Simulation method and device for subway passenger behaviors and electronic equipment

Abstract

The invention provides a simulation method and device for subway passenger behaviors and electronic equipment, and belongs to the field of data processing.The method comprises the steps that grid processing is conducted on a subway model scene, and a grid map is obtained; determining a target moving path in the grid map according to the initial position of a target passenger and a preset destination; obtaining a current state of a target passenger, wherein the current state is used for indicating a riding process; determining a behavior decision of the target passenger through a preset behavior tree according to the current state, wherein the behavior decision is used for indicating the next state of the target passenger; and simulating behaviors of the target passenger according to the behavior decision, the target moving path and a social force model. And simulation precision and authenticity can be improved.

Description

Technical Field

[0001] This application belongs to the field of data processing, and specifically relates to a simulation method, device, and electronic device for subway passenger behavior. Background Technology

[0002] With the acceleration of global urbanization, subways, as the core carrier of urban rail transit, bear increasing passenger transport pressure. As highly densely populated areas, subway stations' operational efficiency, safety, and passenger experience directly affect the stability of the urban transportation system. Especially during peak hours, complex passenger behaviors, such as route selection, yielding, queuing, and congestion, can lead to safety hazards and operational bottlenecks. Therefore, accurately predicting and simulating passenger behavior through simulation technology is of great value for optimizing subway station design, developing emergency evacuation plans, and managing passenger flow.

[0003] Current simulation methods often focus on modeling single behavioral dimensions, while passenger behavior in subway scenarios is highly dynamic, multi-objective, and socially interactive, requiring consideration of both individual decision-making logic and group movement patterns. Traditional methods struggle to balance simulation scalability, behavioral diversity, and computational efficiency, leading to discrepancies between simulation results and real-world scenarios. For example, passengers cannot adjust their priority based on real-time congestion information, limiting their practical application in engineering. Furthermore, existing methods rely on fixed parameters or probability distributions to generate behavior, failing to reflect passenger heterogeneity. Group behavior coupling effects, such as herd mentality and panic propagation, are not adequately modeled, resulting in distorted simulation results for key phenomena like congestion propagation and bottleneck formation. Summary of the Invention

[0004] This application provides a simulation method, device, and electronic equipment for subway passenger behavior, offering a hybrid simulation framework that overcomes the limitations of a single model. While ensuring computational efficiency, it achieves high-fidelity simulation of the passenger's "decision-action-environment" closed loop. Simultaneously, it unifies intelligent decision-driven processes with group dynamics evolution to address the high-density, high-dynamic subway operation scenarios.

[0005] This application provides a simulation method for subway passenger behavior, including: The subway model scene is processed into a grid to obtain a grid map; The target passenger's movement path is determined in the grid map based on the passenger's initial location and preset destination; Obtain the current status of the target passenger, which is used to indicate the boarding process; Based on the current state, the behavioral decision of the target passenger is determined through a preset behavior tree, and the behavioral decision is used to indicate the next state of the target passenger; The behavior of the target passenger is simulated based on the behavioral decision, the target movement path, and the social force model.

[0006] According to the simulation method for subway passenger behavior provided in the embodiments of this application, the step of simulating the behavior of the target passenger based on the behavior decision, the target movement path, and the social force model includes: determining multiple arrival points from the grid map based on the target movement path; determining the arrival point farthest from the preset destination among the multiple arrival points as the target arrival point; and simulating the behavior of the target passenger based on the behavior decision, the target arrival point, and the social force model.

[0007] According to the simulation method for subway passenger behavior provided in the embodiments of this application, the simulation of the target passenger's behavior based on the behavior decision, the target arrival point, and the social force model includes: determining the movement process of the target passenger from its current location to the target arrival point using the social force model; determining the target passenger's behavior process based on the behavior decision; simulating the target passenger's behavior based on the movement process and the behavior process; determining the next arrival point of the target arrival point among a plurality of arrival points as the target arrival point, the plurality of arrival points being sorted according to their distance from the preset destination, with the farther the distance from the preset destination, the higher the sorting; repeating the above actions until the target passenger reaches the preset destination.

[0008] According to the simulation method for subway passenger behavior provided in the embodiments of this application, the step of determining the movement process of the target passenger from the current position to the target arrival point through the social force model includes: determining the first interaction force between the target passenger and other passengers at the target arrival point; determining the second interaction force between the target passenger and an obstacle at the target arrival point; determining the self-driving force of the target passenger moving towards the target arrival point; and determining the movement process of the target passenger from the current position to the target arrival point based on the self-driving force, the first interaction force, and the second interaction force.

[0009] According to the simulation method for subway passenger behavior provided in the embodiments of this application, the step of determining multiple arrival points from the grid map based on the target movement path includes: taking the centroid of each grid in the grid map as a movement node; determining the target grid corresponding to the target movement path; and determining the movement node corresponding to the target grid as the multiple arrival points.

[0010] According to the simulation method for subway passenger behavior provided in the embodiments of this application, determining the mobile node corresponding to the target grid as the plurality of arrival points includes: determining whether the target grid is an edge grid of the grid map; if it is the edge grid, then determining a point at a preset distance along the normal direction of the mobile node corresponding to the edge grid as the arrival point corresponding to the edge grid.

[0011] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the simulation method for subway passenger behavior as described above.

[0012] This application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the simulation method for subway passenger behavior as described above.

[0013] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the simulation method for subway passenger behavior as described above.

[0014] The simulation method, device, and electronic equipment for subway passenger behavior provided in this application first perform gridding processing on the subway model scene to obtain a grid map. Then, based on the initial position and preset destination of the target passenger, the target movement path is determined in the grid map. Next, the current state of the target passenger is obtained, which is used to indicate the boarding process. Then, based on the current state, the target passenger's behavioral decision is determined through a preset behavior tree. The behavioral decision is used to indicate the next state of the target passenger. Finally, the behavior of the target passenger is simulated based on the behavioral decision, the target movement path, and a social force model. This solution integrates a hybrid simulation framework of behavior tree and social force model, breaking through the limitations of a single model. While ensuring computational efficiency, it achieves high-fidelity simulation of the passenger's "decision-action-environment" closed loop. It also achieves the unification of intelligent decision-driven and group dynamics evolution to cope with high-density and high-dynamic subway operation scenarios. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1This is one of the flowcharts of a simulation method for subway passenger behavior provided in this application.

[0017] Figure 2 This is the architecture diagram of the behavior tree proposed in this application.

[0018] Figure 3 This is the second flowchart of a simulation method for subway passenger behavior provided in this application.

[0019] Figure 4 This is a block diagram of the functional units of a simulation device for subway passenger behavior provided in this application.

[0020] Figure 5 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0023] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0024] Current simulation methods often focus on modeling single behavioral dimensions, while passenger behavior in subway scenarios is highly dynamic, multi-objective, and socially interactive. Traditional methods struggle to balance simulation scalability, behavioral diversity, and computational efficiency, leading to discrepancies between simulation results and real-world scenarios.

[0025] To address the aforementioned problems, this application provides a simulation method, apparatus, and electronic device for subway passenger behavior. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0026] See also Figure 1 , Figure 1 This is one of the flowcharts illustrating a simulation method for subway passenger behavior provided in this application. The simulation method for subway passenger behavior includes the following steps.

[0027] S101, perform gridding on the subway model scene to obtain a grid map.

[0028] The process begins by scanning the subway model scene, followed by meshing the accessible parts of the scene. Specifically, triangulation or quadrilateral meshing can be used. This involves dividing the accessible areas into multiple small planar regions using triangles or quadrilaterals to accurately describe the shape and spatial location of objects.

[0029] S102, determine the target movement path in the grid map based on the target passenger's initial location and preset destination.

[0030] In simulations, the destination of the target passenger (i.e., the pedestrian in the following text) can be determined by setting hyperparameters. Alternatively, a preset destination can be automatically determined based on the target passenger's initial location. For example, if the target passenger's initial location is inside the subway, then the preset destination is the subway entrance; if the target passenger's initial location is the subway entrance, then the preset destination is the subway waiting area. This target path can be determined using A. Algorithm acquisition.

[0031] S103, Obtain the current status of the target passenger.

[0032] The current status is used to indicate the boarding process, including: purchasing tickets, going through the security checkpoint, swiping the turnstile card, and waiting for the subway.

[0033] S104, determine the target passenger's behavioral decision based on the current state using a preset behavior tree.

[0034] The behavioral decision is used to indicate the next state of the target passenger. See also... Figure 2 Based on the assessment of pedestrian states, the next action of the traveler can be determined. For example, the next action may be to move, exit the station, purchase a ticket, pass through the turnstile, or wait for a train. This behavior tree can realize hierarchical modeling of complex passenger intentions (such as adjusting goal priorities and responding to environmental feedback) through modular and scalable decision logic.

[0035] S105, Simulate the behavior of the target passenger based on the behavioral decision, the target movement path, and the social force model.

[0036] Specifically, the behavioral process of the target passenger can be determined based on behavioral decisions, and the movement process of the target passenger can be determined through a social force model based on the target movement path. The behavior of the target passenger can then be simulated based on the behavioral and movement processes. After simulating the behavior of the target passenger, a corresponding simulation animation can be generated.

[0037] As can be seen, in this embodiment, the subway model scene is first processed into a grid to obtain a grid map. Then, the target passenger's movement path is determined in the grid map based on the initial position and preset destination. Next, the target passenger's current state is obtained, which is used to indicate the boarding process. Then, based on the current state, the target passenger's behavioral decision is determined through a preset behavior tree. The behavioral decision is used to indicate the target passenger's next state. Finally, the target passenger's behavior is simulated based on the behavioral decision, the target movement path, and the social force model. This solution integrates a hybrid simulation framework of behavior tree and social force model, breaking through the limitations of a single model. While ensuring computational efficiency, it achieves high-fidelity simulation of the passenger's "decision-action-environment" closed loop. It also achieves the unification of intelligent decision-driven and group dynamics evolution to cope with high-density and high-dynamic subway operation scenarios.

[0038] In one possible embodiment, simulating the behavior of the target passenger using a social force model based on the behavioral decision and the target movement path includes: determining multiple arrival points from the grid map based on the target movement path; determining the arrival point farthest from the preset destination among the multiple arrival points as the target arrival point; and simulating the behavior of the target passenger using a social force model based on the behavioral decision and the target arrival point.

[0039] Each grid cell corresponds to a destination point. Since typical social force models follow the "shortest path" principle—meaning the driving force directly points to the target point—pedestrians don't "intelligently" avoid obstacles. Furthermore, when faced with L-shaped or similar obstacles, they may be "captured" by the concave surface and unable to reach their destination. Therefore, this scheme uses a navigation algorithm to calculate paths between the partitioned map grid and the origin and destination points, obtaining the reachable key points along the path, i.e., multiple destination points. Then, the movement process is simulated sequentially using the social force model based on these destination points.

[0040] As can be seen, in this embodiment, determining the target arrival point based on the navigation algorithm and then performing simulation through the social force model can improve the accuracy and feasibility of the simulation.

[0041] In one possible embodiment, simulating the behavior of the target passenger based on the behavioral decision, the target arrival point, and the social force model includes: determining the movement process of the target passenger from its current location to the target arrival point using the social force model; determining the behavior process of the target passenger based on the behavioral decision; simulating the behavior of the target passenger based on the movement process and the behavior process; determining the next arrival point among the plurality of arrival points as the target arrival point, wherein the plurality of arrival points are sorted according to their distance from the preset destination, with the farther the distance from the preset destination, the higher the ranking; and repeating the above actions until the target passenger reaches the preset destination.

[0042] This behavioral process can include, for example, the queuing process, ticket purchase process, and security check process of the target passenger. When acquiring the movement process, based on the determined target path, the distance of each arrival point on the target path from the preset destination can be sorted, and then the arrival point farthest from the preset destination can be taken as the target arrival point. Then, the movement process can be judged based on the target arrival point using a social force model.

[0043] In practical implementation, when the social force model assesses a user's movement from their current location to their target destination, it can generate temporary arrival points along the route. This allows for the simulation of pedestrian dynamics such as avoidance and following. Since a passenger's walking direction is determined by the attraction of the target point, the repulsion of surrounding pedestrians, and obstacle avoidance forces, the movement may not perfectly follow the trajectory of the target path. For example, if a user's current behavioral decision is to queue at the turnstile, with the target destination being inside the turnstile gate of a subway station, the social force model will determine the passenger's movement while queuing. This could involve the passenger moving to the end of the current queue or moving to another turnstile gate.

[0044] As can be seen, in this embodiment, the behavior state of subway passengers is determined by behavior trees, and the navigation-social force queuing model is used to drive the movement of passengers, thereby improving the realism of the simulation results.

[0045] In one possible embodiment, a first interaction force between the target passenger and other passengers at the target arrival point is determined; a second interaction force between the target passenger and an obstacle at the target arrival point is determined; a self-driving force for the target passenger to move toward the target arrival point is determined; and the movement process of the target passenger from the current position to the target arrival point is determined based on the self-driving force, the first interaction force, and the second interaction force.

[0046] Among them, the social force model is based on Newtonian dynamics and describes the force model of the interaction between pedestrians and surrounding pedestrians and the environment. The formula is as follows:

[0047] in, Self-driving force, that is, the self-driving force of a pedestrian moving towards their destination; For pedestrians The quality; and These are the interaction forces between pedestrians and the interaction forces between pedestrians and obstacles, respectively.

[0048]

[0049] in, Let be the pedestrian's expected velocity vector; This refers to the actual speed of the pedestrian. This is the relaxation time for the pedestrian to change from their actual speed to their desired speed.

[0050] As can be seen, in this embodiment, determining the destination described by the self-driving force in the social force model as the target arrival point can improve the accuracy and precision of the simulation.

[0051] In one possible embodiment, determining multiple arrival points from the grid map based on the target movement path includes: using the centroid of each grid in the grid map as a movement node; determining the target grid corresponding to the target movement path; and determining the movement node corresponding to the target grid as the multiple arrival points.

[0052] After triangulation and meshing, the centroid of the triangular mesh can be calculated and used as the moving node. When determining multiple arrival points, the moving nodes of all meshes covered by the target movement path can be used as arrival points. Alternatively, nodes on the same path in the same direction of movement that are at a predetermined distance can be used as arrival points. Specifically, multiple arrival points include nodes of the meshes corresponding to the starting point and the initial point in each direction of movement along the target movement path.

[0053] As can be seen, in this embodiment, determining the arrival point based on the centroid can reduce the amount of computation while improving accuracy and stability.

[0054] In one possible embodiment, determining the mobile node corresponding to the target grid as the plurality of arrival points includes: determining whether the target grid is an edge grid of the grid map; if it is an edge grid, then determining a point at a preset distance along the normal direction of the mobile node corresponding to the edge grid as the arrival point corresponding to the edge grid.

[0055] Specifically, for a destination point located at the edge of the map grid, it is extended by a preset distance along the normal direction of that point, and the corresponding destination point is determined as the extended point.

[0056] As can be seen, in this embodiment, moving the arrival point of the edge can prevent problems such as characters clipping through walls or getting stuck in corners, thus improving the realism of the simulation.

[0057] The following is combined with Figure 3 This application will now be introduced.

[0058] First, the first element in the current path point list is designated as the target point; that is, among the multiple arrival points corresponding to the target movement path, the one furthest from the preset destination is the target point. Then, the system determines if the target passenger will reach the target point. If not, the user's behavior decision is to move to the target point, and the target passenger moves accordingly. Upon reaching the target point, it is determined whether queuing is required. If not, the service is used, and a corresponding animation is played, depicting the user's behavior, such as swiping a card to exit a turnstile. After the animation finishes, the next element is designated as the target point, and the above steps are repeated. If queuing is required, it is determined whether there are similar service points nearby, such as other turnstiles. If similar service points exist, it is checked whether their queues are shorter. If so, that similar service point is designated as a temporary target point, and the user moves to that temporary target point. If not, or if there are no similar service points nearby, the temporary target point is designated as the end of the queue for that service point. Then, it is determined whether the user has reached the head of the queue. If so, the user moves to the service point's service area and plays the corresponding animation. If the leader of the queue has not been reached, a temporary target point is set behind the person in front of the current leader.

[0059] This application uses behavior trees to make decisions about the behavioral states of subway passengers and employs a navigation-social force queuing model to drive passenger movement. Integrating the social force model into the navigation and queuing algorithms makes pedestrian behavior more consistent with realistic simulation scenarios. Through the dynamic coupling of behavior trees and the social force model, a unified approach of intelligent decision-making and group dynamics evolution is achieved in subway passenger simulation, providing a novel technical path for handling high-density, highly dynamic subway operation scenarios.

[0060] The following describes a simulation device for subway passenger behavior provided in this application. The simulation device for subway passenger behavior described below corresponds to the simulation method for subway passenger behavior described above.

[0061] See also Figure 4The simulation device 400 for subway passenger behavior includes: a processing unit 401 for performing gridding processing on a subway model scene to obtain a grid map; a first determining unit 402 for determining a target movement path in the grid map based on the target passenger's initial position and preset destination; an acquisition unit 403 for acquiring the target passenger's current state, which is used to indicate the boarding process; a second determining unit 404 for determining the target passenger's behavioral decision based on the current state through a preset behavior tree, which is used to indicate the target passenger's next state; and a simulation unit 405 for simulating the target passenger's behavior based on the behavioral decision, the target movement path, and a social force model.

[0062] In one possible embodiment, in simulating the behavior of the target passenger based on the behavioral decision, the target movement path, and the social force model, the simulation unit 405 is specifically configured to: determine multiple arrival points from the grid map based on the target movement path; determine the arrival point farthest from the preset destination among the multiple arrival points as the target arrival point; and simulate the behavior of the target passenger based on the behavioral decision, the target arrival point, and the social force model.

[0063] In one possible embodiment, in simulating the behavior of the target passenger based on the behavioral decision, the target arrival point, and the social force model, the simulation unit 405 is specifically configured to: determine the movement process of the target passenger from its current location to the target arrival point using the social force model; determine the behavior process of the target passenger based on the behavioral decision; simulate the behavior of the target passenger based on the movement process and the behavior process; determine the next arrival point of the target arrival point among the plurality of arrival points as the target arrival point, wherein the plurality of arrival points are sorted according to their distance from the preset destination, with the greater the distance from the preset destination, the higher the ranking; and repeat the above actions until the target passenger reaches the preset destination.

[0064] In one possible embodiment, in determining the movement process of the target passenger from the current location to the target arrival point using the social force model, the simulation unit 405 is specifically configured to: determine a first interaction force between the target passenger and other passengers at the target arrival point; determine a second interaction force between the target passenger and an obstacle at the target arrival point; determine the self-driving force of the target passenger moving towards the target arrival point; and determine the movement process of the target passenger from the current location to the target arrival point based on the self-driving force, the first interaction force, and the second interaction force.

[0065] In one possible embodiment, in determining multiple arrival points from the grid map based on the target movement path, the simulation unit 405 is specifically configured to: use the centroid of each grid in the grid map as a movement node; determine the target grid corresponding to the target movement path; and determine the movement node corresponding to the target grid as the multiple arrival points.

[0066] In one possible embodiment, in determining whether the mobile node corresponding to the target grid is one of the plurality of arrival points, the simulation unit 405 is specifically configured to: determine whether the target grid is an edge grid of the grid map; if it is an edge grid, determine a point at a preset distance along the normal direction of the mobile node corresponding to the edge grid as the arrival point corresponding to the edge grid.

[0067] See also Figure 5 , Figure 5 This is a schematic diagram of the structure of the electronic device provided in this application. For example... Figure 5 As shown, the electronic device may include a processor 510, a communications interface 520, a memory 530, and a communication bus 540, wherein the processor 510, communications interface 520, and memory 530 communicate with each other via the communication bus 540. The processor 510 can call logical instructions in the memory 530 to execute a simulation method for subway passenger behavior. This method includes: performing gridding processing on the subway model scene to obtain a grid map; determining a target movement path in the grid map based on the target passenger's initial position and preset destination; acquiring the target passenger's current state, which is used to indicate the boarding process; determining the target passenger's behavioral decision based on the current state through a preset behavior tree, which is used to indicate the target passenger's next state; and simulating the target passenger's behavior based on the behavioral decision, the target movement path, and a social force model.

[0068] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0069] On the other hand, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a simulation method for subway passenger behavior provided by the above methods. The method includes: performing gridding processing on a subway model scene to obtain a grid map; determining a target movement path in the grid map based on the initial position and preset destination of the target passenger; acquiring the current state of the target passenger, the current state being used to indicate the passenger's boarding process; determining a behavioral decision for the target passenger based on the current state through a preset behavior tree, the behavioral decision being used to indicate the next state of the target passenger; and simulating the behavior of the target passenger based on the behavioral decision, the target movement path, and a social force model.

[0070] In another aspect, this application also provides a computer program product, including a computer program that, when executed by a processor, implements any of the above-described simulation methods for subway passenger behavior. The method includes: performing gridding processing on a subway model scene to obtain a grid map; determining a target movement path in the grid map based on the initial position and preset destination of the target passenger; acquiring the current state of the target passenger, the current state being used to indicate the passenger's boarding process; determining a behavioral decision for the target passenger based on the current state using a preset behavior tree, the behavioral decision being used to indicate the next state of the target passenger; and simulating the behavior of the target passenger based on the behavioral decision, the target movement path, and a social force model.

[0071] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0072] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A simulation method for subway passenger behavior, characterized in that, include: The subway model scene is processed into a grid to obtain a grid map; The target passenger's movement path is determined in the grid map based on the passenger's initial location and preset destination; Obtain the current status of the target passenger, which is used to indicate the boarding process; Based on the current state, the behavioral decision of the target passenger is determined through a preset behavior tree, and the behavioral decision is used to indicate the next state of the target passenger; The behavior of the target passenger is simulated based on the behavioral decision, the target movement path, and the social force model.

2. The method according to claim 1, characterized in that, The simulation of the target passenger's behavior based on the behavioral decision, the target movement path, and the social force model includes: Multiple arrival points are determined from the grid map based on the target movement path; The destination point that is furthest from the preset destination among the plurality of destination points is determined as the target destination point; The behavior of the target passenger is simulated based on the behavioral decision, the target arrival point, and the social force model.

3. The method according to claim 2, characterized in that, The simulation of the target passenger's behavior based on the behavioral decision, the target arrival point, and the social force model includes: The social force model is used to determine the movement process of the target passenger from its current location to its target destination. The process of determining the target passenger's behavior based on the behavioral decision; The behavior of the target passenger is simulated based on the movement process and the behavior process; The next arrival point among the multiple arrival points is determined as the target arrival point. The multiple arrival points are sorted according to their distance from the preset destination, with the greater the distance from the preset destination, the higher the ranking. Repeat the above actions until the target passenger reaches the preset destination.

4. The method according to claim 3, characterized in that, The process of determining the movement of the target passenger from its current location to the target destination using the social force model includes: Determine the first interaction force between the target passenger and other passengers at the target arrival point; Determine the second interaction force between the target passenger and the obstacle at the target arrival point; Determine the self-driving force that drives the target passenger to move toward the target arrival point; The movement process of the target passenger from the current position to the target arrival point is determined based on the self-driving force, the first interaction force, and the second interaction force.

5. The method according to claim 2, characterized in that, The step of determining multiple arrival points from the grid map based on the target movement path includes: Use the centroid of each grid in the grid map as the moving node; Determine the target grid corresponding to the target's movement path; The mobile node corresponding to the target grid is determined as one of the multiple arrival points.

6. The method according to claim 5, characterized in that, Determining the mobile node corresponding to the target grid as the plurality of arrival points includes: Determine whether the target grid is an edge grid of the grid map; If it is the edge mesh, then the point at a preset distance along the normal direction of the moving node corresponding to the edge mesh is determined as the arrival point corresponding to the edge mesh.

7. A simulation device for subway passenger behavior, characterized in that, include: The processing unit is used to mesh the subway model scene to obtain a mesh map; The first determining unit is used to determine the target movement path in the grid map based on the initial location and preset destination of the target passenger; The acquisition unit is used to acquire the current status of the target passenger, and the current status is used to indicate the boarding process; The second determining unit is used to determine the target passenger's behavior decision based on the current state through a preset behavior tree, and the behavior decision is used to indicate the target passenger's next state. The simulation unit is used to simulate the behavior of the target passenger based on the behavioral decision, the target movement path, and the social force model.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the simulation method for subway passenger behavior as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the simulation method for subway passenger behavior as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the simulation method for subway passenger behavior as described in any one of claims 1 to 6.

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