Method and device for determining the reduction formula of the traffic speed of people in crowded places based on Monte Carlo simulation

Abstract

The invention relates to the field of crowd evacuation simulation in buildings, and solves the problem of how to quantitatively calculate the reduction of the traffic rate of people at the location where crowding occurs due to crowd congestion. The invention provides the following scheme, wherein the method comprises: according to the reduction coefficient R of the traffic rate Cv in the crowded place, Cv The calculation formula is used to determine the reduction factor R Cv The key variable required is the total time t required for all people to pass through the crowd sum ; The total time t required for people to pass through the crowded area sum Parameter analysis of the influencing factors was carried out to determine the corresponding influencing rules and value ranges of the parameters of each influencing factor; corresponding t sum Data set; sum The data set is converted into the corresponding reduction factor R Cv The data set, namely the fitting data set, is used to fit the personnel traffic rate reduction formulas for the two types of crowd gathering forms. It is also applicable to the field of macro evacuation simulation methods.

Description

Technical Field

[0001] The invention relates to the technical field of crowd evacuation simulation in buildings. Background Art

[0002] When an emergency occurs in a building, if the people inside can evacuate quickly and safely, casualties can be effectively reduced or even avoided. This highlights the importance of efficient evacuation in ensuring the safety of people in the building. With the development of computing technology, evacuation process simulation provides an effective way to study the evacuation process and predict the evacuation results. A set of accurate crowd evacuation simulation methods can generalize the evacuation behavior characteristics of people, simulate the evacuation process of people under different environments, conditions and numbers, and effectively reduce the research cost. According to the different driving modes, the existing evacuation simulation methods can be divided into two categories, namely model-driven methods and data-driven methods. Model-driven methods generally provide guidance for human behavior decisions based on hypothetical scenarios and rules or abstract mathematical models; while data-driven methods obtain real data through motion capture, sensor positioning or video tracking to drive personnel evacuation simulation. Since data-driven methods are often obtained through training of specific target scenarios, they are highly dependent on the data source scenarios and can often only be used in the same scenarios, and are not very applicable to different scenarios. Therefore, in actual engineering applications, engineers and some highly versatile commercial software still mainly use model-driven methods.

[0003] Model-driven evacuation simulation methods can be divided into two types according to the degree of refinement of the modeling of the research object: microscopic methods and macroscopic methods. The microscopic method takes individual personnel as the research object, and describes the possible movement behaviors of pedestrians under different conditions by modeling the perception, decision-making ability and psychological characteristics of each pedestrian, so as to simulate the diversity of the crowd and the differences between individuals. Since the microscopic method performs refined behavioral modeling at the level of evacuation individuals, the computational complexity of the corresponding simulation model is greatly increased. As a result, when such models are used to simulate the evacuation of large-scale buildings, it takes a long time to calculate only one working condition, and the computational efficiency is very low. Unlike the microscopic method, the macroscopic method takes the entire crowd as the research object, focusing on simulating the overall behavior and dynamic changes of the group of personnel. The mutual influence between each person is ignored, thereby reducing the complexity and amount of calculation, and greatly improving the computational efficiency. Therefore, for large-scale personnel evacuation scenarios, such as large shopping malls, hospitals, airports, stadiums, etc., the use of macroscopic methods can more efficiently simulate the global evacuation situation.

[0004] The macro method can help security management and decision-makers evaluate and analyze the efficiency and safety of personnel evacuation as a whole, and provide an important basis for formulating scientific and effective emergency plans. Existing macro evacuation simulation methods generally regard crowded locations as inaccessible to avoid stampedes caused by extreme crowding. However, in most real evacuation processes, when the crowd is in a crowded state, people are often able to pass through the crowded location, but the movement speed of people passing through the crowded place will be significantly slowed down.

[0005] It can be seen that the existing research generally adopts the method of treating the location where crowding occurs as unpassable, which cannot truly reflect the impact of crowd crowding on the evacuation process. In addition, crowd crowding has a serious impact on the overall evacuation efficiency in the building, and it is necessary to accurately and reasonably consider its impact in the simulation process. Therefore, it is necessary to provide a quantitative and reasonable method to more accurately and reasonably consider the impact of crowd crowding on the evacuation process in macro simulation. Summary of the invention

[0006] The present invention solves the problem in the prior art of how to quantitatively calculate the reduction in the speed of personnel passing through a location where crowding occurs due to crowding.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] A method for determining a formula for reducing the speed of traffic in crowded places based on Monte Carlo simulation, the method comprising:

[0009] Step 1: Based on the speed of traffic in congested areas Cv Reduction factor R Cv The calculation formula is used to determine the reduction factor R Cv The key variable required is the total time required for all people to pass through the crowd. t sum ;

[0010] Step 2: The total time required for people to pass through the crowded area t sum Perform parameter analysis on the influencing factors to determine the influencing factors and the corresponding influencing rules and value ranges of the influencing factors;

[0011] Step 3: Establish corresponding t sum The data set includes two different types of crowd gathering forms, including a first type of crowd gathering form and a second type of crowd gathering form;

[0012] Step 4: Based on formula (4), t sum The data set is converted into the corresponding reduction factorR Cv The data set is the fitting data set.

[0013] (4);

[0014] Step 5: According to the fitting data sets of the two types of crowd gathering forms, respectively fit and determine the personnel passage rate reduction formulas of the two types of crowd gathering forms.

[0015] Furthermore, a preferred implementation is provided, in which corresponding t sum The method of the dataset is:

[0016] For each type of crowd gathering form t sum All data sets design at least one working condition based on the passable width W of the congested area and the number of people N who are ready to pass through;

[0017] For each working condition, an independent AnyLogic measurement model was established based on the Monte Carlo method according to the crowd gathering pattern to determine the crowd passing time under this working condition. t sum .

[0018] Furthermore, a preferred embodiment is provided, wherein the personnel traffic rate reduction formulas for the two types of crowd gathering forms also include a step of confirming the value of pedestrian space occupancy OPS in the two types of crowd gathering forms, that is, the OPS of the first type of crowd gathering form is uniformly 0.4, and the OPS of the second type of crowd gathering form is uniformly 0.3.

[0019] Further, a preferred embodiment is provided, wherein the reduction factor The fitting method is: the reduction coefficient R Cv The value is retained to 0.001, and the reduction factor R Cv The data format of the data set is adjusted to column input to complete data fitting.

[0020] Furthermore, a preferred embodiment is provided, wherein the reduction coefficient for the second type of crowd gathering form is The fitting method of is consistent with that of the first type of crowd aggregation pattern.

[0021] Furthermore, a preferred embodiment is provided, wherein the reduction coefficient of the first type of crowd gathering form is The calculation formula is:

[0022] (5).

[0023] Furthermore, a preferred embodiment is provided, wherein the reduction factor of the second type of crowd gathering form is The calculation formula is:

[0024] (6).

[0025] Solution 2: This solution proposes a device for determining a formula for reducing the speed of traffic in crowded places based on Monte Carlo simulation, and the device includes:

[0026] The judgment module is used to determine the traffic speed in congested areas. Cv Reduction factor R Cv The calculation formula is used to determine the reduction factor R Cv The key variable required is the total time required for all people to pass through the crowd. t sum ;

[0027] Determination module for the total time required for people to pass through crowded areas t sum Perform parameter analysis on the influencing factors to determine the influencing factors and the corresponding influencing rules and value ranges of the influencing factors;

[0028] The test module is used to establish corresponding t sum The data set includes two different types of crowd gathering forms, including a first type of crowd gathering form and a second type of crowd gathering form;

[0029] The calculation module is used to convert t sum The data set is converted into the corresponding reduction factor R Cv The data set is the fitting data set.

[0030] (4);

[0031] The data fitting module is used to fit and determine the personnel passage rate reduction formulas of the two types of crowd gathering forms respectively according to the fitting data sets of the two types of crowd gathering forms.

[0032] Solution three: A computer device includes a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the method for determining the formula described in any one of Solution one.

[0033] Solution 4: A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for determining the formula described in any one of Solution 1.

[0034] The present invention is beneficial in that:

[0035] The present invention obtains the reduction coefficients of two types of crowd gathering forms through the above technical solution R Cv The calculation formulas are as follows, namely, the reduction formulas for the traffic speed of people in crowded places, formula (5) and formula (6). The formulas can quantitatively calculate the reduction degree of the traffic speed of people in crowded places due to crowd congestion.

[0036] Applying formula (5) and formula (6) to the macroscopic evacuation simulation method can enable the macroscopic evacuation simulation to accurately and reasonably consider the impact of crowd congestion on the evacuation process, and further improve the authenticity of the simulation results of the macroscopic evacuation simulation method.

[0037] That is, the present invention is also applicable to the field of macroscopic evacuation simulation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a schematic diagram of the first type of crowd gathering pattern described in Implementation Method One.

[0039] Figure 2 This is a schematic diagram of the second type of crowd gathering pattern described in Implementation Method One.

[0040] Figure 3 Schematic diagram of the basic attribute settings of pedestrians in the AnyLogic measurement model described in Implementation 1.

[0041] Figure 4 The reduction coefficient for the first type of crowd gathering pattern described in Implementation Method 6 Schematic diagram of the function surface of the calculation formula, where the OPS of each working condition under the first type of crowd gathering form is uniformly taken as 0.4.

[0042] Figure 5 The reduction coefficient for the second type of crowd gathering pattern described in Implementation Method 7 Schematic diagram of the function surface of the calculation formula, where the OPS of each working condition under the second type of crowd gathering form is uniformly taken as 0.3. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the implementation methods of the present application clearer, the technical solutions in the implementation methods of the present application will be clearly and completely described below in conjunction with the drawings in the implementation methods of the present application. Obviously, the described implementation methods are only part of the implementation methods of the present application, not all of the implementation methods.

[0044] Implementation method 1, see Figure 1 The present embodiment is described as follows: a method for determining a formula for reducing the speed of traffic in a crowded area based on Monte Carlo simulation, the method comprising:

[0045] Step 1: Based on the speed of traffic in congested areas Cv Reduction factor R Cv The calculation formula is used to determine the reduction factor R Cv The key variable required is the total time required for all people to pass through the crowd. t sum ;

[0046] Step 2: The total time required for people to pass through the crowded area t sum Perform parameter analysis on the influencing factors to determine the influencing factors and the corresponding influencing rules and value ranges of the influencing factors;

[0047] Step 3: Establish corresponding t sum The data set includes two different types of crowd gathering forms, including a first type of crowd gathering form and a second type of crowd gathering form;

[0048] Step 4: Based on formula (4), t sum The data set is converted into the corresponding reduction factor R Cv The data set is the fitting data set.

[0049] (4);

[0050] Step 5: According to the fitting data sets of the two types of crowd gathering forms, respectively fit and determine the personnel passage rate reduction formulas of the two types of crowd gathering forms.

[0051] Implementation Method 2: This implementation method further limits the method for determining the traffic speed reduction formula for crowded places based on Monte Carlo simulation described in Implementation Method 1. For two different types of crowd gathering forms, corresponding t sum The method of the dataset is:

[0052] For each type of crowd gathering form t sum The datasets are based on the width of traffic in congested areas. W and the number of people ready to pass N To design at least one operating condition;

[0053] For each working condition, an independent AnyLogic measurement model was established based on the Monte Carlo method according to the crowd gathering pattern to determine the crowd passing time under this working condition. t sum .

[0054] Implementation method three. This implementation method further limits the method for determining the formula for reducing the traffic speed of people in crowded places based on Monte Carlo simulation described in implementation method two. The formula for reducing the traffic speed of people in each of the two types of crowd gathering forms also includes a step of confirming the value of the pedestrian space occupancy OPS in the two types of crowd gathering forms, that is, the OPS of the first type of crowd gathering form is 0.4, and the OPS of the second type of crowd gathering form is 0.3.

[0055] Implementation method 4: This implementation method further limits the method for determining the reduction formula for the traffic speed of people in crowded places based on Monte Carlo simulation described in implementation method 1. The reduction coefficient The fitting method is: the reduction coefficient R Cv The value is retained to 0.001, and the reduction factor R Cv The data format of the data set is adjusted to column input to complete data fitting.

[0056] Implementation method 5: This implementation method further limits the method for determining the reduction formula of the traffic speed of people in crowded places based on Monte Carlo simulation described in implementation method 4. The reduction coefficient under the second type of crowd gathering form is The fitting method of is consistent with that of the first type of crowd aggregation pattern.

[0057] Implementation method 6: This implementation method further limits the method for determining the reduction formula of the traffic speed of people in crowded places based on Monte Carlo simulation described in implementation method 3. The reduction coefficient of the first type of crowd gathering form is The calculation formula is:

[0058] (5).

[0059] Implementation method 7: This implementation method further limits the method for determining the reduction formula for the traffic speed of people in crowded places based on Monte Carlo simulation described in implementation method 3. The reduction coefficient of the second type of crowd gathering form is The calculation formula is:

[0060] (6).

[0061] Embodiment 8: This embodiment proposes a device for determining a formula for reducing the speed of traffic in crowded places based on Monte Carlo simulation, the device comprising:

[0062] The judgment module is used to determine the traffic speed in congested areas. Cv Reduction factor R Cv The calculation formula is used to determine the reduction factor R Cv The key variable required is the total time required for all people to pass through the crowd. t sum ;

[0063] Determination module for the total time required for people to pass through crowded areas t sum Perform parameter analysis on the influencing factors to determine the influencing factors and the corresponding influencing rules and value ranges of the influencing factors;

[0064] The test module is used to establish corresponding t sum The data set includes two different types of crowd gathering forms, including a first type of crowd gathering form and a second type of crowd gathering form;

[0065] The calculation module is used to convert t sum The data set is converted into the corresponding reduction factor R Cv The data set is the fitting data set.

[0066] (4);

[0067] The data fitting module is used to fit and determine the personnel passage rate reduction formulas of the two types of crowd gathering forms respectively according to the fitting data sets of the two types of crowd gathering forms.

[0068] Embodiment 9. This embodiment proposes a computer device, including a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the method for determining the formula described in any one of embodiments 1 to 7.

[0069] Embodiment 10: This embodiment proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method for determining the formula described in any one of embodiments 1 to 7 are implemented.

[0070] Implementation 11: This implementation proposes an example, which is used to explain the above implementations 1 to 10. The specific contents are as follows:

[0071] Step 1: This implementation method can obtain the traffic speed in the congested area through the formula CvReduction factor R Cv The key variable required to calculate the reduction factor is the total time required for all people to pass through the congestion. t sum

[0072] The actual traffic speed of people in this implementation method Cv The ideal speed Cv 0 has the following relationship, as shown in the following formula.

[0073] (1)

[0074] in, is the reduction factor of the evacuation speed of personnel at the congestion site. In the ideal case where the interaction between personnel is not considered, the speed of personnel passing through the congestion site is Cv 0, and its calculation formula is as follows:

[0075] (2)

[0076] in, C=⌊W / D⌋ , indicating the number of people who can pass through this position side by side, V 0 is the evacuation speed of personnel. D The diameter of the incompressible space occupied by a single person. W The actual speed of people passing through a crowded area. Cv The definition is as follows:

[0077] (3)

[0078] in, N is the total number of people preparing to pass through the congestion, The total time required for everyone to pass through the congestion.

[0079] Combining equations (1) and (3), we can obtain the reduction factor of a crowded area in a specific evacuation process: The calculation formula is as follows:

[0080] (4)

[0081] During the evacuation simulation, the movement speed of personnel in an emergency evacuation event V 0, Personnel body size D are all fixed basic parameters. For a congested area with a passable width of W, the ideal traffic speed can be calculated by formula (2): Cv 0, that is Cv0 does not change with the change of the evacuation plan. Therefore, from formula (4), if we want to obtain the reduction coefficient R of a certain crowded place in a specific evacuation process, Cv , you just need to know the corresponding N and t sum The total number of people ready to pass through the congested area N In evacuation simulation, it can be directly obtained in real time, and the crowd passing time t sum It needs to be measured through some settings and cannot be obtained directly. In summary, to calculate the reduction factor of a congested area R Cv The main difficulty lies in obtaining the corresponding crowd passing time t sum , which is the total time required for everyone to pass through the congestion t sum is to calculate the reduction factor R Cv Key variables required.

[0082] Therefore, from formula (4), we can know that if we want to obtain the reduction coefficient of a crowded place in a specific evacuation process, , you just need to know the corresponding N and The total number of people ready to pass through the congested area N In evacuation simulation, it can be directly obtained in real time, and the crowd passing time It needs to be measured through some settings and cannot be obtained directly. In summary, to calculate the reduction factor of a congested area The main difficulty lies in obtaining the corresponding crowd passing time .

[0083] Step 2: This step will further introduce the time for people to pass through t sum Carry out parameter analysis on the influencing factors to determine the corresponding influencing rules and reasonable value range of each influencing parameter;

[0084] Among them, the crowd passes through time It means that in a crowded situation, the number of people is N The total time it takes for the crowd to pass through the congestion. In this embodiment, a simulation experiment is performed on the evacuation simulation software AnyLogic to determine the crowd passing time. .

[0085] In step 1, we will perform a computational experiment to determine the crowd passage time The AnyLogic evacuation simulation model is referred to as the AnyLogic measurement model. After analysis, the AnyLogic measurement model must at least meet the following functional requirements:

[0086] 1) Crowd state requirements: The crowd state in the simulation experiment is a crowded state;

[0087] 2) Timing: The measurement model can accurately measure the time it takes for people to pass through crowded areas. , that is, determine the time when the first person passes through the narrow place ( ), and the time when the last person passes through the narrow place ( ), = .

[0088] 3) Monte Carlo experiment: Considering the randomness of the micro-evacuation simulation method used in AnyLogic, we need a time measurement model that can perform MC calculation experiments to reflect the total time. The randomness of .

[0089] In this embodiment, it is considered that the crowd passing time t sum The factors that affect the measurement results are mainly the following:

[0090] 1. Width of traffic in crowded places W ;

[0091] 2. Number of people waiting to pass through crowded areas N ;

[0092] 3. Spatial form of crowded areas;

[0093] 4. Average pedestrian space occupation (OPS) in crowded areas;

[0094] 5. The number of repetitions of the Monte Carlo experiment.

[0095] From formula (4), we can see that the crowd passing time t sum and reduction factor R Cv Therefore, the above five groups of people have t sum The factors affecting the R Cv Therefore, the above factors are analyzed to determine the impact on the time of people passing through. t sum The parameter analysis process of the influence law of the above factors can also obtain the effect of the above factors on the reduction coefficient. R CvThe law of influence.

[0096] Secondly, this implementation method analyzes the spatial form of crowded crowd gathering areas (hereinafter uniformly referred to as crowd gathering form), the average pedestrian space occupation (OPS), and the number of Monte Carlo calculation experiments. These three factors affect the time it takes for crowds to pass through. t sum and reduction factor R Cv Then, the reasonable value of the average pedestrian space occupation (OPS) and the number of repetitions of the Monte Carlo calculation experiment in the AnyLogic time determination model was determined.

[0097] a. For the gathering states of the two types of people, this implementation method has further investigated the drawings of various buildings and found that the spatial forms of different crowded evacuation scenes will lead to different spatial forms of crowded areas (hereinafter uniformly recorded as crowd gathering forms). There is no necessary correspondence between the crowd gathering form and the passable width W of the crowded area, and W cannot be used to reflect the difference in crowd gathering forms. Therefore, in order to distinguish the reduction coefficients for different crowd gathering forms, In order to take into account the influence of the crowd gathering pattern, this implementation method considers the independent parameter analysis for each type of crowd gathering pattern. Combined with the survey results of the drawings of various types of buildings, we believe that the crowd gathering patterns can be mainly divided into two categories, as follows:

[0098] The first type of crowd gathering pattern: the size of the crowd gathering area in both directions is not restricted, and it is only necessary to keep the length-to-width ratio of the area within a reasonable range (in this spatial environment, the crowd gathering pattern is generally a semicircle, that is, the length-to-width ratio is 2).

[0099] The second type of crowd gathering form: The spatial size of the area where the crowd can gather is limited in a certain direction, and an upper limit on the size in this direction needs to be set.

[0100] The following are two types of aggregation patterns: Figure 1 and Figure 2 shown. Figure 1 and Figure 2 The number of people waiting to pass N and the passable width W both take the same value (N=50, W=2.0M).

[0101] b. Parameter analysis of the number of repetitions of the Monte Carlo experiment:

[0102] In order for the crowd to pass through time t sum The output of the measurement model has an error convergence within the allowable range (0.1s). We need to determine the appropriate number of Monte Carlo experiment repetitions. We choose the passable width W = 2.4m, the number of people to pass N = 20, and the average pedestrian space OPS = 0.5m2 / people condition (i.e. the above three conditions remain unchanged), only the number of Monte Carlo simulations is changed to observe the crowd passing time determined by the AnyLogic simulation model t sum The AnyLogic model selected the first type of clustering, and the layout of the simulation scenario was consistent with Figure 1 The final t sum The results of parameter analysis on the number of repetitions of the Monte Carlo experiment are shown in Table 1.

[0103]

[0104] As shown in Table 1, when the number of repetitions of the Monte Carlo experiment reaches 3000 or more, the result of the crowd passing time converges to 7.7s, and the error is always less than 0.1s, meeting the accuracy requirement. Considering the calculation accuracy and efficiency, this study uses 5000 times as the number of repetitions of the Monte Carlo experiment under each working condition.

[0105] c. Parameter analysis of pedestrian space occupancy:

[0106] The occupancy of pedestrian space is a physical quantity used to describe the density of pedestrians in a certain area. Its unit is m 2 / person, compared to density (person / m 2 ), which can more intuitively reflect the density and congestion of pedestrians. In the present invention, the pedestrian space occupancy is uniformly recorded as OPS. When the pedestrian space occupancy is 0.28~0.65 m 2 / person, pedestrians are greatly restricted when walking in the team, and the flow rate drops sharply. They can only move forward with the crowd. It is very uncomfortable to stay in this density for a long time. 0.18-0.28 m 2 / people, standing will inevitably come into contact with others, and it is impossible to move in the pedestrian queue. The queue can only last for a short time, and people will feel very uncomfortable. In this implementation, the pedestrian space occupancy is analyzed by parameters to determine the effect of the change of pedestrian space occupancy within 0.28~0.65 on the reduction factor R Cv The law of influence.

[0107] For the first type of aggregation, we established the following in AnyLogic: Figure 1 In the simulation scenario shown in the figure, when the passable width W = 2.4m and the number of people to pass N = 20 remain unchanged, the change of OPS from 0.3 to 0.6 has an effect on the reduction factor R CvIn the spatial environment corresponding to this type of working condition, the crowd gathering shape in a crowded state is generally a semicircle, so we set the length-width ratio of the initial crowd gathering area in this type of working condition to 2 to further determine the actual length and width of the initial crowd gathering area. The specific values ​​of the modeling parameters of the AnyLogic measurement model under each working condition are shown in Table 2.

[0108]

[0109] See Table 2 for an explanation of this embodiment, where Length represents the size of the initial crowd gathering area in the direction perpendicular to the direction of the people's advance, and Width represents the size of the initial crowd gathering area in the direction parallel to the direction of the people's advance. In addition, in the AnyLogic measurement model, Length corresponds to the size in the Y-axis direction of the simulation scene coordinate system, and Width corresponds to the size in the X-axis direction of the simulation scene coordinate system. Finally, the crowd passing time under each working condition in the first type of gathering form is obtained. t sum The corresponding speed reduction factor in congested areas R Cv The specific results are shown in Table 3.

[0110]

[0111] For the second type of crowd gathering, we built the following in AnyLogic: Figure 2 In the simulation scenario shown in the figure, when the passable width W = 2.0m and the number of people to pass N = 20 remain unchanged, the change of OPS from 0.3 to 0.6 affects the reduction factor. R Cv The specific values ​​of the modeling parameters of the AnyLogic time measurement model under each condition are shown in Table 7. In the spatial environment corresponding to this type of condition, the size of one direction of the initial crowd gathering area is limited by the actual spatial environment and cannot be increased after reaching a certain fixed value. In order to be consistent with the spatial dimension symbols in Table 2, in the working conditions of Table 4, this limited dimension direction is still recorded as Length, and the direction perpendicular to Length is recorded as Width.

[0112]

[0113] Finally, the crowd passing time under each working condition in the second type of gathering form is obtained. t sum The corresponding speed reduction factor in congested areas R Cv See Table 5.

[0114]

[0115] From the results in Tables 3 and 5, it can be seen that when the value range is 0.28~0.65 m 2 / person, the impact of pedestrian space occupation on the reduction coefficient only occurs at the 0.001 decimal place (and the difference in reduction coefficients corresponding to adjacent pedestrian space occupation values ​​does not exceed 5%). Therefore, we can assume that when the value range is 0.28~0.65 m 2 / person, the change of pedestrian space occupation OPS has little effect on the reduction coefficient and can be ignored within the error tolerance range. In addition, in the improved macro evacuation simulation method proposed in this embodiment, the information on the area occupied by the crowded and gathered areas of pedestrians has been simplified, so it is impossible to obtain the real-time and accurate value of the pedestrian space occupation. In other words, it is impossible to quantitatively reflect the effect of pedestrian space occupation OPS on the reduction coefficient in real time in the macro evacuation simulation method proposed in this embodiment. R Cv impact.

[0116] In summary, we consider that under the same type of gathering form, for different working conditions, we use a unified pedestrian space occupation to design the spatial size of the initial crowd gathering area in the AnyLogic measurement model. Considering that in the case of emergency evacuation, people are in a state of panic, and the crowding level will often increase to a certain extent. Then, the specific values ​​of pedestrian space occupation OPS under the two types of gathering forms are as follows:

[0117] The first type of crowd gathering form: Since the size of the gathering area in both directions is not restricted, the crowding degree is relatively mild, and the value is 0.4;

[0118] The second type of crowd gathering form: Since the size of the gathering area in a certain direction is limited, the crowding degree is relatively denser, and the value is 0.3;

[0119] Step 3: Establish corresponding t for two different types of crowd gathering forms. sum Data set. (Further explanation: ① The t corresponding to each type of crowd gathering form sum The data sets are designed with different working conditions according to the passable width W of the crowded area and the number of people N who are ready to pass through. ② For each working condition, according to the crowd gathering form (with Figure 1 Correspondingly, an independent AnyLogic model was established to determine the crowd passing time t under this condition. sum );

[0120] After the above parameter analysis, we have determined the difference between the two types of crowd gathering forms, as well as the number of Monte Carlo simulation repetitions and the value of pedestrian space occupancy OPS. Next, we conduct working condition design under the two types of crowd gathering forms to establish the respective t sum Specifically, for a certain type of crowd gathering, we design different working conditions according to the passable width W of the crowded area and the number of people N who are ready to pass through. For each of these working conditions, according to the crowd gathering form ( Figure 1 Correspondingly, an independent AnyLogic model was established to determine the crowd passage time under this condition. t sum .

[0121] a. The first type of crowd gathering (OPS=0.4) sum The process of establishing the data set

[0122] In the first type of crowd gathering (OPS=0.4), we designed different working conditions according to the passable width W of the crowded area and the number of people N who are ready to pass through, as shown in Table 6. For each working condition, an independent AnyLogic measurement model was established to measure the crowd passing time under that working condition. , the simulation scene layout of the model and Figure 1 Consistent.

[0123]

[0124] The basic attributes of pedestrians in the AnyLogic model are as follows: Figure 3 shown. Figure 3 In the above equation, uniform() indicates uniform distribution, normal() indicates normal distribution. The physical meanings of the four values ​​in normal distribution are shown in Table 7. A Monte Carlo simulation with 5000 repetitions was performed in the time measurement model, and the crowd passing time was finally obtained. , fill in the corresponding position in Table 6.

[0125]

[0126] b. The second type of crowd gathering (OPS=0.3) t sum The process of establishing the data set

[0127] The second type of crowd gathering (OPS=0.3) t sumThe process of establishing the dataset is the same as that of the first type of crowd gathering. The main difference in the process of establishing the dataset is that the simulation scene layout of the AnyLogic time measurement model corresponding to the second type of crowd gathering is different. Figure 2 The working conditions under this type of gathering form and the corresponding crowd passing time t sum , as shown in Table 8.

[0128]

[0129] Step 4: According to formula (4), t sum The data set is converted into the corresponding reduction factor R Cv The data set is the fitting data set;

[0130] According to formula (4), the crowd can be sum , W, N calculate the corresponding reduction factor R Cv Therefore, the values ​​in Table 6 and Table 9 can be t sum The data set is converted into the corresponding reduction factor R Cv Among them, the reduction coefficient under the first type of crowd gathering form is R Cv The data set is shown in Table 9; the reduction coefficient under the second type of crowd gathering form R Cv The data set is shown in Table 10.

[0131]

[0132] Reduction coefficient for the second type of crowd gathering R Cv The data set is shown in Table 10.

[0133]

[0134] Step 5: According to the gathering forms of the two types of people R Cv The data set is used to fit the personnel passage rate reduction formulas of the two types of crowd gathering forms.

[0135] Through a preliminary analysis of the trend of the data change in the reduction coefficient table, this implementation method believes that the reduction coefficient value surface should be a bounded surface, and its boundary is the number of people waiting to pass N> the number of people who can pass side by side in a crowded place (W / D). Therefore, we consider fitting the formula form only through the reduction coefficient data under the working condition with reduction (the existing data in the table), and setting boundary conditions for it.

[0136] This step uses the software TableCuRcve 3D reduction coefficient The calculation formula has been determined by fitting the data. In the data fitting process, we first retain the reduction coefficient value to 0.001 and adjust the data format to the data input format required by TableCuRcve 3D, that is, input by column. Finally, the reduction coefficient R under the first type of crowd gathering form is obtained by fitting. Cv The calculation formula is shown in formula (5).

[0137] (5)

[0138] The function surface corresponding to formula (5) is as follows Figure 4 As shown, Figure 4 The calculation form of the function surface obtained by fitting is as follows:

[0139]

[0140] DF Adj

[0141] a=61.279523, b=-17.414264, c=50.722801, d=-40.31783.

[0142] in, Eqn The formula is numbered in the software TableCuRcve 3D, that is, the fitting formula number here is 302417328; 2 is the fitting coefficient between the fitting formula and the original fitting data, r 2 The closer it is to 1, the better the fitting effect is; DF Adj r 2 is the fitting coefficient after degree of freedom correction, which is 2 On the basis of , the number of independent variables is introduced to more accurately evaluate the fitting effect of the model; FitStdErr is the standard error, which indicates the degree of dispersion between the original data and the fitting function; Fstat is the F comparison statistic, which is used to compare the degree of fit of different fitting functions for sample data. The larger the F comparison statistic, the better the fit; a, b, c, d correspond to the fixed coefficients in front of each item in the fitting function, and the actual values ​​are all taken to 3 decimal places and substituted into the corresponding final reduction formula, that is, formula (5).

[0143] Reduction coefficient for the second type of crowd gathering (OPS=0.3) R Cv The calculation formula is shown in formula (6).

[0144] (6)

[0145] The function surface corresponding to formula (6) is as follows Figure 4 shown. Figure 4 The calculation form of the function surface obtained by fitting is as follows:

[0146]

[0147] DF Adj

[0148] a=0.66476192,b=0.47307113,c=0.001273305,d=-1.0864365,

[0149] e=-0.00077315676, f=-0.17843281.

[0150] Among them, Eqn is the number of the formula in the software TableCuRcve 3D, that is, the fitting formula number here is 303; r2 is the fitting coefficient between the fitting formula and the original fitting data. The closer r2 is to 1, the better the fitting effect is; DF Adj r2 is the fitting coefficient after the degree of freedom correction. On the basis of r2, it introduces the factor of the number of independent variables to more accurately evaluate the fitting effect of the model; FitStdErr is the standard error, which indicates the degree of discreteness between the original data and the fitting function; Fstat is the F comparison statistic, which is used to compare the degree of fit of different fitting functions for sample data. The larger the F comparison statistic, the better the fit; a, b, c, d, e correspond to the fixed coefficients in front of each item in the fitting function, and the actual values ​​are all taken to 3 decimal places and substituted into the corresponding final reduction formula, that is, formula (6).

[0151] So far, we have obtained the reduction coefficients for two types of crowd gathering forms: R Cv The calculation formula (Formula (5), Formula (6)) is the reduction formula for the speed of people passing through the congested area. In practical application, the total number of people preparing to pass through the congested area at the current moment can be used to calculate the speed of people passing through the congested area. N , together with the width of traffic in congested areas W , substituting into the reduction formula corresponding to the spatial form of the crowded evacuation scene (Formula (5) or Formula (6)), the reduction coefficient of the crowded place at the current moment can be obtained.

[0152] Embodiment 12: This embodiment provides an example, which is used to explain the above embodiments 1 to 10. The specific contents are as follows:

[0153] This implementation can be used to quantitatively calculate the degree of reduction in the speed of people passing through the location where the crowding occurs. When it is necessary to determine the degree of reduction in the speed of people passing through different types of people (people with significantly different basic attributes) in a crowded place, the reduction formula can be determined according to the method proposed in this patent to meet the above requirements.

[0154] In this embodiment, the basic attributes of the specific type of people we selected are as follows:

[0155] First, the diameter of the incompressible space occupied by a single person is unified as D =0.4m,

[0156] Second, the basic movement characteristics of the crowd are shown in Table 7.

[0157] The motion feature data in Table 7 are derived from the motion data of people in the real evacuation video. The motion feature data does not distinguish the types of people in detail, but only summarizes the valid motion data of all people in the video into a statistical sample set. In other words, the specific type of people we selected in this embodiment represents a relatively broad type of people, and does not distinguish between the gender, age, and occupational characteristics of the people. If there is a need in the future, the basic attributes of the corresponding people in various aspects can be determined according to different types of people, and then the reduction formula corresponding to the specific type of people can be determined using this method.

[0158] The key results of each step in the process of determining the personnel passage rate reduction formula for a specific type of people selected in this embodiment at a certain congestion occurrence location (the passable width is W).

[0159] Step 1: Derivation of the traffic velocity in congested areas Cv Reduction factor R Cv The key variable required to determine the reduction factor is the total time required for all people to pass through the congestion. t sum ;

[0160] From the working principle of step 1, we can know that for a specific evacuation process, the reduction coefficient of a crowded place is The calculation formula is shown in formula (1), and the total time required for everyone to pass through the congested area is t sum is the calculation reduction factor R Cv Key variables required.

[0161] Step 2: Time of crowd passing t sum Carry out parameter analysis on the influencing factors to determine the corresponding influencing rules and reasonable value range of each influencing parameter;

[0162] In this step, we first determined the reasonable value of the average pedestrian space occupation (OPS) of this group of people under the two types of crowd gathering forms, namely:

[0163] a. The first type of crowd gathering form: Since the size of the gathering area is not restricted in both directions, the crowding degree is relatively mild, and the value is 0.4;

[0164] b. The second type of crowd gathering form: Since the size of the gathering area in a certain direction is limited, the crowding degree is relatively denser, and the value is 0.3;

[0165] Then, the present embodiment determines that the time required for this type of people to pass through the crowded place is measured. t sum The AnyLogic simulation model performed a Monte Carlo experiment with 5000 repetitions.

[0166] Step 3: Establish corresponding t sum Datasets;

[0167] For a certain type of crowd gathering, we can calculate the width of the crowded area. W and the number of people ready to pass N To design different working conditions. For each working condition, according to the crowd gathering form (with Figure 1 Correspondingly, an independent AnyLogic model was established to determine the time it takes for such people to pass through under this condition. t sum Finally, we get the first type of crowd gathering form (OPS=0.4) t sum The data set is shown in Table 6. The second type of crowd gathering form (OPS=0.3) t sum The dataset is shown in Table 7.

[0168] Step 4: According to formula (4), sum The data set is converted into the corresponding reduction factor R Cv The data set is the fitting data set;

[0169] According to formula (4), the crowd can be t sum , W , NCalculate the corresponding reduction factor R Cv Finally, the reduction factor R of the first type of crowd gathering form (OPS=0.4) is obtained. Cv The data set is shown in Table 9. The reduction factor of the second type of crowd gathering form (OPS=0.3) R Cv The data set is shown in Table 10.

[0170] Step 5: According to the gathering forms of the two types of people R Cv The data set is used to fit the personnel passage rate reduction formulas of the two types of crowd gathering forms.

[0171] This implementation uses the software TableCuRcve 3D to reduce the coefficient R Cv Finally, we fitted the data to determine the calculation formula, that is, the reduction formula. Finally, we obtained the reduction coefficient under the first type of crowd gathering form (OPS=0.4). R Cv The calculation formula is shown in formula (5). The reduction coefficient under the second type of crowd gathering form (OPS = 0.3) R Cv The calculation formula is shown in formula (6).

[0172] So far, we have obtained the reduction coefficients of the specific types of people selected in this embodiment under the two types of crowd gathering forms: R Cv The calculation formula (Formula (5), Formula (6)) is the formula for reducing the traffic speed of this type of people in crowded places.

[0173] The descriptions in this specification are only preferred embodiments of the present invention, and the scope of rights of the present invention cannot be limited thereto; in addition, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner.

[0174] In addition, in the absence of mutual contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples. In addition, the terms "first" and "second" are used only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. Any process or method description in the flowchart or otherwise described herein may be understood to represent a module, fragment or portion of a code including one or more N executable instructions for implementing the steps of a custom logic function or process, and the scope of the preferred embodiment of the present invention includes other implementations, in which the functions may be performed in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by those skilled in the art of the present invention.

[0175] For the purpose of this specification, the "computer-readable medium" in the tenth embodiment can be any device that can contain, store, communicate, propagate or transmit a program for use in an instruction execution system, device or apparatus or in conjunction with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or N wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM).

[0176] Those skilled in the art will appreciate that the above are only preferred embodiments of the present invention, and the features described in the various embodiments and / or claims of the present disclosure may be combined or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. The above are not intended to limit the present invention, and the above are only preferred embodiments of the present invention, and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art may still modify the technical solutions described in the aforementioned embodiments, or perform equivalent substitutions on some of the technical features therein, and any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

[0177] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for determining a formula for reducing the traffic speed of people in crowded places based on Monte Carlo simulation, characterized in that: The method comprises: Step 1: Based on the speed of traffic in congested areas Cv Reduction factor R Cv The calculation formula is used to determine the reduction factor R Cv The key variable required is the total time required for all people to pass through the crowd. t sum ; Step 2: The total time required for people to pass through the crowded area t sum Perform parameter analysis on the influencing factors to determine the influencing factors and the corresponding influencing rules and value ranges of the influencing factors; Step 3: Establish corresponding t sum The data set includes two different types of crowd gathering forms, including a first type of crowd gathering form and a second type of crowd gathering form; Step 4: Based on formula (4), t sum The data set is converted into the corresponding reduction factor R Cv The data set is the fitting data set. （4）； Step 5: According to the fitting data sets of the two types of crowd gathering forms, the personnel passage rate reduction formulas of the two types of crowd gathering forms are respectively fitted and determined; Among them, step 2 specifically includes: The influencing factors include the passable width W of the crowded area, the number of people N waiting to pass through the crowded area, the spatial form of the crowded crowd gathering area, the average pedestrian space occupancy OPS in the crowded crowd gathering area, and the number of repetitions of the Monte Carlo experiment; The corresponding influencing rules of the influencing factors include analyzing the spatial form of the crowded crowd gathering area, the average pedestrian space occupancy OPS, and the number of repetitions of the Monte Carlo calculation experiment. The three factors have an impact on the crowd passing time t sum and reduction factor R Cv The impact of The range of values ​​includes determining the average pedestrian space occupancy OPS, the number of repetitions of the Monte Carlo calculation experiment in the AnyLogic time measurement model; The first type of crowd gathering form described in step 3 is: the size of the crowd gathering area in both directions is not restricted; The second type of crowd gathering form is: the spatial size of the area where the crowd can gather is limited in a certain direction, and an upper limit of the size in this direction needs to be set.

2. The method for determining the reduction formula of the traffic speed of people in crowded places based on Monte Carlo simulation according to claim 1 is characterized in that: For two different types of crowd gathering forms, corresponding t sum The method of the dataset is: For two different types of crowd gathering forms, corresponding t sum The method of the dataset is: For each type of crowd gathering form t sum All data sets design at least one working condition based on the passable width W of the congested area and the number of people N who are ready to pass through; For each condition, an independent AnyLogic measurement model was established based on the Monte Carlo method to determine the crowd passing time under the condition according to the crowd gathering pattern. t sum .

3. The method for determining the reduction formula of the traffic speed of people in crowded places based on Monte Carlo simulation according to claim 2 is characterized in that: The personnel traffic rate reduction formulas for the two types of crowd gathering forms also include a step of confirming the value of pedestrian space occupancy OPS in the two types of crowd gathering forms, that is, the OPS of the first type of crowd gathering form is uniformly 0.4, and the OPS of the second type of crowd gathering form is uniformly 0.

3.

4. The method for determining the reduction formula of the traffic speed of people in crowded places based on Monte Carlo simulation according to claim 1 is characterized in that: The reduction factor The fitting method is: the reduction coefficient R Cv The value is retained to 0.001, and the reduction factor R Cv The data format of the data set is adjusted to column input to complete data fitting.

5. The method for determining the reduction formula of the traffic speed of people in crowded places based on Monte Carlo simulation according to claim 1 is characterized in that: The reduction coefficient for the second type of crowd gathering The fitting method of is consistent with that of the first type of crowd aggregation pattern.

6. A device for determining a formula for reducing the speed of traffic in crowded places based on Monte Carlo simulation, characterized in that: The device comprises: The judgment module is used to determine the traffic speed in congested areas. Cv Reduction factor R Cv The calculation formula is used to determine the reduction factor R Cv The key variable required is the total time required for all people to pass through the crowd. t sum ; Determination module for the total time required for people to pass through crowded areas t sum Perform parameter analysis on the influencing factors to determine the influencing factors and the corresponding influencing rules and value ranges of the influencing factors; The test module is used to establish corresponding t sum The data set includes two different types of crowd gathering forms, including a first type of crowd gathering form and a second type of crowd gathering form; The calculation module is used to convert t sum The data set is converted into the corresponding reduction factor R Cv The data set is the fitting data set. （4）； A data fitting module is used to fit and determine the personnel passage rate reduction formulas of the two types of crowd gathering forms respectively according to the fitting data sets of the two types of crowd gathering forms; The determination module specifically includes: The influencing factors include the passable width W of the crowded area, the number of people N waiting to pass through the crowded area, the spatial form of the crowded crowd gathering area, the average pedestrian space occupancy OPS in the crowded crowd gathering area, and the number of repetitions of the Monte Carlo experiment; The corresponding influencing rules of the influencing factors include analyzing the spatial form of the crowded crowd gathering area, the average pedestrian space occupancy OPS, and the number of repetitions of the Monte Carlo calculation experiment. The three factors have an impact on the crowd passing time t sum and reduction factor R Cv The impact of The range of values ​​includes determining the average pedestrian space occupancy OPS, the number of repetitions of the Monte Carlo calculation experiment in the AnyLogic time measurement model; The first type of crowd gathering form described in the test module is: the size of the crowd gathering area in both directions is not restricted; the second type of crowd gathering form is: the spatial size of the crowd gathering area in one direction is limited, and an upper limit on the size in this direction needs to be set.

7. A computer device comprising a memory and a processor, characterized in that A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the method for determining the formula described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for determining the formula according to any one of claims 1 to 5 are implemented.

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