JP7441080B2 - 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 crowd behavior.

In recent years, simulators that predict crowd behavior have been used to predict the effects of spatial design and improvements, introduction of operational measures, and improvements in spaces where large numbers of people gather. Furthermore, as one of the methods used in such a simulator, multi-agent simulation is known in which each individual forming a crowd is represented by data called an agent.
When simulating an action that involves movement using a multi-agent simulation, the simulation is performed by setting a plurality of destinations for the agent to go to within the space to be simulated, and setting a destination for each agent.
For example, Patent Document 1 describes that the destination of each individual imitating an evacuee is determined to be the exit closest to the location of the individual.

JP 2019-185074 Publication

However, in multi-agent simulations performed by conventional simulators, agents determine destinations based on uniform criteria, which can result in unnatural behavior in which multiple agents head to the same destination all at once.
For example, in the conventional technology that determines a destination based on the distance to the destination, agents located close to each other end up heading to the same destination all at once. It is also possible to decide on a destination based on the number of waiters, but in that case, the agents head all at once to the destination with the least number of waiters, and when they arrive at that destination, the number of waiters at that destination increases. As the number of agents increases, the behavior of heading all at once to the destination with the second lowest number of queues is repeated while reducing the number of agents moving at the same time.

The present invention has been made in view of the above problems, and aims to provide a simulator, a simulation method, and a simulation program that can eliminate the behavior of a plurality of moving agents unnaturally concentrating on one destination.

According to one aspect of the present invention, a simulator is provided that simulates the behavior of a plurality of agents moving to a plurality of destinations in a predetermined space. The simulator includes a statistical information storage means for storing statistical information regarding waiting times at each of a plurality of destinations for each agent, an agent selection means for sequentially selecting a target agent to be processed from among the plurality of agents, and a plurality of agents. arrival number prediction means for predicting the number of arrivals of other agents to each of a plurality of destinations according to the actions of other agents including at least one agent other than the target agent among the agents, and statistical information on the number of arrivals and the target agent. a waiting time prediction means for predicting the waiting time at each of a plurality of destinations based on the above; an evaluation value calculation means for calculating an evaluation value that increases as the waiting time for a destination is shorter; and a behavior simulating means for simulating the behavior of a target agent moving to the ground.

According to another aspect of the present invention, a simulation method is provided for simulating the behavior of a plurality of agents moving to a plurality of destinations in a predetermined space. The simulation method involves a process of reading statistical information about waiting times at each of a plurality of destinations, which is stored for each agent in a predetermined storage device, and a process of sequentially selecting a target agent to be processed from among the plurality of agents. and a process of predicting the number of arrivals of other agents to each of the plurality of destinations according to the actions of other agents including at least one agent other than the target agent among the plurality of agents, and statistical information of the number of arrivals and the target agent. A process for predicting the waiting time at each of a plurality of destinations based on the above, a process for calculating an evaluation value that increases as the waiting time for a destination is shorter, and a target agent that moves to the destination based on the evaluation value. A computer executes a process that simulates the behavior of

According to still another aspect of the present invention, a simulation program is provided that simulates the behavior of a plurality of agents moving to a plurality of destinations in a predetermined space. The simulation program includes a process of reading statistical information regarding waiting times at each of a plurality of destinations, which is stored for each agent in a predetermined storage device, and a process of sequentially selecting a target agent to be processed from among the plurality of agents. and a process of predicting the number of arrivals of other agents to each of the plurality of destinations according to the actions of other agents including at least one agent other than the target agent among the plurality of agents, and statistical information of the number of arrivals and the target agent. A process for predicting the waiting time at each of a plurality of destinations based on the above, a process for calculating an evaluation value that increases as the waiting time for a destination is shorter, and a target agent that moves to the destination based on the evaluation value. A computer executes a process that simulates the behavior of.

According to the present invention, in a simulation simulating the crowd behavior of a plurality of moving agents, it is possible to eliminate the behavior of these agents unnaturally concentrating on one destination.

FIG. 1 is a schematic configuration diagram of an example of a simulator according to an embodiment. FIG. 2 is a functional block diagram of an example of a simulator according to an embodiment. FIG. 3 is a schematic diagram of an example of map information of a target space stored in a spatial information storage means. FIG. 3 is a diagram showing an example of spatial information (behavior purpose information and destination information) stored in a spatial information storage means. FIG. 3 is a diagram showing an example of first agent information stored in an agent information storage means. It is a figure which shows an example of the 2nd agent information stored in an agent information storage means. FIG. 7 is a diagram showing an example of third agent information stored in agent information storage means. It is a figure which shows an example of the 4th agent information stored in an agent information storage means. It is a figure which shows an example of the statistical information stored in a statistical information storage means. (a) and (b) are explanatory diagrams of an example of a method of calculating the probability of success in achieving the first behavioral objective. (a) to (c) are explanatory diagrams of an example of a method of calculating the probability of success in achieving the second and subsequent behavioral objectives. (a) and (b) are diagrams illustrating how the probability distribution stored in the statistical information storage means is updated by the statistical information updating means and differences occur for each agent. 3 is a flowchart of an example of the overall processing of the simulation method according to the first embodiment. 3 is a flowchart of an example of statistical information update processing. 3 is a flowchart of an example of statistical information update processing. It is a flow chart of an example of action decision processing. It is a flowchart of an example of evaluation value calculation processing. 12 is a flowchart of an example of agent state update processing. FIG. 7 is an explanatory diagram of a method for predicting the number of agents arriving at a destination in the second embodiment. It is a functional block diagram of an example of a waiting time calculation means in a 2nd embodiment. 12 is a flowchart of an example of a required time distribution calculation process in the second embodiment.

Embodiments of the present invention will be described below with reference to the drawings. Note that the embodiments of the present invention shown below exemplify devices and methods for embodying the technical idea of the present invention. is not limited to the following: The technical idea of the present invention can be modified in various ways within the technical scope defined by the claims.

As an embodiment of the present invention, an example of a simulator that predicts the behavior of a plurality of people using seats, toilets, and shops in a theater having seats, toilets, and shops will be shown.
That is, in this embodiment, the space to be simulated (target space) is a theater, the individuals making up the crowd are people, and the purpose of each individual's behavior is to "take a seat" in the theater seat and "``Urban'' and ``Shopping.'' Hereinafter, the data of an individual in the simulator will be referred to as an "agent."

Further, in this embodiment, an example will be described in which a simulation implementer (hereinafter referred to as "user") uses a simulator to predict the behavior of a crowd during intermission time. Furthermore, an example will be described in which the prediction of crowd behavior changes after a plurality of intermission times. Incidentally, for the sake of simplicity, the periods before and after the curtain will be omitted.
Note that the theater described in this specification is just an example of a target space, and the target of the present invention is not limited to this.

(First embodiment)
(composition)
FIG. 1 is a schematic configuration diagram of an example of a simulator according to a first embodiment.
The simulator 1 includes an operation input section 2, a file input/output section 3, a storage section 4, a control section 5, and a display section 6. Of these, the file input/output section 3, storage section 4, and control section 5 can be realized by a so-called computer, and the operation input section 2 and display section 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 and used for inputting data and the like. The operation input section 2 is connected to the control section 5 , converts a user's operation into an operation signal, and outputs the signal to the control section 5 .

The file input/output unit 3 includes a DVD (Digital Versatile Disc) drive, a USB (Universal Serial Bus) interface, a network interface, etc., and one side is connected to an external device (not shown), a recording medium, a network, etc., and the other side is a control unit. 5, inputs data to the control unit 5 as a file, and outputs data from the control unit 5 as a file.
The storage unit 4 is a memory device such as a ROM (Read Only Memory) or 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 and outputs this information to and from the control unit 5.

The control unit 5 is composed of arithmetic devices 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 a various processing unit by reading and executing programs from the storage unit 4, and stores various data in the storage unit 4 and reads them out. 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 receives data input by the user by operating the operation input unit 2, and displays the file input/output unit 3. acquire and output data from the outside as a file through the computer, perform a simulation based on the acquired data to predict the behavior of the crowd, output the prediction result data as a file from the file input/output unit 3, and/or make a prediction. The resulting data is converted into an image 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, and is connected to the control unit 5 and displays prediction results by the control unit 5. The user visually checks the displayed prediction results and considers the behavior of the crowd.

(Functions of simulator 1)
FIG. 2 is a functional block diagram of an example of the simulator 1 according to the first embodiment.
The operation input unit 2 functions as a part of the condition setting means 20, 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 60.
The storage unit 4 functions as a simulation condition storage means 40, a spatial information storage means 41, an agent information storage means 42, a statistical information storage means 43, and the like.

The control unit 5 executes a computer program stored in the storage unit 4 by a calculation device such as a CPU, DSP, or MCU, thereby updating the evaluation value calculation means 50, the destination selection means 51, the behavior simulation means 52, and the statistical information update. It functions as means 53 and the like.
The condition setting means 20 receives input of various conditions necessary for simulation, and stores the input conditions in the simulation condition storage means 40, the spatial information storage means 41, the agent information storage means 42, and the statistical information storage means 43.

The condition setting means 20 is realized by, for example, the operation input section 2 and the control section 5 working together, and the control section 5 causes the storage section 4 to store a value input by the user by operating the operation input section 2. . The condition setting means 20 may include a file input/output unit 3 that inputs a file in which a part or all of the conditions are written, and the control unit 5 inputs a file specified by the user by operating the operation input unit 2. The values obtained through the input/output unit 3 and written in the file are stored in the storage unit 4.
The conditions inputted by the condition setting means 20 and stored in the simulated condition storage means 40 include, for example, the settings of the upper limit T of the virtual time, the action decision interval, the observation interval, and the basic speed of the agent (the distance the agent attempts to travel in one time). including.

The simulation of the first embodiment is performed while advancing virtual time. For example, the process of updating agent information at each virtual time while increasing the virtual time by 1 from 0 until the virtual time reaches (T-1) is repeated. A process may be added in which input of a termination instruction by the user is accepted through the operation input unit 2, and the process is forcibly terminated when the termination instruction is input.
The virtual time simulates the actual passage of time, but is advanced each time the simulator completes one hour's worth of calculations, and is clocked at a faster rate than the actual passage of time. For example, one virtual time corresponds to 0.1 seconds of actual time, and the virtual time is expressed as an integer starting from 0 and increasing by 1.

The spatial information storage means 41 stores map information representing the target space, behavioral purpose information that defines the behavioral purpose that can be achieved in the target space, and destination information regarding a destination that is a place where the behavioral purpose can be achieved in the target space. Store spatial information including. Note that in the target space, for at least one behavioral objective, there are a plurality of destinations where this behavioral objective can be achieved.
FIG. 3 is a schematic diagram of an example of map information 100, and FIG. 4 is a diagram showing an example of action purpose information 110 and destination information 111. These spatial information, except for the "queue" included in the destination information 111, are set in advance by the user's input operation using the condition setting means 20.

In the map information 100 shown in FIG. 3, line segments represent walls, and areas other than the line segments are movable areas in which the agent can move.
Further, Roman numerals I to X are destination IDs given to each destination, and indicate the location of each destination in the map information 100 of FIG. 3.
Double circles Q1 to Q10 indicate the positions of representative points representing the positions of each destination I to X. In reality, the positions of destinations I to X, which are areas, are defined as point information to facilitate handling.
Circles Q11 to Q21 indicate waypoints through which the agent can move in the movable area while avoiding walls. A route along which an agent moves in a movable area is defined by a local route (hereinafter referred to as a "local route") connecting representative points, transit points, and representative points and transit points.

See FIG. 4. The behavioral purpose information 110 includes the “behavioral purpose name” of each behavioral purpose in association with the behavioral purpose ID (1 to 3).
The destination information 111 also includes the "destination name", "representative point", "gender restriction", "number of service counters", "achievable ``behavioral purpose'', ``service time distribution'', and ``queue''.

“Destination name” is the name of the destination. For example, destination I is "guest seats," destination II is "women's restroom 1," destination III is "women's restroom 2," destination IV is "men's restroom 1," and destination V is "Men's Restroom 2", and destinations VI to X are "Stand 1" to "Stand 5".
The "representative point" is the XY coordinate value of the representative points Q1 to Q10 described above.

“Gender restriction” is one of the attribute restrictions for agents who can use the destination, and is the gender of the agent who can use the destination.
The "number of service counters" is the maximum number of agents that can use the destination at the same time. In the following explanation, "service" means the use of a destination to achieve a behavioral purpose. For example, if the destination is a "toilet", the service is the provision of a place to relieve themselves, and if the destination is a "store", the service is sales services.

"Achievable behavioral objectives" are behavioral objectives that can be achieved at the destination.
The "service time distribution" is statistical information that represents the time required for a service at the destination (hereinafter referred to as "service time") using a probability distribution. For example, the average value and variance when the service time is expressed as a normal distribution are stored. The service time distribution is used to determine the service time required by each agent to achieve its behavioral objective at the destination.

The "queue" is a buffer in which the agent IDs of agents staying at the destination are arranged in the order of arrival at the destination. The queue is sequentially updated by the behavior simulator 52.
An agent whose order in the queue is less than or equal to the number of service windows is an agent receiving service at the destination, and an agent whose order in the queue is greater than the number of service windows is an agent waiting for service. Further, the number of agents waiting for service is the waiting number (waiting number).

In the example of the spatial information 111 in FIG. 4, four agents #9, #63, #39, and #83 have arrived at the kiosk 1, and since the number of service counters is 1, the agent #9 at the head of the queue Indicates that only the customer is receiving the service (for example, paying at the cash register). In addition, the remaining agents #63, #39, and #83 are waiting for service (either selecting a product or waiting at the register), indicating that the number of agents waiting is three.

See FIG. 2. The agent information storage means 42 and the statistical information storage means 43 store information regarding each of the plurality of agents forming the crowd. Hereinafter, the information stored in the agent information storage means 42 will be referred to as "agent information."
Examples of agent information 120, 121, 122, and 123 are shown in FIGS. 5, 6, 7, and 8, respectively. In each figure, character strings shown in angle brackets next to numerical values have meanings added in consideration of the ease of viewing the figure.
For example, in the initial position of the agent information 120, the value (4.55, 14.00) is actually stored in the location where "Door 1" is written as (4.55, 14.00); 1” coordinates. Note that in the following explanation, the value may be omitted and the meaning may be used instead.

The agent information 120 in FIG. 5 is a part of information set by a user's input operation using the condition setting means 20. As agent information 120, a combination of agent ID, creation time, initial position, and gender is stored. The initial position is the initial value of the agent's position, and indicates the position at which the agent is generated at the generation time.
The example in FIG. 5 shows that female agent #1 is generated at the coordinates of "door 1" at virtual time 0 within "interlude 1." Further, descriptions of "Intermission 2" and "Intermission 3" will be omitted. Furthermore, it is shown that the image is generated at the coordinates of "Door 3" at virtual time 99600 within "Interlude 4". Note that in the simulation example of the first embodiment, the same agent is generated multiple times.

The agent information 121 in FIG. 6 is also set by the user using the condition setting means 20 through an input operation. The agent information 121 includes combinations of "action purpose ID", "achievement deadline", "desired achievement level", "terminal purpose flag", and "achievement flag" in association with the combination of agent ID and generation time. . However, among these pieces of information, the achievement flag is updated as appropriate by the behavior simulation means 52.

The "action purpose ID" is the ID of the purpose (action purpose) for which the agent acts in the target space in each scene (interlude). Multiple action objectives can be set for each agent in each scene.
The "achievement deadline" is the deadline for achieving each action objective of the agent in each scene, and a virtual time after the generation time of the agent is specified.
The "desired achievement level" is the priority level of the agent in each scene for achieving each action objective. For example, the degree of desired achievement may be the subjective degree of satisfaction (utility) that the agent can obtain by achieving the behavioral purpose. The higher the degree of desired achievement, the higher the priority level of the behavioral purpose.

The "terminal purpose flag" is a flag that indicates the purpose of the action that must be achieved at the end of the action in each scene. The action goal for which the value "1" is set in the final goal flag is constrained to be the last action goal in the order of achievement. On the other hand, the action goals for which the value "0" is set in the terminal goal flag can be achieved in any order, and whether or not they are achieved is arbitrary.
The "achievement flag" is a flag indicating whether or not each action objective of the agent in each scene has been achieved. The initial value of the achievement flag is "0"; a value of "0" indicates that the achievement has not been achieved, and a value of "1" indicates that the achievement has been achieved.

In the example of FIG. 6, "Agent #1" in "Intermission 1" must complete "Urage" by "3 minutes later" and "Seated" with the final condition of achieving "Seated". ” and “Shopping” within 10 minutes, and the desired achievement levels for “Sitting,” “Urling,” and “Shopping” are “35,” “100,” and “40,” respectively. It also shows that the behavioral objectives of the students have not been achieved.

Agent information 122 in FIG. 7 is information generated at each time. The agent information 122 includes combinations of "destination ID", "local route", "travel speed", "position", and "situation" in association with the combination of agent ID and virtual time.
"Destination ID" indicates the ID of the destination to which each agent is moving at the time indicated by the virtual time. Note that in the example of the simulator 1 of the first embodiment, the destination ID at the generation time is left blank.
"Local route" indicates a local route along which each agent is moving at the time point indicated by the virtual time. Note that in the example of the simulator 1 of the first embodiment, the local route at the generation time and the time when the vehicle is not moving is left blank.

"Movement speed" indicates the amount of movement that each agent attempts to move per unit time (one virtual time) at the time indicated by the virtual time. In the example of agent information 122 in FIG. 7, the moving speed is expressed as a vector.
The moving speed is just the amount of movement that each agent intends, and does not necessarily match the amount of movement that the agent will move at the time indicated by the virtual time. For example, due to physical constraints between agents, the position at the current time may not be the sum of the position at the previous time and the moving speed at the current time.
Further, for convenience, the moving speed at the generation time and the moving speed from reaching the destination to achieving the action objective that can be achieved at the destination are assumed to be 0 vectors.

“Position” is the position of each agent in the target space at the point in time indicated by the virtual time. The initial value of the position of each agent is the initial position set at the generation time of the agent with reference to the agent information 120 described above.
The coordinates of the representative point of the destination are set from the time the user reaches the destination until the user achieves the action objective that is achievable at the destination.

"Status" is the status of each agent at the time indicated by the virtual time, and the possible values are "moving,""waiting,""processing," and "achieved."
"Moving" indicates that the destination has not been reached and the vehicle is moving.
"Waiting" indicates that the destination has been reached but the vehicle is waiting for service.
"Processing" indicates that the service is being received at the destination.
"Achieved" indicates that the user completed the service at the destination and achieved the behavioral purpose.
In the example of the simulator 1 of the first embodiment, for convenience, "achievement" is specified as the status at the generation time.

The example in FIG. 7 illustrates the agent information 122 at the end of the simulation.
For example, "Agent #1" in "Intermission 1" moves in the order of "Women's Restroom 1", "Concession 2", and "Audience Seat", and achieves the action objective in "Concession 2" and "Audience Seat" at times "2909" and "2969". In addition, in the process, it was recorded that he was waiting for service at "Kiosk 2" from time "1322" to "2686", and that he was waiting for service at "2687" at time "2687". It is recorded that the service was received from ``2908'' to ``2908''.

The agent information 123 in FIG. 8 is generated as necessary by the behavior simulation means 52 in order to determine whether or not the service has been received. The agent information 123 includes the service completion time in association with the agent ID. The information contained in the agent information 123 is retained while the corresponding agent is receiving service at the current destination, and is deleted when the service is completed.

"Agent ID" indicates an agent receiving service at the destination (that is, an agent whose status is "processing"). The destination receiving the service can be specified by the agent information 122.
The "service completion time" is the time when the agent finishes receiving the service at the destination.
The agent information 123 in FIG. 8 includes service completion times for agents #9, #34, #76, #5, #36, etc. at the head of the queue of stands 1 to 5 illustrated in the spatial information 111 in FIG. Retained.

The achievement flag included in the agent information 121 and the position and status included in the agent information 122 are examples of status values and represent the status of the agent.
The behavioral objectives, the deadline for achieving each behavioral objective, the desired achievement level for each behavioral objective, and the final objective flag for each behavioral objective included in the agent information 121, as well as the destination, local route, and movement speed included in the agent information 122, are as follows: This is an example of a behavioral parameter. Behavioral parameters are parameters that act on the agent's state values.
Note that the agent information 120, 121, 122, and 123 shown in FIGS. 5 to 8 are merely examples, and the configuration of the agent information of the present invention is not limited thereto.

See FIG. 2. The statistical information storage means 43, together with the agent information storage means 42, stores information regarding each of the plurality of agents making up the crowd.
The information stored in the statistical information storage means 43 is statistical information regarding the time required to achieve the behavioral purpose at each destination. By storing such statistical information for each agent, it is possible to simulate differences in the knowledge that each individual in the real world has about the required time. That is, the statistical information storage means 43 stores statistical information regarding the time required for each agent to achieve an action objective at a plurality of destinations.

The time required for each behavioral purpose is defined as the sum of the travel time to the destination, the waiting time at the destination, and the service time at the destination.
The waiting time at a destination is modeled as being calculated as the product of the number of people waiting at the destination divided by the number of service counters at the destination and the service time.

Further, the travel time to reach the destination is modeled as being calculated by summing the results of dividing the route length of each local route to the destination by the travel speed of the local route.
For this reason, the statistical information storage means 43 stores, for each agent, the probability distribution of the waiting number for each destination (waiting number distribution), the probability distribution of the service time for each destination (service time distribution), and the travel speed for each local route. The probability distribution (moving speed distribution) is stored as statistical information.
For example, each of the waiting number distribution, service time distribution, and moving speed distribution is modeled using a normal distribution. Then, in order to update each distribution with observed values, joint probability distributions of the parameters of the normal distributions of the waiting number distribution, service time distribution, and moving speed distribution are used. The statistical information storage means 43 stores four parameters of the joint probability distribution of the waiting number distribution, the service time distribution, and the moving speed distribution: the mean value μ, the degree of freedom ν, the hypothetical sample number κ, and the scale parameter λ. do. The joint probability distribution is expressed as Ν・χ ⁻² (μ, ν, κ, λ).

The initial values of the waiting number distribution, service time distribution and moving speed distribution are set in advance via the condition setting means 20. The initial value may be created based on experience, common sense, actually measured statistical information, etc. The initial value may be common to all agents, or may be a different initial value for each agent.
Alternatively, some of the combinations of agents and required times may not be set, and the statistical information storage means 43 may not store the combinations in the initial settings. Thereby, it is possible to simulate a lack of knowledge among individuals in the real world, such as agent #1 not knowing the existence of the store 4, for example.
In this embodiment, the initial settings are such that initial values of distributions (waiting number distribution and service time distribution) regarding one or more destinations corresponding to each action purpose are stored for each agent. The destination to be memorized is determined by a random lottery in advance. Furthermore, in this embodiment, the initial setting for the local route is to store the initial value of the distribution (traveling speed distribution) regarding the local route for all combinations of agents and local routes.
Furthermore, in this embodiment, the statistical information storage means 43 stores, for each destination, the initial value of the distribution regarding the destination, in addition to the information for each agent. This information is used as an initial value when each agent observes the destination for the first time.

As will be described later, the statistical information of each agent is individually updated and referenced based on observed values obtained in accordance with the actions of the agent. As a result, just as the experiential knowledge of individuals differs from one another in the real world, they become different from each other as virtual time passes.
Furthermore, the statistical information storage means 43 adds observed values for combinations of agents and required times that are not stored (that is, destinations and local routes unknown to the agent) when obtained. Memorization may simulate the acquisition of new knowledge by individuals in the real world.

FIG. 9 shows an example of statistical information 130 and 131 stored in the statistical information storage means 43.
The statistical information 130 includes "waiting number distribution" and "service time distribution" that are associated with the combination of agent ID and destination ID.
The "waiting number distribution" and "service time distribution" are joint probability distributions of the "waiting number distribution" and "service time distribution" that the agent has stored for the destination, respectively.
In the example of statistical information 130 in FIG. 9, for agent #1, the waiting number distribution and service time distribution for customer seats, women's restroom 1, men's restroom 1, store 1, store 2, and store 3 are stored, and the waiting number distribution and service time distribution for agent #1 are stored. The distribution of toilets 2, kiosks 4, and kiosks 5 is not stored.
The statistical information 131 includes a "moving speed distribution" associated with a combination of an agent ID and a local route. The "travel speed distribution" is a joint probability distribution of the "travel speed distribution" that the agent has stored for the local route.
The statistical information storage means 43 is an example of a "destination storage means" described in the claims.

See FIG. 2. The evaluation value calculation means 50 calculates an evaluation value to be used as a selection criterion when each agent selects a destination to achieve an action objective in each scene (interlude).
As mentioned above, the agent may set multiple behavioral objectives to be achieved in each scene, so the behavioral objectives to be achieved in each scene are defined as a permutation of the multiple behavioral objectives (hereinafter referred to as "behavior pattern"). ) is expressed by
In addition, there may be multiple destinations where the same behavioral objective can be achieved. In this case, there are multiple options for destinations that each achieve the purpose of action.
The evaluation value calculation means 50 calculates an evaluation value for each selectable destination option for each behavior pattern. Then, evaluation value information in which agents, options, and evaluation values are associated with each other is output to the destination selection means 51. Note that the destination options that can be selected for the action pattern are permutations of destinations, and these permutations are hereinafter referred to as "destination patterns."

For this purpose, the evaluation value calculation means 50 first generates information on behavior patterns. Specifically, the evaluation value calculation means 50 calculates, for each combination having an action objective ID whose achievement flag is 0, among the combinations of agent ID and generation time (each scene) stored in the agent information 121, A permutation is generated in which action goal IDs whose achievement flags are 0 are arranged in order from among action goal IDs associated with . However, the evaluation value calculation means 50 sets the behavioral purpose for which the terminal purpose flag is set at the end of the permutation.
For example, for agent #1 in the agent information 121 in FIG. 6, the behavior pattern when the virtual time is 0 is P ₁ = {errands, shopping, sitting}, P ₂ = {shopping, errands, sitting}. , P ₃ = {use, sitting}, P ₄ = {shopping, sitting}, and P ₅ = {sitting}.

Next, the evaluation value calculation means 50 generates destination pattern information. Specifically, the evaluation value calculation means 50 determines, for each behavior pattern, that the "achievable behavioral purpose" in the spatial information 111 matches each element of the behavior pattern, the gender restriction is satisfied, and the behavior pattern is generated. The destination ID stored in the statistical information 130 of the agent selected is used as a destination ID option for each element of the behavior pattern. Then, for each behavior pattern, the evaluation value calculation means 50 selects one destination ID from the options obtained for each element of the behavior pattern, and assigns the selected destination ID to each element of the behavior pattern. A permutation of destination IDs arranged in this order is generated as a destination pattern.
For example, for the action pattern P ₁ of agent #1 in intermission 1 in the agent information 121 in FIG. The following destination patterns are M ₁₁ = {women's toilet 1, shop 1, audience seats}, M ₁₂ = {women's toilet 1, shop 2, audience seats}, M ₁₃ = {women's toilet 1, shop 3, audience seats}. It becomes a street.

Then, the evaluation value calculating means 50 calculates the evaluation value E for each destination pattern according to the following equations (1) to (4).

In equation (1), P _i is the i-th behavior pattern among the behavior patterns in each scene of the agent whose evaluation value is to be calculated, and M _ij is the i-th behavior pattern among the destination patterns corresponding to the behavior pattern P _i . This is the j-th destination pattern, and N _i is the number of behavioral objectives included in the behavioral pattern P _i .
P _is indicates the s-th element of the action pattern P _i (that is, the s-th action purpose to be executed).
m _ijs indicates the s-th element (ie, the s-th destination) of the destination pattern M _ij .
p(P _is , m _ijs ) is the probability of achieving the behavioral purpose P _is at the destination m _ijs by the deadline for achieving the behavioral purpose P _is . Hereinafter, this probability will be referred to as success probability. As mentioned above, the deadline for achieving the behavioral purpose P _is is set as the agent information 121, and as shown in FIG. specified. Details of the method for calculating the success probability p (P _is , m _ijs ) will be described later.

In equation (1), V(P _is ) is the desired degree of achievement of the behavioral purpose P _is . As mentioned above, the desired achievement degree V (P _is ) is set as the agent information 121, and as shown in FIG. degree V (P _is ) is specified.
For example, in the agent information 121 of FIG. 6, the desired achievement level for the action pattern P1 of agent #1 in interlude 1 is 100 when s=1, 40 when s=2, and 35 when s=3.

In other words, the evaluation value can be calculated by summing the success probabilities of each destination corresponding to a plurality of behavioral objectives to be achieved, weighted by the desired achievement level of the behavioral objective corresponding to the destination.
Note that, as another embodiment, weighting based on the degree of desired achievement may be omitted.
In addition, as yet another embodiment, instead of the sum of weighted success probabilities or the sum of success probabilities, other increases such as a product of weighted success probabilities or a product of success probabilities, such as a product of weighted success probabilities or a product of success probabilities, are used such that the higher the success probability, the higher the evaluation value. You can also define and use functions.

The above explanation regarding equation (1) can be summarized as follows.
The evaluation value calculation means 50 calculates for each agent, based on the statistical information stored in the statistical information storage means 43, each agent who achieves the behavioral purpose P _is at a plurality of destinations m _ijs by the deadline for achieving the behavioral purpose P _is . The success probability p (P _is , m _ijs ) is predicted, and the evaluation value E is calculated according to the success probability p (P _is , m _ijs ).
At this time, in order to select a destination for an agent who is trying to achieve a plurality of behavioral objectives P _is , a combination of destinations m _ijs that achieve each of the plurality of behavioral objectives P _is is selected from among the plurality of destinations m _ijs . By selecting a plurality of destination patterns M _ij that are, the evaluation value E is calculated according to the probability p (P _is , m _ijs ) of achieving each of the plurality of action objectives P _is at _the destination m _ijs belonging to the destination pattern M ij. Calculate.
In addition, in connection with the fact that the priority of the behavioral purpose is different for each agent, the evaluation value calculation means 50 is configured to calculate the priority of the behavioral purpose by selecting a destination according to the difference in the priority of the agent. An evaluation value E is calculated by weighting the probability p (P _is , m _ijs ) of achieving each of P _is by the desired achievement level V (P _is ) of a plurality of behavioral objectives.

An example of the method for calculating the success probability p (P _is , m _ijs ) shown by equations (2) to (4) will be described below.
First, in order to use equations (3 ₎ and ( ₄ ), the evaluation value calculation means ₅₀ calculates each action objective ( A required time distribution (f ₁ (T d ), _{f 2 (} T _d ), ...), which is a probability distribution of the required time T _d when executing P i1 , P _{i2 ,} ...), is calculated.

Here, the required time distribution f _s (T _d ) is the travel time distribution when traveling from the s-1st destination m _{ij (s-1)} to the s-th destination m _ijs , and the travel time distribution when traveling from the s-th destination m _ij (s-1) to the s-th destination m It is defined as the sum of the waiting time distribution at the destination m ijs and the service time distribution related to the achievement of the behavioral purpose at the destination m _ijs . However, m _ij0 is the current position of the agent.
Furthermore, ^{the variance σ 2 =} λ/ There is a relationship of (ν-2).
Furthermore, when each distribution is represented by a normal distribution, the average value of the sum distribution is the sum of the average values of the original distributions, and the variance of the sum distribution is the sum of the variances of the original distributions.

In order to calculate f _s (T _d ), the evaluation value calculating means 50 calculates the travel time distribution. The evaluation value calculation means 50 processes a processing target in which the statistical information storage means 43 stores the parameters of the joint probability distribution of the travel speed distribution of the local route from the destination m _{ij (s-1)} to the destination m _ijs . A normal distribution representing the movement speed distribution of each local route is derived from the read parameters. The evaluation value calculation means 50 obtains the travel time distribution of each local route by converting the speed axis in the travel speed distribution of each local route into a time axis by dividing the distance of the local route, and calculates the travel time of these local routes. The travel time distribution to the destination is calculated by summing the distribution.

In order to calculate f _s (T _d ), the evaluation value calculating means 50 also calculates the waiting time distribution. The evaluation value calculation means 50 reads the parameters of the joint probability distribution of the waiting number distribution and the service time distribution of the destination m _ijs from the statistical information 130 of the agent to be processed stored in the statistical information storage means 43, and also Read out the number of service counters in the _area . The evaluation value calculation means 50 derives a normal distribution representing a waiting number distribution and a service time distribution from the read parameters. The evaluation value calculating means 50 calculates the waiting time distribution by dividing the average value of the waiting number distribution (this becomes the waiting number) by the number of service counters read out and multiplying the result by the service time distribution. Note that the mode value may be used instead of the average value, or a value determined by a random lottery in which the winning probability is determined by the waiting number distribution may be used. Further, the number of waiters at the destination may be a value observed at the current time by the agent to be processed (limited to cases where the corresponding observed value is obtained). Alternatively, the observed value of the number of waiting times at the destination may be held in the storage unit 4 for several hours, and the held value may be used.

In order to calculate f _s (T _d ), the evaluation value calculating means 50 also calculates the service time distribution. The evaluation value calculation means 50 reads the parameters of the joint probability distribution of the service time distribution of the destination m _ijs from the statistical information 130 of the agent to be processed stored in the statistical information storage means 43, and calculates the service time from the read parameters. Derive the normal distribution representing the time distribution.
Then, the evaluation value calculation means 50 calculates the sum of the travel time distribution, waiting time distribution, and service time distribution for each s as the required time distribution f _s (T _d ) for the s.

Next, the evaluation value calculating means 50 calculates the remaining time T _r (P _is ) until the deadline T _lim (P _iS ) for achieving the behavioral purpose P _is according to equation (4).
The remaining time when s=1, that is, the remaining time T _r (P _i1 ) until the deadline T _lim (P _i1 ) for achieving the first action objective P i1 of the action pattern P _i is _a scalar. When s=1, the evaluation value calculating means 50 calculates the difference obtained by subtracting the current time t _c from the achievement deadline T _lim (P _i1 ) as the remaining time T _r (P _i1 ).
The remaining time when s>1, that is, the remaining time until the deadline for achieving the second and subsequent behavioral objectives P _i2, P _{i3, .} . . of the behavioral pattern P _i is a probability distribution. In other words, the time remaining to achieve the second and subsequent behavioral objectives P _i2, P _i3, ... is the required time T ¹ _d , ..., T ^s- for the behavioral objectives to be executed before that behavioral objective. ¹ _d , and the required time T ¹ _d , ..., T ^s-1 _d for the purpose of action is defined as a random variable that follows the required time distribution f ₁ (T _d ) to f _s-1 (T _d ), respectively. , the remaining time T _r of the second and subsequent behavioral objectives P _i2, P _{i3, .} . . is also calculated as a random variable according to a probability distribution. This probability distribution is defined as remaining time distribution g _s (T _r ). Note that in this embodiment where the required time distribution is a normal distribution, the remaining time distribution is also a normal distribution.
When s>1, the evaluation value calculation means 50 calculates the difference between the deadline T _lim (P _is ) for achieving the s-th action objective P _is and the current time t _c , and further calculates the difference between the 1st to s−1th action The total sum of time distributions f ₁ (T _d ) to f _s-1 (T _d ) required to achieve objectives P _i1 to P _{i (s} -1) is determined. The obtained distribution is called a summation distribution. Then, the evaluation value calculation means 50 calculates the remaining time distribution g _s (T _r ) using the value obtained by subtracting the average value of the sum distribution from the difference and the variance of the sum distribution. In other words, the average value of the remaining time distribution g _s (T _r ) is the value obtained by subtracting the average value of the above sum distribution from the above difference, and the variance of the remaining time distribution g _s (T _r ) is the variance of the above sum distribution. .

Next, the evaluation value calculation means 50 calculates the success probability p(P _is , m _ijs ) according to equation (2) and equation (3). The probability of success is the probability of achieving the behavioral objective by the achievement deadline, so it can be defined as ``the probability that the time required to achieve the behavioral objective will be less than or equal to the remaining time.''

When s=1, since the remaining time T _r (P _i1 ) calculated by Equation (4) is a scalar, the value F _s (T _r ) expressed by Equation (3) is also the success probability p expressed by Equation (2). (P _i1 , m _ij1 ) is also a scalar. Specifically, the evaluation value calculation means 50 integrates the probability density represented by the required time distribution f _s (T _d ) with respect to the required time T _d in the range from −∞ to the remaining time _Tr , and calculates an integral value F _{s .} (T _r ) is calculated as success probability p (P _i1 , m _ij1 ).
In this embodiment, since the required time distribution f _s (T _d ) is a normal distribution, the integral value F _s (T _r ) is determined by the required time distribution f _s (T _d ) as shown in equation (3). It can be calculated from the average value μ, the variance σ ² and the remaining time T _r (T _r (P _i1 ) calculated by equation (4)). erf in equation (3) is an error function. Then, as shown in Equation (2), the integral value F _s (T _r ) calculated using Equation (3) directly becomes the success probability p (P _i1 , m _ij1 ).
(a) of FIG. 10 shows an example of the required time distribution f ₁ (T _d ) required to achieve the first behavioral purpose P _i1 , and (b) of FIG. 10 shows an example of the required time distribution f ₁ (T _d ). The cumulative distribution function F ₁ (T _r ) of F 1 (T r ) is shown. μ ₁ in the figure is the average value of the required time distribution f ₁ (T _d ), and the value of the cumulative distribution function F ₁ (T _r ) when the required time is T _r (P _i1 ) is the success probability p (P _i1 , m _ij1 ).

When s>1, the remaining time T _r (P _is ) calculated by equation (4) has a distribution g _s (T _r ). Therefore, the success probability p (P _is , m _ijs ) is the probability that the remaining time at the time of completing the s-1th behavioral purpose is T _r (P _is ), as shown in equation (2). The probability obtained by multiplying g _s (T _r ) by the probability F _s (T _r ) of being able to achieve the s-th behavioral objective in the remaining time T _r (P _is ) is expressed as a range from −∞ to ∞ for the remaining time T _r This is the value obtained by integrating up to . The probability F _s (T _r ) at that time is calculated using equation (3).

Since this formula cannot be solved analytically, the evaluation value calculating means 50 of the first embodiment calculates the success probability p(P _is , m _ijs ) numerically, for example, as described below. Specifically, a sample of the probability density distribution g _s (T _r ) of the remaining time is extracted using a sampling method, and the sample obtained by sampling is combined with the distribution of time required to achieve the sth behavioral objective P _is f _s (T _d ), the success probability p (P _is , m _ijs ) is calculated.
In the present embodiment, the evaluation value calculation means 50 calculates the remaining time distribution g _s (T _r ) until the deadline for achieving the s-th (s=2, 3,...) behavioral purpose P _is at arbitrary time intervals. Discretize and calculate histogram.

(a) of FIG. 11 shows an example of the remaining time distribution g _s (T _r ), and (b) of FIG. 11 shows a histogram of the remaining time distribution g _s (T _r ). (c) of FIG. 11 shows the cumulative distribution function F _s (T _r ) of the required time distribution f _s (T _d ) required to achieve the s-th behavioral objective P _is . μ _rem in the figure is the average value of the remaining time distribution g _s (T _r ), and μ _s is the average value of the required time distribution f _s (T _d ).
The evaluation value calculation means 50 calculates the probability of occurrence (frequency) of each bin (each section) b1 to b5 of the histogram and the cumulative distribution function value F _s (T _r1 ) of the representative value T _r1 to T _r5 of each bin b1 to b5. The sum of products of ~F _s (T _r5 ) is calculated as success probability p (P _is , m _ijs ). Note that the probability of occurrence of bin b1 is calculated from the integral value of the remaining time distribution g _s (T _r ) from -∞ to the right end T _r1 +w/2 of the bin b1, assuming that the width of the bin is _w . It can be calculated by subtracting the integral value from -∞ of T _r ) to the left end T _r1 -w/2 of bin b1. The same applies to the occurrence probabilities of other bins. Furthermore, in this embodiment where the remaining time distribution g _s (T _r ) is a normal distribution, the integral value up to the right end of the bin is the mean value μ and variance σ of the time g _s (T _r ), as in equation (3). ² and the time at the right end of the bin to an error function, and the integral value to the left end of the bin can ^be calculated by applying the mean value μ and variance σ of g _s ( _Tr ) to the error function. It can be calculated by applying it to

Note that the method for calculating the success probability p(P _is , m _ijs ) of the second and subsequent behavioral objectives P _is is merely an example, and the present invention is not limited thereto. The success probability p(P _is , m _ijs ) may be calculated by a numerical analysis method such as performing an approximate calculation using another sampling method. For example, sampling methods such as the Box-Mueller method (when the target distribution is a normal distribution), the rejection sampling method, and the MCMC method can be used.

The explanation of equations (2) to (4) can be summarized as follows.
The evaluation value calculation means 50 calculates _, for each destination pattern M _ij corresponding to the behavior pattern P _{i , the} probability p(P _is , m _ijs ) is predicted based on the probability density distribution f _s (T _d ) of the time T _d required to achieve each of the plurality of behavioral objectives P _is .
In addition, the evaluation value calculation means 50 calculates the probability density distribution f ₁ (T _d ) to f _s-1 (T _d ) of the time required for the behavioral purpose to be achieved first among the plurality of behavioral purposes P _is and the achievement deadline. The probability density distribution g _s (T _r ) of the remaining time is determined based on the probability density distribution g _s (T _r ) of the remaining time, and the probability p (P _is , m _ijs ).
Further, the evaluation value calculation means 50 calculates the remaining time based on the histogram of the remaining time distribution g _s (T _r ) and the cumulative distribution function of the required time distribution f _s (T _d ) required to achieve the behavioral purpose to be achieved later. , the sum of the products of the histogram frequency and the probability of the cumulative distribution function in each section of the histogram is calculated as probability p(P _is , m _ijs ).
Furthermore, since the evaluation value E is calculated according to the probability p (P _is , m _ijs ), the evaluation value E becomes higher as the waiting time at the destination becomes shorter.

The evaluation value calculating means 50 calculates the above-mentioned evaluation value E when a predetermined timing (hereinafter referred to as "action determination timing") for determining the agent's action arrives.
The action decision timing is, for example, the generation time of each agent, the achievement of the action objective (excluding the last action objective), or the timing at which a predetermined action decision interval (for example, 10 virtual times) has arrived. good.

Specifically, the evaluation value calculation means 50 detects an "existing agent" existing in the target space from the agent information 120. The existing agent is an agent that was generated before the current virtual time (hereinafter referred to as current time) and has an action goal ID set with a terminal goal flag of 1 and an achievement flag of 0.
By detecting an agent whose status one time ago was "achieved" in the agent information 122, the evaluation value calculation means 50 determines that the action decision timing of the existing agent has arrived immediately after the generation time and immediately after achieving the action objective. Determine what has been done.
Furthermore, since the difference between the current time and the generated time is a multiple of the action decision interval, it is determined that the action decision timing has arrived for all existing agents.

See FIG. 2. The destination selection means 51 selects a destination for each agent based on the evaluation value information input from the evaluation value calculation means 50, associates the selected destination with the agent ID, and stores the agent information in the agent information storage means 42. The information is stored in the information 122.
Specifically, for example, the destination pattern M _ij with the highest evaluation value is selected for each agent, and the ID of the first destination in the selected destination pattern M _ij is associated with the agent ID and the current time. New time data is generated and added to the agent information 122.
For existing agents for which the current time is not the action decision timing, new current time data is generated in which the destination ID of one time ago is associated with the agent ID and the current time, and is added to the agent information 122.

Alternatively, for example, a lottery may be held to select a winning destination pattern with a probability of winning depending on the height of the evaluation value E of each destination pattern M _ij , and the winning destination pattern may be selected.
Alternatively, for example, a lottery may be held to select a winning destination pattern with a probability of winning depending on the ranking of the evaluation value E of each destination pattern M _ij , and the winning destination pattern may be selected. In this case, the relationship between ranking and probability is determined in advance.

As mentioned above, the method of selecting the destination with the highest evaluation value corresponds to the rational selection behavior performed by individuals in the real world. On the other hand, the method of selecting a destination by random lottery corresponds to the intuitive selection behavior performed by individuals in the real world. The destination may be selected by drawing a lottery for each agent to determine which of these two methods to use, and then selecting the winning method. Alternatively, which of these two methods to use may be assigned to each agent, and the destination may be selected by the assigned method.

See FIG. 2. The behavior simulation means 52 refers to the spatial information 111 stored in the spatial information storage means 41 and the agent information stored in the agent information storage means 42 at each virtual time t. Based on this information, the behavior simulation means 52 simulates the behavior in which each of the plurality of agents moves to the selected destination and achieves the behavioral purpose.
The behavior simulation means 52 generates an agent whose generation time has arrived as an existing agent.

The behavior simulation means 52 reads an agent ID whose generation time matches the current time from the agent information 120, generates current time data in which the agent ID, current time, initial position, and status "achieved" are associated with each other, and It is added to the information 122.
The action simulating means 52 deletes the agent whose terminal goal flag is 1 and whose action goal achievement flag has been updated to 1. Note that in the simulator 1 of the first embodiment, instead of explicitly deleting the agent, data after the next virtual time is not added to the agent information 122.

The behavior simulation means 52 updates the state of the agent (that is, the existing agent) whose current time data is stored, from the agent information 122. As described above, the destination selection means 51 generates current time data for the existing agents.
The behavior simulation means 52 extracts data of the existing agent one time ago and data of the current time.

If the situation one time ago was "moving" or "achieved", the behavior simulation means 52 calculates the moving speed of the agent and updates the position.
Specifically, the behavior simulation means 52 determines the moving speed and position of the agent at the current time. First, the behavior simulation means 52 reads from the spatial information 111 the representative point of the destination of each corresponding agent at the current time t, the movable area, and the way point.

The behavior simulating means 52 moves the agent i to the position xi(t-1) via a route connecting the representative point with the position xi(t-1) of the corresponding agent i one time before, and via arbitrary transit points within the movable area. Select the shortest route among the routes connecting the and representative points.
Next, the behavior simulator 52 moves toward the point closest to the position xi(t-1) among the intermediate points and representative points on the selected shortest route, and moves a vector whose size is equal to the agent's basic speed to the target point. Calculated as the speed of movement to bring it closer to the ground.

Next, the behavior simulator 52 adds the calculated moving speed to the position xi(t-1) to determine the current position xi(t) of the agent i and updates the agent information 122. In addition, at this time, a walking model such as a social force model or RVO (Reciprocal Velocity Obstacles) is applied to the agent to be processed and the walls and other agents in its vicinity, and the movement speed is adjusted before addition. is preferable.

Then, for an agent whose position at the current time does not match the representative point of the destination, the status at the current time in the agent information 122 is updated to "moving".
On the other hand, for an agent whose current position matches the representative point of the destination (an agent who has reached the destination), the destination queue (queue of agents staying at the destination) and Update status.

First, the queue and number of service counters at the destination at the current time are read from the spatial information 111. The spatial information 111 is updated by adding the ID of the agent that has reached the destination to the read queue.
At this time, if the number of agents in the queue after being added is less than or equal to the number of service windows, the current status in the agent information 122 is updated to "processing".
Furthermore, the service time distribution regarding the destination is read from the spatial information 111, and the Box-Mueller method is applied to the read normal distribution to calculate the service time at the destination.

That is, a random number is generated, and the service time is randomly determined by a lottery using the read normal distribution and the random number, with an occurrence probability according to the normal distribution.
A set of the ID of the agent that has reached the destination and the service completion time obtained by adding the calculated service time to the current time is added to the agent information 123.
On the other hand, if the number of agents in the queue after being added is greater than the number of service windows, the current status in the agent information 122 is updated to "on standby".

For an agent whose status one time ago was "on standby" (an agent on standby), the behavior simulation means 52 sets the moving speed at the current time to 0 and maintains the position one time ago.
Further, if the order of waiting agents in the queue becomes less than or equal to the number of service windows due to processing of another agent, the status of the waiting agent is updated to "processing". Otherwise, the status remains "waiting".

For agents whose status one time ago was "processing" (processing agents), the behavior simulation means 52 reads the service completion time from the agent information 123.
When the current time reaches the service completion time, the behavior simulation means 52 updates the status of the current time in the agent information 122 to "achieved". In addition, by referring to the agent information 121 and the spatial information 111, the agent information 121, which has reached the service completion time, selects the action purpose whose generation time is less than or equal to the current time and which corresponds to the destination at the current time. Update the achievement flag of the action objective closest to 1 to 1. Furthermore, the data on the service completion time of the agent is deleted from the agent information 123.

When the current time reaches the service completion time, the behavior simulation means 52 further specifies that the agent whose service completion time has reached the service completion time is later than the agent in the queue of the destination where the agent who has reached the service completion time is receiving the service. The agent in the queue is updated by moving the agent forward one by one, and the agent ID of the agent is deleted from the queue. Furthermore, the behavior simulation means 52 calculates the service time for the agents whose order matches the number of service windows in the updated queue, and adds the combination of agent ID and service completion time to the agent information 123. , updates the current status of the agent in the agent information 122 to "Processing".

See FIG. 2. The statistical information updating means 53 updates statistical information 130 (waiting number distribution, service time distribution) and statistical information 131 (travel speed distribution) based on the observed value of the required time for each destination.
The statistical information updating means 53 may update the statistical information 130 and 131 of each agent according to the required time of the agent. In other words, it is assumed that the agent has observed its own required time.
That is, the statistical information 130 and 131 stored about the target agent may be updated according to the observed value of the time required for the agent to be processed (target agent) to achieve the behavioral purpose among the multiple agents.

Further, the statistical information updating means 53 may update the statistical information 130 and 131 of each agent according to the value observed by the agent in the required time of an agent other than the agent (hereinafter referred to as other agent).
That is, the statistical information 130 and 131 of the target agent may be updated according to observed values regarding the time required for other agents to achieve their behavioral goals.

The statistical information updating means 53 acquires observed values for destinations existing within the visible range of each agent and agents existing within the same range. For this purpose, the statistical information updating means 53 sets the visibility range of each agent. The angular range and visible distance of the visible range may be changed depending on the situation of each agent. For example, the visibility range during standby or processing may be set wider than the visibility range during movement. The angular range and visible distance of the visible range may be changed depending on the moving speed of each agent. Also, with reference to the spatial information 111, a visible range is set excluding areas blocked by obstacles such as walls (blind spots).
Specifically, the statistical information updating means 53 updates an area within a range of 90 degrees to the left and right in the direction of movement for a moving agent, excluding blind spots, and an area centered around the agent's position for an agent that is waiting or processing. The area excluding blind spots from the 360 degree range is set as the visible range.

The statistical information, observation values and observation timing for updating it are as follows.
The observation values for updating the movement speed distribution stored as knowledge for each local route of each agent are the movement speed of the agent and the movement speed of other agents on the local route, and are determined at predetermined observation intervals (for example, 10 observed at virtual time).
In this embodiment, a common observation interval is set for all agents, but a different observation interval may be set for each agent.
Furthermore, in this embodiment, the starting point of the observation interval for each agent is the generation time of the agent. It may also be a common starting point for all agents.

The observation value for updating the waiting number distribution stored as knowledge for each destination of each agent is the waiting number at the destination, and is observed by referring to the queue at the above observation interval.
The observed value for updating the service time distribution stored as knowledge for each destination of each agent is the service time required for the agent to receive the service at the destination. It is observed when the behavioral purpose is achieved.

Alternatively, the observed value may be the service time of another agent at the destination where the agent is on standby. In this case, the observed value may be observed at the time when the rank of the agent in the queue for the destination changes. In this case, for example, the behavior simulation means 52 notifies the statistical information updating means 53 of the agent whose queue has changed and the destination, and the statistical information updating means 53 that has received the notification stores the notification time in the storage unit 4. Then, the elapsed time since the previous notification is determined as the service time.

Furthermore, the statistical information updating means 53 simulates the situation in which each individual adds a destination and a local route seen for the first time to its knowledge.
In other words, the statistical information updating means 53 statistically updates information on destinations that exist within the visible range of each agent but are not included in the agent's statistical information 130 and local routes that are not included in the agent's statistical information 131. Add to information 130.
The statistical information updating means 53 is an example of the "location information updating means" described in the claims.

Specifically, when there is a destination within the visible range of each agent that is not included in the statistical information 130 of the agent, the statistical information updating means 53 updates the agent ID of the agent and the destination of the destination. Data in which initial values of parameters of the joint probability distribution of the waiting number distribution and the service time distribution are associated with the combination with the ID is added to the statistical information 130. As described above, this initial value is stored in the statistical information storage means 43 as an initial value for each destination.
Furthermore, when there is a local route that is not included in the statistical information 131 of the agent within the visible range of each agent, the statistical information updating means 53 updates the agent ID of the agent and the both end points of the local route. Then, data in which the initial values of the parameters of the joint probability distribution of the moving speed distribution are associated is added to the statistical information 131. As described above, this initial value is stored in the statistical information storage means 43 as an initial value for each destination.

The updating of the parameters of the joint probability distribution (mean value μ, degree of freedom ν, hypothetical sample size κ, scale parameter λ) by the statistical information updating means 53 is performed using the Bayesian algorithm expressed by, for example, the following equations (5) to (8). This may be done by an estimation method.

In the above equation, μ ₀ , ν ₀ , κ ₀ , and λ ₀ are the parameters of the joint probability distribution stored in the statistical information storage means 43, and μ ₁ , ν ₁ , κ ₁ , and λ ₁ are the parameters of the updated This is the joint probability distribution parameter. Further, N is the number of observed values, and x _a is the average value of the observed values.

Next, referring to FIGS. 12(a) and 12(b), we will explain how the probability distribution stored in the statistical information storage means 43 is updated by the statistical information updating means 53 and differences occur for each agent. explain.
Probability distributions 140 and 150 indicate the initial values of the service time distributions of the kiosk 2 for agents #1 and #59. The initial values of both agents #1 and #59 are N(600, 2500).

Probability distributions 141 and 151 show the service time distributions of agents #1 and #59 at the shop 2 after the virtual time has passed and the statistical information update means 53 has sufficiently updated them. The service time distribution 141 of agent #1 is N(480, 25700), and the service time distribution 151 of agent #59 is N(550, 17100). That is, the service time distribution 151 has a larger average value and smaller variance than the service time distribution 141.

This means that, while agent #1's kiosk 2 is faster on average than agent #59's kiosk 2, it is often significantly faster or significantly delayed than that average. It can be seen that it is a stable store. As a result, for example, if agent #1 has little remaining time and the service time at another kiosk is shorter on average, then agent #1 will select kiosk 2 on a do-or-go basis.
On the other hand, it can be seen that the shop 2 for agent #59 has a stable service time, although it is slightly slower on average than the shop 2 for agent #1. As a result, for example, agent #59 can easily select store 2 when the remaining time is 550 hours or more.

In this way, even for the same destination, information on the required time varies depending on the agent. Similarly, even for the same agent, there will be differences in information on the time required between destinations to achieve the same action objective.
Differences in information about the required time can cause differences in behavior between agents, and it is possible to simulate differences in behavior in the real world due to differences in experience knowledge among individuals.

See FIG. 2. The prediction result output means 60 outputs a prediction result that is a result of the simulation. The prediction result includes at least some or all of the information stored in the agent information storage means 42, and preferably further includes some or all of the information stored in the spatial information storage means 41.
The prediction result output means 60 is realized by, for example, the control section 5 and the display section 6 working together, and displays the prediction result read from the storage section 4 on the display section 6. Further, the prediction result output means 60 may output the prediction result as a data file, may output it as an image file, may display it as an image, or may output the prediction result in one or more of these formats. You can also output it.

For example, the prediction result output means 60 generates an image of the prediction result by converting a map of the target space into an image, and superimposing and drawing a figure representing the agent at the coordinates corresponding to the position of each agent on the image. The image is output as a file and/or output to the display unit 6.
The output timing of the prediction result may include the middle of the simulation as well as the time when the simulation is finished. That is, part or all of the above information obtained up to the middle of the simulation may be output as an intermediate prediction result.

(motion)
Next, a simulation method using the simulator 1 of the first embodiment will be explained.
FIG. 13 is a flowchart of an example of the overall processing of the simulation method by the simulator 1 of the first embodiment.
First, in step S1, the user sets simulation conditions, spatial information, initial values of agent information, and initial values of statistical information as simulation conditions. Setting is performed through the condition setting means 20. Then, in step S2, the virtual time t is initialized to 0.

In step S3, an agent whose generation time has arrived is generated. Specifically, the behavior simulator 52 searches the agent information 120 for an agent whose generation time is set to be the same as the current time t. When a corresponding agent is detected, the ID of the corresponding agent is added to the agent information 122 along with the initial position. The added agent becomes an existing agent.
In step S4, it is determined whether or not there is an existing agent (an agent that was generated before the virtual time and has an action objective ID set with an achievement flag of 0). If there is an existing agent (step S4: Y), the process advances to step S5. If there is no existing agent (step S4: N), the process advances to step S8.

In step S5, statistical information update processing is performed. In the statistical information update process, the statistical information 130 and 131 stored in the statistical information storage means 43 are updated based on the observed values observed by the existing agents. Details of the statistical information update process will be described later.
In step S6, action determination processing is performed. In the action determination process, a destination that the agent aims to reach is selected based on the action purpose set in the agent information 121 and the probability distribution set in the statistical information 130 and 131. Details of the action determination process will be described later.

In step S7, agent status update processing is performed. In the agent status update process, the status of the existing agent is updated according to the destination determined in step S6. Details of the agent status update process will be described later.
In step S8, the virtual time t is incremented by "1".
In step S9, it is determined whether the virtual time t has reached the simulation end time T stored in the simulation condition storage means 40. If the virtual time t reaches the end time T (step S9: Y), the process proceeds to step S10. If the virtual time t has not reached the end time T (step S9: N), the process returns to step S3.
In step S10, the prediction result, which is the result of the simulation, is output, and the process ends.

Next, the statistical information update process in step S5 will be described with reference to FIGS. 14 and 15. The statistical information update process is executed by the behavior simulation means 52 and the statistical information update means 53.
In step S20, the behavior simulator 52 sequentially selects existing agents and sets them as agents of interest to be processed.

In step S21, the behavior simulator 52 determines whether the observation timing for updating the statistical information 130 and 131 for the agent of interest has arrived. When the observation timing has arrived (step S21: Y), the process advances to step S22. If the observation timing has not arrived (step S21: N), the process proceeds to step S35.
In step S22, the statistical information updating means 53 sets the visible range of the agent of interest. The statistical information updating means 53 determines whether a destination that is not included in the statistical information 130 of the agent of interest exists within the visible range.

If a destination not included in the statistical information 130 exists within the visible range (step S22: Y), the process proceeds to step S23. If a destination that is not included in the statistical information 130 does not exist within the visible range (step S22: N), the process proceeds to step S24.
In step S23, the statistical information updating means 53 adds the data of the destination to the statistical information 130 of the agent of interest. Thereafter, the process proceeds to step S24.

In step S24, the statistical information updating unit 53 determines whether a local route that is not included in the statistical information 131 of the agent of interest exists within the visible range. If a local route not included in the statistical information 131 exists within the visible range (step S24: Y), the process advances to step S25. If there is no local route not included in the statistical information 131 within the visible range (step S24: N), the process proceeds to step S26.
In step S25, the statistical information updating means 53 adds the data of the local route to the statistical information 131 of the agent of interest. Thereafter, the process proceeds to step S26.

In step S26, the statistical information updating means 53 determines whether the status of the agent of interest is "on the move." If the status of the agent of interest is "on the move" (step S26: Y), the process advances to step S27. If the status of the agent of interest is not "on the move" (step S26: N), the process advances to step S28.
In step S27, the statistical information updating means 53 acquires the moving speed and local route of the agent of interest, and calculates the speed on the local route. Thereafter, the process advances to step S28.

In step S28, the statistical information updating unit 53 determines whether the status of other agents existing within the visible range of the agent of interest is "moving". If the status of another agent within the visible range is "moving" (step S28: Y), the process advances to step S29. If the status of other agents within the visible range is not "moving" (step S28: N), the process proceeds to step S30.
In step S29, the statistical information updating means 53 acquires the moving speed and local route of another agent that is moving and exists within the visible range, and calculates the speed on the local route. After that, the process proceeds to step S30.

In step S30, the statistical information updating means 53 determines whether the speed has been calculated in either step S27 or S29. If the speed has been calculated (step S30: Y), the process advances to step S31. If the speed is not calculated (step S30: N), the process advances to step S32.
In step S31, the statistical information updating means 53 uses the calculated speed as an observed value to update the moving speed distribution of the local route whose speed was observed in the statistical information 131 of the agent of interest. After that, the process proceeds to step S32.

In step S32, the statistical information updating means 53 determines whether any destination exists within the visible range of the agent of interest. If the destination exists within the visible range (step S32: Y), the process advances to step S33. If the destination does not exist within the visible range (step S32: N), the process proceeds to step S35.
In step S33, the statistical information updating means 53 obtains the number of waiting destinations existing within the visible range. In step S34, the statistical information updating means 53 uses the obtained waiting number as an observed value to update the waiting number distribution of the destination whose waiting number is observed in the statistical information 130 of the agent of interest. Thereafter, the process proceeds to step S35.

In step S35, the statistical information updating means 53 determines whether the service completion time of the agent of interest has arrived. If the service completion time has arrived (step S35: Y), the process advances to step S36. If the service completion time has not arrived (step S35: N), the process proceeds to step S37.
In step S36, the statistical information updating means 53 calculates the service time required for the agent of interest. After that, the process advances to step S37.

In step S37, the statistical information updating means 53 determines whether or not the order of the agent in the queue has been updated, if the status of the agent of interest is on standby. If the order has been updated (step S37: Y), the process advances to step S38. If the order is not updated (step S37: N), the process advances to step S39.
In step S38, the statistical information updating means 53 calculates the service time required for other agents whose services have been completed at the destination waiting for the service of the agent of interest. Thereafter, the process advances to step S39.

In step S39, the statistical information updating means 53 determines whether the service time has been calculated in either step S36 or S38. If the service time has been calculated (step S39: Y), the process advances to step S40. If the service time is not calculated (step S39: N), the process advances to step S41.
In step S40, the statistical information updating means 53 uses the calculated service time as an observed value, and uses the service time of the destination where the service time is observed (i.e., the destination where the noted agent receives the service) out of the statistical information 130 of the noted agent. Update the distribution. After that, the process advances to step S41.
In step S41, the statistical information updating means 53 determines whether the processes of steps S21 to S40 have been executed for all existing agents. When the process has been executed for all existing agents (step S41: Y), the statistical information update process ends. If there is an unprocessed existing agent (step S41: N), the process returns to step S20.

Next, with reference to FIG. 16, the action determination process in step S6 will be described. The action determination process is executed by the evaluation value calculation means 50 and the destination selection means 51.
In step S50, the evaluation value calculation means 50 sequentially selects existing agents and sets them as agents of interest to be processed.
In step S51, the evaluation value calculation means 50 determines whether the timing for determining the action of the agent of interest has arrived. When the action decision timing has arrived (step S51: Y), the process proceeds to step S52. If the action decision timing has not arrived (step S51: N), the process proceeds to step S59.

In step S52, the evaluation value calculation means 50 generates one or more behavior patterns P _i .
In step S53, the evaluation value calculation means 50 generates one or more destination patterns M _ij for each of the behavior patterns P _i .
In step S54, the evaluation value calculation means 50 sequentially selects the destination patterns _Mij and sets them as the pattern of interest to be processed.

In step S55, the evaluation value calculation means 50 performs evaluation value calculation processing. In the evaluation value calculation process, an evaluation value E is calculated for the pattern of interest. Details of the evaluation value calculation process will be described later.
In step S56, the evaluation value calculation means 50 determines whether all destination patterns M _ij have been set as patterns of interest. If all the destination patterns M _ij are set as patterns of interest (step S56: Y), the process advances to step S57. If there is a destination pattern M _ij that is not set as a pattern of interest (step S56: N), the process returns to step S54.

In step S57, the destination selection means 51 selects one of the destination patterns M _ij based on the evaluation value.
In step S58, the destination selection means 51 newly generates current time data in which the ID of the first destination in the selected destination pattern _Mij is associated with the agent ID and the current virtual time, and the agent information 122 is added. The process then proceeds to step S60.

On the other hand, in step S59, the destination selection means 51 newly generates current time data in which the destination ID of one time ago is associated with the agent ID and the current virtual time, and adds it to the agent information 122. The process then proceeds to step S60.
In step S60, the evaluation value calculation means 50 determines whether the processes of steps S51 to S59 have been executed for all existing agents. When the process has been executed for all existing agents (step S60: Y), the action determination process ends. If there is an unprocessed existing agent (step S60: N), the process returns to step S50.

The evaluation value calculation process in step S55 will be described with reference to FIG. 17. The evaluation value calculation process is executed by the evaluation value calculation means 50.
In step S70, the evaluation value calculating means 50 calculates, for each destination included in the pattern of interest, a required time distribution that is a probability distribution of the time required to achieve the behavioral objective at the destination.
In step S71, the evaluation value calculation means 50 initializes a variable s indicating the ranking of the destination included in the pattern of interest to "1". In the following steps S73 to S77, each success probability of achieving the behavioral objective by the achievement deadline at the sth destination included in the pattern of interest is calculated. Furthermore, the evaluation value calculation means 50 initializes the evaluation value E to "0".

In step S72, the evaluation value calculation means 50 determines whether the variable s is "1", that is, whether the success probability of the first destination is calculated. If the variable s is "1" (step S72: Y), the process advances to step S73. If the variable s is not "1" (step S72: N), the process proceeds to step S75.
In step S73, the evaluation value calculation means 50 calculates the remaining time until the deadline for achieving the behavioral purpose to be achieved at the first destination.

In step S74, the evaluation value calculation means 50 calculates the probability of success based on the remaining time and the distribution of time required for the action objective at the first destination. Thereafter, the process proceeds to step S78.
On the other hand, in step S75, the evaluation value calculation means 50 calculates the total distribution of time required to achieve the behavioral objective at the 1st to s-1th destinations.

In step S76, the evaluation value calculation means 50 calculates the remaining time distribution until the deadline for achieving the action objective to be achieved at the sth destination, based on the total required time distribution.
In step S77, the evaluation value calculating means 50 calculates the success probability based on the remaining time distribution and the required time distribution for the sth destination. Thereafter, the process proceeds to step S78.
In step S78, the evaluation value calculation means 50 adds to the evaluation value E the product obtained by multiplying the desired achievement level by the success probability calculated in step S74 or S77.

In step S79, the evaluation value calculation means 50 increases the variable s by "1".
In step S80, the evaluation value calculation means 50 determines whether or not the processing of steps S72 to S79 has been completed for all destinations in the pattern of interest. If the processing has been completed for all destinations (step S80: Y), the processing advances to step S81. If there is an unprocessed destination (step S80: N), the process returns to step S72.
In step S81, the evaluation value calculation means 50 outputs evaluation value information in which agents, patterns of interest, and evaluation values are associated with each other to the destination selection means 51. After that, the evaluation value calculation process ends.

Next, the agent status update process in step S7 will be explained with reference to FIG. The agent state update process is realized by the destination selection means 51 and the behavior simulation means 52.
In step S90, the behavior simulator 52 sequentially selects existing agents and sets them as agents of interest to be processed.
In step S91, the behavior simulation means 52 determines whether the destination selection means 51 has newly selected the destination pattern of the agent of interest. If a new destination pattern is selected (step S91: Y), the process advances to step S92. If a new destination pattern is not selected (step S91: N), the process advances to step S93.

In step S92, the destination selection means 51 updates the destination ID stored as an action parameter in the agent information 122 to the destination ID at the head of the newly selected destination pattern. Thereafter, the process advances to step S93.
In step S93, the behavior simulation means 52 updates the situation of the agent of interest.

In step S94, the behavior simulator 52 determines whether the status of the agent of interest is "moving" or "achieved." If the status is "moving" or "achieved" (step S94: Y), the process advances to step S95. If the status is not "moving" or "achieved" (step S94: N), the process advances to step S96.
In step S95, the behavior simulator 52 calculates the moving speed of the agent of interest and updates the position. Thereafter, the process advances to step S96.

In step S96, the behavior simulator 52 determines whether the agent of interest has achieved all the behavioral objectives (that is, has achieved the behavioral objective for which the terminal objective flag is 1). If all behavioral objectives have been achieved (step S96: Y), the process advances to step S97. If there is an unachieved action objective (step S96: N), the process proceeds to step S98.
In step S97, the behavior simulation means 52 deletes the agent of interest. Thereafter, the process advances to step S98.
In step S98, the behavior simulation means 52 determines whether or not the processing of steps S91 to S97 has been completed for all existing agents. If there is an unprocessed existing agent (step S98: N), the process returns to step S90. When the processing is completed for all existing agents (step S98: Y), the agent status update processing ends.

(Modified example)
(1) In the first embodiment, the required time is defined as the sum of travel time, waiting time, and service time.
Alternatively, the travel time may be taken as the required time (ie, the waiting time and service time are zero). As a result, it is possible to perform simulations for behavioral purposes such as delivery, where waiting time and service time can be considered to be zero.

(2) In the first embodiment, the case where the relationship between the purpose of action and the destination is one-to-one or one-to-many is illustrated. The relationship between the purpose of action and the destination may be a many-to-one or many-to-many relationship. For example, it is possible to simulate a situation in which service times differ between different services at the same destination. In this case, it is desirable to store or update statistical information on service time distribution for each combination of purpose of action and destination.

(3) In the first embodiment, an example has been described in which evaluation value calculation by the evaluation value calculation means 50 and destination selection by the destination selection means 51 are repeated at action determination intervals. Alternatively, repeating evaluation value calculation and destination selection at action determination intervals may be omitted.
In this case, for example, the destination pattern selected by the destination selection means 51 is stored in the agent information storage means 42, and the behavior simulation means 52 sequentially selects the destination pattern from the top of the destination patterns stored for each agent. May act to move the agent.

(4) In the first embodiment, an example has been described in which an achievement deadline is set for each behavioral purpose. Alternatively or in addition to this, a single achievement deadline (that is, an achievement deadline common to a plurality of action objectives) may be set for each interlude and for each agent. For example, the end time of the intermission may be set as the completion deadline. In this case, the same processing as in the above embodiment may be performed on the assumption that the same achievement deadline is set for all action objectives of each agent during each intermission.
Furthermore, a single completion deadline common to all agents may be set for each intermission.

(5) In the first embodiment, if there is a destination that is not included in the statistical information 130 of the agent within the visible range of the agent, data on the destination is added to the statistical information 130 of the agent. Alternatively or in addition to this, an information source such as a signboard that informs the agent of information regarding the destination may be set within the target space. If there is an information source within the agent's visual range that informs of a destination that is not included in the agent's statistical information 130, data on the destination may be added to the agent's statistical information 130. Similarly, an announcement that broadcasts information regarding the destination within the target space may be simulated. When an announcement informing of a destination that is not included in the statistical information 130 of the agent is broadcast, data on the destination may be added to the statistical information 130 of the agent.

(Second embodiment)
When calculating the required time distribution, the evaluation value calculating means 50 of the first embodiment predicts the waiting time based on the waiting number distribution of the destination stored in the statistical information 130. Furthermore, for destinations within visual range, waiting times are predicted based on the observed number of waiting times.
However, the prediction of waiting time may change by observing the movements of other agents toward the destination.
Therefore, when selecting the destination pattern of the agent to be processed, the evaluation value calculation means 50 of the second embodiment predicts the waiting time at the destination based on the behavior of other agents. For example, waiting time at a destination is predicted based on the direction of movement of other agents.

See FIG. 19. Now, reference numeral 200 indicates the target agent of interest for which the waiting number distribution is calculated (that is, the target agent of the action determination process in FIG. 16), and the broken line indicates the visible range of the target agent 200.
Reference numerals 201a to 201c indicate other agents within the visible range of the agent of interest 200, and reference numeral 202 indicates other agents outside the visible range.

Further, reference numerals 203a, 204b, and 205a indicate agents receiving service at destinations VI, VII, and VIII, respectively; reference numerals 203b and 203c indicate agents waiting for service at destination VI; reference numeral 204b indicates agents receiving service at destination VI; Agents waiting for service at destination VII, reference numerals 205b and 205c indicate agents waiting for service at destination VIII.
The evaluation value calculation means 50 of the second embodiment predicts the number of waiting times for destinations VI, VII, and VIII based on the moving directions of other agents 201a to 201c within the visible range.

The evaluation value calculation means 50 of the second embodiment includes a waiting time calculation means 70 shown in FIG. The waiting time calculation means 70 includes an agent selection means 71 , a visibility range setting means 72 , a moving direction acquisition means 73 , an arrival number prediction means 74 , and a waiting time prediction means 75 .
The waiting time calculation means 70 selects an agent of interest to be processed from among the existing agents.

The visibility range setting means 72 sets the visibility range of the agent of interest. The angular range and visible distance of the visible range may be changed depending on the situation of the agent of interest. For example, the visibility range during standby or processing may be set wider than the visibility range during movement.
For example, if the agent of interest is moving, the range may be 90 degrees left and right in the direction of movement, and if the agent is on standby or processing, the range may be 360 degrees centered on the agent's position.
The angular range and visible distance of the visible range may be changed depending on the moving speed of each agent. Preferably, the spatial information 111 stored in the spatial information storage means 41 is used to set the visible range by excluding areas blocked by obstacles such as walls within the visible range.

The movement direction acquisition means 73 acquires the movement speed of another agent within the visible range from the agent information 122 stored in the agent information storage means 42 as the movement direction of the other agent.
The arrival number prediction means 74 calculates the destination d stored in the statistical information 130 of the agent of interest based on the moving direction of other agents within the visible range and the representative point of the destination stored in the spatial information 111. Predict the number of arrivals of other agents to _i . For example, the number of arrivals predicting means 74 may predict the number of arrivals _Rdi of other agents reaching the destination based on the following equation (9).

In equation (9), a is another agent that is determined to be heading to the destination _di stored in the statistical information 130. For example, as shown in FIG. 19, if the destination d _i exists within a predetermined angle θ to the left and right of the movement direction of the other agent 201b, the arrival number prediction means 74 predicts that the other agent 201b will head toward the destination d _i. It can be concluded that Furthermore, in equation (9), r _a represents the number of all destinations that are within a predetermined angle θ to the left and right of the moving direction of the other agent a, among the destinations stored in the statistical information 130 of the agent of interest.

The waiting time prediction means 75 predicts the waiting time distribution for the destinations stored in the statistical information 130 of the agent of interest. The waiting time distribution is calculated by the product of the future number of waiting times at the destination divided by the number of service counters and the service time distribution required when carrying out the behavioral purpose at that destination.
When the destination is within the visible range of the agent of interest, the waiting time prediction means 75 sets the future waiting number for the destination as the sum of the waiting number for the destination and the number of arrivals at the destination. That is, the waiting time distribution is predicted based on the observation result of the number of other agents existing at the destination and the number of agents arriving.

If the destination does not exist within the visibility range of the agent of interest, a representative value is sampled from the distribution of the number of people waiting at the destination stored in the statistical information 130 of the agent of interest, and the number of people arriving at the destination is used as the representative value. The added sum is the future waiting number for the destination.
That is, the waiting time distribution is predicted based on the number of arrivals at the destination and the statistical information on the waiting time at the destination stored in the statistical information 130.

Next, with reference to FIG. 21, the required time distribution calculation process by the evaluation value calculation means 50 of the second embodiment will be described.
In step S100, the evaluation value calculation means 50 calculates the travel required to reach each destination included in the pattern of interest (destination pattern) based on the movement speed distribution of the local route stored in the statistical information 131 of the agent of interest. Calculate the time distribution.

In step S101, the evaluation value calculation means 50 calculates the service time distribution at each destination included in the attention pattern (destination pattern) based on the service time distribution at the destination stored in the statistical information 130 of the attention agent. Calculate.
In step S102, the number-of-arrivals prediction means 74 predicts the number of arrivals of other agents heading to the destination stored in the statistical information 130 of the agent of interest.

In step S103, the waiting time prediction unit 75 calculates the observed values of the number of arrivals of agents heading for the destination and the number of waiting at the destination, or the number of arrivals of agents heading for the destination and the relevant agent stored in the statistical information 130 of the noted agent. Predict the future number of people waiting at a destination based on the representative value of the distribution of people waiting at the destination.

In step S104, the waiting time prediction means 75 calculates the waiting time distribution at the destination based on the future number of waiting times, the number of service counters, and the service time distribution.
In step S105, the evaluation value calculation means 50 calculates the sum of the travel time distribution, service time distribution, and waiting time distribution as the required time. After that, the required time distribution calculation process ends.

Note that in each of the above embodiments, an example has been shown in which the waiting number distribution, service time distribution, moving speed distribution, and each distribution associated therewith are held as normal distributions, but these distributions may be held as a histogram. In that case, calculations between distributions are calculations between corresponding bins, and updating of the distribution based on observed values is updating of the bin values. Further, each distribution can be held as a formulated distribution other than a normal distribution, and in that case, a calculation method and an update method suitable for the distribution to be adopted may be used, such as performing calculations on the distribution by a sampling method.

Further, in the second embodiment, an example of a simulator that predicts the behavior of a plurality of people in using seats, toilets, and shops in a theater having seats, toilets, and shops has been described, but the present invention is not limited to this. It's not something you can do. The simulator, simulation method, and simulation program of the second embodiment can also be applied to the case of simulating the behavior of a non-human agent (for example, a vehicle).

(Effects of embodiment)
(1) The simulator 1 simulates the behavior of multiple agents moving to multiple destinations in a predetermined space. The simulator 1 includes a statistical information storage means 43 that stores statistical information regarding waiting times at each of a plurality of destinations for each agent, and an agent selection means 71 that sequentially selects a target agent to be processed from among the plurality of agents. an arrival number prediction means 74 for predicting the number of arrivals of other agents to each of the plurality of destinations according to the actions of other agents including at least one agent other than the target agent among the plurality of agents; a waiting time prediction means 75 that predicts the waiting time at each of a plurality of destinations based on statistical information of the agent; and an evaluation value calculation means 50 that calculates an evaluation value that increases as the waiting time for a destination becomes shorter. A behavior simulating means 52 is provided that simulates the behavior of the target agent moving to the destination based on the evaluation value.

In this way, by simulating the behavior of the target agent moving to the destination based on the evaluation value, which increases as the waiting time decreases, multiple agents with the same behavioral purpose will unnaturally concentrate on one destination. Behavior can be resolved.

(2) The simulator 1 may include visibility range setting means 72 for setting the visibility range of the target agent. The waiting time prediction means 75 may predict the number of arrivals according to the actions of other agents existing within the visible range.
This makes it possible to simulate the behavior of an agent who visually recognizes the behavior of other agents and predicts the waiting time at each of a plurality of destinations.

(3) The simulator 1 may include a movement direction acquisition means 73 that acquires the movement direction of at least one other agent other than the target agent among the plurality of agents. The number of arrivals predicting means 74 may predict the number of arrivals according to the movement direction of another agent as the behavior of the other agent.
This makes it possible to simulate the behavior of an agent who predicts waiting time based on the movement direction of other agents.
(4) The simulator 1 may include visibility range setting means 72 for setting the visibility range of the target agent. The waiting time prediction means 75 predicts the waiting time at each of a plurality of destinations based on the number of arrivals and the statistical information of the target agent for destinations that are not within the visible range, and predicts the waiting time for each of the destinations that are within the visible range. The waiting time may be predicted based on the observed number of waiting times and the number of arrivals at each of a plurality of destinations.
This makes it possible to simulate behavior that predicts waiting times for destinations that are within the visible range based on observation results, and based on the agent's own knowledge for destinations that are not within the visible range.

(5) The number of destinations predicted means 74 determines a plurality of candidates for destinations that other agents are expected to reach in the future according to the movement direction of the other agents, and the number of destinations predicted based on the reciprocal of the number of candidates. can be predicted.
This makes it possible to calculate the number of other agents reaching the destination.
(6) The visibility range setting means 72 may set the visibility range according to the moving speed of the target agent.
This allows the visibility range to be set appropriately.

(7) The statistical information storage means 43 may store location information of the destination for each agent. When a destination whose position information is not stored in the statistical information storage means 43 of the target agent comes within the visual range of the target agent, the statistical information updating means 53 updates the position information of the destination with the statistics of the target agent. It may be added to the information storage means 43. The number of arrivals predicting means 74 predicts the number of arrivals for the destination whose position information is stored in the statistical information storage means 43, and the waiting time predicting means 75 predicts the number of arrivals for the destination whose position information is stored in the statistical information storage means 43. You can predict the waiting time at the location.
This makes it possible to simulate the bias in knowledge of each agent regarding the destination.

DESCRIPTION OF SYMBOLS 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...Simulation condition storage means, 41...Spatial information storage means , 42... Agent information storage means, 43... Statistical information storage means, 50... Evaluation value calculation means, 51... Destination selection means, 52... Action simulation means, 53... Statistical information updating means, 60... Prediction result outputting means, 70 ...Time calculation means, 71...Agent selection means, 72...Visibility range setting means, 73...Movement direction acquisition means, 74...Number of arrivals prediction means, 75...Time prediction means, 100...Map information, 110...Action purpose information, 111 ... Spatial information, 120, 121, 122, 123... Agent information, 130, 131... Statistical information

Claims (9)

A simulator that simulates the behavior of multiple agents moving to multiple destinations in a predetermined space,
statistical information storage means for storing statistical information regarding waiting times at each of the plurality of destinations for each of the agents;
agent selection means for sequentially selecting a target agent to be processed from among the plurality of agents;
arrival number prediction means for predicting the number of arrivals of the other agents to each of the plurality of destinations according to the actions of other agents including at least one agent other than the target agent among the plurality of agents;
waiting time prediction means for predicting waiting time at each of the plurality of destinations based on the number of arrivals and the statistical information of the target agent;
an evaluation value calculation means for calculating an evaluation value that increases as the waiting time for the destination becomes shorter;
behavior simulating means for simulating the behavior of the target agent moving to the destination based on the evaluation value;
A simulator characterized by comprising: comprising a visibility range setting means for setting a visibility range of the target agent;
2. The simulator according to claim 1, wherein the number-of-arrivals prediction means predicts the number of arrivals according to the behavior of the other agent existing within the visible range. comprising a visibility range setting means for setting a visibility range of the target agent;
The waiting time prediction means predicts the waiting time at each of the plurality of destinations, based on the number of arrivals and the statistical information of the target agent, with respect to the destinations that are not within the visible range, and predicting the waiting time for the destinations existing within a visual range based on the observed number of waiting times at each of the plurality of destinations and the number of arrivals;
The simulator according to claim 1, characterized in that: visibility range setting means for setting a visibility range of the target agent;
destination storage means for storing location information of the destination for each agent;
When a destination whose location information is not stored in the destination storage means of the target agent enters the visible range of the target agent, the position information of the destination is stored in the destination storage means of the target agent. A means for updating location information to be added;
The number of arrivals predicting means predicts the number of arrivals for a destination whose location information is stored in the destination storage means,
The waiting time prediction means predicts the waiting time at a destination whose location information is stored in the destination storage means.
The simulator according to claim 1, characterized in that: The simulator according to any one of claims 2 to 4, wherein the visibility range setting means sets the visibility range according to a moving speed of the target agent. comprising a movement direction acquisition means for acquiring the movement direction of at least one other agent other than the target agent among the plurality of agents;
The simulator according to any one of claims 1 to 5, wherein the number of arrivals predicting means predicts the number of arrivals according to a moving direction of the other agent as the action of the other agent. The destination number predicting means determines a plurality of candidates for the destination that the other agent is expected to reach in the future according to the direction of movement of the other agent, and calculates the number of destinations based on the reciprocal of the number of candidates. The simulator according to any one of claims 1 to 6, characterized in that the simulator predicts. A simulation method for simulating the behavior of multiple agents moving to multiple destinations in a predetermined space, the method comprising:
a process of reading statistical information regarding waiting times at each of the plurality of destinations, which is stored for each agent in a predetermined storage device;
a process of sequentially selecting a target agent to be processed from among the plurality of agents;
A process of predicting the number of arrivals of the other agents to each of the plurality of destinations according to the actions of other agents including at least one agent other than the target agent among the plurality of agents;
A process of predicting waiting time at each of the plurality of destinations based on the number of arrivals and the statistical information of the target agent;
a process of calculating an evaluation value that increases as the waiting time for the destination becomes shorter;
a process of simulating the behavior of the target agent moving to the destination based on the evaluation value;
A simulation method characterized by being executed by a computer. A simulation program that simulates the behavior of multiple agents moving to multiple destinations in a predetermined space,
a process of reading statistical information regarding waiting times at each of the plurality of destinations, which is stored for each agent in a predetermined storage device;
a process of sequentially selecting a target agent to be processed from among the plurality of agents;
A process of predicting the number of arrivals of the other agents to each of the plurality of destinations according to the actions of other agents including at least one agent other than the target agent among the plurality of agents;
A process of predicting a waiting time at each of the plurality of destinations based on the number of arrivals and the statistical information of the target agent;
a process of calculating an evaluation value that increases as the waiting time for the destination becomes shorter;
a process of simulating the behavior of the target agent moving to the destination based on the evaluation value;
A simulation program that is executed by a computer.

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