JPH0475199A - Crowd walk simulation system - Google Patents

Abstract

PURPOSE:To easily analyze and predict crowd action with high accuracy by simulating walkers by using resiliency and attractive force. CONSTITUTION:This crowd walk simulation system sets a travel area and simulates the action of a crowd in the travel area. Runners, obstacles, and a destination point are so set that the resiliency depending upon a distance operates between runners and between a runner and an obstacle, and the constant attractive force irrelevant to the distance operates between the runner and destination point. Consequently, the action of the crowd can be simulated with high accuracy by using a motion equation based upon the resiliency and attractive force.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to a crowd walking simulation system that sets a walking area and simulates the flow of a crowd in the walking area.

[Conventional technology]

In the management and operation of event facilities, comprehensive leisure facilities, and various entertainment venues, it is necessary to analyze the number of people, their trends, and crowd trends, and provide appropriate guidance and guidance based on the analysis. In particular, an unspecified number of people gather at complex leisure facilities and mall-like commercial facilities for various purposes. Therefore, for comprehensive safety management and business management of a group of facilities, it is desirable to be able to provide a variety of information according to each purpose. To this end, it is essential to understand the flow of people and analyze trends.

For example, when a crowd has accumulated and it is necessary to guide the crowd, conventionally, people have been mobilized to guide the crowd using placards, megaphones, ropes, etc., or by announcements. In this case, a television monitor system is often adopted as a conventional monitoring system for grasping the entire situation.

[Invention or problem to be solved]

However, to date, no system has been put into practical use that can grasp the number of people, crowd movements, etc., analyze them in real time, and automatically provide guidance as needed based on the analysis results.

Is it possible to monitor the movements of crowds, etc. relatively easily with the TV monitor method? A lifeguard always stays in front of the monitor screen and monitors the movements of the crowd, etc. based on the observer's intuitive judgment. Therefore, maintaining and operating the system requires not only manpower but also time and cost, and there is also the problem that it is not possible to respond quickly in real time. In particular, when the monitoring range becomes wider, there is a problem in management that the discovery of the occurrence of a serious emergency situation may be overlooked or delayed at an early stage.

In addition, facilities that try to manage visitors use a method of counting people at the entrance gate using a revolving system or a photocell system, but after entering the premises, it is possible to keep track of how many people are in each area. I can't do it. Therefore, in order to grasp the number of people in each area, there is a method of counting the number of people using ultrasonic waves from the upper part of the detection area, but this method has the problem of large errors due to changes in the weather.

Is there another method that uses still image processing? With this method, the necessary information is extracted manually from each image frame by frame, which requires a lot of labor and time. be.

Methods using placards, megaphones, robes, etc., or information announcements require manpower, and especially in cases of heavy congestion, they may become a nuisance to guides or traffic, which can lead to dissatisfaction from the crowd. Sometimes it becomes. Furthermore, it may be difficult to convey accurate instructions to the entire group when guiding people to help in an emergency. Furthermore, the method of using guidance announcements has the problem that it is difficult to convey information when the surrounding area is noisy.

The present invention is intended to solve the above-mentioned problems, and its purpose is to provide a crowd walking simulation system that can grasp crowd trends in real time and perform safety management and management of spaces where an unspecified number of people gather. That's true. Another object of the present invention is to analyze crowd trends and perform appropriate processing according to the situation.

[Means to solve the problem]

To this end, the present invention provides a crowd walking simulation system that sets a walking area and simulates the flow of crowds in the walking area. The present invention is characterized in that a repulsion force that depends on the distance acts between the pedestrian and the obstacle, and a constant attraction force that is independent of the distance acts between the pedestrian and the target point. Then, a positive charge is applied to the pedestrian and the obstacle, and a negative charge is applied to the destination point, the target walking area is represented by a magnetic field, and the pedestrian or the destination is simulated based on Coulomb's law and the equation of motion. In the crowd walking simulation system of the present invention, distances between pedestrians and between pedestrians and obstacles depend on the distance between pedestrians, obstacles, and destination points. The setting is such that a repulsive force acts between the pedestrians and the target point, and a constant attractive force is applied between the pedestrian and the target point, regardless of the distance.The equation of motion based on the repulsive force and attractive force can be used to accurately measure the flow of the crowd. It can be simulated.

〔Example〕

Examples will be described below with reference to the drawings.

11D is a diagram for explaining one embodiment of the crowd walking simulation system according to the present invention, FIG. 2 is a diagram for explaining the setting conditions of each element, and FIG. 3 is a diagram for explaining an example of simulation processing. This is a diagram for

First, the basic concept of the model applied in the system of the present invention, walking space (walking area), walking principle, and pedestrians will be explained.

The basic idea of the model is to apply a positive charge to each pedestrian, wall, pillar, handrail, etc. as shown in Figure 2 (at).
As shown in FIG. 4(b), a negative charge is applied to the destination point.

As a result, each pedestrian receives a repulsive force from other pedestrians and obstacles, and an attractive force from the destination point.

Then, the target walking area is represented by a magnetic field, and based on Coulomb's law and the equation of motion, the pedestrian moves toward the destination while avoiding other pedestrians and obstacles, thereby realizing a simulation.

In addition, regarding the expression of walking space, we faithfully reproduce the flow of people in areas where there is a certain regularity in terms of walking, such as around obstacles such as walls and pillars, ticket gates, and near entrance to facilities. Therefore, a walking space is constructed using various charges. For example, pedestrians can be allocated by setting the magnetic dipoles shown in FIG. 4(d) at the ends of the partition walls near the entrance gates as shown in FIG. 2(e). If such a magnetic dipole is not used, some pedestrians will come to a complete stop within the edge line circle. In addition, by installing the magnetic double layer shown in Figure (f) at the entrance to facilities and rooms as shown in Figure (g), pedestrians located near the edge line can be smoothly guided inside. Aspirate (induce).

The walking area is expressed as an enclosed area by connecting arbitrary vectors representing walls in a clockwise direction, as shown in FIG. 2(h). In addition, sequential numbers are assigned so that the contact numbers of the connection points (vertices) of wall vectors are always clockwise when viewed from the walking area. This makes it possible to determine whether the pedestrian is in a normal position (located within the walking area).

In addition, among the connection points of wall vectors, those that are convex toward the walking area, for example, 2.6.7.1 shown in FIG.
3.14.18.20.21, 23-26 are recognized as "corners".

Note that the position within the walking area is defined by two-dimensional coordinates (X-Y).

The walking principle will be treated as follows.

Each pedestrian has a territory with a radius of 3 m, as shown in FIG.

In addition, a constant attraction force is applied from the destination point regardless of the distance. Note that the attraction force does not follow Coulomb's law. If there are multiple forces being received by a pedestrian, each force is expressed as a composite (vector sum) as shown in (j) of the same figure.

Pedestrian expressions are handled as follows.

Multiple groups of pedestrians with different walking routes are set based on given conditions, and walking speed, occurrence, and
Parameters such as the final destination, the density of occurrence, and the number of people in the group (20 to 110 people) are defined. That is, each pedestrian belongs to one of the groups, and the pedestrian data is fixed data for the group and is given for each group. Group data consists of walking speed, location, density of occurrence, number of people in the group, and walking route. Data for each pedestrian in any population (
speed, location) are automatically given by random numbers based on group data.

The input format of pedestrian data for each simulation is, for example, as follows.

1st line, 2nd pedestrian data is group input or individual input (-〇 indicates group input) 2nd line, group number 3rd line Destination, search format, occurrence location hector walking speed, occurrence density, occurrence time 4 Line, number of route display contacts 5th line Enter data for one group every 4 lines after the route display contact number (3 consecutive digits).

Regarding the walking route, in addition to the starting and final destination points, multiple points are specified as excursion routes along the way.

These are given as a sequence of wall contact numbers, known as corners.

However, this is because it takes into account the walking characteristics of humans, who try to proceed in the shortest distance to their destination despite interference from obstacles and other pedestrians.

The walking speed is given as the average human speed under good walking conditions, and is given some variation using random numbers based on this.

The occurrence density is given by the setting conditions of each simulation pattern.

Once within the walking area, a pedestrian recognizes a designated corner as a destination point and moves forward from that point receiving attraction, despite interference (repulsion) from obstacles and other pedestrians. , after passing there, the same cycle is repeated every unit time until the next specified corner is reached.

Here, special elements of the simulation model regarding pedestrian space include, for example, the ``Pedestrian generation/final destination point'' and the ``Aqua Museum entrance gate J1 F viewing/appreciation point.'' acts based on a logic different from the above-mentioned walking principle.

[Pedestrian occurrence/final destination point J is determined whether the pedestrian occurs or arrives on a line segment that connects the vertices of the wall instead of a point, or is determined by a random number on the line segment for each pedestrian. The point (occurrence/arrival) is determined.

After each pedestrian reaches the [Aqua Museum Entrance Gate], they remain at that point until they receive service for a preset period of time (equivalent to waiting time). Also, if a queue has already been formed by the time you arrive,
You will line up at the end of the shortest line.

A "viewing/appreciation point" is provided by setting a plurality of windows at equal intervals on a line connecting the vertices of the wall. When all the counters are occupied, a queue is formed in the same way.

However, ``In the case of Entrance Gate J, you start moving toward the next destination point after the service ends, but in this case, you go to the next destination point after going around the end of the queue, so - , a temporary destination point is set near the end of the queue, and after passing there, the vehicle heads to the original destination point.

These three types of special elements have a linear shape with respect to the walking area, so they cannot be expressed by wall contact numbers. Therefore, for the relevant parts, new sequential numbers are assigned to lines connecting adjacent contact points, and parameters such as service hours, number of gates, and number of counters are set.

Next, the simulation calculation procedure (flow chart)
This will be explained in detail below using FIG.

(Sl) Before actually generating a group of pedestrians and simulating their behavior, various environmental data are set. First, facility plan layout data for setting walking areas and data regarding pedestrian groups for each group described above are input.

Pedestrian data can be divided into fixed data as a group to which pedestrians belong, and pedestrian files containing variable data that each person has during walking behavior.Here, we will use the former group data (18 patterns) as shown below. input is performed.

■Number of people in the group, ■Occurrence/final destination point, ■Walking speed, ■Occurrence density, ■Occurrence time, ■Route display contact number (migratory route).

(S2) Corners (
It is recognized whether the walking area is convex (convex with respect to the walking area). The determination method is to recognize the connection point as a corner if the angle θ formed by the two vectors forming the connection point of the wall is sin θ>0. Therefore, for example, in the example shown in FIG. 3(a), when the values of sin θ are calculated for each of θ1 to θ5, sin θ, 〉0, s
A and C, where inθ3>0, are recognized as corners.

(S3) Recognize the adjacency relationship between the vertices recognized as corners in the process (2) above. This is referred to when determining a temporary target point when a pedestrian enters a search walking state, which will be described later. The adjacency relationship is determined to be "connected" when looking from one vertex to another vertex and satisfying the following conditions.

■Visibility ■Can only see the wall on one side of the opponent's vertex This adjacency relationship is expressed, for example, by the following matrix in the walking space shown in No. 311 (bl).

12'345678910 If it is connected, it is "1", and if it is not connected, it is "0". Note that since this is a directed flag, the adjacency relationship matrix is not necessarily a symmetric matrix.

(S4) A pedestrian occurs within the walking area, and the position coordinates of all the pedestrians are calculated for each unit time based on the above-mentioned walking principle. At this time, the calculation of the magnetic force that the pedestrian receives is performed for each pedestrian. Therefore, in order to prevent pedestrians belonging to a certain group from receiving attraction from the destination points of pedestrians of other groups, points other than the pedestrian destination points that are the subject of calculation do not have a negative charge. Do it like this.

(S5) Determine the board destination point of each pedestrian and check the walking route.

Processing when passing a corner during walking route checking is performed as follows.

Pedestrians receive attraction from designated corners and points on line segments, and reach their final destination by passing through them one after another. However, if you do not actually receive the service, that is, if you give a negative charge to the corner (corresponding to Haton) designated for walking the shortest route on the excursion route as shown in Figure 3 (C1). , the pedestrian [after hitting the wall (corner) once instead of passing through the edge of the wall]
This results in unnatural movements such as "heading towards the next corner." Therefore, in reality, a natural flow is achieved by setting a temporary destination point slightly inside the walking area of the designated corner, as shown in FIG. 3(d).

Regarding the recognition of passing the destination point, as shown in Figure 3 (e), is it considered that the pedestrian has passed when the walls on both sides that make up the corner are visible (shaded area in the figure)? In actual calculation, the following method is used.

The angle θ formed by the vector A connecting the coordinates where the pedestrian is located and the coordinates of the destination point, and the wall vector B from the destination point
seek. And in the same figure (as shown in fl, sin θ〉
If it is 0, it means that it has not passed, and as shown in the same figure (g), si
If nθ≦0, it is determined that the line has passed.

(S6) For example, waiting lines occur at [Aqua Museum entrance gates] and [viewing/appreciation points]. When pedestrians line up in queues, they have a different perception from normal walking, and In this case, as shown in the figure (h), a virtual point matrix with an interval of 50an is set in front of each window provided on the line segment, and each pedestrian is As soon as each pedestrian sees a counter, he or she secures a position (virtual point) with the shortest waiting time, i.e., the minimum queue length.At that point, the corresponding virtual point (- ) becomes the board destination point for the pedestrian, and attracts it by being charged with a negative point charge.

(S7) to (S8) Calculate the speed and direction from the magnetic force received by each pedestrian.

(S9) Display each pedestrian along with the walking area on the screen.

(SIO) Store data in pedestrian file.

This data storage process writes state changes for each unit time into a pedestrian file containing variable data held by each person during the above-mentioned walking action. For pedestrians, files include, for example, the following data: device coordinates, ■ walking speed direction, magnitude and upper limit, ■ final destination point, ■ next destination point, ■ walking route (route display contact number), ■ existing Passing points, ■Walking type (route-dependent walking or search walking), ■Walking status (not occurring or occurring, normal walking, waiting queue (nearing a queue, in a queue, leaving the queue,
During the simulation, some pedestrians may lose sight of their next destination due to interference (repulsion) from other pedestrians. At that time, the pedestrian in question enters a state of search walking, and sets the optimal corner as a temporary destination point based on the current position coordinates and the position coordinates of the destination point.

The criteria for selecting the optimal corner are as shown below.

■ Pedestrians remember the corners they have passed and, in principle, do not pass there again. This is to prevent falling into a pedestrian or infinite loop. Therefore, the optimal corner is selected from among the corners that have not been passed yet.

■ Refer to the corner-to-corner adjacency relation matrix of the lost destination point, and select corners that are in an adjoining relation from there.

■ Next, of the two walls that make up the corner, choose one where only one wall is visible. If all the corners in front of the pedestrian are showing walls on both sides,
From there, it will be a dead end.

■ Pedestrians generally choose the corner in front of them in their direction of travel, or if there is a dead end or a corner in front of them or a corner they have already passed, they choose the corner behind them. to be selected.

■ If there are multiple corners that satisfy these conditions, select the corner closest to the lost destination point as the temporary destination point.

While repeating such search walking, each time the pedestrian passes a corner selected as a temporary destination point, he or she determines whether or not the lost destination point can be seen from that point. If you can see it, you will know that you have returned to your normal walking state. This logic makes it possible to escape from search walking using the shortest route.

For example, if there is a walking space as shown in FIG. 3(i), the area where pedestrians who have not yet passed can see the destination point will be the shaded area. At this time, a pedestrian who has entered the area (a) due to interference from other pedestrians will lose sight of his or her next destination. This determination is made based on whether a vector directed from the pedestrian toward the destination point intersects with an arbitrary wall vector. At this time, the pedestrian enters a state of search walking and selects the optimal board destination point from Corner Q and RSK.

In other words, the corner K or board destination point satisfies all the conditions from ■ to ■ (■ is irrelevant in this case).

Note that the determination (2) of the corner in front of the pedestrian as seen from the pedestrian is performed by calculating the inner product of vectors. For example, for corners R and K, PN・PK>0, PN−P
If R>0, both are positive, so they are considered to be in the front.

Regarding corner Q, PN-PQ<0, and it is determined that it is not in front.

Next, an example of simulation using Coulomb's law and equation of motion under the conditions set as above will be explained.

According to Coulomb's law, the force F1 received from the pillar and the force F2 received from other pedestrians are expressed as follows.

In addition, the attraction force F from the destination point. is constant and does not follow Coulomb's law.

There is a relationship between r and the distance from point P to the pillar, and the same is true for F, so F can be expressed as follows.

F=F, 10F a.

Electric charge held by the pillar If the above equations (1) and (2) are expressed according to equation (4), F2 The distance F2 from point P to the pedestrian is Q21 The electric charge K held by the pedestrian is a proportionality constant 4πε0... ... (5) ε. =8.855 Xl0 In general, when force F is a unit vector, F = F, i + F, j ... (3) i:
Since the unit vector in the X direction, the unit vector in the jay direction, and the coordinates of point P are (xo, yo), F
xx.

F r ...... (6) It becomes. However, since the origin of this coordinate is based on the point where the pillar and other pedestrians are located, it is necessary to correct it. The coordinates of the pillar are (Xl, y,), and the coordinates of other pedestrians are (X
2, yi ), then equations (5) and (6) become
F2) j) ...... (8) Force F at point P
, can be expressed as the vector sum of F, , F, and Fo, so F2 =F+, +Fz +Fo (9
), and when decomposed into the X direction and the X direction component side, F3
= (F, ++ 10Fyz) 10 (F, 2+ 10F a, 1) + (F,.H1FIIO+1 =< Fx ++ F 82+p XO) ++ (F
yI×FF2+Fy0)j・・・・・・(lO)(1
Substituting equations (7) and (8) into equation 0)...
(11) Since the forces applied to point P are not necessarily three, (1
1) The general solution of the equation is as follows.

...... (12) l Obstacle m in the pedestrian's territory: Other pedestrians in the pedestrian's territory Due to the force F expressed by equation (12), the pedestrian I want to move on.

2 t m: Mass of pedestrian a: Acceleration of pedestrian 1 Displacement of pedestrian (absolute value) Integrating equation (13) over t gives L--a t2 + Vt +47-- (14)■・
Pedestrian speed 1, initial value Similarly, by substituting equation (15) into equation (14) from equation (13), L= t2+Vt+l ・=-(16)m Here, assuming the unit time is 1 (t=1), In order to find the absolute displacement, if the initial value is O (l=0), equation (16) is as follows: The initial value of m ■ is ■. Then, V is expressed as a function of t, ■(t)=V(t-1) 10V. becomes. Regarding (■), set an upper limit and check that the walking speed is not extremely high.

Decompose the absolute displacement measurement into X direction and X direction components12)
Substituting the formula gives +VW.

+Va.

Therefore, the position coordinates of the pedestrian at time t are (Xo.

y4), the position (x+, y
+) is 2m” r 1V8゜10Xo+ 1■7゜1y0 Does the applicant propose a moving object identification analysis management system that separately identifies pedestrians through image processing and analyzes and processes their movements? By using the invention in combination with this system, it can contribute to the management of comprehensive leisure parks, large-scale exhibition halls such as trade fairs, etc. The moving object identification analysis management system will be described below.

FIG. 4 is a diagram showing one embodiment of the moving object identification analysis management system, FIG. 5 is a diagram showing an example of an analysis model, and FIG. 6 is a diagram for explaining the processing procedure.

In FIG. 4, the image memory 2.3 stores video data (image data), and this image data is
For example, the surveillance area is photographed from above using an imaging device such as a CCD camera or a video camera. Switching part l
The input image data is switched to be alternately stored in the image memory 2.3 at predetermined time intervals. The moving object extraction section 4 extracts a moving object by taking the difference between the images stored in the image memory 2.3, and the image memory 5.6 stores the image data from which the movement thereof has been extracted. The position extraction unit 7 extracts the position of the dynamic object at each time from the dynamic image data in the image memory 5.6, and the analysis unit 8 extracts the position of the moving object in the monitoring area from the temporal change in the extracted dynamic position. This method analyzes trends based on a collection of moving objects by determining the moving speed, etc., or counting the number of moving objects.

Next, the processing procedure will be explained using the model shown in FIG.

In the model shown in FIG. 5, a camera 13 photographs a monitoring area 11 from above, and image data including a moving object 12 such as a target human is input into an image processing device 14. The image processing device 14 extracts the image data of the moving object 12 by calculating the difference between the previous and subsequent image data at regular time intervals, and further performs a position extraction process of the moving object 12. Then, transfer the extracted position data to the host computer 15,
Perform data analysis. That is, in the model shown in FIG. 5, the image processing device 14 is configured up to the position extraction section 7 shown in FIG. 4, and the host computer 15 is configured after the analysis section 8.

To explain the processing by the moving object extraction section 4 and the position extraction section 7, first, the switching section 1 receives the sixth
Images f1, f2, f3 in the order of figure (al, (b), (C))
is taken in. The switching unit 1 switches the image data so that the first image f1 is stored in the image memory 2 and the next image f2 is stored in the image memory 3. Therefore, the dynamic extraction unit 4 reads the image data stored in the image memory 2.3, calculates the difference between the images f1 and f2, and stores the image m12 in the image memory 5. Image fl, f
As shown in FIG. 6(d), the image m12 obtained as the difference between
Only the emotions of the moving object (person) 12, who moved but were not in the same position, remain. Background parts such as roads and surrounding stationary objects that overlap in images fl and f2 are at the same position in images fl and f2 and do not change, so they are erased by taking the difference. In this case, even if the emotional axis of the person 12 who has moved is correct, some of the overlapping parts of images f1 and f2 may be removed, but this can be compensated for by processing such as blank filling or dilation. .

Furthermore, when the third image f3 is captured, the switching unit 1
Now, the image data is switched so that the image f3 is stored in the image memory 2 again. Similarly, the dynamic extraction unit 4 reads the image data stored in the image memory 2.3 and calculates the difference between the images f2 and f3.
An image m23 shown in tel in the figure is obtained and stored in the image memory 6. When the difference images m12 and m23 are stored in the image memories 5 and 6, respectively, the position extraction unit 7 calculates the logical AND of the images m+2 and m23 to obtain a position extracted image a123 as shown in the same figure. The position where the moving object 12 was at the time of the image f2 is extracted.

The analysis unit 8 extracts the time-series position extraction images ao12, a123, and a as shown in FIG.
234 is obtained, the speed and trend are analyzed from the direction and distance of movement by comparing these center of gravity positions.

The above processing is performed continuously at regular time intervals, and the captured images f1, f2, ..., m12, m23, etc.
......, and a123, a234, ...
By determining ・, the individual trends and overall trends of moving objects (people) are analyzed. In this trend analysis, for example, the number of moving objects and changes in the number of moving objects are determined by counting the number of moving objects, and changes in the overall movement direction and speed are also determined.

As mentioned above, images are captured from the same angle at different times, and changes are extracted by taking the difference between these images, so only the image information of the moving object can be extracted without being affected by the environment in which the moving object is placed. Furthermore, by performing a logical product between the difference images, the position of the moving object can be extracted at each time point. Therefore, by counting the number of moving objects, it is possible to calculate the waiting time or detect the occurrence of an emergency situation based on changes in the number, overall direction of movement, changes in speed, etc.

FIGS. 7 and 8 are diagrams for explaining an example in which a camera is installed at an entrance and exit to analyze trends.

As shown in FIG. 7, if the vicinity of the entrance and exit of the facility is set as a monitoring area, and the monitoring area 22 is photographed from above with the camera 21 and the above processing is performed on the image data at regular intervals, for example, the entrance and exit It is possible to calculate the number of people entering and leaving the venue per unit time, as well as their flow (speed). Furthermore, by adding the number of people entering at the entrance and subtracting the number of people leaving at the exit, it is possible to determine the number of people entering at any given time, its increase/decrease, the status of residence, etc. Furthermore, by performing a similar analysis on the entrance and the area on the way to the entrance, it is possible to determine the number of people lining up at the entrance, and it is also possible to calculate the waiting time from this number of people and the flow (speed) at the entrance and exit of the venue. .

At the entrance and exit, the moving direction of the moving object is determined, as shown in FIG. Therefore, by monitoring the moving direction and moving speed, it is possible to detect the occurrence of an abnormal condition at the venue. Examples of abnormal patterns include, for example, when the moving speed suddenly increases or suddenly stops, or when the flow changes in the opposite direction at the inlet. Therefore, if the system detects an abnormality based on changes in the pattern of the flow or its direction, the details of the abnormality cannot be grasped, so by issuing an alarm and notifying the operator as shown in Figure 8, the system can detect an abnormality. countermeasures such as confirming the details of the abnormality can be taken quickly. It is also possible to set a no-go zone or an emergency day area as a monitoring area, detect the entry of a moving object, and determine an abnormality. The criteria for judgment may be changed depending on the time of day. Since these abnormalities are determined based on the movement of the moving object, the actual situation can be confirmed by a staff member at the scene or by checking on a monitor screen.

FIG. 9 is a diagram showing an example of the configuration of an visitor monitoring system in a wide area facility.

In FIG. 9, the video switcher 32 switches the video signals of the surveillance cameras 31-1 to 31-18, and the time generator 36 switches the video switcher 32, calculates the computer 34, and controls the logger 3.
It generates timing signals such as sampling for each area in 5. Image processing device 33, personal computer 3
4 is the same as the image processing device 14 and host computer 15 previously explained with reference to FIG. It is. The calculation unit 37 is
For example, the number of people in a predetermined section is calculated by adding the number of people on the entrance side and subtracting the number of people on the exit side as described above from the sampling data for each area accumulated in the logger 35.

The data storage section 38 stores calculation data of the number of people in each section, and the trend analysis section 39 trends the data in each section stored in the data storage section 38 to analyze and predict the retention situation. be. In other words, if the number of people in a certain section exceeds a certain value, it is determined that there is a stagnation situation, and if there is an increasing trend and the number exceeds a certain percentage, a stagnation situation is predicted. The display device 40 outputs the retention status analyzed by the trend analysis section 39.

In addition, the trend analysis unit 39 sets a flow pattern that eliminates the stagnation by predicting the stagnation situation in each area, and uses the pattern as guidance information on the display device 40.
It may also be configured to output to a display board or other display board. In setting the pattern in this case, selection patterns corresponding to the residence situation in each area may be prepared in advance, and selection patterns may be selected from among them according to certain conditions.

The crowd walking simulation system of the present invention can predict the situation after a certain period of time in real time by using information on pedestrians and speed obtained by the above system, and can provide signs at optimal locations. I can do it.

Note that the present invention is not limited to the above embodiments, and various modifications are possible. For example, in the above embodiment, repulsion and attraction were applied by applying positive and negative charges, but in a broader sense, the charge in the embodiment can be defined as a moving force factor, a magnetic field, or a magnetic dipole. , magnetic double layer, etc. as driving force factors, it goes without saying that these may be replaced with other factors and set to exert repulsive force and attractive force.

〔Effect of the invention〕

As is clear from the above explanation, according to the present invention, a pedestrian is simulated using repulsion force and attraction force.
Crowd behavior can be analyzed and predicted easily and with high accuracy. Furthermore, by combining it with a separately proposed moving object identification analysis management system, it is possible to realize a flexible sign device aimed at crowd behavior management. Therefore, it is possible to efficiently manage crowd safety and business management in various large-scale venues and facilities.

[Brief explanation of the drawing]

Fig. 1 is a diagram for explaining one embodiment of the crowd walking simulation system according to the present invention, Fig. 2 is a diagram for explaining the setting conditions of each element, and Fig. 3 is an example of simulation processing. Figure 4 is a diagram for explaining one embodiment of the moving object identification analysis management system, Figure 5 is a diagram showing an example of an analysis model, Figure 6 is a diagram for explaining the processing procedure, and Figure 7 is a diagram for explaining the processing procedure. 8 and 8 are diagrams for explaining an example of installing cameras at entrances and exits to analyze trends, and FIG. 9 is a diagram showing an example of the configuration of an visitor monitoring system in a wide area facility. 1...Switching unit, 2.3.5 and 6...Image memory, 4
. . . Moving object extraction section, 5. . . Position extraction section, 8. . . Analysis section. Applicant Shimizu Corporation Sub-Agent Patent Attorney Ryukichi Abe (7 others) (a) (b) ・Column (C) (d) ・Wall walking area side (e) Figure 2 (g) Step 0′ region side slnθ1>0, so “not passed” Figure 2 ('') Figure 3 (e) sin θ! 0, so "pass" (c) Figure 3 (b) Figure 3 (h) (i) (d) Figure 4 Figure 5- and Figure 6 (a) Increased power? +) Roshō 1 & things (b) (C) ~ (f) Hashiri 1 hoe 4 shells Figure 8 Tsuta 1 Figure 9

Claims (8)

[Claims] (1) A crowd walking simulation system that sets a walking area and simulates the flow of crowds in the walking area, in which pedestrians, obstacles, and destination points are separated by pedestrians, pedestrians, and obstacles, respectively. A crowd walking simulation system characterized in that a repulsion force that depends on the distance acts between pedestrians and a target point, and a constant attraction force that is independent of the distance acts between pedestrians and a target point. (2) Give a positive charge to pedestrians and obstacles and a negative charge to the destination point, represent the target walking area with a magnetic field, and simulate the pedestrian moving toward the destination based on Coulomb's law and equation of motion. The crowd walking simulation system according to claim 1, characterized in that: (3) Crowd walking according to claim 1, characterized in that pedestrians are set to receive repulsive force only when other pedestrians or obstacles enter within a fixed radius of 180° in the direction of travel. simulation system. (4) A wall is represented by connecting arbitrary vectors, and if the value of the sine of the angle formed by the two vectors forming the connection point of the wall is positive, the connection point is recognized as a corner. The crowd walking simulation system according to claim 1. (5) The crowd walking simulation system according to claim 1, wherein corners on the way to the destination point are sequentially used as temporary destination points to simulate up to the final destination point. (6) The crowd walking simulation system according to claim 5, wherein a temporary target point is set on the walking area side of the corner. (7) The crowd walking simulation system according to claim 5, wherein passage of the temporary target point is recognized on the condition that walls on both sides of the temporary destination point are visible. (8) The crowd walking simulation system according to claim 7, wherein passage is determined based on an angle between a vector connecting the coordinates of the pedestrian and the coordinates of the destination point and a wall vector from the target point.