Urban rail station passenger flow risk identification method and system based on particle flow dynamic simulation

Abstract

The present invention provides a method and system for identifying passenger flow risks at urban rail stations based on particle flow dynamic simulation. This method belongs to the technical field of urban rail transit operation and maintenance. The method uses extrusion pressure and duration as passenger flow risk evaluation indicators, performs grading, and constructs an urban rail station passenger flow risk matrix. The method obtains geometric data of the area to be simulated, determines the passenger physical entity model and force model, and uses particle flow software (PFC) to construct a dynamic simulation model of local passenger flow risks at the station. The method also establishes relationships between the maximum extrusion pressure within the passenger flow and the ratio of the extrusion pressure duration in each level interval and macroscopic passenger flow parameters such as the initial density and initial speed of the passenger flow. The method calculates the extrusion pressure and duration using a fitting function based on the simulation results under the corresponding scenario, and determines the risk level by comparing the results to the risk matrix. The method can provide a theoretical basis for the quantitative grading of passenger flow risks, offer decision-making support for urban rail operators in formulating targeted passenger flow control measures, implement proactive safety control, and effectively ensure passenger travel safety.

Description

Technical Field

[0001] The present invention relates to the technical field of urban rail transit operation and maintenance, and in particular to a method and system for identifying passenger flow risks in urban rail transit stations based on particle flow dynamic simulation. Background Art

[0002] The short-term gathering of passengers in urban rail transit stations exacerbates the mutual influence between passengers and increases the load intensity of various equipment and facilities, which has a significant impact on the normal operation of urban rail stations and the safety of passengers. Therefore, timely and accurate risk identification of station passenger flow, research on the evolution mechanism and assessment methods of passenger flow risks, and the implementation of targeted and effective risk control measures are the top priorities for urban rail operation safety management. However, the risk characteristics and risk points of urban rail station passenger flow at different times under different risk scenarios vary. Faced with the dynamic risks of station passenger flow, relevant management departments currently use passenger flow control measures based on subjective experience, which lack theoretical support.

[0003] Currently, studies of passenger flow risk at stations mostly focus on accident mechanisms and risk influencing factors. Quantitative evaluations are primarily measured through passenger flow density, but there is no clear threshold for passenger flow density. High passenger concentration is one of the fundamental causes of passenger flow risk at urban rail stations. Currently, widely used passenger flow simulation technologies have limitations and shortcomings when simulating high-density passenger flows. The combination of the particle discrete element method and social force models offers a promising approach to addressing this issue. Furthermore, high passenger flow concentration does not necessarily lead to accidents such as stampedes. The true cause of passenger flow accidents is the jostling of passengers within the flow, leading to individual instability, falls, or crushing injuries. Therefore, crowd forces are a crucial basis for characterizing passenger flow risk. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for identifying passenger flow risks in urban rail stations based on particle flow dynamic simulation, so as to solve at least one technical problem existing in the above-mentioned background technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In one aspect, the present invention provides a method for identifying passenger flow risks in urban rail stations based on granular flow dynamic simulation, comprising:

[0007] Using squeeze force and duration as passenger flow risk evaluation indicators, we classify the levels and construct a passenger flow risk matrix for urban rail stations.

[0008] According to the geometric data of the area to be simulated, the passenger physical model and force model are determined, and the particle flow software PFC is used to build a dynamic simulation model of the local risk of passenger flow in the station, and the dynamic simulation of the local risk of passenger flow in the area to be simulated is carried out;

[0009] Based on the simulation results, the relationship between the ratio of the duration of the maximum squeezing pressure within the passenger flow to the duration of the squeezing pressure in each level interval and the macroscopic passenger flow parameters of the initial passenger flow density and initial passenger flow speed is established;

[0010] According to the passenger flow data, the squeeze force and duration are calculated by fitting the function according to the simulation results in the corresponding scenario, and the risk level is determined by comparing it with the risk matrix.

[0011] Preferably, the squeezing force and duration are used as passenger flow risk evaluation indicators, and a level classification is performed to construct a passenger flow risk matrix for urban rail stations, including:

[0012] Determine the extrusion pressure level division interval and the duration division interval;

[0013] The passenger flow risk measurement of urban rail transit stations is determined by the relationship between risk and the two measurement indicators of squeeze pressure and duration, which is expressed as follows:

[0014] R = f(F, t);

[0015] Where R is the passenger flow risk; F is the internal squeezing force of the passenger flow; t is the duration of the squeezing force;

[0016] Determine the number of passenger flow risk levels N for urban rail transit stations based on actual needs;

[0017] The risk matrix cells are graded and the urban rail transit passenger flow risk matrix is ​​constructed.

[0018] Preferably, the risk matrix cells are graded to construct an urban rail transit passenger flow risk matrix, including:

[0019] All cells of the risk matrix are numbered in sequence, the probability comparison values ​​between each two cells are calculated, and then the similarity measurement values ​​between cells are calculated to obtain the initial similarity matrix between cells;

[0020] Get the minimum value of the similarity measure between cells and cluster the two corresponding cells into a new class;

[0021] Get the minimum value in the initial similarity matrix in turn. If the corresponding cell has been merged into one category, continue to get the next minimum value; if the corresponding cell belongs to two categories, calculate the inter-class similarity measure and repeat the calculation until it is greater than the previously obtained inter-class similarity measure value, then the corresponding two categories are clustered into a new category.

[0022] Preferably, the geometric data of the area to be simulated is obtained, the passenger physical model and the force model are determined, and the particle flow software PFC is used to construct a dynamic simulation model of the local risk of passenger flow in the station, including:

[0023] Obtain geometric data of the area to be simulated;

[0024] Construct a passenger physical entity model, including the passenger individual space model and passenger contact model;

[0025] Analyze the passenger force and motion characteristics according to the simulation scenario and build a passenger motion model;

[0026] The above steps are implemented using command statements in the particle flow software PFC and the built-in FISH language, including the generation of wall boundaries, passenger attribute settings and passenger force function definitions, and the calculation of the resultant force acting on passengers. Passengers begin to move and come into contact under the action of various forces, thus realizing the dynamic simulation of local passenger flow risks in urban rail transit stations.

[0027] Preferably, by changing the parameters such as the initial density and initial speed of the passenger flow in the simulation scenario, the values ​​of the internal squeezing pressure of the passenger flow changing with time under different initial conditions are recorded, and the relationship between the maximum squeezing pressure and the duration ratio of the squeezing pressure in each level interval and the macroscopic passenger flow parameters of the initial density and initial speed of the passenger flow are established by function fitting.

[0028] Preferably, passenger flow data is obtained, and the squeezing force and duration are calculated according to the fitting function of the simulation results in the corresponding scenario, and the risk level is determined by comparing with the risk matrix, including:

[0029] Obtain the corresponding basic passenger flow data required in the station area at a certain time interval;

[0030] The maximum squeezing force of passenger flow during the interval is calculated by fitting a function. If the maximum squeezing force exceeds the maximum squeezing force interval, it is directly determined as the highest risk level. If it does not exceed the maximum squeezing force interval, the duration of the time period less than the force and the duration of the force's subordinate interval is calculated based on the fitted squeezing force duration ratio function of each interval.

[0031] Calculate the cumulative duration of each extrusion pressure interval during the statistical period, and obtain the corresponding risk level from the corresponding risk matrix. Finally, the maximum risk level shall be taken as the standard.

[0032] In a second aspect, the present invention provides a system for identifying passenger flow risks in urban rail stations based on particle flow dynamic simulation, comprising:

[0033] The first construction module is used to classify the passenger flow risk using squeezing force and duration as passenger flow risk evaluation indicators, and to construct a passenger flow risk matrix for urban rail stations;

[0034] The simulation module is used to determine the passenger physical model and force model based on the geometric data of the area to be simulated, and use the particle flow software PFC to build a dynamic simulation model of local passenger flow risks in the station to perform dynamic simulation of local passenger flow risks in the area to be simulated;

[0035] The second construction module is used to establish the relationship between the ratio of the maximum squeezing pressure duration within the passenger flow to the squeezing pressure duration of each level interval and the macro passenger flow parameters of the initial passenger flow density and initial passenger flow speed based on the simulation results;

[0036] The calculation module is used to calculate the squeezing force and duration according to the passenger flow data and the fitting function of the simulation results in the corresponding scenario, and determine its risk level by comparing it with the risk matrix.

[0037] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the urban rail station passenger flow risk identification method based on particle flow dynamic simulation as described above is implemented.

[0038] In a fourth aspect, the present invention provides a computer program product, comprising a computer program, which, when running on one or more processors, is used to implement the urban rail station passenger flow risk identification method based on particle flow dynamic simulation as described above.

[0039] In a fifth aspect, the present invention provides an electronic device comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the urban rail station passenger flow risk identification method based on particle flow dynamic simulation as described above.

[0040] The beneficial effects of the present invention are as follows: starting from the perspective of crowd force, the particle flow theory is introduced, and the professional particle flow software PFC2D is used to dynamically simulate the local risk of station passenger flow, and the relationship between the passenger contact force at the micro level and the macro traffic characteristic parameters is established; considering the dynamic change characteristics of passenger flow, the relationship between the duration ratio of the squeezing force in each level interval and the macro traffic characteristic parameters is established; using squeezing force and duration as passenger flow risk evaluation indicators, passenger flow risk is graded according to the passenger flow risk matrix constructed based on the hierarchical clustering idea, effectively realizing the quantitative evaluation of the local risk of urban rail transit passenger flow, and providing a theoretical basis for station passenger flow risk classification warning and the formulation of passenger flow control measures.

[0041] Additional advantages of the present invention will be more clearly given in the following description or learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 This is a flowchart for implementing the method for identifying local risk of passenger flow in urban rail transit stations based on particle flow dynamic simulation according to an embodiment of the present invention.

[0044] Figure 2 This is a schematic diagram of the numbering of the passenger flow risk matrix of an urban rail transit station according to an embodiment of the present invention.

[0045] Figure 3 This is a passenger flow risk matrix diagram of an urban rail transit station according to an embodiment of the present invention.

[0046] Figure 4 This is a schematic diagram of a bottleneck area scenario in a corridor according to an embodiment of the present invention.

[0047] Figure 5 This is a screenshot of the passenger flow simulation process in the bottleneck area of ​​the corridor according to an embodiment of the present invention.

[0048] Figure 6 This is a flow chart of the algorithm for the local risk identification model of passenger flow in urban rail transit stations according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention.

[0050] Those skilled in the art will understand that unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.

[0051] It should also be understood that terms, such as those defined in commonly used dictionaries, should be understood to have a meaning consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless otherwise defined herein.

[0052] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a," "an," "said," and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the stated features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, and / or groups thereof.

[0053] In the description of this specification, reference to the terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means 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. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless otherwise inconsistent.

[0054] To facilitate understanding of the present invention, the present invention is further explained below with reference to specific embodiments in conjunction with the accompanying drawings. However, the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0055] Those skilled in the art should understand that the drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily necessary for implementing the present invention.

[0056] Example 1

[0057] This embodiment 1 provides a system for identifying passenger flow risks in urban rail stations based on particle flow dynamic simulation, including:

[0058] The first construction module is used to classify the passenger flow risk using squeezing force and duration as passenger flow risk evaluation indicators, and to construct a passenger flow risk matrix for urban rail stations;

[0059] The simulation module is used to determine the passenger physical model and force model based on the geometric data of the area to be simulated, and use the particle flow software PFC to build a dynamic simulation model of local passenger flow risks in the station to perform dynamic simulation of local passenger flow risks in the area to be simulated;

[0060] The second construction module is used to establish the relationship between the ratio of the maximum squeezing pressure duration within the passenger flow to the squeezing pressure duration of each level interval and the macro passenger flow parameters of the initial passenger flow density and initial passenger flow speed based on the simulation results;

[0061] The calculation module is used to calculate the squeezing force and duration according to the passenger flow data and the fitting function of the simulation results in the corresponding scenario, and determine its risk level by comparing it with the risk matrix.

[0062] In this embodiment 1, the above-mentioned system is used to implement a method for identifying passenger flow risks in urban rail stations based on particle flow dynamic simulation, including:

[0063] Using squeeze force and duration as passenger flow risk evaluation indicators, we classify the levels and construct a passenger flow risk matrix for urban rail stations.

[0064] According to the geometric data of the area to be simulated, the passenger physical model and force model are determined, and the particle flow software PFC is used to build a dynamic simulation model of the local risk of passenger flow in the station, and the dynamic simulation of the local risk of passenger flow in the area to be simulated is carried out;

[0065] Based on the simulation results, the relationship between the ratio of the duration of the maximum squeezing pressure within the passenger flow to the duration of the squeezing pressure in each level interval and the macroscopic passenger flow parameters of the initial passenger flow density and initial passenger flow speed is established;

[0066] According to the passenger flow data, the squeeze force and duration are calculated by fitting the function according to the simulation results in the corresponding scenario, and the risk level is determined by comparing it with the risk matrix.

[0067] Using squeeze force and duration as passenger flow risk evaluation indicators, we categorize them into different levels and construct a passenger flow risk matrix for urban rail stations, including:

[0068] Determine the extrusion pressure level division interval and the duration division interval;

[0069] The passenger flow risk measurement of urban rail transit stations is determined by the relationship between risk and the two measurement indicators of squeeze pressure and duration, which is expressed as follows:

[0070] R = f(F, t);

[0071] Where R is the passenger flow risk; F is the internal squeezing force of the passenger flow; t is the duration of the squeezing force;

[0072] Determine the number of passenger flow risk levels N for urban rail transit stations based on actual needs;

[0073] The risk matrix cells are graded and the urban rail transit passenger flow risk matrix is ​​constructed.

[0074] The risk matrix cells are graded to construct the urban rail transit passenger flow risk matrix, including:

[0075] All cells of the risk matrix are numbered in sequence, the probability comparison values ​​between each two cells are calculated, and then the similarity measurement values ​​between cells are calculated to obtain the initial similarity matrix between cells;

[0076] Get the minimum value of the similarity measure between cells and cluster the two corresponding cells into a new class;

[0077] Get the minimum value in the initial similarity matrix in turn. If the corresponding cell has been merged into one category, continue to get the next minimum value; if the corresponding cell belongs to two categories, calculate the inter-class similarity measure and repeat the calculation until it is greater than the previously obtained inter-class similarity measure value, then the corresponding two categories are clustered into a new category.

[0078] Obtain the geometric data of the area to be simulated, determine the passenger physical model and force model, and use the particle flow software PFC to build a dynamic simulation model of the local risk of station passenger flow, including:

[0079] Obtain geometric data of the area to be simulated;

[0080] Construct a passenger physical entity model, including the passenger individual space model and passenger contact model;

[0081] Analyze the passenger force and motion characteristics according to the simulation scenario and build a passenger motion model;

[0082] The above steps are implemented using command statements in the particle flow software PFC and the built-in FISH language, including the generation of wall boundaries, passenger attribute settings and passenger force function definitions, and the calculation of the resultant force acting on passengers. Passengers begin to move and come into contact under the action of various forces, thus realizing the dynamic simulation of local passenger flow risks in urban rail transit stations.

[0083] By changing the parameters such as the initial density and initial speed of the passenger flow in the simulation scenario, the values ​​of the internal squeezing pressure of the passenger flow changing with time under different initial conditions are recorded, and the relationship between the maximum squeezing pressure and the duration ratio of the squeezing pressure in each level interval and the macroscopic passenger flow parameters of the initial density and initial speed of the passenger flow are established through function fitting.

[0084] Obtain passenger flow data, calculate the squeeze force and duration based on the simulation results of the corresponding scenario using a fitting function, and determine the risk level by comparing it with the risk matrix, including:

[0085] Obtain the corresponding basic passenger flow data required in the station area at a certain time interval;

[0086] The maximum squeezing force of passenger flow during the interval is calculated by fitting a function. If the maximum squeezing force exceeds the maximum squeezing force interval, it is directly determined as the highest risk level. If it does not exceed the maximum squeezing force interval, the duration of the time period less than the force and the duration of the force's subordinate interval is calculated based on the fitted squeezing force duration ratio function of each interval.

[0087] Calculate the cumulative duration of each extrusion pressure interval during the statistical period, and obtain the corresponding risk level from the corresponding risk matrix. Finally, the maximum risk level shall be taken as the standard.

[0088] Example 2

[0089] In this embodiment 2, a method for identifying local risk of passenger flow in urban rail stations based on particle flow dynamic simulation is provided, which specifically includes the following steps:

[0090] S1: Using squeeze force and duration as passenger flow risk evaluation indicators, we classify the levels and construct a passenger flow risk matrix for urban rail stations;

[0091] S2: Obtain the geometric data of the area to be simulated, determine the passenger physical model and force model, and use the particle flow software PFC to build a dynamic simulation model of the local risk of station passenger flow;

[0092] S3: Based on the simulation results, the relationship between the maximum squeezing pressure inside the passenger flow and the duration ratio of the squeezing pressure in each level interval and the macroscopic passenger flow parameters such as the initial passenger flow density and initial speed is established;

[0093] S4: Obtain passenger flow data, calculate the squeeze force and duration based on the simulation results fitting function under the corresponding scenario, and determine its risk level by comparing it with the risk matrix.

[0094] Said S1 comprises:

[0095] S11: Determine the extrusion pressure level division interval and the duration division interval;

[0096] S12: Determine the passenger flow risk measurement for urban rail transit stations, that is, the relationship between risk and the two measurement indicators of squeeze pressure and duration, which is expressed as follows:

[0097] R=f(F,t) (1)

[0098] Among them, R is the passenger flow risk; F is the internal squeezing pressure of the passenger flow; and t is the duration of the squeezing pressure.

[0099] S13: Determine the number of risk classification levels N for passenger flow at urban rail transit stations based on actual needs;

[0100] S14: Classify the risk matrix cells and construct the urban rail transit passenger flow risk matrix.

[0101] The S14 specifically includes:

[0102] S141: All cells of the risk matrix are numbered in sequence, the probability comparison values ​​between each two cells are calculated, and then the similarity measurement value between the cells is calculated according to the following formula to obtain the initial similarity matrix between the cells.

[0103] d AB =|P(R a >R b )-0.5|,(a∈A,b∈B) (2)

[0104] Among them, the probability comparison value between cells P(Ra >R b ) is obtained by Monte Carlo simulation. First, define the counting variable count, then randomly generate points a and b in two comparison cells A and B, and compare their risk values ​​R according to the corresponding risk measures. If R a >R b , then set count = count + 1, otherwise it remains unchanged. m Then calculate count / N m The value of is the probability that the risk level of cell A is higher than that of cell B.

[0105] S142: Obtain the minimum value of the similarity measure between cells and cluster the two corresponding cells into a new class.

[0106] S143: Obtain the minimum value in the initial similarity matrix in sequence. If the corresponding cells have been merged into one category, continue to obtain the next minimum value; if the corresponding cells belong to two categories respectively, calculate the inter-class similarity measure according to the following formula.

[0107]

[0108] Where n is the number of cells in class A, and m is the number of cells in class B.

[0109] S144: Repeat S143. Each time a new inter-class similarity measure is obtained, it is compared with the previously obtained inter-class similarity measure. If the previously obtained inter-class similarity measure is smaller, the two corresponding classes are clustered into a new class; otherwise, S144 is repeated. This step is mainly to avoid the problem of obtaining clustering priority when the similarity between cells is strong but the similarity between their classes is weak.

[0110] S145: Based on the clustering result generated by S144, S143 and S144 are repeated until all cells are clustered into N levels.

[0111] The S2 specifically includes:

[0112] S21: Obtain geometric data of the area to be simulated.

[0113] S22: Construct a passenger physical entity model, including a passenger individual space model and a passenger contact model.

[0114] Among them, the passenger individual space model is related to the passenger space demand. The present invention abstracts the passengers as circular particles on a two-dimensional plane with the maximum shoulder width of the passengers as the diameter; secondly, the particle contact theory is introduced, and the Hertz-Mindlin contact model is selected to characterize the contact behavior between passengers.

[0115] S23: Analyze the passenger force and motion characteristics based on the scenario to be simulated, and construct a passenger motion model.

[0116] Among them, the passenger's movement process is divided into active behavior and passive behavior. Active behaviors such as driving, braking, avoidance, and overtaking are realized through the improved social force model, and passive behaviors such as squeezing and friction between passengers are realized through the discrete contact model.

[0117] S24: The above steps are implemented using the command statements in the particle flow software PFC and the built-in FISH language, including the generation of wall boundaries, the setting of passenger attributes and the definition of passenger force functions, the calculation of the resultant force acting on passengers, and the movement and contact of passengers under the action of various forces, thus realizing the dynamic simulation of local passenger flow risks in urban rail transit stations.

[0118] The S3 specifically includes:

[0119] By changing the initial density, initial speed and other parameters of the passenger flow in the simulation scenario, the values ​​of the internal squeezing pressure of the passenger flow changing with time under different initial conditions are recorded, and the relationship between the maximum squeezing pressure and the duration ratio of the squeezing pressure in each level interval and the initial density, speed and other parameters of the passenger flow are established through function fitting.

[0120] (1) Relationship between maximum squeezing pressure and initial passenger flow parameters

[0121] The maximum value of the squeezing force data in each set of simulation data and the corresponding initial passenger flow speed, initial density and other values ​​are obtained, and function fitting is performed on the recorded multiple sets of data to obtain the relationship between the maximum squeezing force and the initial passenger flow speed and initial density.

[0122] (2) Relationship between the duration ratio of squeeze pressure in each level interval and initial passenger flow parameters

[0123] According to the squeezing pressure levels divided in S11, the simulation data are statistically analyzed. The proportion of the cumulative time that the squeezing pressure is in each level interval in each set of simulation data to the total time is calculated and recorded. Function fitting is performed on the recorded multiple sets of data to obtain the relationship between the proportion of the squeezing pressure duration in each level interval and the initial passenger flow speed and initial density.

[0124] The S4 specifically includes:

[0125] S41: Obtain the corresponding basic passenger flow data required by the station area at a certain time interval Vt.

[0126] S42: Calculate the maximum passenger squeeze force F during the Vt period by fitting the function max If the maximum passenger flow squeeze pressure exceeds the maximum squeeze pressure interval, it is directly determined as the highest risk level; if it does not exceed, the duration of the time period less than the force and the duration of the force's subordinate interval is calculated based on the fitted squeeze pressure duration ratio function of each interval.

[0127] S43: Calculate the cumulative duration of each extrusion pressure interval during the statistical period, and obtain the corresponding risk level corresponding to the risk matrix respectively, and finally take the maximum risk level as the standard.

[0128] The method described in Example 2 introduces granular flow theory from the perspective of crowd forces and utilizes the professional granular flow software PFC2D to dynamically simulate local risks in station passenger flow. This establishes a relationship between microscopic passenger contact forces and macroscopic traffic characteristic parameters. Furthermore, taking into account the dynamic characteristics of passenger flow, a relationship is established between the duration ratio of squeeze pressure in each level interval and macroscopic traffic characteristic parameters. Using squeeze pressure and duration as passenger flow risk assessment indicators, passenger flow risk is graded based on a passenger flow risk matrix constructed using hierarchical clustering. This effectively achieves a quantitative assessment of local risks in urban rail transit passenger flow and provides a theoretical basis for station passenger flow risk grading and early warning, as well as the formulation of passenger flow control measures.

[0129] Example 3

[0130] like Figures 1 to 6 As shown, this embodiment 3 provides a method for identifying local risk of passenger flow in urban rail stations based on particle flow dynamic simulation, see Figure 1 , the method comprises the following steps:

[0131] S1: Using squeeze force and duration as passenger flow risk evaluation indicators, we classify the levels and construct a passenger flow risk matrix for urban rail stations;

[0132] S2: Obtain the geometric data of the area to be simulated, determine the passenger physical model and force model, and use the particle flow software PFC to build a dynamic simulation model of the local risk of station passenger flow;

[0133] S3: Based on the simulation results, the relationship between the maximum squeezing pressure inside the passenger flow and the duration ratio of the squeezing pressure in each level interval and the macroscopic passenger flow parameters such as the initial passenger flow density and initial speed is established;

[0134] S4: Obtain passenger flow data, calculate the squeeze force and duration based on the simulation results fitting function under the corresponding scenario, and determine its risk level by comparing it with the risk matrix.

[0135] Said S1 specifically includes:

[0136] (1) Set the extrusion pressure level range to (0,250], (250,400], (400,700], (700,1000], the unit is N; the duration level range is (0,5], (5,10], (10,15], (15,20], the unit is min;

[0137] (2) The passenger flow risk measurement of urban rail transit stations is R: [ln(F+1660.03)] 3.17 ×[ln(t+7.28)] 0.43

[0138] (3) The number of passenger flow risk classification levels for urban rail transit stations is N = 4;

[0139] (4) Classify the risk matrix cells and construct the urban rail transit passenger flow risk matrix. The specific steps are as follows:

[0140] Step 1: Number all cells from shorter time to longer time and smaller squeeze force to larger squeeze force. The risk matrix is ​​a 4×4 matrix, so the initial cell sample size is 16. The number of the risk matrix to be designed is as follows: Figure 2 shown.

[0141] Step 2: Calculate the probability comparison value of each cell by Monte Carlo simulation method, subtract 0.5 from it and take the absolute value to obtain the risk matrix cell similarity matrix. Some data are shown in Table 1.

[0142] Table 1 Cell similarity matrix (partial)

[0143]

[0144]

[0145] Step 3: Select the cells corresponding to the minimum value of the similarity measure in turn, and continue clustering by comparing the similarity between cells with the similarity between classes until four classes are clustered. The clustering process is shown in the following table:

[0146] Table 2 Clustering process of passenger flow risk matrix of urban rail stations

[0147]

[0148] The final four-level risk matrix obtained by clustering is as follows Figure 3 As shown, the numbers in the cells are the corresponding risk level values:

[0149] The S2 specifically includes:

[0150] (1) This embodiment selects the bottleneck area of ​​the corridor and escalator of Xi'erqi subway station for dynamic simulation of passenger flow risk. The geometric data of this area is as follows: Figure 4 shown.

[0151] (2) A passenger physical entity model is constructed, and the passengers are abstracted as circular particles on a two-dimensional plane with the maximum shoulder width of the passengers as the diameter; secondly, the particle contact theory is introduced, and the Hertz-Mindlin contact model is selected to characterize the contact behavior between passengers.

[0152] (3) In this simulation scenario, the passenger force and motion characteristics are as follows:

[0153] 1) This scenario is a one-way flow scenario. Passengers can choose to leave from the corridor by taking the escalator or the stairs. Passengers tend to take the escalator to transfer. Therefore, it is assumed that all passengers initially choose to take the escalator. When the passenger flow density within the queue area at the escalator entrance exceeds a certain value, passengers closer to the stairs will choose to leave via the stairs.

[0154] 2) Passengers in the corridor and descending staircase areas are subject to self-driven forces, inter-passenger forces, and passenger-wall forces. These inter-passenger forces and passenger-wall forces include repulsive and contact forces. The self-driven and repulsive force functions are defined using the FISH language within PFC, while contact forces are calculated using the built-in contact model in PFC.

[0155] The formulas for calculating the self-driving force and repulsive force are as follows:

[0156] Self-propulsion: When the passenger cannot clearly see the target location, the expected speed is affected by the actual speed of the passenger in the desired direction of movement. The calculation formula is as follows.

[0157]

[0158]

[0159]

[0160]

[0161] in, Individual driving force for passengers; m i is the equivalent mass of passenger i; τ i is the reaction time of passenger i; is the expected speed of passenger i at time t after being affected by other passengers; is the actual passenger velocity vector at time t; V i 0 is the initial expected speed of passenger i; is the expected movement direction of the passenger at time t; is the expected target location of passenger i; is the current actual location of passenger i; is the average speed of other passengers in the direction expected by passenger i. The angle between the expected direction of passenger i and the direction of the line connecting passenger i to other passengers is used to determine whether other passengers are in the direction expected by passenger i. p is the passenger conformity coefficient, p∈[0,1], where p=0 means that the passenger's expected speed is not affected by surrounding passengers. The larger the p value, the more obvious the conformity mentality. N jIt represents the number of other passengers within the affected range after the improvement of passenger i. If it is zero, it means that the passenger can clearly see the target point and the expected speed is the initial expected speed. If it is not zero, it means that the passenger's expected speed is affected by other passengers within the affected range. The value is p0.

[0162] Repulsive force between passengers: Taking into account the distance between passengers, their positional relationships, and their relative speeds, the improved expression for the repulsive force exerted on passenger i by passenger j is shown below.

[0163]

[0164]

[0165]

[0166]

[0167] in, is the repulsive force of passenger j on passenger i; A is the force intensity; B is the action range constant; r i 、r j are the particle radii of passenger i and passenger j respectively; d ij is the distance between the centroids of passengers i and j; is the direction of the repulsive force, the unit vector pointing from passenger j to passenger i; is the angle between the line connecting the centers of passengers i and j and the actual velocity direction of passenger i; λ is the anisotropic form factor, which ranges from [0,1]; is the normal velocity difference between passenger i and passenger j, is the tangential velocity difference between passenger i and passenger j, in is perpendicular to The unit vector of is the maximum speed difference between passenger i and passenger j, which is related to the maximum speed of the passengers. s n 、s t are the sensitivity coefficients of normal velocity difference and tangential velocity difference respectively; l is the spatial distance required by the passenger psychologically.

[0168] Repulsive force between passengers and walls: When the distance between passengers and walls is less than the psychologically required space and a collision is likely, passengers will take measures such as deceleration and braking to maintain the distance from the wall. In other words, the wall exerts a repulsive force on the passengers. The calculation formula is shown below.

[0169]

[0170]

[0171]

[0172] Among them, f iw (t) pr is the repulsive force between the passenger and the wall; A w B is the intensity of the psychological repulsion between the guest and the wall; w is the range of repulsive force between passengers and walls; r i is the radius of passenger i; d iw is the shortest distance between passenger i and the wall; Vv is the direction of the repulsive force between passenger i and the wall, which is a unit vector pointing from the wall to passenger i and perpendicular to the wall. iw is the relative velocity difference between the passenger and the wall, is the relative velocity difference between the passenger and the wall, i.e. the maximum velocity of the passenger; s w is the rate difference sensitivity coefficient; l w δ is the psychological distance between the passenger and the wall; iw It is the angle between the passenger's velocity direction and the direction the passenger points to the wall. If its cosine value is greater than 0, it means that the passenger has a tendency to move toward the wall, which may produce a repulsive force. Otherwise, there is no repulsive force.

[0173] The net force acting on the passenger is:

[0174]

[0175] Passengers are displaced under the action of the above-mentioned combined force, realizing dynamic simulation of passenger flow, such as Figure 6 shown.

[0176] 3) Passengers in the escalator area move forward at the same speed as the escalator without applying any external force;

[0177] 4) The passengers in the descending staircase area and the escalator area do not affect each other. That is, when calculating the repulsive force between passengers and the average speed of surrounding passengers in the descending staircase area, the passengers in the escalator area are not considered.

[0178] The S3 specifically includes:

[0179] By changing the initial density and initial speed of the passenger flow in the corridor area, the time-varying values ​​of the passenger flow internal squeezing pressure at the bottleneck of the corridor and escalator under different initial conditions were recorded. The relationship between the maximum squeezing pressure and the duration ratio of the squeezing pressure in each level interval and the initial density and speed of the passenger flow was established through function fitting. Figure 4 As shown in the figure, the initial passenger flow density is set by generating different numbers of particles; the initial passenger velocity direction is the initial expected movement direction of each passenger. Assuming that the initial velocity of each passenger is the same, that is, the initial passenger flow velocity, the initial passenger flow velocity is set by giving different values.

[0180] (1) Relationship between maximum squeezing pressure and initial passenger flow parameters

[0181] The maximum value of the squeezing force data and the corresponding initial passenger flow speed, initial density and other values ​​in each set of simulation data are obtained. Some simulation output results are shown in Table 3:

[0182] Table 3. Simulation results of maximum squeeze force under different initial passenger flow conditions (partial)

[0183]

[0184] By fitting multiple sets of recorded data, we can obtain the relationship between the maximum squeezing force in the bottleneck area of ​​the corridor and the initial passenger flow speed and initial density:

[0185]

[0186] Goodness of fit R 2 The value is 0.961467, which has a good fit and can be used to describe the relationship between the maximum extrusion pressure, initial density, and velocity in the corridor scenario.

[0187] (2) Relationship between the duration ratio of squeeze pressure in each level interval and initial passenger flow parameters

[0188] According to the divided extrusion pressure levels, the simulation data are statistically analyzed, and the proportion of the cumulative time of the extrusion pressure in each level interval to the total time in each set of simulation data is calculated and recorded, as shown in Table 4:

[0189] Table 4 Statistics on the duration of squeeze pressure in the bottleneck area of ​​the corridor

[0190]

[0191] Function fitting was performed on multiple sets of recorded data to obtain the relationship between the duration ratio of the squeezing pressure in each level interval of the bottleneck area of ​​the corridor and the initial speed and initial density of the passenger flow.

[0192] P F∈(0,250] =0.9853-0.0602ρ0ln(ρ0)-0.0516v0ln(v0) (17)

[0193]

[0194] P F∈(400,700] =-0.0226+0.0018ρ0 3 +0.0119v0 2.5 (19)

[0195]

[0196] like Figure 6 As shown, the S4 specifically includes:

[0197] Step 1: Obtain the initial density and initial speed data of passenger flow in the corridor area at a certain time interval Vt.

[0198] Step 2: Calculate the maximum passenger squeeze force F for each Vt period max If the maximum passenger flow squeeze pressure exceeds the maximum squeeze pressure interval range, it is directly determined as the highest risk level; if it does not exceed, the duration of the time period less than the force and the force's subordinate interval is calculated based on the fitted squeeze pressure duration ratio function of each interval. The calculation results of the bottleneck area risk assessment index are shown in Table 5:

[0199] Table 5 Calculation results of passenger flow risk evaluation index in bottleneck area

[0200]

[0201] Step 3: Calculate the cumulative duration of each extrusion pressure interval during the statistical period, and obtain the corresponding risk level based on the corresponding risk matrix. The results are shown in Figure 6.

[0202] Table 6 Calculation results of passenger flow risk level in bottleneck area

[0203]

[0204] Taking the maximum risk level as the standard, the passenger flow risk level in the bottleneck area during this period is 2.

[0205] In summary, this embodiment uses squeeze pressure and duration as passenger flow risk indicators and constructs a passenger flow risk matrix for urban rail transit stations based on hierarchical clustering. Secondly, the granular flow simulation software PFC is used to implement dynamic simulation of passenger flow risk in a one-way bottleneck area scenario. Based on the simulation results, the relationship between the maximum squeeze pressure and the duration ratio of each squeeze pressure interval and the initial passenger flow density and speed is established. Finally, passenger flow data for the bottleneck area during a certain period of time is obtained. Based on the simulation results, the fitting function and the passenger flow risk matrix are used, and the algorithm for calculating the local risk level of urban rail transit passenger flow is used to determine the risk level of the bottleneck area during that period of time. Analysis of case studies shows that this method has certain practical value and can provide a quantitative basis for identifying local risk of passenger flow in urban rail transit stations.

[0206] Example 4

[0207] Embodiment 4 of the present invention provides a non-transitory computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, a method for identifying passenger flow risks in urban rail stations based on granular flow dynamic simulation is implemented. The method includes:

[0208] Using squeeze force and duration as passenger flow risk evaluation indicators, we classify the levels and construct a passenger flow risk matrix for urban rail stations.

[0209] According to the geometric data of the area to be simulated, the passenger physical model and force model are determined, and the particle flow software PFC is used to build a dynamic simulation model of the local risk of passenger flow in the station, and the dynamic simulation of the local risk of passenger flow in the area to be simulated is carried out;

[0210] Based on the simulation results, the relationship between the ratio of the duration of the maximum squeezing pressure within the passenger flow to the duration of the squeezing pressure in each level interval and the macroscopic passenger flow parameters of the initial passenger flow density and initial passenger flow speed is established;

[0211] According to the passenger flow data, the squeeze force and duration are calculated by fitting the function according to the simulation results in the corresponding scenario, and the risk level is determined by comparing it with the risk matrix.

[0212] Example 5

[0213] Embodiment 5 of the present invention provides a computer program (product), including a computer program. When the computer program is executed on one or more processors, the computer program is used to implement a method for identifying passenger flow risks in urban rail stations based on particle flow dynamic simulation. The method includes:

[0214] Using squeeze force and duration as passenger flow risk evaluation indicators, we classify the levels and construct a passenger flow risk matrix for urban rail stations.

[0215] According to the geometric data of the area to be simulated, the passenger physical model and force model are determined, and the particle flow software PFC is used to build a dynamic simulation model of the local risk of passenger flow in the station, and the dynamic simulation of the local risk of passenger flow in the area to be simulated is carried out;

[0216] Based on the simulation results, the relationship between the ratio of the duration of the maximum squeezing pressure within the passenger flow to the duration of the squeezing pressure in each level interval and the macroscopic passenger flow parameters of the initial passenger flow density and initial passenger flow speed is established;

[0217] According to the passenger flow data, the squeeze force and duration are calculated by fitting the function according to the simulation results in the corresponding scenario, and the risk level is determined by comparing it with the risk matrix.

[0218] Example 6

[0219] Embodiment 6 of the present invention provides an electronic device, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing a method for identifying passenger flow risks in urban rail stations based on particle flow dynamic simulation. The method includes:

[0220] Using squeeze force and duration as passenger flow risk evaluation indicators, we classify the levels and construct a passenger flow risk matrix for urban rail stations.

[0221] According to the geometric data of the area to be simulated, the passenger physical model and force model are determined, and the particle flow software PFC is used to build a dynamic simulation model of the local risk of passenger flow in the station, and the dynamic simulation of the local risk of passenger flow in the area to be simulated is carried out;

[0222] Based on the simulation results, the relationship between the ratio of the duration of the maximum squeezing pressure within the passenger flow to the duration of the squeezing pressure in each level interval and the macroscopic passenger flow parameters of the initial passenger flow density and initial passenger flow speed is established;

[0223] According to the passenger flow data, the squeeze force and duration are calculated by fitting the function according to the simulation results in the corresponding scenario, and the risk level is determined by comparing it with the risk matrix.

[0224] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0225] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0226] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0227] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide the functions for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0228] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solutions disclosed in the present invention without the need for creative work should be included in the scope of protection of the present invention.

Claims

1. A method for identifying passenger flow risk in urban rail stations based on particle flow dynamic simulation, characterized in that: include: Taking squeeze pressure and duration as passenger flow risk evaluation indicators, a graded classification is performed to construct a passenger flow risk matrix for urban rail stations, including: numbering all cells of the risk matrix in sequence, calculating the probability comparison value between each two cells, and then calculating the similarity measure value between cells to obtain the initial similarity matrix between cells; obtaining the minimum value of the similarity measure between cells, and clustering the two corresponding cells into a new class; sequentially obtaining the minimum value in the initial similarity matrix, if the corresponding cell has been merged into one class, then continue to obtain the next minimum value; if the corresponding cell belongs to two classes respectively, then calculate the similarity measure between classes, and repeat the calculation until it is greater than the previous similarity measure value between classes, and then cluster the corresponding two classes into a new class; According to the geometric data of the area to be simulated, the passenger physical model and force model are determined, and the particle flow software PFC is used to build a dynamic simulation model of the local risk of passenger flow in the station, and the dynamic simulation of the local risk of passenger flow in the area to be simulated is carried out; Based on the simulation results, the relationship between the ratio of the duration of the maximum squeezing pressure within the passenger flow to the duration of the squeezing pressure in each level interval and the macroscopic passenger flow parameters of the initial passenger flow density and initial passenger flow speed is established; According to the passenger flow data, the squeeze force and duration are calculated by fitting the function according to the simulation results in the corresponding scenario, and the risk level is determined by comparing it with the risk matrix.

2. The urban rail station passenger flow risk identification method based on particle flow dynamic simulation according to claim 1 is characterized in that: Using squeeze force and duration as passenger flow risk evaluation indicators, we categorize them into different levels and construct a passenger flow risk matrix for urban rail stations, including: Determine the extrusion pressure level division interval and the duration division interval; The passenger flow risk measurement of urban rail transit stations is determined by the relationship between risk and the two measurement indicators of squeeze pressure and duration, which is expressed as follows: R = f(F, t); Where R is the passenger flow risk; F is the internal squeezing force of the passenger flow; t is the duration of the squeezing force; Determine the number of passenger flow risk levels N for urban rail transit stations based on actual needs; The risk matrix cells are graded and the urban rail transit passenger flow risk matrix is ​​constructed.

3. The urban rail station passenger flow risk identification method based on particle flow dynamic simulation according to claim 1 is characterized in that: Obtain the geometric data of the area to be simulated, determine the passenger physical model and force model, and use the particle flow software PFC to build a dynamic simulation model of the local risk of station passenger flow, including: Obtain geometric data of the area to be simulated; Construct a passenger physical entity model, including the passenger individual space model and passenger contact model; Analyze the passenger force and motion characteristics according to the simulation scenario and build a passenger motion model; The above steps are implemented using command statements in the particle flow software PFC and the built-in FISH language, including the generation of wall boundaries, passenger attribute settings and passenger force function definitions, and the calculation of the resultant force acting on passengers. Passengers begin to move and come into contact under the action of various forces, thus realizing the dynamic simulation of local passenger flow risks in urban rail transit stations.

4. The urban rail station passenger flow risk identification method based on particle flow dynamic simulation according to claim 3 is characterized in that: By changing the initial density and initial speed parameters of the passenger flow in the simulation scenario, the values ​​of the internal squeezing pressure of the passenger flow changing with time under different initial conditions are recorded, and the relationship between the maximum squeezing pressure and the duration ratio of the squeezing pressure in each level interval and the macroscopic passenger flow parameters of the initial density and initial speed of the passenger flow are established through function fitting.

5. The urban rail station passenger flow risk identification method based on particle flow dynamic simulation according to claim 3 is characterized in that: Obtain passenger flow data, calculate the squeeze force and duration based on the simulation results of the corresponding scenario using a fitting function, and determine the risk level by comparing it with the risk matrix, including: Obtain the corresponding basic passenger flow data required in the station area at a certain time interval; The maximum squeezing force of passenger flow during the interval is calculated by fitting a function. If the maximum squeezing force exceeds the maximum squeezing force interval, it is directly determined as the highest risk level. If it does not exceed the maximum squeezing force interval, the duration of the time period less than the force and the duration of the force's subordinate interval is calculated based on the fitted squeezing force duration ratio function of each interval. Calculate the cumulative duration of each extrusion pressure interval during the statistical period, and obtain the corresponding risk level from the corresponding risk matrix. Finally, the maximum risk level shall be taken as the standard.

6. A passenger flow risk identification system for urban rail stations based on particle flow dynamic simulation, characterized in that: include: The first construction module is used to perform grade classification based on squeezing force and duration as passenger flow risk evaluation indicators, and construct a passenger flow risk matrix for urban rail stations. The module includes: sequentially numbering all cells in the risk matrix, calculating the probability comparison value between each cell, and then calculating the similarity measure value between cells to obtain an initial similarity matrix between cells; obtaining the minimum value of the similarity measure between cells, and clustering the two corresponding cells into a new class; sequentially obtaining the minimum value in the initial similarity matrix, and if the corresponding cells have been merged into one class, continuing to obtain the next minimum value; if the corresponding cells belong to two classes respectively, calculating the similarity measure between classes, and repeating the calculation until it is greater than the previously obtained similarity measure between classes, and then clustering the corresponding two classes into a new class; The simulation module is used to determine the passenger physical model and force model based on the geometric data of the area to be simulated, and use the particle flow software PFC to build a dynamic simulation model of local passenger flow risks in the station to perform dynamic simulation of local passenger flow risks in the area to be simulated; The second construction module is used to establish the relationship between the ratio of the maximum squeezing pressure duration within the passenger flow to the squeezing pressure duration of each level interval and the macro passenger flow parameters of the initial passenger flow density and initial passenger flow speed based on the simulation results; The calculation module is used to calculate the squeezing force and duration according to the passenger flow data and the fitting function of the simulation results in the corresponding scenario, and determine its risk level by comparing it with the risk matrix.

7. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the urban rail station passenger flow risk identification method based on particle flow dynamic simulation as described in any one of claims 1 to 5 is implemented.

8. A computer program product, characterized in that It comprises a computer program, which, when running on one or more processors, is used to implement the urban rail station passenger flow risk identification method based on particle flow dynamic simulation as described in any one of claims 1 to 5.

9. An electronic device, characterized in that: include: A processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to execute instructions for implementing the urban rail station passenger flow risk identification method based on particle flow dynamic simulation as described in any one of claims 1 to 5.

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