KR102108850B1 - Method and apparatus for pedestrian simulation using cellualar automata - Google Patents

Abstract

Determining a pedestrian group including a pedestrian cell corresponding to a pedestrian located in an indoor space among a plurality of pedestrian groups, determining a walking direction indicating a direction in which the pedestrian cell moves within the indoor space network, and for simulation Provided is a method and apparatus for simulating walking based on a CA algorithm through the step of simulating walking by calculating a probability that a pedestrian cell moves to another cell based on walking data and a walking direction.

Description

METHOD AND APPARATUS FOR PEDESTRIAN SIMULATION USING CELLUALAR AUTOMATA}

The present invention relates to a method and apparatus for simulating indoor walking conditions using a CA algorithm.

Recently, studies are being conducted to provide mobility convenience for the weak. There are studies to facilitate the use of public transportation for the disabled, and studies on accessibility and mobility on the sidewalk for the disabled. However, the above studies have not been performed on the traffic and walking weaks in the indoor space. Therefore, when the public, the weak, and the walking weak are mixed in the indoor space, a study capable of accurately simulating the walking situation in the indoor space is required. The evacuation strategy of the building can be established through a sophisticated simulation of the walking situation in the interior space, and appropriate firefighting design can be performed at the construction stage.

The problem to be solved by the present invention is to provide a method for simulating walking based on a CA algorithm.

In addition, a problem to be solved by the present invention is to provide an apparatus for simulating walking based on a CA algorithm.

In addition, the problem to be solved by the present invention is to provide another device for simulating walking based on a CA algorithm.

According to an embodiment of the present invention, a method for simulating walking is provided based on a cellular automata (CA) algorithm that models an indoor space as an indoor space network composed of a plurality of cells. The walking simulation method determines a pedestrian group including a pedestrian cell corresponding to a pedestrian located in an indoor space among a plurality of pedestrian groups, and determines a walking direction indicating a direction in which the pedestrian cell moves within the indoor space network. Steps, and the step of simulating walking by calculating the probability that a pedestrian cell moves to another cell based on the walking data for the simulation and the walking direction, the plurality of pedestrian groups, the public pedestrian group, children's pedestrian group, wheelchair pedestrian The group, and the elderly pedestrian group, and the occupied spaces of the plurality of pedestrian groups are different.

In the walking simulation method, walking data includes initial speeds of a pedestrian cell, and each pedestrian cell included in a plurality of pedestrian groups may have different initial speeds.

In the walking simulation method, the initial speed of the pedestrian cell belonging to the general pedestrian group is the fastest, the initial speed of the pedestrian cell belonging to the child pedestrian group is the next fastest of the general pedestrian group, and the initial speed of the pedestrian cell belonging to the wheelchair pedestrian group is the child. It is the next fastest of the pedestrian group, and the initial speed of the pedestrian cell belonging to the old pedestrian group may be the slowest.

The walking simulation method may further include the step of simulating walking again by calculating a moving speed of a pedestrian cell by changing a configuration ratio of a plurality of pedestrian groups after the step of simulating walking.

In the walking simulation method, among the plurality of pedestrian groups on the indoor space network, the pedestrian cell of the public pedestrian group occupies the smallest occupied space, and the pedestrian cell of the wheelchair pedestrian group occupies the largest occupied space.

In the walking simulation method, calculating a probability that a pedestrian cell moves to another cell through Equation 1 when it is located at P _{i, j} includes: a left movement factor of the pedestrian cell and a right movement factor of the pedestrian cell through Equation 1; Including a step of calculating, if the left shift factor is greater than the right shift factor, the pedestrian cell moves to the left, and if the right shift factor is greater than the left shift factor, the pedestrian cell can shift to the right.

According to another embodiment of the present invention, an apparatus for simulating walking is provided based on a cellular automata (CA) algorithm that models an indoor space as an indoor space network composed of a plurality of cells. The walking simulation apparatus, among a plurality of pedestrian groups, determines a pedestrian group including a pedestrian cell corresponding to a pedestrian located in the indoor space, and determines a walking direction indicating a direction in which the pedestrian cell moves within the indoor space network A pedestrian determination unit and a walking speed calculation unit that simulates walking by calculating the probability of a pedestrian cell moving to another cell based on the walking data and the initial direction of the walking direction and the pedestrian cell, and the plurality of pedestrian groups comprises: , A children's pedestrian group, a wheelchair pedestrian group, and an elderly pedestrian group, and the occupied spaces of the plurality of pedestrian groups are different.

According to another embodiment of the present invention, an apparatus for simulating walking is provided based on a cellular automata (CA) algorithm that models an indoor space as an indoor space network composed of a plurality of cells. The walking simulation apparatus, among a plurality of pedestrian groups, determines a pedestrian group including a pedestrian cell corresponding to a pedestrian located in the indoor space, and determines a walking direction indicating a direction in which the pedestrian cell moves within the indoor space network Includes a pedestrian decision unit, a database for storing walking data for walking simulation in the indoor space network of a pedestrian cell, and a walking speed calculation unit for simulating walking by calculating the moving speed of the pedestrian cell based on the walking data and walking direction Then, the plurality of pedestrian groups includes a public pedestrian group, a children's pedestrian group, a wheelchair pedestrian group, and an elderly pedestrian group, and the occupied spaces of the plurality of pedestrian groups are different.

According to an embodiment of the present invention, it is possible to elaborately simulate a pedestrian's walking in an indoor space in consideration of various types of pedestrians.

1 is a block diagram showing a simulation apparatus for simulating indoor walking conditions according to an embodiment.
2 is a flowchart illustrating a method for simulating indoor walking conditions according to an embodiment.
3 is a conceptual diagram illustrating some examples of indoor spatial network data representing an indoor space according to an embodiment.
4 is a conceptual diagram showing a pedestrian cell of the CA algorithm according to an embodiment.
5 is a conceptual diagram showing a pedestrian type of the CA algorithm according to FIG. 4.
6 is a conceptual diagram showing a relationship between a pedestrian cell and a neighboring pedestrian cell of a CA algorithm according to an embodiment.
7 is a flowchart illustrating a method for moving a pedestrian forward according to an embodiment.
8 is a flowchart illustrating a method of moving a pedestrian sideways when the time step is odd according to an embodiment.
9 is a flowchart illustrating a method of moving a pedestrian sideways when the time step is even according to an embodiment.
10 is a diagram showing a simulation screen captured during a walking simulation according to an embodiment.
11 is a block diagram showing a walking simulation apparatus according to another embodiment.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art to which the present invention pertains can easily practice. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. In addition, in order to clearly describe the present invention in the drawings, parts irrelevant to the description are omitted, and like reference numerals are assigned to similar parts throughout the specification.

In this specification, redundant description of the same components is omitted.

Also, in this specification, when a component is referred to as being 'connected' or 'connected' to another component, it may be directly connected to or connected to the other component, but other components in the middle It should be understood that may exist. On the other hand, in this specification, when a component is referred to as being 'directly connected' or 'directly connected' to another component, it should be understood that no other component exists in the middle.

In addition, the terms used in this specification are only used to describe specific embodiments, and are not intended to limit the present invention.

Also, in this specification, a singular expression may include a plural expression unless the context clearly indicates otherwise.

Also, in the present specification, terms such as 'include' or 'have' are only intended to indicate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, and one or more of the terms are included. It should be understood that the existence or addition possibilities of other features, numbers, steps, actions, components, parts or combinations thereof are not excluded in advance.

Also in this specification, the term 'and / or' includes a combination of a plurality of listed items or any one of a plurality of listed items. In this specification, 'A or B' may include 'A', 'B', or 'both A and B'.

Also, in this specification, detailed descriptions of well-known functions and configurations that may obscure the subject matter of the present invention will be omitted.

1 is a block diagram showing a simulation apparatus for simulating an indoor walking situation according to an embodiment, FIG. 2 is a flow chart showing a simulation method of an indoor walking situation according to an embodiment, and FIG. 3 is an embodiment It is a conceptual diagram showing some examples of indoor space network data representing indoor space according to the present invention.

Referring to FIG. 1, the walking simulation apparatus 100 according to an embodiment includes a pedestrian determination unit 110, a walking speed calculation unit 120, and a database 130.

The walking simulation apparatus 100 according to an embodiment simulates a pedestrian's walking located in an indoor space based on a cellular automata (CA) algorithm. That is, the walking simulation apparatus 100 may simulate how each pedestrian represented by at least one cell moves within the indoor space network based on a CA algorithm that models the indoor space as a plurality of cells.

The pedestrian determining unit 110 divides pedestrians walking in the indoor space into a plurality of different pedestrian groups. In addition, the pedestrian determining unit 110 determines a starting cell and a walking direction in which each type of pedestrian is located in the indoor spatial network when starting walking simulation. The starting cell of the pedestrian and the walking direction are determined randomly or according to a predetermined method. In the simulation, each pedestrian moves in the walking direction determined by the pedestrian determination unit 110 in the cell determined by the pedestrian determination unit 110.

The walking speed calculator 120 determines a speed at which the pedestrian moves every time step. The walking speed calculator 120 may determine in what speed and direction the pedestrian moves according to the opposite pedestrian walking in the direction opposite to the walking direction from the front of the pedestrian's walking direction. In the simulation according to an embodiment, it is assumed that the pedestrian moves in the front or the left and right directions of the walking direction in the indoor space network represented by the cell.

The database 130 stores various types of walking data for walking simulation. Hereinafter, walking data stored in the database 130 will be described.

Referring to FIG. 2, first, walking data is input to the database 130 of the walking simulation apparatus 100 (S110). The walking data input to the database 130 includes indoor spatial network data and pedestrian attribute data for indicating the indoor space. In addition, the walking data may include data indicating the output format of the simulation device. Table 1 below shows walking data of a CA model according to an embodiment.

division Walking data unit Indoor space
network Unit cell length m Area of unit cell m ² The length of the network m Network area m ² Simulation time s (seconds) Interval time s (seconds) Time step s (seconds) Pedestrian property Pedestrian cell traffic by direction Person / h (hour) / m Pedestrian Group Composition % Initial speed of the pedestrian cell for each pedestrian group m / s Capacity per pedestrian group Person / h (hour) / m Output format Pedestrian space use distribution per time interval Average traffic speed per direction per hour interval m / s Individual cell share / maximum share among individual cells %

Walking data according to an embodiment is collected from walking speed data such as research cases and reports on general and disaster situations. The database 130 may store and store walking data according to criteria such as a situation, a location, and pedestrian age. In the same age group, it can be clearly divided into middle school students and high school students. The indoor spatial network data varies according to a simulation scenario according to an embodiment. Referring to FIG. 3, (a) is indoor spatial network data showing a general corridor, and is 30 m long and 4.5 m wide (0.5 m Х9 cells). (b) is the indoor space network data showing the corridor with obstacles, and the length and width are the same as the normal corridor. One hatched cell in the middle of the hallway represents a normal pedestrian, and several cells painted in black represent obstacles present in the hallway. (c) is the indoor space network data showing a narrow staircase, the length is 10m, and the width is 1.2m (about 0.5mХ2 cells). (d) is the indoor space network data showing a wide staircase, the length is 10m, and the width is 2.5m (0.5mХ5 cells). In the center of a wide staircase, a plurality of black cells representing railing obstacles are located.

Next, the pedestrian determining unit 110 determines a plurality of different pedestrian groups to which the pedestrian belongs (S120). In addition, the pedestrian determining unit 110 determines the starting cell and the walking direction in the indoor space network of the pedestrian cells corresponding to the pedestrians included in each pedestrian group. Pedestrians of various levels may exist in the indoor space network according to an embodiment. Pedestrians may be classified as fast pedestrians, normal pedestrians, and slow pedestrians according to the walking speed, and may be classified as general pedestrians, wheelchair pedestrians, elderly pedestrians using a cane, crutches, etc. according to the size of the area occupied when walking. In the simulation according to an embodiment, the area occupied by one general pedestrian (occupied space) was considered to be 0.25 m ² (0.5 m × 0.5 m).

4 is a conceptual diagram showing a pedestrian cell of the CA algorithm according to an embodiment, FIG. 5 is a conceptual diagram showing a pedestrian type of the CA algorithm according to FIG. 4, and FIG. 6 is a pedestrian cell and a neighbor of the CA algorithm according to an embodiment It is a conceptual diagram showing the relationship between pedestrian cells.

Referring to FIG. 4, in the CA algorithm according to an embodiment, when the current pedestrian cell is P _{i, j} , each pedestrian cell is a left-right cell ( P _{i, j-1 ,} P _{i, j + 1} ) of the current cell or It is assumed that it can move to the upper and lower cells P _{i-1, j and} P _{i + 1, j} . Referring to FIG. 5, in the CA algorithm according to an embodiment, occupied spaces of pedestrian cells included in each pedestrian group are different. In FIG. 5, the walking direction of each pedestrian cell is assumed to be from left to right of the ground. Referring to FIG. 5, a pedestrian cell of a pedestrian group including a general pedestrian and a child occupies one cell, and there are three movable cells, one cell located to the right of the current cell and one cell located up and down. Pedestrian cells in the pedestrian group, including pedestrians using crutches, occupy two (or three) cells up and down, and the movable cells are two cells located to the right of the cell and one each located up and down. Cells are all four. The pedestrian cell of the pedestrian group including the pedestrian using the cane occupies two cells to the left and right, and the movable cells are five, one cell on the right and two cells on the top and bottom respectively. The pedestrian cell of the pedestrian group including the pedestrian in the wheelchair occupies four cells up, down, left, and right, and the movable cells are two cells located on the right and two cells located up and down, all six. And the initial speed of each pedestrian group is different. The initial speed of each pedestrian group is the fastest for the public pedestrian group (for example, 1.2 m / s), followed by the child pedestrian group (for example, 0.9 m / s), the wheelchair pedestrian group and the crutches pedestrian group. This is followed (eg 0.6m / s) and the elderly pedestrian group is the slowest (eg 0.3m / s).

The variables of the pedestrian movement model of the CA algorithm according to an embodiment are shown in Table 2 below.

variable Content (unit) Gap _f Number of cells up to pedestrians ahead Gap _l Number of cells to the left front pedestrian (cell) Gap _r Number of cells to the right front pedestrian V (t) Pedestrian speed at time t (cell / unit time) V _max Maximum pedestrian speed (cell / unit time) X (t) Pedestrian cell position at time t Gap _f (t) Cell count to the corrected forward pedestrian P _noise Irregular deceleration probability Pr Randomization probability Dp Distance to recognize facing pedestrians located in front (20cell) Pdg Deceleration probability of forward gap P _o Coercive career change probability P _m Chance to change course at full speed P _l Chance to change course at less than maximum speed

Referring to FIG. 6, the number of cells Gap _f from the pedestrian cell n to the front pedestrian cell is 7, the number of cells Gap _l to the left front pedestrian cell is 2, and the number of cells Gap _r to the right front pedestrian cell is 5 .

Referring back to FIG. 2, the walking speed calculation unit 120 calculates the moving speed of the pedestrian cell based on the walking data stored in the database 130 and the starting cell and the walking direction of the pedestrian determined by the pedestrian determination unit 110. By doing so, the movement state of the pedestrian cell is simulated (S130). That is, the simulation of the pedestrian movement situation can be performed by calculating the movement speed of each type of pedestrian cell. In general, it is assumed that the pedestrian cell determines the moving speed and the moving direction in a direction to avoid collision with the opposing pedestrian cell, and each pedestrian cell moves forward if the effective space up to the opposing pedestrian is secured, and the size of the remaining effective space ( Lateral movement is determined according to the number of cells). In the walking simulation according to an embodiment, the pedestrian cell moves according to the following rules. Equation 1 below shows the probability that a pedestrian cell moves to another cell when it is located in the cell P _{i, j} . P _{i, j} may be obtained as a sum of the probability of each cell / the probability of the total movable area cell.

이다.In Equation 1, N is the reciprocal of the sum of the probability of all movable cells,

to be.

는 정적 플로어 필드(static floor field)의 민감 파라미터(sensitive parameter)이고,

는 동적 플로어 필드(dynamic floor field)의 민감 파라미터이고,

는 정적 값(static value)이며,

는 동적 값(dynamic value)이다. 그리고 수학식 1의

는 셀 (i,j)가 비어있을 때 0이고, 경계에 의해 점유되어 있을 때 1이다.

은 셀 (i,j)가 보행자에 의해 점유되어 있을 때 0이고, 비어있을 때 1이다. In Equation 1,

Is a sensitive parameter of the static floor field,

Is a sensitive parameter of the dynamic floor field,

Is a static value,

Is a dynamic value. And Equation 1

Is 0 when cell (i, j) is empty, and 1 when occupied by boundary.

Is 0 when cell (i, j) is occupied by pedestrians and 1 when empty.

The forward movement rule according to Equation 1 is as shown in Equation 2 below.

In addition, the side movement rule according to Equation 1 is as shown in Equation 3 below.

In addition, the left lateral movement rule and the right lateral movement rule together with normalization are as shown in Equation 4 below.

In Equation 4, P _l is the left side movement factor of each pedestrian cell and P _r is the right side movement factor of each pedestrian cell. Therefore, if P _r is greater than P _l , the pedestrian cell moves to the right, and if P _l is greater than P _r , the pedestrian cell moves to the left. Hereinafter, a method of moving forward and a side of a pedestrian will be described in detail with reference to FIGS. 7 to 9.

7 is a flowchart illustrating a method for moving a pedestrian forward according to an embodiment, and FIG. 8 is a flowchart illustrating a method for moving a pedestrian sideways when the time step according to an embodiment is odd, and FIG. 9 is a flowchart for one embodiment It is a flow chart showing a method of moving a pedestrian sideways when the time step is even.

In the walking simulation according to an embodiment, the speed of each pedestrian cell may be accelerated or decelerated, and the deceleration probability is assumed to be irregular.

Referring to FIG. 7, the walking speed calculator 120 determines whether to accelerate or decelerate the pedestrian cell in consideration of the initial speed and effective space of each pedestrian group (S210). The walking speed calculating unit 120 updates the speed of the pedestrian cell at each time step.

The acceleration rules of the pedestrian cell are as follows and Equation 5.

-If the effective space ahead is greater than the speed at time t, the speed at time t + 1 is increased by 1 cell / s. The speed of the pedestrian cell cannot exceed the maximum speed (V _max ).

The deceleration rules of the pedestrian cell are as follows and Equation 6.

-If the forward effective space is smaller than the speed of time t, the speed of time t + 1 is reduced to the effective space.

Then, the walking speed calculator 120 determines whether the pedestrian cell is irregularly decelerated in consideration of the irregular deceleration probability P _noise (S220). The irregular deceleration rules of the pedestrian cell are as follows and Equations 7 and 8.

-If the random deceleration probability P _noise is greater than the random sampling probability P _r , the speed of the pedestrian cell is decelerated by 1 acceleration / cell.

-If the random deceleration probability P _noise is smaller than the random sampling probability P _r , it depends on the speed determined by the acceleration and deceleration rules.

The walking speed calculating unit 120 updates the speed of the forward movement at the time t + 1 of each pedestrian cell as described above.

In addition, the walking speed calculator 120 determines whether each pedestrian cell moves sideways (S230). The lateral movement of each pedestrian cell is performed when the lateral condition, the forward condition, and the path change probability condition are all satisfied. In addition, the path change of each pedestrian cell may be divided into a coercive path change according to the presence or absence of an opposing pedestrian, a path change according to a potential preference of a pedestrian wishing to walk at a maximum speed, and a path change to optimize the passage time. The forward conditions, lateral conditions, and probability conditions that must be satisfied for each type of course change are as follows.

-Coercive course change: Each pedestrian cell can perform course change to avoid collisions if an opposite pedestrian exists within the forward recognition distance.

First, when the time step is odd, there is an opposite pedestrian within the forward recognition distance (forward condition), if the left cell in the walking direction is not occupied (lateral condition), and if P ₀ ≥ P _r , the pedestrian cell proceeds to the left cell To change.

And, when the time step is even, if the opposite pedestrian exists in the forward recognition distance (forward condition), the right cell in the walking direction is non-occupied (lateral condition), and if P ₀ ≥ P _r , the pedestrian cell proceeds to the right cell To change.

-Preferred course change: It is assumed that the pedestrian cell performs a course change to maintain the maximum speed, and that the pedestrian does not actually walk in a straight line. When v (t) = v _max, and there is no opposing walker within the front recognition distance, a preferred path change can be performed.

First, if the time step is odd and the left cell in the walking direction is non-occupied (lateral condition), gap _f (t) ≤ gap _l (forward condition), and P _m ≥P _r , the pedestrian cell changes course to the left do.

Then, if the time step is even and the right cell in the walking direction is non-occupied (lateral condition), gap _f (t) ≤ gap _l (forward condition), and P _m ≥P _r , the pedestrian cell changes course to the right. do.

-Optimized travel time course: Pedestrian cells change course to optimize their travel time even at the maximum speed or less. When v (t) <v _max, and there is no opposing walker within the front recognition distance, a travel time optimization course change can be performed.

First, if the time step is odd, the left cell is non-occupied (lateral condition), gap _f (t) ≤ gap _l (forward condition), and if P _l ≥ P _r (probability condition), the pedestrian cell proceeds to the left To change.

And, if the time step is even, the right cell is non-occupied (lateral condition), gap _f (t) ≤ gap _l (forward condition), and if P _l ≥ P _r (probability condition), the pedestrian cell proceeds to the right To change.

10 is a diagram showing a simulation screen captured during a walking simulation according to an embodiment.

Referring to FIG. 10, in the indoor space network (4.5m × 30m), pedestrian groups represented by different occupied spaces are walking at different moving speeds. Each pedestrian group may accelerate or decelerate the movement speed depending on the presence or absence of an opposing pedestrian cell, decelerate randomly, or change the direction of movement.

Referring again to FIG. 2, the walking simulation apparatus 100 according to an embodiment compares pedestrian density, pedestrian speed, and walking traffic flow rate according to the pedestrian composition ratio based on the results of the walking simulation performed as shown in FIG. 10 (S140) ). Pedestrian density can be expressed as the number of pedestrians located within the area of the indoor space network (unit: persons / m ² ). The final speed of the pedestrian is determined according to the acceleration rules, deceleration rules, and irregular deceleration rules described above. Pedestrian traffic flow rate can be expressed as pedestrian density × pedestrian speed, and can be calculated in minute intervals (unit: persons / (m · min)). For example, the walking simulation apparatus 100 may repeat the walking simulation while reducing the ratio of normal people by 75% to 15% according to a predetermined indoor space scenario. When the proportion of normal pedestrians is 75% (initial value), the proportion of child pedestrians may be 15.55%, the proportion of elderly pedestrians may be 5.79%, and the proportion of wheelchair pedestrians may be 3.66%. Then, in the repeated walking simulation, the composition ratio of each pedestrian group is changed.

11 is a block diagram showing a walking simulation apparatus according to another embodiment.

The walking simulation apparatus according to an embodiment may be implemented as a computer system, for example, a computer-readable medium. Referring to FIG. 11, the computer system 1100 includes a processor 1110 communicating with a bus 1170, a memory 1130, a user interface input device 1150, a user interface output device 1160, and a storage device It may include at least one of (1140). Computer system 1100 may also include a communication device 1120 coupled to a network. The processor 1110 may be a central processing unit (CPU) or a semiconductor device that executes instructions stored in the memory 1130 or the storage device 1140. The memory 1130 and the storage device 1140 may include various types of volatile or nonvolatile storage media. For example, the memory may include read only memory (ROM) and random access memory (RAM).

Thus, embodiments of the present invention may be implemented as a computer-implemented method or as a non-transitory computer-readable medium having computer-executable instructions stored thereon. In one embodiment, when executed by a processor, computer readable instructions may perform a method according to at least one aspect of the present disclosure.

Although the embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements of those skilled in the art using the basic concept of the present invention defined in the following claims are also provided. It belongs to the scope of rights.

Claims (15)

As a method for simulating walking based on a cellular automata (CA) algorithm that models an indoor space performed by a computer-implemented walking simulation device into an indoor space network composed of a plurality of cells,
Determining a pedestrian group including a pedestrian cell corresponding to a pedestrian located in the indoor space among a plurality of pedestrian groups, and determining a walking direction indicating a direction in which the pedestrian cell moves within the indoor space network, and
Simulating the walking by calculating the walking data for the simulation and the probability that the pedestrian cell moves to another cell based on the walking direction
Including,
The plurality of pedestrian groups includes a public pedestrian group, a children pedestrian group, a wheelchair pedestrian group, and an elderly pedestrian group,
The number of cells occupied in the indoor space network by the pedestrian cell of the wheelchair pedestrian group or the pedestrian cell of the elderly pedestrian group is set differently from the number of cells occupied by the pedestrian space of the public pedestrian group in the indoor space network,
When the walking direction of each pedestrian cell is assumed from left to right of the ground,
Pedestrian cells in the pedestrian group including general pedestrians and children occupy one cell, and the movable cells are all three, one cell located to the right of the current cell and one cell located up and down,
A pedestrian group of a pedestrian group including a pedestrian using crutches occupies two cells up and down, and four movable cells are two cells located to the right of the cell and one cell located up and down, respectively.
The pedestrian cell of the pedestrian group including the pedestrian using the cane occupies 2 cells from side to side, and the movable cell is 5 cells, 1 cell on the right and 2 cells on the top, respectively.
The pedestrian simulation method of a pedestrian group of a pedestrian group including a pedestrian in a wheelchair occupies four cells up, down, left, and right, and the movable cells are two cells located on the right and two cells located on the top, each of which is six. In claim 1,
The walking data includes an initial speed of the pedestrian cell, and each pedestrian cell included in the plurality of pedestrian groups has different initial speeds. In claim 2,
The initial speed of the pedestrian cell belonging to the public pedestrian group is the fastest, the initial speed of the pedestrian cell belonging to the child pedestrian group is the next fastest of the public pedestrian group, and the initial speed of the pedestrian cell belonging to the wheelchair pedestrian group is the child A walking simulation method, which is the next fastest of a pedestrian group, and has the slowest initial speed of a pedestrian cell belonging to the elderly pedestrian group. In claim 1,
After the step of simulating the walking, the step of simulating the walking again by calculating the movement speed of the pedestrian cell by changing the configuration ratio of the plurality of pedestrian groups
Walking simulation method further comprising a. In claim 1,
On the indoor space network, the pedestrian simulation method of the public pedestrian group among the plurality of pedestrian groups occupies the smallest number of cells, and the pedestrian cell of the wheelchair pedestrian group occupies the largest number of cells. In claim 1,
The step of simulating the walking,
And calculating a probability of moving to the other cell through Equation 1 below when the pedestrian cell is located at P _{i, j} .
[Equation 1]

(N: reciprocal of the sum of the probability of all movable cells,

: Sensitive parameter of static floor field,

: Sensitive parameter of dynamic floor field,

: Static value,

: Dynamic value,

: 0 when cell (i, j) is empty and 1 when occupied by boundary,

: 0 when cell (i, j) is occupied by pedestrians and 1 when empty In claim 6,
When the pedestrian cell is located at P _{i, j} , calculating the probability of moving to the other cell through Equation 1,
And calculating a left shift factor of the pedestrian cell and a right shift factor of the pedestrian cell through Equation 1,
When the left shift factor is greater than the right shift factor, the pedestrian cell moves to the left, and when the right shift factor is greater than the left shift factor, the pedestrian cell moves to the right. As a device for simulating walking based on a cellular automata (CA) algorithm that models an indoor space as an indoor space network composed of a plurality of cells,
Among the plurality of pedestrian groups, a pedestrian determination unit for determining a pedestrian group including a pedestrian cell corresponding to a pedestrian located in the indoor space, and determining a walking direction indicating a direction in which the pedestrian cell moves within the indoor space network , And
A walking speed calculation unit that simulates the walking by calculating the probability of the pedestrian cell moving to another cell based on the walking data and the initial speed of the walking cell and the pedestrian cell
Including,
The plurality of pedestrian groups includes a public pedestrian group, a children pedestrian group, a wheelchair pedestrian group, and an elderly pedestrian group,
The number of cells occupied in the indoor space network by the pedestrian cell of the wheelchair pedestrian group or the pedestrian cell of the elderly pedestrian group is set differently from the number of cells occupied by the pedestrian space of the public pedestrian group in the indoor space network,
When the walking direction of each pedestrian cell is assumed from left to right of the ground,
Pedestrian cells in the pedestrian group including general pedestrians and children occupy one cell, and the movable cells are set to three as cells located to the right of the current cell and one cell located at the top and bottom, respectively.
The pedestrian cell of the pedestrian group including the pedestrian using crutches occupies two cells up and down, and the movable cells are set to four, two cells located to the right of the cell and one cell located up and down, respectively.
The pedestrian cell of the pedestrian group including the pedestrian using the cane occupies two cells to the left and right, and the movable cell is set to five, one cell on the right and two cells on the top and bottom, respectively.
Pedestrian cells in the pedestrian group, including pedestrians in wheelchairs, occupy 4 cells up, down, left, and right, and the movable cell is a walking simulation device that is set to 6 as two cells located at the right and two cells located at the top and bottom. . In claim 8,
The walking data includes an initial speed of the pedestrian cell, and each pedestrian cell included in the plurality of pedestrian groups has different initial speeds. In claim 9,
The initial speed of the pedestrian cell belonging to the public pedestrian group is the fastest, the initial speed of the pedestrian cell belonging to the child pedestrian group is the next fastest of the public pedestrian group, and the initial speed of the pedestrian cell belonging to the wheelchair pedestrian group is the child A walking simulation device that is the next fastest in the pedestrian group and has the slowest initial speed of the pedestrian cell belonging to the elderly pedestrian group. In claim 8,
The walking speed calculating unit, after simulating the walking, by simulating the walking again by calculating the movement speed of the pedestrian cell by changing the configuration ratio of the plurality of pedestrian groups, walking simulation apparatus. In claim 8,
On the indoor space network, a pedestrian simulation device, among the plurality of pedestrian groups, the pedestrian cell of the public pedestrian group occupies the smallest number of cells, and the pedestrian cell of the wheelchair pedestrian group occupies the largest number of cells. In claim 8,
The walking speed calculation unit,
When the pedestrian cell is located at P _{i, j} , a walking simulation apparatus for calculating a probability of moving to the other cell through Equation 1 below.
[Equation 1]

(N: reciprocal of the sum of the probability of all movable cells,

: Sensitive parameter of static floor field,

: Sensitive parameter of dynamic floor field,

: Static value,

: Dynamic value,

: 0 when cell (i, j) is empty and 1 when occupied by boundary,

: 0 when cell (i, j) is occupied by pedestrians and 1 when empty In claim 13,
The walking speed calculation unit,
When calculating the probability, the left movement factor of the pedestrian cell and the right movement factor of the pedestrian cell are calculated through Equation 1,
When the left shift factor is greater than the right shift factor, the pedestrian cell moves to the left, and when the right shift factor is greater than the left shift factor, the pedestrian cell moves to the right. As a device for simulating walking based on a cellular automata (CA) algorithm that models an indoor space as an indoor space network composed of a plurality of cells,
Among the plurality of pedestrian groups, a pedestrian determining unit that determines a pedestrian group including a pedestrian cell corresponding to a pedestrian located in the indoor space, and determines a walking direction indicating a direction in which the pedestrian cell moves within the indoor space network ,
A database for storing walking data for walking simulation of the pedestrian cell in the indoor space network, and
A walking speed calculation unit that simulates the walking by calculating the moving speed of the pedestrian cell based on the walking data and the walking direction
Including,
The plurality of pedestrian groups includes a public pedestrian group, a children pedestrian group, a wheelchair pedestrian group, and an elderly pedestrian group,
The number of cells occupied in the indoor space network by the pedestrian cell of the wheelchair pedestrian group or the pedestrian cell of the elderly pedestrian group is set differently from the number of cells occupied by the pedestrian space of the public pedestrian group in the indoor space network,
When the walking direction of each pedestrian cell is assumed from left to right of the ground,
The pedestrian cell of the pedestrian group including the general pedestrian and the children occupies one cell, and the movable cell is set to three as a cell located to the right of the current cell and one cell located at the top and bottom, respectively.
The pedestrian cell of the pedestrian group including the pedestrian using crutches occupies two cells up and down, and the movable cell is set to four, two cells located to the right of the cell and one cell located up and down, respectively,
The pedestrian cell of the pedestrian group including the pedestrian using the cane occupies two cells to the left and right, and the movable cell is set to five, one cell located on the right and two cells located on the top and bottom, respectively.
Pedestrian cells in the pedestrian group, including pedestrians in wheelchairs, occupy 4 cells up, down, left, and right, and the movable cell is a walking simulation device that is set to 6 as two cells located at the right and two cells located at the top and bottom. .

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