the other is a control unit. It is connected to 5, the data is input to the control unit 5 as a file, and the data is output from the control unit 5 as a file.

The storage unit 4 is a memory device such as a ROM (Read Only Memory) and a RAM (Random Access Memory), and stores various programs and various data. The storage unit 4 is connected to the control unit 5 and inputs / outputs these information to and from the control unit 5.

The control unit 5 is composed of arithmetic units such as a CPU (Central Processing Unit), a DSP (Digital Signal Processor), and an MCU (Micro Control Unit). The control unit 5 is connected to the storage unit 4 and operates as various processing units by reading and executing a program from the storage unit 4, and stores and reads various data in the storage unit 4. Further, the control unit 5 is also connected to the operation input unit 2, the file input / output unit 3 and the display unit 6, and the user operates the operation input unit 2 to acquire the input data, and the file input / output unit 3 is used. Data is acquired and output as a file from the outside via the file, simulation is performed based on the income data to predict the behavior of the crowd, and the prediction result data is output as a file from the file input / output unit 3 and / or predicted. The resulting data is imaged and displayed on the display unit 6.

The display unit 6 is a display device such as a liquid crystal display or a CRT (Cathode Ray Tube) display, is connected to the control unit 5, and displays the prediction result by the control unit 5. The user visually recognizes the displayed prediction result and considers the behavior of the crowd.

(Function of simulator 1)
FIG. 2 is a functional block diagram of an example of the simulator 1 according to the embodiment.
The operation input unit 2 functions as a part of the condition setting means 20 and the like, and the file input / output unit 3 functions as a part of the condition setting means 20 and a part of the prediction result output means 52 and the like.
The storage unit 4 functions as a spatial information storage means 40, an attraction area storage means 41, an individual information storage means 42, and the like.
The control unit 5 executes a computer program stored in the storage unit 4 by an arithmetic unit such as a CPU, DSP, MCU, etc., so that the action determination means 50, the individual information changing means 51, a part of the prediction result output means 52, etc. Functions as.

The condition setting means 20 receives input of various conditions necessary for the simulation, and stores the input conditions in the storage unit 4. The condition setting means 20 is realized, for example, in collaboration with the operation input unit 2 and the control unit 5, and the control unit 5 stores the value input by the user by operating the operation input unit 2 in the storage unit 4. .. The condition setting means 20 may include a file input / output unit 3 for inputting a file in which a part or all of the conditions are described, and the control unit 5 files a file specified by the user by operating the operation input unit 2. The value acquired through the input / output unit 3 and recorded in the file is stored in the storage unit 4.

The simulation of this embodiment is performed while advancing the virtual time. For example, the process of updating the individual information for each virtual time is repeated while increasing the virtual time by 1 from 0 until all the individuals reach the movement target position. The upper limit T of the virtual time may be set by the condition setting means 20, and the process may be repeated until the virtual time reaches (T-1). Further, the operation input unit 2 may accept the input of the end instruction by the user, and may add a process of forcibly terminating when the end instruction is input.

The virtual time is a simulation of the passage of actual time, but it is advanced every time the simulator finishes the calculation for one hour, and is ticked faster than the passage of actual time. For example, one hour of virtual time corresponds to one actual second, and the virtual time is represented by an integer starting from 0 and increasing by one.

The spatial information storage means 40 stores spatial information including a movable area in which an individual can move in the target space and a plurality of moving target positions. The spatial information is preset by an input operation by the user using the condition setting means 20.
See FIG. 3 (a). The spatial information has, for example, a map 100 of a movable area in the target space defined in the two-dimensional xy coordinate system. The map 100 includes coordinate information representing the movable area 110 indicated by hatching, and in the example of FIG. 3A, the movable area 110 has coordinates (0,20)-(30,20)-. It is defined as a T-shaped closed polygon connecting (30,10)-(20,10)-(20,0)-(10,0)-(10,10)-(0,10).

The spatial information has the target position ID that identifies each movement target position and the coordinate information in association with each other for a plurality of movement target positions defined in the target space.
For example, the movement target position may be a linear region indicating the positions of the west contact port, the east contact port, and the south contact port, respectively. In the example of FIG. 3A, the target position IDs of the western contact port I, the east side contact port II, and the south side contact port III are set to "I", "II", and "III", respectively, and each position is set to a line segment (line segment). Set to 0,20)-(0,10), line segment (30,10)-(30,20) and line segment (10,0)-(20,0).

The spatial information may include a representative point of each movement target position as auxiliary information of the movement target position. In the example of FIG. 3B, the representative points Pt1, Pt2, and Pt3 of the western contact port I, the east side contact port II, and the south side contact port III are the midpoints (0, 15) of the line segment of the western contact port I, respectively. It is the midpoint (30, 15) of the line segment of the east side contact port II and the midpoint (15, 30) of the line segment of the south side contact port III.
In addition, the spatial information may also store an intermediate target point for forming a representative route to each movement target position as another auxiliary information of the movement target position. When there is an obstacle in the target space, the intermediate target point may be appropriately set so as to be the intermediate point of the route for avoiding the obstacle. In the example of FIG. 3B, Pi1 (11,11) and Pi2 (19,11) are set as intermediate target points.

See FIG. The attraction area storage means 41 stores the attraction area information regarding the attraction area that can be a flow line by attracting the individual by the guidance for guiding the individual.
Guidance is, for example, instructions, displays, signs, spatial shapes, voice guidance, etc., and attraction is the action of attracting an individual by guidance. The attraction area is set within the movable area.

For example, the attraction area related to the guidance display for right-hand traffic is the area on the right side of a road or passage. For example, an attraction area is set on the right side of the road corresponding to an arrow (guidance) painted on the right side of the road. Further, for example, the attraction area relating to the lanes divided to guide to each of a plurality of different points is an area corresponding to a lane provided on a road or a passage (for example, an area within each lane).
In the above example, the guidance is displayed in the attraction area, but the attraction area does not have to coincide with the place where the guidance is displayed, and may be an area corresponding to the guidance perceived other than the visual sense. ..

For example, the guide area set on the right side of the road section corresponding to the guide display such as a sign or guide light installed in front of the road section of right-hand traffic or the pylon leading to the right side is the guide display or the spatial shape. This is an example of the case where the change of is provided outside the attraction area.
Further, for example, the attraction area set on the right side of the road section corresponding to the guidance broadcast of the right-hand traffic performed in the road section of the right-hand traffic and / or toward the vicinity of the road section is an instruction perceived by other than the visual sense. This is an example of an attraction area where an individual is attracted by guidance or guidance.

The attraction area information is set in advance by the user using the condition setting means 20.
See FIG. The attraction area information 120 has information on the attraction area ID, the movement target position information, the coordinate information, the attraction degree, and the standard speed for each of the plurality of attraction areas. The attraction area storage means 41 stores the attraction area ID, the movement target position information, the coordinate information, the attraction degree, and the standard speed in association with each other for each attraction area.

The attraction area ID is an identifier of the attraction area.
The movement target position information indicates the movement target position of the individual to be attracted to the attraction area. That is, the attraction area is set to attract an individual moving to a specific movement target position, and the movement target position information is an attraction to attract a specific movement target position and an individual moving to this movement target position. The correspondence with the area is shown.
For example, for a road that runs on the right side extending from east to west, the attraction area that acts on people heading east is set on the south side of the road, and the attraction area that acts on people heading west is set on the north side of the road. Will be done.

The coordinate information of the attracting area indicates the position and shape of the attracting area set in the movable area 110.
The degree of attraction represents the degree of ease of attracting an individual to the attraction area.
The standard velocity represents the standard velocity vector (movement amount per unit time) of the individual in the attraction region.

For example, in the first row of the attraction area information 120 shown in FIG. 4, the attraction area having the attraction area ID “i” is associated with the movement target position I (that is, the western contact port I), and the coordinates (0,20). It is a rectangular region connecting − (0,15) − (15,15) − (15,20), and represents having an attraction degree of “1.0” and a standard velocity (-1,0).
For example, in the fourth line, the attraction area having the attraction area ID “iv” is associated with the movement target position II (that is, the east side contact port II), and the coordinates (0,15)-(0,10)-(15, 10) It is a rectangular region connecting − (15,15), and represents having an attraction degree “1.0” and a standard speed (1,0).
The attraction areas 121 to 129 of the attraction areas IDs “i” to “ix” set by the attraction area information 120 shown in FIG. 4 are shown in FIGS. 5 (a) to 5 (i), respectively. It should be noted that a plurality of attraction regions associated with the same movement target position may be set to have overlapping portions.

See FIG. The individual information storage means 42 stores individual information which is information about each of a plurality of individuals constituting the community.
6 (a) and 6 (b) show examples of individual information 130 and 131, respectively. The individual information 130 in FIG. 6A is time-independent information, and the individual information 131 in FIG. 6B is time-dependent individual information. The individual information 130 and 131 shown in FIGS. 6 (a) and 6 (b) are merely examples, and the structure of the individual information of the present invention is not limited thereto.

The individual information may include, for example, at least a state value and an action parameter. The individual information may further include the degree of attraction for each individual.
(Individual state value)
The state value indicates the state of each individual at each virtual time. In the present embodiment, the state value indicates the position of the individual in the target space.
The initial value of the position of each individual is set as the initial position by the input operation by the user using the condition setting means 20. In the present embodiment, the initial value of the position of each individual may be any of the western contact port I, the east side contact port II, and the south side contact port III.

In the example of FIG. 6A, the initial position of the individual with the individual ID “0” is set to the position (17.78,0) of the south contact port III.
After setting the initial position, the position of each individual is updated at any time by the individual information changing means 51 with the passage of the virtual time. For example, the individual with the individual ID "0" at the virtual time "1" moves to the position (17.78, 1.3), and by the virtual time "463", the position of the western contact port I (0, 15.21). ) Is moving.

(Individual behavior parameters)
The behavior parameter represents the behavior of each individual at each virtual time, and is a parameter that affects the state value.
For example, the behavior parameters are the purpose of each individual's action, the means of action determined by the individual to achieve the purpose, and the per unit time (1 virtual time) due to the individual performing the means of the action. It may include the change amount (state change amount) of the state value (for each).

For example, the purpose of the action in the present embodiment may be movement to the A station, movement to the B station, movement to the audience seats, or the like, and the identifier assigned to each purpose may be stored as an action parameter.
Further, for example, the means of action in the present embodiment is movement to a movement target position, and the target position ID of the movement target position selected as the movement target as the means of action may be stored as an action parameter. Hereinafter, the movement target position selected as the movement target may be referred to as "selected target position".

For example, the target position ID "I" is for an individual whose western contact port is the selected target position, the target position ID "II" is for the individual whose eastern contact port is the selected target position, and the south contact port is the selected target position. The target position ID "III" is stored in a certain individual. In the example of FIG. 6A, the western contact port I is designated as the selection target position of the individual with the individual ID “0”.
The selection target position, which is the means of action, is determined according to the purpose of the action, and the simulator 1 of the present embodiment relates to the processing after the selection target position is set. Therefore, the purpose of the action is described below. The description is omitted.

The state change amount in the present embodiment is a movement amount per unit time (per virtual time), and a vector that is a set of an increment value in the x direction and an increment value in the y direction is stored as the state change amount.
For example, the amount of movement when moving one unit distance to the north is (0,1), the amount of movement when moving one unit distance to the northwest is (0.707, -0.707), and so on. To. If the movement amount of the individual whose virtual time t position is (5,15) is (0,1), the virtual time (t + 1) position is updated to (5,16).

In the example of FIG. 6B, at the virtual time "0", the movement amount of the individual with the individual ID "0" and the ID "1" is (0,1.3), and the movement of the individual with the individual ID "99" is The quantity is (1.3,0). The movement amount of the individual with the individual ID "0" at the virtual time "463" is (-1.09,0), and the movement amount of the individual with the individual ID "1" at the virtual time "572" is (1.1, 0.01), and the amount of movement of the individual with the individual ID “99” at the virtual time “789” is (0, −1.09).

(Degree of attraction)
The degree of attraction is a parameter expressing the individuality of an individual, and is a value indicating the degree of easiness of being attracted to the attracting region of each individual. Individuals with a higher degree of attraction are more likely to be attracted to the attraction area, and individuals with a lower degree of attraction are less likely to be attracted. The initial value of the degree of attraction is set in advance by an input operation by the user using the condition setting means 20, and then appropriately updated by the individual information changing means 51.
In the embodiment that does not consider the degree of attraction for each individual, the degree of attraction may not be included in the individual information.

In the example of FIG. 6A, a value "1.0" is set as an initial value of the degree of attraction of an individual with an individual ID "0". In the example of FIG. 6B, the degree of attraction of the individual with the individual ID “0” is updated to “0.85” by the virtual time “463”.
For example, the range of attraction degree is "0" or more and "1" or less. For example, a person corresponding to an individual having an attraction degree of "0" is a person who acts in the real world completely ignoring guidance such as right-hand traffic, and corresponds to an individual having an attraction degree of "1". A person is a person who obstinately follows guidance such as right-hand traffic in the real world.

In addition, the individual information may include any of a time constant for each individual, a region of action for each individual, and a generation time for each individual. The time constant for each individual is the time required for the degree of attraction of the individual to become the same as the degree of attraction of other individuals (hereinafter, may be referred to as "surrounding individuals") existing in the surroundings. It is the time required from when the degree of attraction of the individual becomes the same as the degree of attraction of surrounding individuals to when it returns to the initial value.

The time constant is expressed as the reciprocal of the speed at which the degree of attraction of the individual changes. The larger the time constant, the longer it takes for the individual to become the same as the degree of attraction of surrounding individuals, and the smaller the time constant. In a short time, it becomes the same as the degree of attraction of surrounding individuals.
The time constant is preset by an input operation by the user using the condition setting means 20.
For example, the range of the time constant is "1" or more and "1000" or less.

In the real world, a person corresponding to an individual whose time constant is "1" should act according to the guidance as soon as the surrounding person follows the guidance such as right-hand traffic, and the surrounding person ignores the guidance. He is a person who immediately ignores himself and acts. The person corresponding to the individual whose time constant is "1000" is a person who does not easily change the intention of whether or not the surrounding person follows the guidance such as right-hand traffic in the real world. be.
In the example of FIG. 6A, the value "1" is set as the time constant of the individual with the individual ID "0", and the value "1000" is set as the time constant of the individual with the individual ID "1".

The area of action for each individual indicates the attraction area around the individual and the area where the surrounding individual acts on the individual. That is, the attraction region that attracts an individual is only the attraction region that exists in the action region of the individual, and the other individuals that affect the degree of attraction of the individual are the surrounding individuals that exist in the action region of the individual. Only.
The action area is calculated and updated by the individual information changing means 51.

FIG. 7 shows an example of the working area. For example, the action area is set around the individual so as to spread in an attractive area around the individual or in a direction of being affected by the surrounding individual. In the present embodiment, the action region Ri of each individual at the virtual time t is defined as a sector having a central angle θ (for example, 60 °) and a radius R (for example, 5 unit distance) centered on the position x _i (t) of the individual. do.
The subscript i (i = 1, 2, ..., M: M is the total number of individuals) is an index representing the individual of interest, and the individual ID may be used as the subscript i. Hereinafter, an individual whose individual ID is "i" may be referred to as an individual i.

The individual information 131 of the present embodiment stores a vector indicating the direction of the fan-shaped center line Lc (that is, the line connecting the intersection of the two radii and the center of the arc) as the working area information representing the working area Ri. The vector has the same value as the movement amount of the same individual at the same time. As a result, the action area is set so as to expand in a biased manner in the moving direction of the individual i, for example.
In the example of FIG. 6 (b), at the virtual time "0", the action area of the individual with the individual ID "0" is centered on the position (17.78, 0), and the direction of the center line Lc is the same vector as the movement amount. It is a fan-shaped region represented by (0, 1.3) with a central angle of 60 ° and a radius of 5 units.

The generation time for each individual is a virtual time for generating information of each individual, and is a virtual time for each individual to appear in the target space.
In the present embodiment, the generation time for each individual is set in order to simulate how a person appears in the target space one after another via the contact port, but all the individuals exist when the virtual time is 0. When simulating the target space, the generation time setting may be omitted.
In the example of FIG. 6A, the individual with individual IDs "0" and "1" appears in the target space at the virtual time "0", and the individual with the individual ID "99" appears in the target space at the virtual time "42". Appear.

See FIG. At each virtual time t, the action determining means 50 stores the spatial information stored in the spatial information storage means 40, the attracted area information stored in the attracted area storage means 41, and the individual information storage means 42. Refer to individual information.
The action determining means 50 determines the action parameter for each individual (that is, determines the action for each individual) based on this information, and outputs the determined action parameter to the individual information changing means 51.

Specifically, the action determining means 50 determines the amount of movement of the individual at each virtual time t.
First, the action determining means 50 uses an acceleration vector e _i (t) for the individual i (i = 1, 2, ..., M) to bring its position closer to the selected target position at the virtual time t. Is calculated.

The action determining means 50 reads out the position x _i (t) of the individual i from the individual information storage means 42 and the selection target position of the individual i, and also from the spatial information storage means 40 to the representative point of the selection target position and the movable area 110. Read the intermediate target point.
The action determining means 50 is a path connecting the position x _i (t) of the individual i and the representative point of the selected target position in the movable region 110, and the position x _i (t) of the individual i via an arbitrary intermediate target point. ) And the shortest route connecting the representative points. At this time, the action determining means 50 may exclude the route deviating from the movable area 110 from the selection target.

Next, the action determining means 50 identifies the point closest to the position x _i (t) of the individual i among the representative points of the intermediate target point and the selected target position on the selected shortest path as the short-term target point. If the shortest path does not pass through the intermediate target point, the representative point of the selected target position is specified as the short-term target point. When the shortest path passes through a plurality of intermediate target points, the intermediate target point closest to the position x _i (t) of the individual i among these intermediate target points is specified as the short-term target point.
The action determining means 50 calculates the unit vector from the position x _i (t) of the individual i toward the short-term target point as the acceleration e _i (t) that approaches the selected target position.

See FIG. In the example of FIG. 8A, as a result of excluding the path R0 deviating from the movable region 110 from the selection target, the position x _i (t) of the individual i, the intermediate target point Pi2, and the representative point Pt2 of the selection target position II are set. The connecting route R1 is the shortest route, and the intermediate target point Pi2 is selected as the short-term target point.
The unit vector e from the position x _i (t) of the individual i toward the representative point of the selected target position may be calculated as the vector e _i (t) without setting the intermediate target point.

Next, the action determining means 50 brings the individual i closer to the attracting region by the attractive force acting on the individual i from the attracting region at the virtual time t for each of the individual i (i = 1, 2, ..., M). The acceleration vector _ai (t) for this is calculated.

The action determining means 50 reads out the position x _i (t) of the individual i, the selection target position of the individual i, and the action area information of the individual i from the individual information storage means 42, and reads out the information of the attraction area from the attraction area storage means 41. ..
The action determining means 50 calculates the working area Ri of the individual i in the target space based on the position x _i (t) of the individual i and the working area information of the individual i. The action determining means 50 detects an attracting region having an overlapping region with the working region Ri and being associated with the same moving target position as the selected target position from the attracting regions.

When detecting the overlapping region between the working region Ri and the attracting region, the action determining means 50 sets a plurality of sample points Ps in the working region Ri at equal intervals in the central angle direction and the radial direction. When any of the sample points Ps is included in the attracting region, the action determining means 50 determines that there is an overlapping region between the acting region Ri and the attracting region.
When an attraction region that has an overlapping region Ro with the action region Ri and is associated with the same movement target position as the selection target position II is detected, the action determining means 50 indicates that the individual i is the attraction target of the attraction region. It is determined that it is an individual ξ, and a vector _ai (t) that is not a zero vector is calculated according to the following equation (1).
When the attracting region having an overlapping region Ro with the working region Ri and being associated with the same movement target position as the selection target position II is not detected, the action determining means 50 determines that the individual i is the attracting target individual ξ in the attracting region. It is determined that this is not the case, and the vector _ai (t) is set to the 0 vector according to the following equation (2).

See FIG. In the example of FIG. 8B, the fan-shaped figure Ri is the action area of the individual i, and the 16 points drawn in the action area Ri are the sample points Ps. In the example of FIG. 8B, the hatched region Ro is an overlapping region between the working region Ri and the attracting region. Of the attracting area 123 (ID = iii) and the attracting area 126 (ID = vi) including one or more of these sample points Ps inside, the attracting area 126 associated with the selection target position II Detected.

In the equation (1), the subscript r is an index of the sample points included in the attracting region, and Σr indicates the sum of the sample points included in the attracting region.
Further, the vector xr ( _t -1) is a sample point (that is, a sample point in the overlapping region Ro) set in the action region Ri of the individual i at the virtual time (t-1) and included in the attraction region. The coordinates. The vector x _i (t-1) is the coordinates of the position of the individual i at the virtual time (t-1). Also, Normalize (・) is an operator that unit-vectorizes (normalizes) a vector.

The difference vector between the vector x _r (t-1) and the vector x _i (t-1) represents a vector representing the direction from the position of the individual i toward the overlapping region Ro of the action region Ri and the attraction region.
Further, the variable S _r indicates the degree of attraction set in the attraction region including the sample point r. In equation (1), the degree of attraction Sr is multiplied by the difference vector between the vector x _r (t-1) and the vector x _i (t-1).

Therefore, the vector _ai (t) in the equation (1) is a weighted average vector obtained by weighting and averaging the unit vectors representing the direction from the position of the individual i toward the overlapping region Ro by the attraction degree of the attracting region, and the duplication. It becomes a vector toward the weighted average point in the region Ro.
Therefore, by following the equation (1), a vector indicating the direction in which the individual _i is brought closer to the attracting region corresponding to the selection target position of the individual i can be calculated as the acceleration _ai (t) that brings the individual i closer to the attracting region. ..

In particular, since the action region Ri is set around the individual i (for example, the range of the radius R centered on the individual i), the vector _ai (t) moves to the attraction region located around the individual i. A vector indicating the direction in which i is approached can be calculated as the acceleration _ai (t) that brings the individual _i closer to the attraction region.
That is, it is possible to calculate the acceleration _ai (t) that brings the attracted individual whose distance between the attracted region corresponding to the selection target position of the individual i is less than the reference to the attracted region closer to the attracted region.
By calculating the acceleration _ai (t) as described above, it is possible to predict the behavior of each individual having a different selection target position under the influence of different attraction depending on the selection target position. Therefore, it is possible to predict the behavior of the crowd in the space where the guidance according to each of the plurality of movement target positions is performed.

The vector _ai (t) becomes larger as the degree of attraction set in the overlapping region Ro is higher, and becomes smaller as the degree of attraction is lower.
Further, when a plurality of attracting regions having overlapping regions Ro are detected, the vector _ai (t) is an average of the number of sample points included in the overlapping regions Ro of each attracting region, and thus the area of the overlapping regions Ro. The larger the attraction region, the higher the contribution to the vector _ai (t).
Therefore, the acceleration _{ai (t) that makes it easier to approach the attracted individual to the attracted region with a higher degree of attraction can be calculated, and the acceleration ai (} t) that makes it easier to approach the attracted individual to the attracted region where the overlapping region Ro is larger can be calculated. Can be calculated.

変数Ｑｒは、重複領域Ｒｏの面積を示し、ベクトルｘ_ｃ（ｔ－１）は仮想時刻（ｔ－１）における重複領域Ｒｏの重心位置である。 The acceleration _ai (t) may be calculated using the area of the overlapping region Ro instead of the sum of the sample points. In this case, for example, the action determining means 50 may calculate the acceleration _ai (t) according to the following equation (3) instead of the equation (1).

The variable QR indicates the area of the overlapping region Ro, and the vector x _c (t-1) is the position of the center of gravity of the overlapping region Ro at the virtual time (t-1).

Further, the direction of the action region Ri of the individual i is determined based on the amount of movement of the individual i. This makes it possible to calculate the acceleration _ai (t) that approaches the attracting region existing in the moving direction of the individual. Therefore, it is possible to calculate the acceleration _ai (t) that brings the individual i closer to the attracting region that exists in the direction in which it is easily affected. Therefore, even if the attraction region is set in a range where the individual i is not easily affected, it is possible to prevent the influence and predict a smooth movement close to the behavior of the individual in the real world.
Instead of the fan-shaped working region Ri shown in FIG. 7, a circular region having a radius R centered on the position x _i (t) of the individual i may be used as the working region Ri.

Further, the action determining means 50 determines the acceleration vector bi (for each of the individuals i (i = 1, 2, ..., M) required for the individual _i to adjust its own velocity to the standard velocity of the attracting region at the virtual time t (). t) is calculated.
When an attracting region that has an overlapping region Ro with the working region Ri and is associated with the same movement target position as the selected target position II is detected, it is determined that the individual i is the attracted individual ξ of the attracting region. Then, a vector _bi (t) that is not a zero vector is calculated according to the equation (4).

式（４）中、Ａは検出された誘引領域、Σ_Ａは検出された誘引領域についての総和を示す。ベクトルＶ_Ａは検出された誘引領域に設定されている標準速度であり、右辺の第一項は検出された誘引領域についてのベクトルＶ_Ａの平均ベクトルとなる。 If the attracting region that has an overlapping region Ro with the working region Ri and is associated with the same movement target position as the selected target position II is not detected, the individual i is determined not to be the attracted individual ξ in the attracting region. According to (5), let the vector bi (t) be a ₀ vector.

In equation (4), A is the detected attracting region, and Σ _A is the sum of the detected attracting regions. The vector _VA is the standard velocity set in the detected attracting region, and the first term on the right side is the average vector of the vector _VA for the detected attracting region.

Therefore, the vector bi (t) is a difference vector between the standard velocity (average) set in the detected attraction region and the velocity of the individual _i , and the movement amount of the individual i corresponds to the selected target position II. The acceleration is close to the standard velocity in the attraction region.
This makes it possible to predict the behavior of the crowd in the space where the speed regulation operation measures corresponding to each of the plurality of movement target positions are carried out. In addition, smooth movements similar to the behavior of individuals in the real world, such as movement along a passage, can be predicted.

図９は式（６）のベクトル合成の説明図である。 Then, the action determining means 50 sets the acceleration e _i (t) approaching the selected target position, the acceleration _ai (t) approaching the attracting region, and the acceleration bi (t ₎ approaching the standard speed of the attracting region according to the following equation (6). By synthesizing, the acceleration vector Δvi (t) of the individual _i at the virtual time t is calculated.

FIG. 9 is an explanatory diagram of the vector composition of the equation (6).

The ni (t) in the equation (6) is the degree of attraction for each individual _i at the virtual time t acquired by reading from the individual information storage means 42.
_{ni (t) is multiplied by the acceleration ai (} t) that approaches the attracting region. Therefore, the higher the degree of attraction, the greater the contribution of the acceleration _ai (t) to the acceleration vector Δv _i (t).
Therefore, it is possible to calculate the acceleration Δv _i (t) that brings the individual i with a higher degree of attraction _ni (t) closer to the attraction region.
In the embodiment in which the degree of attraction ni (t) for each individual _i is not taken into consideration, _ni (t) may be omitted from the formula (6).

The action determining means 50 calculates the movement amount of the individual i at the time t by adding the acceleration Δv _i (t) to the movement amount of the individual i at the time (t-1) one time before (Equation (7)). ..

In equation (7), the vector vi (t) is the amount of movement of the individual _i at time t. Further, the vector v _i (t-1) is the amount of movement of the individual i at the time (t-1) one time before the time (t-1) read from the individual information storage means 42 and acquired.
When the vector _vi (t) exceeds the maximum speed Vmax as a result of the calculation of the equation (7), the action determining means 50 corrects the vector _vi (t) to the maximum speed Vmax. The maximum speed Vmax may be set in advance to a value according to, for example, the type of individual or space.

In this way, the movement amount is calculated based on the acceleration _Δvi (t) obtained by adding the acceleration e _i (t) approaching the selected target position and the acceleration _ai (t) approaching the attraction region, thereby attracting. The amount of movement that brings the individual i closer to both the area and the selection target position can be calculated.
In addition, by calculating the amount of movement based on the acceleration Δv _i (t ₎ obtained by adding the acceleration bi (t) that approaches the standard speed of the attractive region, it is attempted to match the standard speed of the attractive region such as speed regulation. The amount of movement of the individual i can be predicted.

See FIG. The individual information changing means 51 newly creates, updates, and deletes individual information.
When newly creating individual information, the individual information changing means 51 appropriately creates new individual information according to the conditions set by the condition setting means 20, and additionally stores the individual information in the individual information storage means 42.
When updating the individual information, the individual information changing means 51 updates the state value of each individual with the behavior parameter of the individual.

In the present embodiment, the individual information changing means 51 updates by adding the movement amount of the action parameters to the position of the state values. Specifically, the individual information changing means 51 reads the position and the movement amount of each individual from the individual information storage means 42, adds the read movement amount to the read position for each individual, and obtains the position after addition. It is stored in the individual information storage means 42.

Further, the individual information changing means 51 updates the degree of attraction for each individual. When another individual (surrounding individual) exists around the individual i of interest, the individual information changing means 51 brings the degree of attraction of the individual of interest closer to the degree of attraction of the surrounding individual.
The individual information changing means 51 may detect, for example, an individual in the fan-shaped working region Ri shown in FIG. 7 as a surrounding individual. In this case, the individual information changing means 51 reads out the position and action area information of each existing individual from the individual information storage means 42, and the action area of the individual i in the target space from the position of the individual i and the action area information of the individual i. Ri is calculated, and individuals other than the individual i existing in the action region Ri are detected as surrounding individuals.

Instead of the fan-shaped working region Ri shown in FIG. 7, a circular region having a radius R centered on the position of the individual i may be used as the working region Ri.
When the surrounding individual does not exist, the individual information changing means 51 updates the attracting degree of the individual i of interest to approach the initial value of the attracting degree of the individual i.
Specifically, the individual information changing means 51 updates the degree of attraction _ni (t) according to the following equations (8) and (9).

In the equations (8) and (9), the subscript i indicates the individual ID of the individual of interest, t indicates the latest virtual time, and n _i (t) indicates the degree of attraction of the individual i at the virtual time t. .. Further, the subscript k indicates the individual ID of the surrounding individual of the individual i, Σk indicates the total sum of the surrounding individuals, K indicates the total number of the surrounding individuals, and τ _i indicates the time constant of the individual i.

According to the formula (8), when there is a surrounding individual in the individual i of interest, the degree of attraction of the individual i of interest is set to the initial degree of attraction of the surrounding individual and the degree of attraction of the individual i of interest over time. It can be close to the average value with the value. This update corresponds to the synchronization of individuals within the community by acting as a crowd in the real world.
According to the formula (9), when the surrounding individual does not exist in the individual i of interest, the degree of attraction of the individual can be returned to the initial value with the passage of time. This update corresponds to the degree of attraction that has changed due to synchronization with surrounding individuals in the real world, but returns to the original degree of attraction over time.

By doing so, it is possible to make highly accurate predictions in line with the psychology of individuals in the real world. In the embodiment that does not consider the degree of attraction for each individual, the process of updating the degree of attraction _ni (t) may be omitted.
The individual information changing means 51 deletes an individual that has reached the movement target position. This processing means that the individual information of the corresponding individual is set outside the processing target of the action determining means 50.

The prediction result output means 52 outputs the prediction result which is the result of the simulation. The prediction result includes at least a part or all of the information stored in the individual information storage means 42, and preferably further includes a part or all of the information stored in the spatial information storage means 40. Further, the prediction result output means 52 statistically processes the information stored in the individual information storage means 42 or the information and the information stored in the spatial information storage means 40, and uses the obtained statistics as the prediction result. You may output it.

The output timing of the prediction result may include the middle of the simulation in addition to the time when the simulation is completed. That is, a part or all of the above information obtained up to the middle of the simulation may be output as an intermediate prediction result.

Further, the prediction result output means 52 may output the prediction result as a data file, may be output as an image file, may be displayed as an image, or may be displayed in one or more of these formats. You may output it.
The prediction result output means 52 creates an image of the prediction result by imaging a map of the target space including the exit and superimposing and drawing a figure representing the individual on the coordinates corresponding to the position of each individual on the image. The image is output as a file and / or is output to the display unit 6.

(Operation example of Simulator 1)
An operation example of the simulator 1 will be described with reference to the flowcharts of FIGS. 10 to 12.
In step S1, the condition setting means 20 receives the input from the user and sets the simulation conditions. That is, the simulation conditions obtained by the user manually inputting using the condition setting means 20 are set in the spatial information storage means 40, the attraction area storage means 41, and the individual information storage means 42.
The simulation conditions may be acquired by the user designating a file using the condition setting means 20 and reading it from the corresponding file by the corresponding condition setting means 20.

When the simulation conditions are set, the simulator 1 initializes the virtual time to 0 in step S2 and sets the loop processing of steps S2 to S9.
In the virtual time loop processing, first, in step S3, the attraction degree update process for updating the attraction degree of each individual is performed.
With reference to FIG. 11, the attraction degree update process in step S3 will be described.

In step S30, the individual information changing means 51 sequentially sets each of the existing individuals to the individual i to be processed.
In step S31, the individual information changing means 51 reads out the position and action area information of the individual i from the individual information storage means 42, and calculates the action area Ri of the individual i from the position of the individual i and the action area information of the individual i.

In step S32, the individual information changing means 51 reads out the position of an existing individual from the individual information storage means 42, and detects an individual other than the individual i existing in the action region Ri as a surrounding individual.
In step S33, the individual information changing means 51 determines whether or not a surrounding individual exists. If there are surrounding individuals (S33: Y), the process proceeds to step S34. When there is no surrounding individual (S33: N), the process proceeds to step S35.

In step S34, the individual information changing means 51 reads out the degree of attraction of surrounding individuals from the individual information storage means 42. The individual information changing means 51 brings the degree of attraction of the individual i to be processed close to the degree of attraction of surrounding individuals according to the equation (8). After that, the process proceeds to step S36.
In step S35, the individual information changing means 51 reads out the initial value of the degree of attraction of the individual i from the individual information storage means 42. The individual information changing means 51 brings the degree of attraction of the individual i to be processed close to the initial value according to the equation (9). After that, the process proceeds to step S36.

In step S36, the individual information changing means 51 determines whether or not the processing has been completed for all the existing individuals. When the processing for all the existing individuals is completed (S36: Y), the attracted degree update processing is terminated, and the processing returns to step S4 in FIG. If the processing has not been completed for all of the existing individuals (S36: N), the processing returns to S31.

See FIG. In step S4, the action determining means 50 performs an action determining process for calculating the movement amount of each individual.
The action determination process of step S4 will be described with reference to FIG.
In step S40, the action determining means 50 sequentially sets each of the existing individuals to the individual i to be processed.

In step S41, the action determining means 50 reads out the position of the individual i and the selected target position from the individual information storage means 42, and reads out the representative point, the movable area, and the intermediate target point of the selected target position from the spatial information storage means 40. The action determining means 50 calculates the acceleration ei (t) that brings the individual i closer to the selected target position based on the position of the individual i, the representative point of the selected target position, the movable region, and the intermediate target point.

In step S42, the action determining means 50 reads out the position and action area information of the individual i from the individual information storage means 42, and calculates the action area Ri of the individual i from the position of the individual i and the action area information of the individual i.
In step S43, the action determining means 50 reads out the coordinate information of all the attracting regions and the movement target position associated with the attracting region from the attracting area storage means 41. The action determining means 50 detects an attracting region that has an overlapping region Ro with the working region Ri and is associated with the same movement target position as the selection target position of the individual i. When there is an attraction area (S43: Y), the process proceeds to S44. If there is no attraction area (S43: N), the process proceeds to S47.

In step S44, the action determining means 50 reads out the detected attraction degree Sr of the attracting area from the attracting area storage means 41. The action determining means 50 sets the coordinates of the sample points Ps set in the overlapping region Ro, the position of the individual i, and the acceleration _ai (t) that approaches the attracting region based on the attraction degree Sr according to the equation (1). calculate.
In step S45, the action determining means 50 reads out the detected standard speed of the attracting region from the attracting region storage means 41. The action determining means 50 calculates the acceleration bi (t) that brings the movement amount of the individual _i closer to the standard speed according to the equation (4).

In step S46, the action determining means 50 uses the degree of attraction updated in step S3, and according to the equation (6), the acceleration _{e i} (t), the acceleration _ai (t), and the acceleration bi (t). Is vector-synthesized to calculate the acceleration Δv _i (t) of the individual i. After that, the process proceeds to step S48.
When the attraction region having an overlapping region Ro with the action region Ri and associated with the selection target position of the individual i is not detected (step S43: N), the action determining means 50 accelerates in step S47. The acceleration Δv _i (t) of the individual _i is calculated assuming that a _i (t) and the acceleration bi (t) are 0 vectors. After that, the process proceeds to step S48.

In step S48, the action determining means 50 calculates the movement amount of the individual i at the time t by adding the acceleration Δv _i (t) to the movement amount of the individual i at the time (t-1) one time before. 7)). The action determining means 50 stores the calculated movement amount of the individual i in the individual information storage means 42.
In step S49, the action determining means 50 determines whether or not the processing has been completed for all the existing individuals. When the processing is completed for all the existing individuals (S49: Y), the action determination processing is completed, and the processing returns to step S5 in FIG. If the processing has not been completed for all the existing individuals (S49: N), the processing returns to S41.

See FIG. In step S5, the individual information changing means 51 updates the amount of information of the existing individual. Specifically, the individual information changing means 51 reads the position and the movement amount of each individual from the individual information storage means 42, adds the read movement amount to the read position for each individual, and determines the position after the addition. It is stored in the individual information storage means 42.

In step S6, the individual information changing means 51 creates new individual information whose generation time has come according to the conditions set by the condition setting means 20, and additionally stores it in the individual information storage means 42.
In step S7, the individual information changing means 51 deletes the individual that has reached the movement target position.

In step S8, the individual information changing means 51 determines whether or not all the individuals stored in the individual information storage means 42 have reached the movement target position. When all the individuals have reached the movement target position (S8: Y), the process proceeds to step S10. If there are still individuals that have not reached the movement target position (S8: N), the process proceeds to step S9.

In step S9, the simulator 1 advances the virtual time by 1 and returns the process to step S3.
In step S10, the prediction result output means 52 outputs the prediction result which is the result of the simulation. After that, the process ends.

(Modification example)
(1) In the above embodiment, the individual information changing means 51 changes the degree of attraction of an individual, but the degree of attraction may not be changed with time depending on the purpose of the simulation. In that case, the process of updating the degree of attraction by the individual information changing means 51 may be omitted.

(2) In the above embodiment and its modification, the individual information changing means 51 updates the position of the individual without considering the size of the individual and without eliminating the overlap between the individuals, but these physical restrictions The position of the individual may be updated in consideration of.
In this case, for example, the individual information changing means 51 may update the movement amount determined by the action determining means 50 so that the individuals do not overlap each other in the target space.

Further, for example, after the action determining means 50 calculates the amount of movement that brings the individual closer to both the selection target position and the attraction region or the movement amount that brings the individual closer to the selection target position, the action determination means 50 itself transfers the movement amount between the individuals. The individual information changing means 51 may update the position of the individual using the movement amount corrected by the action determining means 50.

(3) In the above embodiment and its modification, an example of a simulation relating to a target space having a plurality of moving target positions is shown, but the present invention can be applied to a target space having a single moving target position. good.
When there is only one moving target position, the storage of the selected target position by the individual information storage means 42 may be omitted. Further, in the process of determining the attracted individual ξ by the action determining means 50, the collation between the moving target position associated with the attracted region and the selected target position may be omitted.

(4) In the above embodiment and its modification, the individual information changing means 51 updates the attracted degree _ni (t) based on the equations (8) and (9), but the following equation (10) is used. The degree of attraction _ni (t) may be updated based on the above.

The second term (N ₁ / τ _i ) on the right-hand side of the equation (10) is the same as the second term on the right-hand side of the equation (8).
The variable N ₂ in the third term (N ₂ / τ _i ) on the right side of the equation (10) may be set according to, for example, the degree of congestion around the individual i of interest.
For example, the degree of congestion around the individual i can be determined based on the number of individuals existing around the individual i and the staying time of the individual i in the target space. In the above-mentioned modification in which the movement amount is corrected in consideration of physical restrictions such as the size of individuals and the overlap between individuals, the degree of congestion can be determined based on the corrected movement amount.

Therefore, the individual information changing means 51 responds to the degree of congestion calculated based on the number of individuals around the individual i, the time spent in the target space, and / or the amount of movement after correction due to physical constraints. The variable N ₂ may be set.
For example, the individual information changing means 51 may reduce the degree of attraction by setting a predetermined negative value in the variable N ₂ when the degree of congestion is “high”. This makes it possible to reflect in the simulation an event in which the psychology of ignoring guidance is likely to occur due to the discomfort felt by congestion in the real world.

Further, for example, the individual information changing means 51 may reduce the degree of attraction by setting a predetermined negative value in the variable N ₂ when the degree of congestion is “low”. As a result, when it is not crowded in the real world, it is possible to reflect in the simulation an event in which the psychology of ignoring guidance and moving freely is likely to occur.
Further, for example, the individual information changing means 51 may increase the degree of attraction by setting a predetermined positive value in the variable N ₂ when the degree of congestion is “medium”. In addition to this, even when the degree of congestion is “low”, a predetermined positive value may be set in the variable N ₂ .

For example, the individual information changing means 51 detects the surrounding individuals of the individual i of interest, and when the number of surrounding individuals is less than a predetermined quietness determination threshold value, determines that the degree of congestion is "low" in advance. The defined negative value may be set in the variable N ₂ to reduce the degree of attraction.
Further, for example, when the number of surrounding individuals exceeds a predetermined congestion determination threshold value, the individual information changing means 51 determines that the degree of congestion is “high” and sets a predetermined negative value in the variable N ₂ . , The degree of attraction may be reduced.
Further, for example, when the number of surrounding individuals is equal to or greater than the quiet determination threshold value and equal to or less than the congestion determination threshold value, the individual information changing means 51 determines that the congestion degree is “medium” and sets a predetermined positive value in the variable N ₂ . , The degree of attraction may be increased.

Further, for example, the individual information changing means 51 calculates the staying time of the individual i in the target space, and when the staying time exceeds a predetermined staying determination threshold value, determines that the degree of congestion is “high” and determines in advance. The negative value may be set in the variable N ₂ to reduce the degree of attraction.
Further, for example, in the modified example in which the movement amount is corrected in consideration of physical restrictions such as the size of individuals and the overlap between individuals as described above, the individual information changing means 51 has the corrected size of the movement amount in advance. When it is less than the predetermined unpleasant speed, it may be determined that the degree of congestion is "high" and a predetermined negative value may be set in the variable N ₂ to reduce the degree of attraction.
As a result, it is possible to reflect in the simulation an event in which the psychology of ignoring the guidance is likely to occur due to the discomfort caused by the speed reduction caused by congestion in the real world.

Further, for example, the individual information changing means 51 determines that the degree of congestion is "medium" or "low" when the magnitude of the corrected movement amount exceeds a predetermined comfort speed, and determines that the degree of congestion is "medium" or "low". The value may be set in the variable N ₂ to increase the degree of attraction.
As a result, it is possible to reflect in the simulation an event in which the psychology of following guidance such as right-side traffic regulation is likely to occur due to the ease of joining a crowd that is flowing smoothly in the real world.

The above-mentioned unpleasant speed and comfortable speed may be values common to all individuals, or different values may be set for each individual. Further, instead of directly referring to the corrected movement amount, the magnitude of the corrected movement amount may be calculated from the time series of the positions of the individuals stored in the individual information storage means 42.

The variable N ₃ in the fourth term (N ₃ / τ _i ) on the right side of the equation (10) may be set according to, for example, the total energy consumption of the individual i of interest. For example, the individual information changing means 51 calculates the total energy consumption _Ei (t) for each individual according to the following equation (11).

In the equation (11), the subscript i is the individual ID of the individual of interest, the variable t is the latest virtual time, the variable E _i (t) is the total energy consumption at the virtual time t, and the variable S _i . Is the amount of energy consumed not depending on the amount of movement of the individual _i , the variable wi is the amount of energy consumed depending on the amount of movement of the individual _i , and the variable vi (t) is the amount of movement of the individual i at the virtual time t. Yes, the variable mi is the weight of the individual _i .

When the total energy consumption E _i (t) exceeds the predetermined fatigue determination threshold value, the individual information changing means 51 may set a predetermined negative value in the variable N ₃ to reduce the degree of attraction. .. As a result, it is possible to reflect in the simulation an event in which the psychology of ignoring guidance is likely to occur due to the accumulation of fatigue associated with activities in the real world.

The time constants of the second term, the third term, and the fourth term on the right side of the equation (10) may be different from each other to be τ _{i, 1} , τ _{i, 2} , and τ _{i, 3} , respectively. In that case, the equation (10) is as follows.

(5) Further, in the above-described embodiment and its modification, the individual information changing means 51 is subject to increase / decrease in the degree of attraction _ni (t-1) one hour before according to the equations (8) to (10). The attraction degree _ni (t) was updated, but the attraction degree _ni (t) of each individual was calculated for each virtual time without depending on the attraction degree _ni (t-1) one hour ago. May be good. For example, the equations (8) to (10) may be updated according to the equation omitting the first term " _ni (t-1) +" on the right side.

(6) In the above embodiment and its modification, an example in which the attraction area storage means 41 sets the attraction degree for each attraction area is shown, but the attraction degree may be a uniform value depending on the purpose of the simulation. In this case, the attraction degree may be excluded from the items stored in the attraction area storage means 41, and the attraction degree may be described as a constant in the program of the action determining means 50.

(7) In the above-described embodiment and its modification, an example in which the spatial information storage means 40 stores a two-dimensional map of space is shown, but a three-dimensional map may be used. In that case, the coordinate information of the attracting area stored by the attracting area storage means 41, the position of the individual stored by the individual information storage means 42, the amount of movement, and the direction of the center line of the acting area are also three-dimensional information.

(8) In the above-described embodiment and its modification, some of the simulation conditions can be set to values according to the situation of the target space in real time. For example, if the situation in the near future is predicted by a simulation in which the position of an actual person is set as the position of each individual, it becomes possible to accurately guide the crowd.

(9) Further, the individual to be simulated is not limited to a human being, but may be an object controlled by a human being (for example, a vehicle driven by a human being).

(10) In the above embodiment and its modified example, each of the action determining means 50 and the individual information changing means 51 counts the individuals existing in the action region Ri which is within a predetermined distance centering on the individual of interest. The number of surrounding individuals was calculated. Instead of this, the target space may be divided into blocks, the number of individuals in each block may be counted, and the number of individuals in the block to which the individual of interest belongs may be used as the number of surrounding individuals.

(11) In the above-described embodiment and its modification, the fan-shaped working region is exemplified as the working region that spreads unevenly in the moving direction, but instead of the fan-shaped, it is separated from the position of the individual by 5 unit distance along the moving direction. A circle or ellipse having a diameter of a line segment connecting the point and the position of the individual may be set as the action region.

(Effect of embodiment)
(1) The simulator 1 predicts the movement of an individual in a predetermined target space. The spatial information storage means 40 stores spatial information including a movement target position set in the target space. The attraction area storage means 41 stores an attraction area set so that an individual moving to a movement target position is attracted. The individual information storage means 42 stores individual information including at least the position of the individual in the target space and the degree of attraction indicating the degree of the individual's easiness of being attracted to the attraction region. The action determining means 50 sets the amount of movement that brings the individual closer to both the attraction region and the movement target position located around the individual. The individual information changing means 51 updates the position of the individual according to the amount of movement. The action determining means 50 sets the amount of movement that brings the individual closer to the attracting region as the degree of attraction increases.

Therefore, it is possible to predict the movement of an individual that is more attracted to the attracting region as the degree of attraction is higher. Therefore, it is possible to predict the behavior of an individual when there is an individual difference in the easiness of being attracted to the attracting region.
(2) The individual information changing means 51 changes the degree of attraction over time. This makes it possible to predict the behavior of an individual when the degree of attraction of the individual changes with the passage of time.

(3) The individual information storage means 42 stores individual information of a plurality of individuals. The individual information changing means 51 updates the degree of attraction of the individual i of interest among the plurality of individuals so as to approach the degree of attraction of surrounding individuals around the individual i.
This makes it possible to predict the behavior of an individual in the community when the degree of attraction of the individual is influenced by other surrounding individuals in the real world.
(4) The individual information changing means 51 calculates the average value of the attraction degree of a plurality of surrounding individuals, and updates the attraction degree of the individual i of interest so as to approach the average value.
This makes it possible to reflect in the simulation the phenomenon in which the individuals in the crowd synchronize by acting as a crowd in the real world.

(5) The individual information storage means 42 stores the movement amount set for the individual i of further interest. The individual information changing means 51 is used when a surrounding individual exists in an action region set around the individual so that the movement amount stored in the individual information storage means 42 is biased and spreads in the movement direction of the individual i. The degree of attraction of individual i is updated so as to approach the degree of attraction of surrounding individuals existing in the area of action.
As a result, the individual is affected by the surrounding individuals existing in the moving direction of the individual i (that is, the direction in which the individual i is easily affected), and is not affected by the surrounding individuals existing in the range in which the individual is not easily affected. The degree of attraction of i can be updated.

(6) The individual information changing means 51 changes the degree of attraction according to the degree of congestion of the individual in the target space.
As a result, it is possible to reflect in the simulation an event in which the psychology of ignoring the guidance is likely to occur due to the discomfort caused by the speed reduction caused by congestion in the real world. In addition, when it is not crowded, it is possible to reflect in the simulation an event in which the psychology of ignoring guidance and moving freely is likely to occur.
(7) The individual information storage means 42 further stores the movement amount set for the individual. The individual information changing means 51 changes the degree of attraction according to the amount of movement set for the individual.
As a result, it is possible to reflect in the simulation an event in which the psychology of ignoring guidance is likely to occur due to the accumulation of fatigue with movement in the real world.

1 ... Simulator, 2 ... Operation input unit, 3 ... File input / output unit, 4 ... Storage unit, 5 ... Control unit, 6 ... Display unit, 20 ... Condition setting means, 40 ... Spatial information storage means, 41 ... Attraction area storage Means, 42 ... Individual information storage means, 50 ... Action determination means, 51 ... Individual information changing means, 52 ... Prediction result output means

Claims (9)

A simulator that predicts the movement of an individual in a given space.
Spatial information storage means that stores spatial information including the movement target position set in the space, and
An attraction area storage means for storing an attraction area set so that the individual moving to the movement target position is attracted.
An individual information storage means for storing individual information including at least the position of the individual in the space and the degree of attraction indicating the degree of attraction of the individual to the attraction region.
An action determining means for setting a movement amount that brings the individual closer to both the attraction region and the movement target position located around the individual.
An individual information changing means for updating the position of the individual according to the amount of movement, and
Equipped with
The behavior determining means is a simulator characterized in that the higher the degree of attraction, the closer the individual is to the attracted region, and the amount of movement is set. The simulator according to claim 1, wherein the individual information changing means changes the degree of attraction over time. The individual information storage means stores individual information of a plurality of the individuals, and stores the individual information.
The individual information changing means updates the attraction degree of the first individual among the plurality of individuals so as to approach the attraction degree of the second individual around the first individual.
The simulator according to claim 2, wherein the simulator is characterized in that. The individual information changing means is characterized in that an average value of the attraction degrees of a plurality of the second individuals is calculated, and the attraction degree of the first individual is updated so as to approach the average value. The simulator according to claim 3. The individual information storage means further stores the movement amount set for the individual, and stores the movement amount.
In the individual information changing means, the second individual exists in an action region set around the individual so that the movement amount stored in the individual information storage means spreads unevenly in the movement direction of the individual. If so, the degree of attraction of the first individual is updated so as to approach the degree of attraction of the second individual present in the area of action.
The simulator according to any one of claims 3 or 4, wherein the simulator is characterized by the above. The simulator according to any one of claims 2 to 5, wherein the individual information changing means changes the degree of attraction according to the degree of congestion of the individual in the space. The individual information storage means further stores the movement amount set for the individual, and stores the movement amount.
The simulator according to any one of claims 2 to 4, wherein the individual information changing means changes the degree of attraction according to the movement amount set for the individual. A simulation method that predicts the movement of an individual in a given space.
Spatial information including the movement target position set in the space, an attraction region set to attract the individual moving to the movement target position, the position of the individual in the space, and the attraction region. An individual information including at least the degree of attraction, which indicates the degree of easiness of attracting the individual to the computer, is stored in a computer in a storage device.
A movement that brings the individual closer to both the attraction region located around the individual and the movement target position so that the higher the degree of attraction is, the closer the individual is to the attraction region located around the individual. The process of setting the amount and
The process of updating the position of the individual according to the amount of movement, and
A simulation method characterized by executing. A simulation program that allows a computer to predict the movement of an individual in a given space.
Spatial information including the movement target position set in the space, an attraction region set to attract the individual moving to the movement target position, the position of the individual in the space, and the attraction region. In the computer storing at least the individual information including the degree of attraction indicating the degree of easiness of attracting the individual to the storage device.
A movement that brings the individual closer to both the attraction region located around the individual and the movement target position so that the higher the degree of attraction is, the closer the individual is to the attraction region located around the individual. The process of setting the amount and
The process of updating the position of the individual according to the amount of movement, and
A simulation program characterized by executing.

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