Urban rail transit capacity adjustment method, device and equipment and storage medium

Abstract

The application provides a kind of urban rail transit transport capacity adjustment method, device, computer equipment and computer storage medium, it is related to the operation management technical field of urban rail transit.It includes: determining the passenger stay factor corresponding to target platform and the linear relationship constructed between each passenger stay factor;Passenger stay factor and linear relationship are input into the station scene simulation model constructed in advance, to obtain the station passenger flow simulation scene under the current transport capacity configuration mode, and then calculate the passenger stay parameter of target platform under the current transport capacity configuration mode;If the passenger stay parameter of target platform is greater than the preset passenger stay threshold, the current transport capacity configuration mode is adjusted to obtain the target transport capacity configuration mode;Wherein, the passenger stay parameter of target platform corresponding to target transport capacity configuration mode is not greater than the preset passenger stay threshold.The method can improve the accuracy of traffic transport capacity resource deployment and provide quantifiable decision basis for dispatch personnel to carry out passenger transport organization deployment.

Description

Technical Field

[0001] This application relates to the field of urban rail transit operation and management technology, specifically to an urban rail transit capacity adjustment method, an urban rail transit capacity adjustment device, computer equipment, and a computer storage medium. Background Technology

[0002] With the continuous advancement of rail transit construction and the increasing sophistication of urban rail transit networks, urban rail transit modes such as subways are gradually becoming one of the main travel options for the public. Correspondingly, this leads to a supply-demand imbalance between the rapidly growing passenger flow at stations and the insufficient capacity of rail transit, resulting in large passenger congestion within stations and consequently affecting the travel needs of both supply and demand sides.

[0003] To address the issue of large passenger congestion within stations, relevant technical solutions typically employ methods such as adding temporary trains to increase transport capacity, setting up barriers to guide passenger walking routes to reduce the number of passengers on the platform within a certain timeframe, and limiting passenger flow at the entrance to the paid area to reduce the number of passengers entering the station within a certain timeframe.

[0004] However, the aforementioned technical solutions rely solely on the manual experience of train dispatchers and station operators to determine whether the number of additional trains, the timing of obstacle placement, and the time for limiting passenger flow at paid area entrances are reasonable. This makes it difficult for the operations organization to combine the actual operating scenarios of the station to obtain the optimal capacity adjustment plan, which in turn makes it difficult to accurately allocate transportation capacity resources. Summary of the Invention

[0005] This application provides a method for adjusting urban rail transit capacity, an apparatus for adjusting urban rail transit capacity, a computer device, and a computer storage medium, thereby overcoming, to some extent, the technical problem that the operation organization is unable to obtain the optimal capacity adjustment plan in combination with the actual operation scenario of the station due to the limitations and defects of related technologies, which leads to the difficulty in accurately allocating transportation capacity resources.

[0006] The first aspect of this application provides a method for adjusting urban rail transit capacity. The method includes: determining a passenger congestion factor corresponding to a target station and determining a linear relationship between these factors; inputting the passenger congestion factor corresponding to the target station and the linear relationship between these factors into a pre-constructed station scenario simulation model to obtain a station passenger flow simulation scenario under the current capacity configuration mode, wherein the station passenger flow simulation scenario includes at least the real-time motion state of a virtual character model corresponding to each individual passenger; calculating the passenger congestion parameter of the target station under the current capacity configuration mode based on the real-time motion state of the virtual character model corresponding to each individual passenger in the station passenger flow simulation scenario; if the passenger congestion parameter of the target station is greater than a preset passenger congestion threshold, adjusting the current capacity configuration mode to obtain a target capacity configuration mode; wherein the passenger congestion parameter of the target station corresponding to the target capacity configuration mode is not greater than the preset passenger congestion threshold.

[0007] A second aspect of this application provides an urban rail transit capacity adjustment device, comprising: a determining module configured to determine a passenger congestion factor corresponding to a target station and determine a linear relationship between the passenger congestion factors; a data input module configured to input the passenger congestion factor corresponding to the target station and the linear relationship between the passenger congestion factors into a pre-constructed station scene simulation model to obtain a station passenger flow simulation scene under the current capacity configuration mode, wherein the station passenger flow simulation scene includes at least the real-time motion state of the virtual character model corresponding to each passenger individual; a parameter calculation module configured to calculate the passenger congestion parameter of the target station under the current capacity configuration mode based on the real-time motion state of the virtual character model corresponding to each passenger individual in the station passenger flow simulation scene; and a mode adjustment module configured to adjust the current capacity configuration mode if the passenger congestion parameter of the target station is greater than a preset passenger congestion threshold to obtain a target capacity configuration mode; wherein the passenger congestion parameter of the target station corresponding to the target capacity configuration mode is not greater than the preset passenger congestion threshold.

[0008] A third aspect of this application provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of any of the above-mentioned urban rail transit capacity adjustment methods.

[0009] A fourth aspect of the embodiments of this application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the urban rail transit capacity adjustment method as described above.

[0010] A fifth aspect of this application provides a computer program product, including a computer program that is executed by a processor to implement the steps of the urban rail transit capacity adjustment method as described above.

[0011] The technical solution of this application has the following beneficial effects:

[0012] This urban rail transit capacity adjustment method determines the passenger congestion factor corresponding to the target platform and establishes a linear relationship between these factors. The passenger congestion factor and the linear relationship between them are input into a pre-built station scenario simulation model to obtain a station passenger flow simulation scenario under the current capacity configuration mode. This simulation scenario includes the real-time motion state of the virtual character model corresponding to each individual passenger. Based on the real-time motion state of the virtual character model corresponding to each individual passenger in the station passenger flow simulation scenario, the passenger congestion parameter of the target platform under the current capacity configuration mode is calculated. If the passenger congestion parameter of the target platform is greater than a preset passenger congestion threshold, the current capacity configuration mode is adjusted to obtain the target capacity configuration mode. The passenger congestion parameter of the target platform corresponding to the target capacity configuration mode is not greater than the preset passenger congestion threshold.

[0013] On the one hand, this method analyzes the factors influencing passenger congestion at target platforms and the linear relationships between these factors, using these as input parameters to construct the real-time movement state of virtual character models corresponding to each individual passenger. This intuitively simulates the actual passenger flow during station operations, calculating the number and frequency of passenger congestion in the current operational simulation scenario. This serves as a standard to determine whether the current passenger transport organization configuration meets the capacity-volume matching standard, allowing for timely adjustments to the current capacity configuration mode. This overcomes the technical problem of difficulty in accurately determining the configuration mode due to the separation of decision-making between station personnel and trains in existing technical solutions. On the other hand, if the number of passenger congestion events is too high, it is necessary to change the parameter settings to adjust the passenger transport organization and allocation mode, thereby deriving the optimal passenger flow evacuation and allocation scheme for different scenarios of platform passenger congestion. This method can be combined with the actual station operation scenario to provide quantifiable decision-making basis for dispatchers in passenger transport organization and allocation, avoiding the technical problems of long processing time and low accuracy caused by manual decision-making. Attached Figure Description

[0014] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0015] Figure 1An architecture diagram of an urban rail transit capacity adjustment system in one application scenario provided in this application embodiment;

[0016] Figure 2 A flowchart illustrating one embodiment of an urban rail transit capacity adjustment method provided in this application;

[0017] Figure 3 A flowchart illustrating one embodiment of the method for determining the linear relationship between various passenger retention factors provided in this application;

[0018] Figure 4 This is a flowchart illustrating one embodiment of a method for constructing a station scene simulation model according to this application.

[0019] Figure 5 A schematic diagram of the structure of an urban rail transit capacity adjustment device provided in one embodiment of this application;

[0020] Figure 6 This is a schematic diagram of one embodiment of a computer device provided in this application. Detailed Implementation

[0021] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0022] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0023] The flowchart shown in the attached diagram is merely an illustrative example and does not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0024] With the rapid development of my country's rail transit construction and the increasingly sophisticated urban rail transit network, subways and similar modes of transportation are gradually becoming one of the main travel options for the public. Correspondingly, this leads to a supply-demand imbalance, with rapidly increasing station passenger flow often exceeding the available rail transit capacity. Typically, large passenger flows at stations are caused by two main factors: morning and evening rush hours, and unforeseen events. Morning and evening rush hours are usually characterized by large passenger volumes during commutes, while unforeseen events cause congestion due to sudden shortages of capacity in certain areas.

[0025] To address large passenger congestion at stations, existing solutions typically include adding temporary trains, setting up barriers to guide passenger flow into the station, and limiting passenger flow at paid area entrances. Adding temporary trains increases transport capacity; setting up barriers to guide passenger flow into the station reduces the number of passengers on the platform within a certain timeframe; and limiting passenger flow at paid area entrances reduces the number of passengers entering the station within a certain timeframe, thus resolving the large passenger congestion issue.

[0026] For example, taking transfer station B on any subway line in area A as an example, the level of congestion at transfer station B will reach its peak from 5 PM to 8 PM on a weekday. During this period, the operation team will usually add extra trains on closed lines, and obstacles will be set up at transfer station B, with the turnstiles closed and open to guide passenger walking routes and increase the walking time in the concourse to alleviate platform congestion.

[0027] However, the inventors discovered that the existing technical solutions rely heavily on the experience of train dispatchers and station operators to determine the rationality of decisions regarding the number of additional trains, the timing of obstacle placement, and the duration of passenger flow restrictions at paid area entrances. There are no quantitative standards to measure whether decisions made to adjust capacity or reduce passenger demand are optimal. Furthermore, the decisions to add trains are route network decisions, while increasing passenger platform walking time is a station decision; these two are not organically combined to generate a holistic capacity adjustment plan, making it difficult to accurately allocate transportation resources. Insufficient allocation of transportation resources fails to address the problem of large passenger congestion within stations, while excessive allocation leads to a waste of resources.

[0028] Therefore, there is an urgent need for a method that can combine the actual operation scenarios of the station to provide dispatchers with quantifiable decision-making basis for the allocation of passenger transport resources, and thus obtain the optimal allocation scheme for passenger flow evacuation and passenger transport organization under different operation scenarios.

[0029] To address the aforementioned issues, this application provides a method for adjusting urban rail transit capacity. This method uses a pre-constructed station simulation model to analyze the factors influencing passenger platform congestion and the specific relationships between these factors. These factors are then set as input parameters for the station simulation model, simulating passenger congestion under different operating scenarios. The method calculates the number and frequency of passenger congestion in different scenarios to determine whether the current passenger transport organization configuration achieves a capacity-volume match. If the number of passenger congestions is excessive, the parameter settings need to be changed to adjust the passenger transport organization and allocation mode, thereby deriving the optimal passenger flow evacuation and allocation scheme for different scenarios. This method, combined with actual station operating scenarios, provides dispatchers with quantifiable decision-making basis for passenger transport organization and allocation.

[0030] System Architecture :

[0031] Based on the above description, this disclosure proposes a method and apparatus for adjusting urban rail transit capacity, which can be applied to... Figure 1 In the system architecture of the exemplary application environment shown.

[0032] Figure 1 This is an architecture diagram of an urban rail transit capacity adjustment system in one application scenario provided in this application embodiment, with reference to... Figure 1 As shown, in this scenario, the system architecture 100 may include a terminal device 101, a server 102, and a network 103.

[0033] The terminal device 101 can be, for example, any device involved in urban rail transit capacity adjustment, such as a mobile phone, tablet computer (PAD), laptop computer, desktop computer, smart TV, smart in-vehicle device, smart wearable device, or aircraft. The terminal device 101 can have a target application installed, which can display the capacity adjustment plan. The application involved in this embodiment can be a software client, or a webpage, mini-program, etc. The server 102 is the server corresponding to the software, webpage, mini-program, etc., and the specific type of client is not limited.

[0034] In this embodiment, the terminal device 101 and the server 102 can communicate directly or indirectly through one or more networks 103. The network 103 can be a wired network or a wireless network. For example, the wireless network can be a mobile cellular network or a Wireless-Fidelity (WIFI) network. Of course, it can also be other possible networks, and this embodiment does not limit them.

[0035] Server 102 can be a backend server for the target application, used to provide corresponding backend services, such as rail transit capacity adjustment plan generation service, etc.

[0036] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, there can be any number of terminal devices, networks, and servers. For example, server 106 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0037] The urban rail transit capacity adjustment method provided in this embodiment can be executed on server 102, and correspondingly, the urban rail transit capacity adjustment device is generally installed in server 102. The urban rail transit capacity adjustment method provided in this embodiment can also be executed in terminal device, and correspondingly, the urban rail transit capacity adjustment device can also be installed in terminal device. The urban rail transit capacity adjustment method provided in this embodiment can also be partially executed on server 102 and partially executed in terminal device; correspondingly, some modules of the urban rail transit capacity adjustment device can be installed in server 102 and some modules can be installed in terminal device.

[0038] Both server 102 and terminal device 101 may include one or more processors, memory, and I / O interfaces for interaction. Furthermore, server 102 may be configured with a database to store trained model parameters. The memory of server 102 and terminal device 101 may also store program instructions required for execution in the urban rail transit capacity adjustment method provided in this embodiment. These program instructions, when executed by the processor, can be used to implement the generation process of the urban rail transit capacity adjustment scheme provided in this embodiment.

[0039] It should be noted that when the urban rail transit capacity adjustment method provided in this application embodiment is executed by either server 102 or terminal device 101 alone, the above application scenario may also include only a single device, either server 102 or terminal device 101. Alternatively, server 102 and terminal device 101 may be considered as the same device. Of course, in practical applications, when the urban rail transit capacity adjustment method provided in this application embodiment is executed by both server 102 and terminal device 101, server 102 and terminal device 101 may also be the same device. That is, server 102 and terminal device 101 may be different functional modules of the same device, or virtual devices virtualized from the same physical device.

[0040] For example, in one exemplary embodiment, the operations manager triggers a simulation start command through terminal device 101. Server 102 determines the passenger congestion factor corresponding to the target station and establishes a linear relationship between each passenger congestion factor. The passenger congestion factor corresponding to the target station and the linear relationship between each passenger congestion factor are input into a pre-built station scene simulation model to obtain a station passenger flow simulation scenario under the current capacity configuration mode. The station passenger flow simulation scenario includes at least the real-time movement state of the virtual character model corresponding to each individual passenger. Based on the real-time movement state of the virtual character model corresponding to each individual passenger in the station passenger flow simulation scenario, the passenger congestion parameter of the target station under the current capacity configuration mode is calculated. If the passenger congestion parameter of the target station is greater than a preset passenger congestion threshold, the current capacity configuration mode is adjusted to obtain a target capacity configuration mode. The passenger congestion parameter of the target station corresponding to the target capacity configuration mode is not greater than the preset passenger congestion threshold. Finally, the server sends the target capacity configuration mode to terminal device 101 for display.

[0041] However, those skilled in the art will readily understand that the above application scenarios are merely illustrative and are not intended to limit the scope of this exemplary embodiment.

[0042] The following describes the method provided by exemplary embodiments of this application in conjunction with the application scenarios described above and with reference to the accompanying drawings. It should be noted that the above application scenarios are only shown to facilitate understanding of the spirit and principles of this application, and the embodiments of this application are not limited in any way. It should also be noted that the following method can be executed by the aforementioned terminal device or server, or by both the terminal device and the server. Here, the method is specifically shown as being executed by the terminal device or server.

[0043] Figure 2 A flowchart of one embodiment of the urban rail transit capacity adjustment method provided in this application is shown below. Figure 2The diagram shown is a schematic representation of an urban rail transit capacity adjustment process provided in an embodiment of this application. This method can be executed by a computer device, which may be... Figure 1 The specific implementation process of this method for the terminal device or server shown includes the following steps 201 to 204:

[0044] Step 201: Determine the passenger dwell factor corresponding to the target platform and determine the linear relationship between each passenger dwell factor.

[0045] Step 202: Input the passenger dwell factor corresponding to the target platform and the linear relationship between each passenger dwell factor into the pre-constructed station scene simulation model to obtain the station passenger flow simulation scene under the current capacity configuration mode. The station passenger flow simulation scene shall at least include the real-time motion state of the virtual character model corresponding to each individual passenger.

[0046] Step 203: Based on the real-time motion state of the virtual character model corresponding to each passenger in the station passenger flow simulation scenario, calculate the passenger dwell parameters of the target platform under the current capacity configuration mode.

[0047] Step 204: If the passenger congestion parameter of the target station is greater than the preset passenger congestion threshold, the current capacity configuration mode is adjusted to obtain the target capacity configuration mode; wherein, the passenger congestion parameter of the target station corresponding to the target capacity configuration mode is not greater than the preset passenger congestion threshold.

[0048] In some embodiments of this disclosure, the method provides the following technical solutions: First, by analyzing the factors influencing passenger congestion at the target platform and the linear relationships between these factors, the method uses these factors as input parameters to construct the real-time motion state of the virtual character model corresponding to each individual passenger. This allows for an intuitive simulation of the actual passenger flow during station operation, and the calculation of the number and frequency of passenger congestion in the current operational simulation scenario. This serves as a standard for judging whether the current passenger transport organization configuration meets the capacity-volume matching standard, thereby enabling timely adjustments to the current capacity configuration mode. This overcomes the technical problem in existing solutions where the separation of decision-making between station personnel and trains makes it difficult to accurately determine the configuration mode. Second, if the number of passenger congestion events is too high, it is necessary to change the parameter settings to adjust the passenger transport organization and allocation mode, thereby deriving the optimal passenger flow evacuation and allocation scheme for different scenarios of passenger congestion at the platform. This method can be combined with the actual station operation scenario to provide quantifiable decision-making basis for dispatchers to conduct passenger transport organization and allocation, avoiding the technical problems of long processing time and low accuracy caused by manual decision-making.

[0049] The following will describe in conjunction with specific embodiments Figure 2 The specific implementation methods of each step in the illustrated embodiment are described in detail below:

[0050] In step 201, the passenger dwell factor corresponding to the target station is determined, and the linear relationship between each passenger dwell factor is determined.

[0051] The target platform is any platform within the operating range of the urban rail transit system, such as the current platform where the train arrives; the passenger dwell factor refers to the factors that affect passengers' dwell time at the target platform.

[0052] Regarding step 201, which involves determining the passenger dwelling factor corresponding to the target platform, the process of determining the passenger dwelling factor corresponding to the target platform will be explained below with reference to specific embodiments:

[0053] In one optional embodiment of this disclosure, the passenger retention factor corresponding to the target platform includes one or more of a first influencing factor within the target platform and a second influencing factor for the train.

[0054] The first influencing factor includes one or more of the number of waiting passengers when the train arrives at the target platform and the number of arriving passengers during the train's stop at the target platform.

[0055] The second influencing factor includes one or more of the following: the train's rated passenger capacity, the actual number of passengers when the train stops at the target platform, the number of passengers getting off the train, the train's stopping time, whether the train skips a stop at the target platform, and whether the train removes passengers at the target platform.

[0056] For example, the reasons that affect passengers staying at the target platform mainly depend on: the number of waiting passengers at the target platform in the direction of train travel when the train arrives, the number of arriving passengers at the platform during the train's stop, and the train's own attributes. These influencing factors can be summarized into two categories: the first influencing factor within the station and the second influencing factor for the train.

[0057] A. Regarding the primary influencing factor within the aforementioned target platform:

[0058] For example, the first influencing factor mainly includes the number of passengers waiting on the target platform when the train arrives at the target platform, and the number of passengers arriving at the target platform during the period when the train stops at the target platform.

[0059] In one optional embodiment of this disclosure, the number of waiting passengers on the target platform includes one or more of the following: the initial number of passengers arriving at the target platform at the start of the simulation, and the target number of passengers arriving at the target platform during the train travel interval.

[0060] The simulation start time refers to the start time of the station simulation model's operation, while the initial passenger count refers to the existing passenger count on the target platform at the start time of the station simulation model's operation. The initial passenger count can be a preset fixed value or a non-preset fixed value; a non-preset fixed value can be, for example, a variable value.

[0061] The initial number of passengers and / or the target number of passengers are determined by the time each passenger spends within the station and the number of passengers whose destination is the target platform. The station includes the target platform.

[0062] For example, the initial number of passengers located at the target platform and the target number of passengers arriving at the target platform during the train travel interval are affected by one or more of the walking efficiency of passengers within the station and the number of passengers whose destination is the target platform during the time period.

[0063] The following will illustrate, with reference to specific embodiments, the methods for determining the travel time of each passenger within the station and the method for determining the number of passengers whose destination is the target platform:

[0064] In one optional embodiment of this disclosure, the travel time of each passenger within the target platform is determined based on at least one of the following methods:

[0065] 1) Passenger transit time through various fixed facilities within the station;

[0066] 2) The passage time of each passenger through obstacles in the station is determined based on the arrangement of the obstacles;

[0067] 3) The passage time of each passenger through the toll gate equipment;

[0068] 4) Passenger travel time in the event of a pre-set emergency within the station;

[0069] 5) The passage time for each passenger when the platform screen doors close at the target platform;

[0070] 6) The individual travel time of each passenger during their movement on the target platform.

[0071] Regarding the passage time of each passenger through various fixed facilities within the station mentioned above (1): the time or walking efficiency of passengers within the target platform can include the passage time of passengers through various fixed facilities within the station. These fixed facilities can include, for example, stairs, escalators, security screening machines, etc.

[0072] Regarding 2) the passage time of each passenger through obstacles in the station: the passage time of each passenger in the station is determined based on the placement of the obstacles.

[0073] These obstacles are guide barriers set up by station staff to reduce the number of passengers on the platform within a certain timeframe. It should be understood that the longer the obstacle is placed and the more turns it makes, the longer the passenger travel time will be.

[0074] Regarding the passage time of each passenger through the toll gate equipment mentioned above (3): Whether the toll gate equipment can be used normally and the direction of use of the toll gate equipment (e.g., the opening and closing direction of the gate) will affect the passenger's passage choice, and thus affect the passage time of each passenger through the toll gate equipment.

[0075] Regarding 4) the travel time of each passenger in the event of a pre-set emergency within the station: In the event of an emergency, the station's solution to the pre-set emergency will affect the walking efficiency of passengers, and thus affect the travel time of each passenger within the target platform. For example, when a small-scale fire occurs within the station, the fire area will be isolated, which will cause passengers to detour, increasing the travel time of passengers within the target platform.

[0076] Regarding point 5) above, the passage time for each passenger at the target platform's platform screen doors closing: The opening and closing efficiency of the platform screen doors affects the rate at which passengers board and alight, thus affecting the number of passengers boarding. Consequently, the fewer passengers board, the more passengers remain on the target platform.

[0077] Regarding 6) the individual travel time of each passenger during their movement on the target platform: Since objective and subjective factors of passengers can affect their individual travel time during their movement on the target platform, the following will explain the individual travel time in combination with various factors.

[0078] Among the subjective factors, passengers exhibit herd mentality: the degree to which a single passenger depends on the walking route chosen by the majority of passengers. For example, when an elevator becomes congested, some passengers may choose to take the stairs, while others continue to wait for the elevator.

[0079] Each passenger has their own walking habits: passengers choose their walking path based on their own habits according to the station's internal layout. For example, when passing through toll gates, will passengers prioritize the toll gate closest to the security checkpoint? Or, regarding boarding speed, for instance, when a train door opens within a passenger's line of sight, can the passenger choose whether to speed up to board the train or wait for the next train?

[0080] From an objective perspective, differences in walking speed, and consequently, walking time, can be caused by factors such as the passenger's own characteristics. These factors include the passenger's gender, size, age, and whether they are carrying luggage. Generally speaking, the more luggage a passenger carries, the slower their walking speed tends to be.

[0081] In an optional embodiment of this disclosure, the number of passengers whose destination is the target station is determined based on at least one of the following methods:

[0082] 1) Based on the fact that the station is a transfer station, determine the number of passengers arriving at the target platform.

[0083] In this implementation, when the station is a transfer station, passengers who get off at other lines at the station during this time period can reach the target platform through the transfer passage, thereby determining the number of passengers who arrive at the target platform.

[0084] 2) Determine the number of passengers arriving at the target platform based on the station's passenger flow control measures.

[0085] In this embodiment, it is necessary to determine the station's flow control status to determine the number of passengers arriving at the target platform. Station flow control is typically divided into three levels: outside the station, outside the paid area, and within the paid area. Each level of flow control affects the number of passengers allowed to pass. For example, if a preset number of passengers has passed through within a certain time period, passengers will be prohibited from passing through in the next time period.

[0086] 3) Determine the number of passengers arriving at the target platform based on the open or closed status of the station entrance and exit.

[0087] In this embodiment, the number of passengers arriving at the target platform can also be determined based on the open or closed status of station entrances and exits. That is, when a station entrance is closed, it will affect the number of passengers entering the station through that entrance, and thus affect the number of passengers arriving at the target platform.

[0088] 4) Determine the number of passengers arriving at the target platform based on the passenger flow entering the station during the simulation period.

[0089] The simulation time period refers to the time between the start and end of the simulation.

[0090] In this embodiment, the number of passengers arriving at the target platform can also be determined based on the passenger flow entering the station during the simulation period. That is, during the simulation period, there will be a continuous flow of passengers entering the station through various entrances and exits, and then reaching their destination (i.e., the target platform).

[0091] It should be noted that the number of passengers arriving at the target platform as described above can be the number of passengers determined by one or more of the above methods combined.

[0092] In the above embodiments, by comprehensively statistically analyzing the first influencing factors affecting passenger congestion at the target platform, the accuracy of passenger congestion factor calculation can be improved, thereby improving the accuracy of model analysis to derive the optimal capacity allocation plan.

[0093] B. Regarding the second influencing factor concerning trains mentioned above:

[0094] In one optional embodiment of this disclosure, the second influencing factor includes one or more of the following: the train's rated passenger capacity, the actual number of passengers when the train stops at the target platform, the number of passengers disembarking, the train's stopping time, whether the train skips a stop at the target platform, and whether the train removes passengers at the target platform.

[0095] The second influencing factor is the impact of train-related factors on passenger congestion.

[0096] For example, the second influencing factor includes one or more of the following: the train's rated passenger capacity, the actual number of passengers when the train stops at the target platform, the number of passengers disembarking, the train's stopping time, whether the train skips a stop at the target platform, and whether the train clears passengers at the target platform.

[0097] The rated passenger capacity of the aforementioned train indicates the upper limit of the number of passengers that the train can carry. It is understandable that the size of the train carriages determines the upper limit of the number of passengers that the train can carry; the smaller the train carriages and the fewer the carriages, the smaller the rated passenger capacity.

[0098] The actual number of passengers boarding and disembarking when the train stops at the target platform determines the maximum number of passengers who can board during the train's stop. In other words, the more passengers boarding and the fewer passengers disembarking, the smaller the maximum number of passengers who can board the train. Furthermore, the length of the train's stop affects the number of passengers boarding and disembarking under the same conditions; that is, the longer the stop, the more people can board, provided it does not exceed the train's rated passenger capacity.

[0099] Whether the train makes a skip stop at the target platform indicates whether, if the train skips the stop (i.e., passes through without stopping), no passengers will board or alight. Conversely, if the train does not skip the stop, passengers can board and alight normally, provided the passenger capacity does not exceed the train's rated capacity. Whether the train removes passengers at the target platform indicates whether, if passengers are removed, passengers will only alight upon arrival at the target platform. Conversely, if the train does not remove passengers at the target platform, passengers can board and alight normally, provided the passenger capacity does not exceed the train's rated capacity.

[0100] Continuing with step 201, regarding the step of determining the linear relationship between various passenger delay factors in step 201, the factors affecting passenger delay are usually multidimensional. The following will combine... Figure 3 The illustrated embodiment provides an exemplary description of the steps for determining the linear relationship between passenger dwelling factors, as described above. (Refer to...) Figure 3 As shown, the method includes steps 301 to 304:

[0101] Step 301: Obtain the distribution ratio of each passenger delay factor and the initial number of passengers arriving at the target platform at the start of the simulation.

[0102] Step 302: Determine whether the initial number of passengers is a preset fixed value.

[0103] If the initial number of passengers is a preset fixed value, then proceed to step 303: determine the linear relationship between passenger retention factors based on formula (1):

[0104]

[0105] Within the system of linear equations shown in formula (1), A 11 To A mn The distribution proportions of each passenger delay factor, x1 to x n For each passenger's delay factor, Y1 to Y m The number of passengers stranded corresponds to each passenger stranding factor, where k is the initial number of passengers and is a preset fixed value.

[0106] Conversely, if the initial number of passengers is not a preset fixed value, then step 304 is executed to determine the linear relationship between the various passenger retention factors based on formula (2):

[0107]

[0108] Within the system of linear equations shown in formula (2), A 11 To A mn The distribution ratio among the various passenger delay factors, x1 to x n For each passenger's delay factor, Y1 to Y m For each passenger delay factor, k1 to k m Let k1 be the initial number of passengers corresponding to each passenger retention factor, and k1 to k m They may differ from each other or be partially the same. Furthermore, the aforementioned non-preset fixed values ​​refer to values ​​that change, not fixed values.

[0109] The above embodiments set multiple passenger retention factors and linear relationships between each passenger retention factor, which can simulate passenger retention situations under different station scenarios, improve the adaptability of the method provided in this disclosure, and thus improve reliability.

[0110] In step 202, the passenger dwell factor corresponding to the target platform and the linear relationship between each passenger dwell factor are input into the pre-constructed station scene simulation model to obtain the station passenger flow simulation scene under the current capacity configuration mode. The station passenger flow simulation scene includes at least the real-time motion state of the virtual character model corresponding to each individual passenger.

[0111] Among them, the station scene simulation model can obtain the station passenger flow simulation scenario under the current capacity configuration mode. The station passenger flow simulation scenario not only includes the station passenger flow and the passenger congestion status of the target platform under the current capacity configuration mode, but also includes the real-time movement status of the virtual character model corresponding to each passenger.

[0112] Furthermore, before inputting the passenger dwell time factors corresponding to the target platform and the linear relationships between these factors into the pre-built station scenario simulation model, it is also necessary to pre-build the station scenario simulation model. The following will combine... Figure 4 The process of constructing a station scene simulation model is illustrated by way of example. The construction of this station scene simulation model can employ a combination of multiple models, including agent modeling, physical modeling, human-computer interaction modeling, and time modeling. It is understood that the construction of the station scene simulation model can also be implemented based on one or some of the above modeling methods, and this disclosure does not impose any special limitations on this.

[0113] Figure 4 This is a flowchart illustrating one embodiment of a method for constructing a station scene simulation model; see also... Figure 4 As shown, the method includes steps 401 to 404:

[0114] 1) Agent modeling:

[0115] Step 401: For each passenger in the station, generate a virtual character model, and perform action proxy on the virtual character model based on the passenger dwell factor corresponding to the target platform to obtain the first action proxy for the virtual character model.

[0116] For example, for each passenger in the station, each Agent in the established model corresponds to a passenger in the station. That is, every time a passenger is generated in the station, a virtual character model is generated through an Agent to act as an agent. The first action agent for the virtual character model includes a series of factors such as the purpose of movement in the station (e.g., passenger entering the station to board the train, getting off the train to exit the station, getting off the train to transfer), the way of walking in the station (e.g., passenger walking speed / travel time), and actions in the station (e.g., entering the station through the device, waiting, selecting different sections, getting on and off the train).

[0117] 2) Physical modeling:

[0118] Step 402: For the set of equipment in the station, generate physical equipment models and perform action proxies on each physical equipment model in the set of equipment to obtain the second action proxy for the physical equipment models.

[0119] The equipment set within the station includes fixed equipment as shown in the above embodiments, such as stairs, escalators, and security screening machines, as well as dynamic equipment, such as toll gates, door switches, and elevator doors.

[0120] In this embodiment, physical modeling is used to simulate the various equipment entities existing in the station, and a second action agent is added to the corresponding dynamic equipment. For example, action simulation is performed for elevator equipment (such as start / stop, availability, one-way or two-way passage, door opening / closing, etc.).

[0121] 3) Human-computer interaction modeling:

[0122] Step 403: Based on the second action agent, the first action agent of the virtual character model is empowered with thought to obtain the third action agent of the virtual character model.

[0123] For example, physical equipment within a station can influence passengers' travel routes and travel time. This embodiment can be used to empower the passenger's primary action agent in a simulated scenario through human-computer interaction modeling. Specifically, data is allocated to the virtual character model. Since the agent, when created, lacks inherent attributes and therefore lacks thought, data allocation empowers it, enabling it to perform actions within the station. Secondly, by allocating actions from the physical model, the decisions made by the agent (i.e., the virtual character model's primary action agent) when passing through virtual equipment are influenced, thus achieving scenario simulation with different outcomes under different circumstances.

[0124] 4) Time modeling:

[0125] Step 404: Construct a station scene simulation model based on the first action agent, the second action agent, and the third action agent within a continuous time period.

[0126] For example, the significance of a station scene simulation model lies in the fact that simulation parameters are switched through different operational instructions over a period of time to obtain various simulation results, thereby providing auxiliary decision-making for the operation and production of urban rail transit. Therefore, discrete event models are not suitable for use, that is, data collection at regular intervals will not yield instructive results. Therefore, a continuous-time approach is used for simulation, for example, the frame rate of each frame is 0.2s, that is, five simulations are performed within 1 second.

[0127] In an optional embodiment of this disclosure, the station scene simulation model constructed above can be a four-layer architecture model based on the Model-View-View model MVVM framework, which includes a presentation layer, an object layer, a business layer, and a data layer.

[0128] Among them, the Model-View-View-Model (MVVM) helps to clearly separate the application's business and presentation logic from the user interface. Maintaining a clear separation between application logic and UI helps solve many development problems and makes the application easier to test, maintain, and evolve.

[0129] It is understood that the station scene simulation model can also be implemented using other frameworks, and this disclosure does not impose any special restrictions on this.

[0130] In this embodiment, by superimposing multiple models such as Agent modeling, physical modeling, human-computer interaction modeling, and time modeling, a station scene simulation model that is infinitely close to the actual scene can be obtained. This facilitates the subsequent accurate simulation of individual passenger dynamics and station operation passenger flow based on the station scene simulation model, thereby improving accuracy.

[0131] Furthermore, after the station scene simulation model is built, the passenger congestion factor corresponding to the target platform and the linear relationship between each passenger congestion factor can be input into the pre-built station scene simulation model, that is, the data import is completed and the model parameters are configured in order to calculate the passenger congestion situation on the platform under different parameter scenarios.

[0132] In an optional embodiment of this disclosure, in response to a configuration operation for a target parameter, the target parameter value corresponding to the configuration operation is used as the model parameter of the station scene simulation model, so that the station scene simulation model generates a station passenger flow simulation scenario under the current capacity configuration mode based on the target parameter value.

[0133] The target parameters include one or more of the following: train type parameters, passenger parameters, passenger flow parameters, station equipment parameters, and capacity configuration mode parameters. Each of these target parameters can also have multiple sub-parameters set to simulate different station operation scenarios based on different parameters.

[0134] In this embodiment, target parameters can be made available to operation and management personnel so that they can flexibly configure them based on the open target parameters to simulate various complex station operation scenarios, thereby exploring the optimal solution of the matrix equation set shown in the above formulas (1) and (2), and thus providing comprehensive auxiliary decision support for dispatchers.

[0135] In response, based on the station scene simulation model constructed above, physical simulations of various equipment within the station can be performed, and the real-time dynamic flow of individual passengers can be simulated based on these equipment, thereby achieving the effect of simulating passenger flow during station operation.

[0136] In step 203, based on the real-time motion state of the virtual character model corresponding to each passenger in the station passenger flow simulation scenario, the passenger dwell parameters of the target platform under the current capacity configuration mode are calculated.

[0137] The passenger congestion parameter includes a combination of one or more of the following: the number of passengers congested and the number of times passengers were congested. Based on the real-time movement status of each individual passenger, it is possible to determine in real time whether they are eligible to board the train.

[0138] For example, based on the real-time movement status of the virtual character model corresponding to each individual passenger in the station passenger flow simulation scenario, parameters such as the number of passengers stranded and the number of times passengers are stranded can be calculated under the current capacity configuration mode.

[0139] It should be noted that the following conditions must be met when calculating passenger congestion parameters to conform to the operational logic in actual scenarios:

[0140] 1) If the same platform receives n trains, and the same passenger experiences delays after all n trains have departed, the delays will be continuous, not discrete. That is, the passenger's delays could occur after the 1st train departure, the 2nd train departure, the 3rd train departure, and so on up to the (n-3rd)th train departure, but after boarding the (n-2nd)th train arrival without further delays; or the passenger could experience continuous delays such as after the 2nd train departure, the 3rd train departure, the 4th train departure, and so on up to the (n-2nd)th train departure, but after boarding the (n-1st)th train arrival without further delays. It will not occur where the passenger is delayed after the 1st train departure, delayed after the 2nd train departure, not delayed after the 3rd train departure, and delayed after the 4th train departure.

[0141] 2) Once a passenger boards a train on the same platform, the same passenger will not reappear within the same train's route cycle.

[0142] 3) Each passenger has a unique identifier.

[0143] In performing step 203, in an optional embodiment of this disclosure, if the passenger congestion parameters include at least the number of passenger congestion instances and the number of passenger congestion instances, step 203 can determine the number of train departures on the target platform; for any target departure number N in the number of train departures, based on the real-time motion state of the virtual character model corresponding to each individual passenger in the station passenger flow simulation scenario, calculate the set of passenger congestion instances corresponding to each passenger congestion instance after the Nth train departure, and obtain the passenger congestion parameters of the target platform based on the number of passenger congestion instances corresponding to each passenger congestion instance set.

[0144] Each individual passenger in the set of stranded passengers has a unique identifier.

[0145] For example, let P be a set of identifiers for the remaining passengers (i.e., stranded passengers) on the platform after each train departure. Then, after the first train departure, the set of stranded passengers on the platform is P(1), after the second train departure, the set of stranded passengers on the platform is P(2), and so on, until the Nth train departure, when the set of stranded passengers on the platform is P(n). Each passenger has a unique identifier within the set. Using a reverse order method, a reverse count is performed after each train departure. First, the number of stranded passengers after the Nth train departure is counted. The calculation process is as follows:

[0146] After the Nth train departs:

[0147] The set of passengers who experienced the first instance of being stranded is: P(n) - P(n) ∩ P(n-1)

[0148] The set of passengers who become stranded for the second time is: P(n)∩P(n-1)

[0149] The set of passengers who become stranded for the third time is: P(n)∩P(n-1)∩P(n-2)

[0150] …

[0151] The set of passengers who become stranded for the Nth time is: P(n)∩P(n-1)∩P(n-2)∩……∩P(2)∩P(1)

[0152] Accordingly, as follows:

[0153] After the N-1th train departs:

[0154] The set of passengers who experienced the first instance of being stranded is: P(n-1) - P(n-1) ∩ P(n-2)

[0155] The set of passengers who become stranded a second time is: P(n-1)∩P(n-2)

[0156] The set of passengers who become stranded for the third time is: P(n-1)∩P(n-2)∩P(n-3)

[0157] …

[0158] The set of passengers who experience the (N-1)th delay is: P(n-1)∩P(n-2)∩P(n-3)∩……∩P(2)∩P(1)

[0159] Based on the above embodiments, by counting the number of identifiers in the set of passengers stranded for each number of times a train is stranded after departure, the number of stranded passengers can be obtained, thus obtaining the passenger stranding parameters of the target platform.

[0160] In step 204, if the passenger congestion parameter of the target station is greater than the preset passenger congestion threshold, the current capacity configuration mode is adjusted to obtain the target capacity configuration mode; wherein, the passenger congestion parameter of the target station corresponding to the target capacity configuration mode is not greater than the preset passenger congestion threshold.

[0161] After calculating parameters such as the number of passenger delays and the number of passengers delayed at the target platform under the current capacity configuration mode in step 203 above, these parameters are compared with a preset passenger delay threshold to determine whether the current capacity configuration mode meets the capacity and passenger volume requirements of the current urban rail transit system. If it does, no adjustment is needed to the current capacity configuration mode, and it can continue to be used; otherwise, if it does not meet the requirements, the current capacity configuration mode needs to be adjusted so that the adjusted target capacity configuration mode meets the capacity and passenger volume requirements of the current urban rail transit system, thereby reducing the number of passenger delays and the large number of delayed passengers.

[0162] For example, when adjusting the current capacity configuration mode, the passenger transport organization and allocation scheme can be optimized by adjusting the input parameters, and the simulation results can be observed to see if passenger congestion is resolved. Through the above embodiments, the optimal passenger flow evacuation and allocation scheme for multiple passenger congestion scenarios on the platform can be obtained through multiple model simulations. Combined with the actual operation scenario of the station, this provides quantifiable decision-making basis for dispatchers to organize and allocate passenger transport.

[0163] To facilitate observation by operators, train departure times can be recorded to form a time domain, which can be used as the horizontal axis (i.e., the X-axis), and the number of stranded passengers or the number of stranded passengers can be used as the vertical axis (i.e., the Y-axis) to draw a curve, so as to dynamically display the number of stranded passengers or the number of stranded passengers at the station for both the up and down directions.

[0164] It should be understood that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order constraint on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the diagram may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0165] To implement the above-mentioned urban rail transit capacity adjustment methods, please refer to [link / reference]. Figure 5One embodiment of this application provides an urban rail transit capacity adjustment device 500, which may include: a determination module 501, a data input module 502, a parameter calculation module 503, and a mode adjustment module 504.

[0166] Among them, the determination module 501 is configured to determine the passenger dwell factor corresponding to the target station and determine the linear relationship between each passenger dwell factor;

[0167] The data input module 502 is configured to input the passenger dwell factor corresponding to the target platform and the linear relationship between each passenger dwell factor into a pre-built station scene simulation model to obtain the station passenger flow simulation scene under the current capacity configuration mode. The station passenger flow simulation scene includes at least the real-time motion state of the virtual character model corresponding to each individual passenger.

[0168] The parameter calculation module 503 is configured to execute the real-time motion state of each passenger's corresponding virtual role model in the station passenger flow simulation scenario, and calculate the passenger dwell parameters of the target platform under the current capacity configuration mode.

[0169] The mode adjustment module 504 is configured to adjust the current capacity configuration mode to obtain the target capacity configuration mode if the passenger congestion parameter of the target station is greater than the preset passenger congestion threshold; wherein the passenger congestion parameter of the target station corresponding to the target capacity configuration mode is not greater than the preset passenger congestion threshold.

[0170] In one optional embodiment of this disclosure, the passenger congestion factor corresponding to the target platform includes one or more of a first influencing factor within the target platform and a second influencing factor for the train; wherein, the first influencing factor includes one or more of the number of waiting passengers when the train arrives at the target platform and the number of arriving passengers during the train's stop at the target platform; the second influencing factor includes one or more of the following: the train's rated passenger capacity, the actual number of passengers when the train stops at the target platform, the number of passengers disembarking, the train's stopping time, whether the train skips a stop at the target platform, and whether the train clears passengers at the target platform.

[0171] In one optional embodiment of this disclosure, the number of waiting passengers includes one or more of the initial number of passengers arriving at the target platform at the start of the simulation and the target number of passengers arriving at the target platform during the train travel interval; the initial number of passengers and / or the target number of passengers are determined by the travel time of each passenger within the station and the number of passengers whose destination is the target platform, and the station includes the target platform.

[0172] In an optional embodiment of this disclosure, the determining module, when determining the movement time of each passenger within the target platform, is implemented based on at least one of the following methods:

[0173] The passage time for each passenger through various fixed facilities within the station;

[0174] The passage time of each passenger through obstacles within the station is determined based on the arrangement of the obstacles.

[0175] The passage time for each passenger through the toll gate equipment;

[0176] The travel time for each passenger in the event of a pre-set emergency within the station;

[0177] The passage time for each passenger at the target platform when the platform screen doors close;

[0178] Each passenger's own travel time during their movement on the target platform.

[0179] In an optional embodiment of this disclosure, the determining module, when determining the number of passengers whose destination is the target station, is implemented based on at least one of the following methods:

[0180] Based on the fact that the station is a transfer station, determine the number of passengers arriving at the target platform;

[0181] Based on the station's passenger flow control measures, determine the number of passengers arriving at the target platform;

[0182] Determine the number of passengers arriving at the target platform based on the open or closed status of the station entrances and exits;

[0183] Based on the passenger flow entering the station during the simulation period, the number of passengers arriving at the target platform is determined.

[0184] In an optional embodiment of this disclosure, the determining module is used to obtain the distribution ratio of each passenger dwelling factor and the initial number of passengers arriving at the target platform at the start time of the simulation.

[0185] If the initial number of passengers is a preset fixed value, the linear relationship between passenger dwell factors is determined based on the following formula (1):

[0186]

[0187] Among them, A 11 To A mn The distribution proportions of each passenger delay factor, x1 to x n For each passenger's delay factor, Y1 to Y m The number of passengers stranded corresponds to each passenger stranding factor, where k is the initial number of passengers and is a preset fixed value.

[0188] Alternatively, if the initial number of passengers is not a preset fixed value, the linear relationship between the various passenger dwell factors is determined based on the following formula (2):

[0189]

[0190] Among them, A 11 To A mn The distribution ratio among the various passenger delay factors, x1 to x n For each passenger's delay factor, Y1 to Y m For each passenger delay factor, k1 to k m Let k1 be the initial number of passengers corresponding to each passenger retention factor, and k1 to k m They are either different or partially the same.

[0191] In an optional embodiment of this disclosure, the device may further include a model building module, which generates a virtual character model for each passenger in the station, and performs action proxy on the virtual character model based on the passenger dwell factor corresponding to the target platform to obtain a first action proxy for the virtual character model; generates physical equipment models for the set of equipment in the station, and performs action proxy on each physical equipment model in the set of equipment to obtain a second action proxy for the physical equipment model; empowers the first action proxy of the virtual character model with thought based on the second action proxy to obtain a third action proxy of the virtual character model; and constructs a station scene simulation model based on the first action proxy, the second action proxy, and the third action proxy within a continuous time period.

[0192] In one optional embodiment of this disclosure, the station scene simulation model is a four-layer architecture model based on the Model-View-View model MVVM framework, which includes a presentation layer, an object layer, a business layer, and a data layer.

[0193] In an optional embodiment of this disclosure, the device may further include a parameter configuration module, which is used to, in response to a configuration operation for a target parameter, use the target parameter value corresponding to the configuration operation as the model parameter of the station scene simulation model, so that the station scene simulation model generates a station passenger flow simulation scenario under the current capacity configuration mode based on the target parameter value; wherein, the target parameter includes one or more of train type parameters, passenger parameters, passenger flow parameters, equipment parameters in the station, and capacity configuration mode parameters.

[0194] In an optional embodiment of this disclosure, the passenger congestion parameters include at least the number of passenger congestion instances and the number of passenger congestion instances. The parameter calculation module 503 is used to determine the number of train departures on the target platform. For any target departure number N in the number of train departures, based on the real-time motion state of the virtual character model corresponding to each passenger individual in the station passenger flow simulation scenario, the set of passenger congestion instances corresponding to each passenger congestion instance after the Nth train departure is calculated. Each passenger individual included in the set of passenger congestion instances corresponds to a unique identifier. Based on the number of passenger congestion instances corresponding to each passenger congestion instance, the passenger congestion parameters of the target platform are obtained.

[0195] Specific limitations regarding the aforementioned urban rail transit capacity adjustment device can be found in the limitations of the urban rail transit capacity adjustment method described above, and will not be repeated here. Each module in the aforementioned urban rail transit capacity adjustment device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0196] In one embodiment, a computer device is provided, the internal structure of which can be as follows: Figure 6 As shown. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores data. The network interface communicates with external terminals via a network connection. When the processor executes the computer program, it implements the above-described urban rail transit capacity adjustment method. It includes: a memory and a processor; the memory stores a computer program; and the processor executes the computer program to implement any step in the above-described urban rail transit capacity adjustment method.

[0197] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, can perform any of the steps in the above-described urban rail transit capacity adjustment method.

[0198] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0199] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0200] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0201] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0202] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0203] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for adjusting urban rail transit capacity, characterized in that, include: Determine the passenger dwell factor corresponding to the target platform, and determine the linear relationship constructed between the passenger dwell factors; The passenger congestion factor corresponding to the target platform includes a first influencing factor within the target platform and a second influencing factor for the train; wherein, the first influencing factor includes one or more of the following: the number of waiting passengers when the train arrives at the target platform, and the number of arriving passengers during the train's stop at the target platform; the second influencing factor includes one or more of the following: the train's rated passenger capacity, the actual number of passengers when the train stops at the target platform, the number of passengers disembarking, the train's stopping time, whether the train skips a stop at the target platform, and whether the train clears passengers from the target platform; determining the linear relationship constructed between the various passenger congestion factors includes: Obtain the distribution ratio of each passenger dwell factor, and obtain the initial number of passengers arriving at the target station at the start of the simulation; If the initial number of passengers is a preset fixed value, the linear relationship between the passenger retention factors is determined based on the following formula: ； in, to The distribution ratio of each passenger's delay factor. to For each passenger's delay factor, to For each passenger delay factor, the number of passengers stranded is... The initial number of passengers, and The preset fixed value; Alternatively, if the initial number of passengers is not a preset fixed value, the linear relationship between the various passenger dwell factors is determined based on the following formula: ； in, to The distribution ratio among various passenger delay factors, to For each passenger's delay factor, to For each passenger delay factor, the number of passengers stranded is... to The initial number of passengers corresponding to each passenger dwell factor, and to They are all different or partially the same; The passenger retention factor corresponding to the target platform and the linear relationship between each passenger retention factor are input into the pre-constructed station scene simulation model to obtain the station passenger flow simulation scenario under the current capacity configuration mode. The station passenger flow simulation scenario includes at least the real-time motion state of the virtual character model corresponding to each individual passenger. Based on the real-time motion state of the virtual character model corresponding to each passenger in the station passenger flow simulation scenario, the passenger dwell parameters of the target platform under the current capacity configuration mode are calculated. If the passenger congestion parameter of the target station is greater than the preset passenger congestion threshold, the current capacity configuration mode is adjusted to obtain the target capacity configuration mode; wherein, the passenger congestion parameter of the target station corresponding to the target capacity configuration mode is not greater than the preset passenger congestion threshold.

2. The method according to claim 1, characterized in that, The number of waiting passengers includes one or more of the initial number of passengers arriving at the target platform at the start of the simulation, and the target number of passengers arriving at the target platform during the train travel interval; the initial number of passengers and / or the target number of passengers are determined by the travel time of each passenger in the station and the number of passengers whose destination is the target platform, and the station contains the target platform.

3. The method according to claim 2, characterized in that, The travel time of each passenger within the target platform is determined based on at least one of the following methods: The passage time for each passenger through various fixed facilities within the station; The passage time of each passenger through obstacles within the station is determined based on the placement of the obstacles. The passage time for each passenger through the toll gate equipment; The travel time for each passenger in the event of a pre-set emergency within the station; The passage time for each passenger when the platform screen doors close at the target platform; The individual travel time of each passenger during their movement within the target platform.

4. The method according to claim 3, characterized in that, The number of passengers whose destination is the target station is determined based on at least one of the following methods: Based on the fact that the station is a transfer station, determine the number of passengers arriving at the target platform; Based on the station's passenger flow control measures, determine the number of passengers arriving at the target platform; The number of passengers arriving at the target platform is determined based on the open or closed status of the station entrance and exit. Based on the passenger flow entering the station during the simulation period, the number of passengers arriving at the target platform is determined.

5. The method according to claim 1, characterized in that, Before inputting the passenger dwelling factor corresponding to the target platform and the linear relationship between the passenger dwelling factors into the pre-built station scene simulation model, the method further includes: For each passenger in the station, a virtual character model is generated, and an action agent is applied to the virtual character model based on the passenger dwell factor corresponding to the target platform to obtain the first action agent for the virtual character model. For the set of equipment in the station, generate physical equipment models, and perform action proxy on each physical equipment model in the set of equipment to obtain a second action proxy for the physical equipment model; Based on the second action agent, the first action agent of the virtual character model is empowered by thought to obtain the third action agent of the virtual character model; The station scene simulation model is constructed based on the first action agent, the second action agent, and the third action agent within a continuous time period.

6. A capacity adjustment device for urban rail transit, characterized in that, include: The determination module is configured to determine the passenger dwell factor corresponding to the target station and determine the linear relationship constructed between the passenger dwell factors; The passenger congestion factor corresponding to the target platform includes a first influencing factor within the target platform and a second influencing factor for the train; wherein, the first influencing factor includes one or more of the following: the number of waiting passengers when the train arrives at the target platform, and the number of arriving passengers during the train's stop at the target platform; the second influencing factor includes one or more of the following: the train's rated passenger capacity, the actual number of passengers when the train stops at the target platform, the number of passengers disembarking, the train's stopping time, whether the train skips a stop at the target platform, and whether the train clears passengers from the target platform; determining the linear relationship constructed between the various passenger congestion factors includes: Obtain the distribution ratio of each passenger dwell factor, and obtain the initial number of passengers arriving at the target station at the start of the simulation; If the initial number of passengers is a preset fixed value, the linear relationship between the passenger retention factors is determined based on the following formula: ； in, to The distribution ratio of each passenger's delay factor. to For each passenger's delay factor, to For each passenger delay factor, the number of passengers stranded is... The initial number of passengers, and The preset fixed value; Alternatively, if the initial number of passengers is not a preset fixed value, the linear relationship between the various passenger dwell factors is determined based on the following formula: ； in, to The distribution ratio among various passenger delay factors, to For each passenger's delay factor, to For each passenger delay factor, the number of passengers stranded is... to The initial number of passengers corresponding to each passenger dwell factor, and to They are all different or partially the same; The data input module is configured to input the passenger dwell factor corresponding to the target platform and the linear relationship between the passenger dwell factors into a pre-built station scene simulation model to obtain a station passenger flow simulation scenario under the current capacity configuration mode. The station passenger flow simulation scenario includes at least the real-time motion state of the virtual character model corresponding to each individual passenger. The parameter calculation module is configured to execute the real-time motion state of each passenger's corresponding virtual role model in the station passenger flow simulation scenario, and calculate the passenger congestion parameters of the target platform under the current capacity configuration mode. The mode adjustment module is configured to adjust the current capacity configuration mode to obtain a target capacity configuration mode if the passenger congestion parameter of the target station is greater than the preset passenger congestion threshold; wherein the target capacity configuration mode corresponds to the passenger congestion parameter of the target station not being greater than the preset passenger congestion threshold.

7. A computer device, comprising: The system includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of the urban rail transit capacity adjustment method according to any one of claims 1 to 5.

8. A 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 steps of the urban rail transit capacity adjustment method according to any one of claims 1 to 5.

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