Coordinated control method for urban rail passenger flow, and electronic device and storage medium

Abstract

A coordinated control method for an urban rail passenger flow, and an electronic device and a storage medium, which belong to the technical field of rail transit control. In order to realize passenger flow management and control without increasing the transport capacity, in the present application, a rail simulation system is used to deduce a predicted passenger flow of an urban rail line during rush hour, which involves data such as entry station IDs, exit station IDs, entry time periods and passenger flow volumes; performing division on the basis of different time periods t and stations s, so as to obtain a flow of passengers arriving at a station s and preparing to enter the station to take a train within the time period t; on the basis of historical passenger flow distribution data of subway operating companies, compiling statistics on passenger flows in various directions among historical passenger flows, and calculating the proportions of the passenger flows in the various directions among the historical passenger flows; constructing a mixed integer programming model for multi-station passenger flow coordinated control; and using a branch and bound algorithm to solve the mixed integer programming model for multi-station passenger flow coordinated control constructed in step S3, so as to obtain an optimal passenger entry flow scheme for stations. The present application realizes the effect of prioritizing the evacuation in a station with a large passenger flow.

Description

Urban rail passenger flow coordinated control method, electronic device and storage medium Technical Field

[0001] The present application belongs to the field of rail transit control technology, and specifically relates to a method for coordinated control of urban rail passenger flow, an electronic device, and a storage medium. Background Art

[0002] During peak urban rail operations, passenger flow at key stations surges. Furthermore, due to the interconnectedness of various rail transit stations and the limitations of rail transport capacity, when a train arrives at a key station, the remaining capacity after passengers disembark is too small, causing waiting passengers to be stranded on the platform. Even if the number of passengers waiting at a station is small, over time, more and more passengers become stranded. When the platform capacity reaches the threshold, the risk factor of waiting increases, which can easily lead to dangerous accidents. Therefore, based on the passenger flow distribution at key stations on the line, it is necessary to implement flow control measures in advance at the corresponding rail transit stations to reserve some train capacity for subsequent rail transit stations, making the overall passenger flow more balanced.

[0003] There are currently two solutions to the problem of coordinated control of rail passenger flow:

[0004] 1. Manually adjust station control measures: Based on passenger flow retention, there are three levels of control intensity: primary, secondary, and tertiary. Primary control primarily controls the speed at which passengers enter the platform, including installing railroad barriers at escalators leading to the platform, or controlling the number and direction of escalators. Secondary control involves installing railroad barrier detours at station entrances or security checkpoints, closing some gates or ticket machines, and thus reducing the speed at which passengers enter rail transit stations or fare collection areas. Tertiary control involves closing some entrances to prevent passengers from entering. Subway staff can flexibly and quickly adjust strategies to control passenger flow based on actual operating conditions. However, the passenger limit is often based on manual experience, and coordination with other stations is often done through telephone calls and work groups, making it impossible to guarantee the accuracy and timeliness of flow control.

[0005] 2. Utilize algorithmic optimization to coordinate passenger flow control at multiple stations: By abstracting scenarios and establishing mathematical models, we optimize train transportation organization plans to improve the matching of transport capacity and volume. Based on the different temporal and spatial distributions of passenger flow, we establish a multi-station coordinated passenger flow control optimization model to control the number of passengers entering a station within a unit time interval to better match train capacity, improve the coordination and matching between passenger demand and transport capacity supply, and alleviate the conflict between passenger demand and transport capacity supply.

[0006] The typical steps for optimizing rail passenger flow control through modeling are as follows: First, analyze the spatiotemporal distribution characteristics of urban rail passenger flow and abstract scenarios for routes with uneven passenger demand distribution. Then, establish a mathematical model, taking into account factors such as passenger demand constraints, station capacity constraints, and train capacity constraints, and use the indicators to be improved as the model's optimization objectives, such as maximizing passenger turnover and minimizing total passenger delays. Finally, use heuristic algorithms or exact solution algorithms to find the optimal solution for the model. However, while these models have a data-based theoretical basis, they do not simultaneously consider the impact of passenger delays at different stations or the impact of transfer passenger flow on flow control.

[0007] Application Contents

[0008] The problem to be solved by this application is to realize passenger flow control without increasing the transport capacity in view of the uneven temporal and spatial distribution of passenger flow during the peak period of urban rail transit, and to propose a method, electronic device and storage medium for coordinated control of urban rail passenger flow.

[0009] To achieve the above objectives, this application is implemented through the following technical solutions:

[0010] In a first aspect, the present application provides a method for coordinated control of urban rail passenger flow, comprising the following steps:

[0011] S1. Use the rail simulation system to obtain the predicted passenger flow during the line's peak period. Using the simulation function in the rail simulation system, predict the passenger flow during the peak period of the urban rail line, including the entry ID, exit ID, entry time period, and passenger flow data. Divide the time interval from the start to the end of the peak period into a set of time periods with a step size of 5 minutes, denoted as T.

[0012] S2: Based on the predicted passenger flow of the urban rail line during peak period obtained in step S1, the passenger flow A arriving at station s and preparing to board the train in time period t is obtained. t，s ;

[0013] According to the historical passenger flow data of the subway operating company, the passenger flow in each direction of the historical passenger flow is counted, and the passenger flow proportion in each direction of the historical passenger flow is calculated to obtain the number of passengers from the oth station s in the time period t. o Departure and arrival at the dth station s d Passenger flow share

[0014] S3. Construct a mixed integer programming model for multi-station passenger flow coordinated control;

[0015] S4. Use the branch and bound algorithm to solve the mixed integer programming model for multi-station passenger flow coordinated control constructed in step S3 to obtain the optimal passenger flow plan for the station.

[0016] Furthermore, in step S2, the passenger flow ratio of each direction of the historical passenger flow is calculated to obtain the passenger flow ratio from the oth station s in the time period t. o Departure and arrival at the dth station s d Passenger flow share The specific implementation method includes the following steps:

[0017] The data is concentrated in time period t, from the oth site s o Departure and arrival at the dth station s d The number of passengers is recorded as From the oth site s o The number of all departing passengers is recorded as Then in time period t, from the oth site s o Departure and arrival at the dth station s d Passenger flow share The calculation expression is:

[0018] Furthermore, the specific implementation method of step S3 includes the following steps:

[0019] S3.1. Construct a mixed integer programming model for multi-station passenger flow coordinated control. The calculation expression is:

[0020] Among them, min represents the minimization function, S represents the station set;

[0021] D t，s represents the actual passenger demand at station s during time period t, including the number of people arriving at the station during time period t and the number of people stranded during the previous period. It is an integer variable in the mixed integer programming model for multi-station passenger flow coordinated control.

[0022] P t,s represents the optimal number of passengers entering station s during time period t, and is an integer variable in the mixed integer programming model for multi-station passenger flow coordinated control;

[0023] S3.2. Construct the constraints of the mixed integer programming model for multi-station passenger flow coordinated control.

[0024] Furthermore, the specific implementation method of step S3.2 includes the following steps:

[0025] S3.2.1. Set the actual passenger demand in the first time period at the beginning of the peak period to be equal to the passenger flow arriving at the station ready to board the bus in the first time period at the beginning of the peak period. The calculation expression is:

[0026] Among them, D 1,s represents the actual passenger demand at station s in the first time period at the beginning of the peak period, A 1，sIt represents the passenger flow arriving at station s in the first time period at the beginning of the peak period and preparing to board the bus;

[0027] S3.2.2. The actual passenger demand in time period t is equal to the sum of the number of passengers arriving at the station ready to board the bus during time period t and the number of passengers stranded in the period before time period t. The calculation expression is:

[0028] Among them, D t-1，s represents the actual ride demand of station s and time period t in the previous period, P t-1，s represents the optimal number of passengers entering the station in the previous period of time t at station s;

[0029] S3.2.3. Set the upper and lower limits of the optimal passenger flow entering the station. The upper and lower limits of the optimal passenger flow entering the station can be greater than or equal to 0.15 times the actual passenger flow and less than or equal to the actual passenger flow. The calculation expression is:

[0030] S3.2.4. Set the number of passengers who do not get off the train arriving at station s during time period t to be equal to the sum of the passenger flow of passengers who arrived at the station before station s before time period t and whose destinations are after station s. The calculation expression is:

[0031] Among them, M t，s It represents the number of passengers who do not get off the train at station s during time period t, and is a continuous variable defined in the mixed integer programming model for multi-station passenger flow coordinated control;

[0032] represents the set of upstream stations of station s; represents the set of downstream stations of station s; Indicates that there is passenger flow from the oth station s o Departure, go to the dth station s d The time period set of , and the time periods in the set satisfy that when the time increases to time period t, the passenger can arrive at station s; Indicates t o From the oth site s in the time period o Departure and arrival at the dth station s d The proportion of passenger flow, is the time period t o Next site o The optimal number of passengers entering the station.

[0033] Furthermore, the specific implementation method of step S4 includes the following steps:

[0034] S4.1. Linearly relax the mixed integer programming model for multi-station passenger flow coordinated control obtained in step S3 into a linear programming model, i.e., without restricting P t,s The value of can be an integer, which is recorded as a relaxed model. Then the simplex algorithm is called to solve it and the optimal solution of the linear programming is obtained.

[0035] S4.2, according to P t，s It is necessary to meet the requirements of its integer feasible domain, from P t，s Choose any non-integer solution variable and perform a binary split. In step S4.1, add the constraint Obtain the model for subproblem 1, denoted as subproblem 1, and add constraints to the relaxed model in step S4.1 Get the model of subproblem 2, denoted as subproblem 2, and call the simplex algorithm to solve it respectively;

[0036] S4.3. Calculate the objective function ∑ in subproblems 1 and 2 obtained in step S4.2. t∈T ∑ s∈S A t，s (D t，s -P t,s ), and take the maximum value among them as the lower bound of the objective function of the mixed integer programming model for multi-station passenger flow coordinated control;

[0037] S4.4. For sub-problems whose objective function value is greater than or equal to the lower bound value of the objective function of the mixed integer programming model for multi-site passenger flow coordinated control and whose solutions are non-integer, further branch and repeat steps S4.2, S4.3, and S4.4 until the integer optimal solution is obtained.

[0038] In a second aspect, the present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the urban rail passenger flow coordinated control method when executing the computer program.

[0039] On the third aspect, the present application provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, it implements the aforementioned method for coordinated control of urban rail passenger flow.

[0040] Beneficial effects of the present application: The urban rail passenger flow collaborative control method described in the present application targets the uneven distribution of passengers during urban rail peak hours, and simultaneously optimizes the following three key decision-making factors: the start and end time of passenger flow control, the stations under collaborative control, and the size of the controlled passenger flow; in view of the different risk levels of passenger flow delays at different rail stations during peak hours, the number of passengers arriving at the station is used as a penalty item for passenger flow delay, thereby achieving the effect of priority evacuation of stations with large passenger flows. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] FIG1 is a schematic flow chart of a method for coordinated control of urban rail passenger flow according to the present application;

[0042] FIG2 is a schematic diagram showing a comparison of the average 15-minute passenger flow limit at multiple stations before and after optimization of an urban rail passenger flow coordinated control method according to the present application;

[0043] FIG3 is a schematic diagram showing a comparison of passenger flow before and after optimization in Qianhai Bay according to a method for coordinated control of passenger flow on urban rail transit in the present application.

[0044] Specific implementation methods of this application

[0045] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application. That is, the specific embodiments described herein are only part of the embodiments of this application, not all of the specific embodiments. Generally, the components of the specific embodiments of this application described and shown in the drawings herein can be arranged and designed in various different configurations, and this application can also have other embodiments.

[0046] Therefore, the following detailed description of the specific embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for protection, but merely represents selected specific embodiments of the present application. Based on the specific embodiments of the present application, all other specific embodiments obtained by those skilled in the art without making any creative work are within the scope of protection of the present application.

[0047] In order to further understand the application content, features and effects of this application, the following specific implementation methods are given as examples and described in detail with reference to Figures 1 to 3 as follows:

[0048] Specific implementation method one:

[0049] Please refer to FIG1 , which is a flow chart of a method for coordinated control of urban rail passenger flow. The method for coordinated control of urban rail passenger flow includes the following steps:

[0050] S1. Use the rail simulation system to obtain the predicted passenger flow during the line's peak period. Using the simulation function in the rail simulation system, predict the passenger flow during the peak period of the urban rail line, including the entry ID, exit ID, entry time period, and passenger flow data. Divide the time interval from the start to the end of the peak period into a set of time periods with a step size of 5 minutes, denoted as T.

[0051] Furthermore, based on the Shenzhen Rail Transit Operation Dynamic Perception System, the predicted passenger flow during peak hours of a certain line is obtained;

[0052] S2: Based on the predicted passenger flow of the urban rail line during peak period obtained in step S1, the passenger flow A arriving at station s and preparing to board the train in time period t is obtained. t，s ;

[0053] According to the historical passenger flow data of the subway operating company, the passenger flow in each direction of the historical passenger flow is counted, and the passenger flow proportion in each direction of the historical passenger flow is calculated to obtain the number of passengers from the oth station s in the time period t. o Departure and arrival at the dth station s d Passenger flow share

[0054] Furthermore, in step S2, the passenger flow ratio of each direction of the historical passenger flow is calculated to obtain the passenger flow from the oth station s in the time period t. o Departure and arrival at the dth station s d Passenger flow share The specific implementation method includes the following steps: collect the data in time period t, from the oth site s o Departure and arrival at the dth station s d The number of passengers is recorded as From the oth site s o The number of all departing passengers is recorded as Then in time period t, from the oth site s o Departure and arrival at the dth station s d Passenger flow share The calculation expression is:

[0055] S3: Constructing a mixed integer programming model for multi-station passenger flow coordinated control. The specific implementation method of step S3 includes the following steps:

[0056] S3.1. Construct a mixed integer programming model for multi-station passenger flow coordinated control. The calculation expression is:

[0057] Among them, min represents the minimization function, S represents the station set;

[0058] D t，s represents the actual passenger demand at station s during time period t, including the number of people arriving at the station during time period t and the number of people stranded during the previous period. It is an integer variable in the mixed integer programming model for multi-station passenger flow coordinated control.

[0059] P t,s represents the optimal number of passengers entering station s during time period t, and is an integer variable in the mixed integer programming model for multi-station passenger flow coordinated control;

[0060] S3.2. Construct the constraints of the mixed integer programming model for multi-station passenger flow coordinated control.

[0061] The specific implementation method of step S3.2 includes the following steps:

[0062] S3.2.1. Set the actual passenger demand in the first time period at the beginning of the peak period to be equal to the passenger flow arriving at the station ready to board the bus in the first time period at the beginning of the peak period. The calculation expression is:

[0063] Among them, D 1,s represents the actual passenger demand at station s in the first time period at the beginning of the peak period, A 1,s It represents the passenger flow arriving at station s in the first time period at the beginning of the peak period and preparing to board the bus;

[0064] The first time period during the peak period is the first 5 minutes of the peak period. At this time, the transport capacity of each station is still sufficient, and the demand for rides only includes the number of passengers arriving at the station. There are no stranded passengers.

[0065] S3.2.2. The actual passenger demand in time period t is equal to the sum of the number of passengers arriving at the station ready to board the bus during time period t and the number of passengers stranded in the period before time period t. The calculation expression is:

[0066] Among them, D t-1，s represents the actual ride demand of station s and time period t in the previous period, P t-1，s represents the optimal number of passengers entering the station in the previous period of time t at station s;

[0067] S3.2.3. Set the upper and lower limits of the optimal passenger flow entering the station. The upper and lower limits of the optimal passenger flow entering the station can be greater than or equal to 0.15 times the actual passenger flow and less than or equal to the actual passenger flow. The calculation expression is:

[0068] S3.2.4. Set the number of passengers who do not get off the train arriving at station s during time period t to be equal to the sum of the passenger flow of passengers who arrived at the station before station s before time period t and whose destinations are after station s. The calculation expression is:

[0069] Among them, M t，s It represents the number of passengers who do not get off the train at station s during time period t, and is a continuous variable defined in the mixed integer programming model for multi-station passenger flow coordinated control;

[0070] represents the set of upstream stations of station s; represents the set of downstream stations of station s; Indicates that there is passenger flow from the oth station s oDeparture, go to the dth station s d The time period set of , and the time periods in the set satisfy that when the time increases to time period t, the passenger can arrive at station s; Indicates t o From the oth site s in the time period o Departure and arrival at the dth station s d The proportion of passenger flow, is the time period t o Next site o Optimal number of passengers entering the station;

[0071] S4. Use the branch and bound algorithm to solve the mixed integer programming model for multi-station passenger flow coordinated control constructed in step S3 to obtain the optimal passenger flow plan for the station.

[0072] Furthermore, the specific implementation method of step S4 includes the following steps:

[0073] S4.1. Linearly relax the mixed integer programming model for multi-station passenger flow coordinated control obtained in step S3 into a linear programming model, i.e., without restricting P t,s The value of can be an integer, which is recorded as a relaxed model. Then the simplex algorithm is called to solve it and the optimal solution of the linear programming is obtained.

[0074] S4.2, according to P t,s It is necessary to meet the requirements of its integer feasible domain, from P t,s Choose any non-integer solution variable and perform a binary split. In step S4.1, add the constraint Obtain the model for subproblem 1, denoted as subproblem 1, and add constraints to the relaxed model in step S4.1 Get the model of subproblem 2, denoted as subproblem 2, and call the simplex algorithm to solve it respectively;

[0075] S4.3. Calculate the objective function ∑ in subproblems 1 and 2 obtained in step S4.2. t∈T ∑ s∈S A t，s (D t，s -P t,s ), and take the maximum value among them as the lower bound of the objective function of the mixed integer programming model for multi-station passenger flow coordinated control;

[0076] S4.4. For sub-problems whose objective function value is greater than or equal to the lower bound value of the objective function of the mixed integer programming model for multi-site passenger flow coordinated control and whose solutions are non-integer, further branch and repeat steps S4.2, S4.3, and S4.4 until the integer optimal solution is obtained.

[0077] This embodiment describes a coordinated urban rail passenger flow control method based on morning peak passenger flow data for Shenzhen Metro Line 1. The total passenger flow (number of boardings) at each station is statistically compared. The analysis is conducted during the peak passenger flow period of 7:30-9:30, the time period with the heaviest morning peak passenger flow. Using the coordinated flow control strategy, the number of passengers served during the morning peak in the Qianhai Bay-Taoyuan section increased by 7,919, while the number of passengers served during the morning peak in the Xixiang-Xin'an section decreased by 3,021. The overall number of passengers served during the morning peak increased by 4,909. Table 1 shows the comparison.

[0078] Table 1 Comparison of total passenger flow of trains at each station (persons)

[0079] The comparison of passenger flow controlled by multiple sites is shown in Figure 2: after optimization of the optimal single-line collaborative flow limiting strategy, the trend of the number of people subject to flow limiting in an average of 15 minutes is more balanced, and there is no large-scale passenger flow detention before optimization.

[0080] The comparison of the number of boarding passengers and the number of stranded passengers at Qianhai Bay Station, a key station, is shown in Figure 3: Due to the large flow of transfer passengers at Qianhai Bay, when the coordinated flow limiting strategy was not adopted, stranded passengers (greater than 600 people) began to appear at 7:50, and the stranded passenger flow increased significantly, resulting in an increase in passenger demand in the subsequent time. The cumulative number of people gathered in a 5-minute time period (the number of new arrivals in the current period + the number of stranded people in the previous period) reached a maximum of 18,019 people, and the average number of people gathered in a 5-minute time period reached 10,934 people; after adopting the coordinated flow limiting strategy, flow limiting measures (greater than 300 people) began to appear at 8:15, with a maximum gathering of 7,259 people in a 5-minute time period and an average gathering of 4,400 people every 5 minutes.

[0081] Specific implementation method two:

[0082] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a method for coordinated control of urban rail passenger flow described in the first embodiment are implemented.

[0083] The computer device of the present application may include a processor and a memory, such as a single-chip microcomputer including a central processing unit. Furthermore, the processor is used to execute the computer program stored in the memory to implement the steps of the above-mentioned urban rail passenger flow coordinated control method.

[0084] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0085] The memory may mainly include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory may include a high-speed random access memory and may also include a non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0086] Specific implementation method three:

[0087] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a method for coordinated control of urban rail passenger flow as described in the first embodiment.

[0088] The computer-readable storage medium of the present application can be any form of storage medium that can be read by the processor of a computer device, including but not limited to non-volatile memory, volatile memory, ferroelectric memory, etc. The computer-readable storage medium stores a computer program. When the processor of the computer device reads and executes the computer program stored in the memory, the steps of the above-mentioned urban rail passenger flow coordinated control method can be implemented.

[0089] The computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0090] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0091] Although the present application has been described above with reference to specific embodiments, various modifications may be made thereto and components may be substituted with equivalents without departing from the scope of the present application. In particular, as long as there are no structural conflicts, the various features of the embodiments disclosed herein may be combined with each other in any manner, and the omission of an exhaustive description of these combinations in this specification is solely for the sake of space and resource conservation. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions within the scope of the claims.

Claims

1. A method for coordinated control of urban rail passenger flow, characterized in that: The steps include: S1. Use the rail simulation system to obtain the predicted passenger flow during the peak period of the line. Use the simulation function in the rail simulation system to predict the passenger flow during the peak period of the urban rail line, including the entry ID, exit ID, entry time period, and passenger flow data; divide the time interval from the beginning to the end of the peak period into a time period set according to a step length of 5 minutes, recorded as T; S2: Based on the predicted passenger flow of the urban rail line during the peak period obtained in step S1, the passenger flow A of arriving at station s and preparing to board the train in time period t is obtained according to different time periods t and stations s. t，s ; According to the historical passenger flow data of the subway operating company, the passenger flow in each direction of the historical passenger flow is counted, and the passenger flow proportion in each direction of the historical passenger flow is calculated to obtain the number of passengers from the oth station s in the time period t. o Departure and arrival at the dth station s d The proportion of passenger flow S3, construct a mixed integer programming model for multi-station passenger flow coordinated control; S4. Use the branch and bound algorithm to solve the mixed integer programming model for the multi-station passenger flow coordinated control constructed in step S3 to obtain the optimal passenger flow plan for the station.

2. The method for coordinated control of urban rail passenger flow according to claim 1, characterized in that: In step S2, the passenger flow proportions of each direction of the historical passenger flow are calculated to obtain the passenger flow from the oth station s in the time period t o Departure and arrival at the dth station s d The proportion of passenger flow The specific implementation method includes the following steps: The data is concentrated in time period t, from the oth station s o Departure and arrival at the dth station s d The number of passengers is recorded as From the oth site s o The number of all departing passengers is recorded as Then in time period t, from the oth station s o Departure and arrival at the dth station s d The proportion of passenger flow The calculation expression is:

3. A method for coordinated control of urban rail passenger flow according to claim 1 or 2, characterized in that: The specific implementation method of step S3 includes the following steps: S3.

1. Construct a mixed integer programming model for multi-station passenger flow coordinated control. The calculation expression is: Among them, min represents the minimization function, S represents the station set; D t，s represents the actual passenger demand of station s in time period t, including the number of people arriving at the station in time period t and the number of people stranded in the previous time period, and is an integer variable of the mixed integer programming model for multi-station passenger flow coordinated control; P t，s represents the optimal number of passengers entering station s in time period t, which is an integer variable of the mixed integer programming model for multi-station passenger flow coordinated control; S3.

2. Construct the constraints of the mixed integer programming model for multi-station passenger flow coordinated control.

4. The method for coordinated control of urban rail passenger flow according to claim 3, characterized in that: The specific implementation method of step S3.2 includes the following steps: S3.2.

1. Set the actual passenger demand in the first time period at the beginning of the peak period to be equal to the passenger flow arriving at the station ready to board the bus in the first time period at the beginning of the peak period. The calculation expression is: Among them, D 1，s represents the actual passenger demand at station s in the first time period at the beginning of the peak period, A 1，s It represents the passenger flow that arrives at station s and prepares to board the bus in the first time period at the beginning of the peak period; S3.2.

2. Set the actual passenger demand in time period t to be equal to the sum of the passenger flow arriving at the station ready to board the bus in time period t and the passenger flow stranded in the previous time period of time period t. The calculation expression is: Among them, D t-1，s represents the actual passenger demand of the previous period of station s and time period t, P t-1，s represents the optimal number of passengers entering the station in the previous period of time t at station s; S3.2.

3. Set the upper and lower limits of the optimal passenger flow entering the station. The values ​​of the upper and lower limits of the optimal passenger flow entering the station can be greater than or equal to 0.15 times the actual passenger flow and less than or equal to the actual passenger flow. The calculation expression is: S3.2.

4. Set the number of passengers who do not get off the train at station s during time period t to be equal to the sum of the passenger flow of the passengers who enter the station before station s before time period t and whose destination is after station s. The calculation expression is: Among them, M t，s It represents the number of passengers who do not get off the train at station s in time period t, and is a continuous variable defined in the mixed integer programming model for multi-station passenger flow coordinated control; represents the set of upstream stations of station s; represents the set of downstream stations of station s; Indicates that there is passenger flow from the oth station s o Departure, go to the dth station s d A set of time periods, and the time periods in the set satisfy that when the time increases to time period t, the passenger can arrive at station s; Indicates t o From the oth site s in the time period o Departure and arrival at the dth station s d The proportion of passenger flow, For time period t o Next site o Optimal number of passengers entering the station.

5. The urban rail passenger flow coordinated control method according to claim 1, characterized in that: The specific implementation method of step S4 includes the following steps: S4.

1. Linearly relax the mixed integer programming model for multi-station passenger flow coordination control obtained in step S3 into a linear programming model, that is, without restricting P t，s The value of can be an integer, which is recorded as a relaxed model. Then the simplex algorithm is called to solve the problem and obtain the optimal solution of the linear programming. S4.

2. According to P t，s It needs to meet the requirements of its integer feasible domain, from P t，s Select any non-integer solution variable from the binary search and add constraints to the relaxed model in step S4.

1. The model of subproblem 1 is obtained, denoted as subproblem 1, and the constraint is added to the relaxed model in step S4.1 The model of subproblem 2 is obtained, recorded as subproblem 2, and the simplex algorithm is called to solve it respectively; S4.

3. Calculate the objective function ∑ in subproblems 1 and 2 obtained in step S4.2 t∈T ∑ s∈S S t，s (D t，s -P t，s ), and take the maximum value as the lower bound of the objective function of the mixed integer programming model for multi-station passenger flow coordinated control; S4.

4. For sub-problems whose objective function values ​​are greater than or equal to the lower bound value of the objective function of the mixed integer programming model for multi-site passenger flow coordinated control and whose solutions are non-integer, further branching is performed, and steps S4.2, S4.3, and S4.4 are repeated until an integer optimal solution is obtained.

6. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the urban rail passenger flow coordinated control method according to any one of claims 1 to 5 when executing the computer program.

7. A storage medium, wherein the storage medium is a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, wherein: When the computer program is executed by a processor, the urban rail passenger flow coordinated control method according to any one of claims 1 to 5 is implemented.

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