Coordinated optimization method for demand-oriented train diagram and passenger flow control

Abstract

The application provides a kind of method for coordinating optimization of train diagram and passenger flow control guided by demand, comprising: obtaining physical characteristics and spatial characteristics in train operation process, expressed as mathematical model;Constructing decision variables and constraint conditions;According to physical characteristics, spatial characteristics, decision variables and constraint conditions, construct initial rail transit train operation diagram and passenger flow control collaborative optimization nonlinear integer programming model;Introduce 0-1 variable and large M constraint to linearize the nonlinear model, and derive the minimum value of large M, obtain the passenger flow control collaborative optimization linear programming model with more compact upper bound;According to the structural characteristics of the optimization objective original problem and the mathematical properties of the passenger flow control collaborative optimization model, design an exact solution algorithm based on Benders decomposition, obtain the standard rail transit train operation diagram, demand guidance plan and passenger flow control strategy.The method of the application improves the service quality and operation efficiency of urban rail transit system, while reducing resource waste.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban public transport operation management, and particularly relates to a traffic train diagram and passenger flow control collaborative optimization method combined with demand guidance. BACKGROUND

[0002] With the rapid development of rail transit systems in megacities, the increasing demand for peak passenger flow has brought great challenges to the carrying capacity of the system. Especially during peak hours, the passenger flow far exceeds the maximum carrying capacity of the rail transit system, resulting in train congestion, prolonged passenger waiting time and other problems. How to effectively guide passengers with flexible travel demand to avoid peak hours and shift to off-peak hours to alleviate the pressure of peak passenger flow and improve the utilization rate of train capacity during off-peak hours, so as to achieve the optimal distribution of overall passenger flow, is a key problem that needs to be solved in the current smart city rail transit system. However, existing academic research and engineering practice have not fully explored this problem. Although demand management and passenger flow control strategies have gradually been valued in the field of rail transit, there is still a lack of systematic research methods and practical solutions in the collaborative optimization of peak demand relief and off-peak resource utilization. Therefore, it is necessary to construct an innovative optimization model from the perspective of demand guidance and passenger flow control to solve this complex travel demand management problem.

[0003] Through the analysis of existing related literature, most of the researches or patents are inclined to study the optimization of train diagram or passenger flow control respectively. At present, there is no research on the collaborative optimization of demand management and train diagram considering service fairness under the pre-travel mode, which is difficult to improve the operation efficiency while ensuring the service quality. SUMMARY

[0004] Therefore, the present application provides a traffic train diagram and passenger flow control collaborative optimization method combined with demand guidance to solve the above problems.

[0005] The application provides a method for coordinating optimization of a train operation diagram and passenger flow control based on demand guidance, comprising the following steps: obtaining physical characteristics and spatial characteristics in a train operation process, and representing the characteristics as mathematical models; constructing decision variables based on the mathematical models of the physical characteristics and the spatial characteristics; constructing constraint conditions according to coupling relationships between reserved passengers and train operation, between reserved passengers and non-reserved passengers, between whether non-reserved passengers are willing to transfer travel time and train remaining capacity, and between fairness relationships between the number of passengers served by a train at each station and the number of waiting passengers; constructing an initial rail transit train operation diagram and passenger flow control coordinated optimization nonlinear integer programming model with a weighted sum of minimized passenger waiting time and government additional subsidies as an optimization objective according to the physical characteristics, the spatial characteristics, the decision variables and the constraint conditions; introducing 0-1 variables and large M constraints to linearize the nonlinear model, and deriving a minimum value of the large M to obtain a passenger flow control coordinated optimization linear programming model with a more compact upper bound; designing an accurate solving algorithm based on Benders decomposition according to structural characteristics of the optimization objective original problem and mathematical properties of the passenger flow control coordinated optimization model, solving the original problem by decomposition into a main problem and a sub-problem with smaller scale, and iteratively solving until a standard rail transit train operation diagram, a demand guidance plan and a passenger flow control strategy are obtained.

[0006] In another implementation manner of the application, the physical characteristics include basic parameters of train operation, maximum passenger capacity of a train and ticket price along a rail transit line; and the spatial characteristics include station distribution along the rail transit line and passenger flow demand of each station in each time period.

[0007] In another implementation manner of the application, the decision variables include train departure indication variables, the number of passengers transferring travel time and passenger flow control related decision variables.

[0008] In another implementation manner of the application, the constraint conditions include train operation diagram constraints, demand guidance strategy constraints, constraints of a dynamic evolution process of reserved passengers, constraints of a dynamic evolution process of non-reserved passengers and coupling constraints between reserved passengers and non-reserved passengers.

[0009] In another implementation manner of the application, the train operation diagram constraints are represented as:

[0010]

[0011]

[0012] wherein z itis a 0-1 indicator variable representing whether train i departs from station s at time t; h min and h max respectively represent the minimum and maximum headway limits; y ist is a 0-1 indicator variable representing whether time t is within the headway time window of train i and i+1 at station s; t represents a discrete time period, t∈T; i represents a train service, i∈I.

[0013] In another implementation of the present application, the demand guidance strategy constraint is represented as:

[0014]

[0015] where τ represents the maximum limit of allowed passenger transfer time; κ uvt't is the number of passengers who transfer their travel time from time t to time t'; D uvt is the dynamic passenger demand arriving at station u at time t and destined for station v; u, v represent stations, u, v∈S.

[0016] In another implementation of the present application, the reserved passenger dynamic evolution constraint is represented as:

[0017]

[0018] where D uvt is the reserved quota allocation plan, i.e., the number of reserved quotas from station u to station v at time t; is the number of reserved passengers from station u to station v among the on-board passengers of train i.

[0019] In another implementation of the present application, the unreserved passenger dynamic evolution constraint is represented as:

[0020]

[0021]

[0022] where ρ iuv is the minimum service ratio; b iuv is the number of unreserved passengers from station u to station v among the on-board passengers of train i; w iuv is the number of passengers waiting for train i at station u to go to station v; r iuv is the number of passengers who are stranded at station u by train i and waiting for a subsequent train to go to station v.

[0023] In another implementation of the present application, the coupling constraint between reserved and unreserved passengers is represented as:

[0024]

[0025] wherein, o iu and o iu respectively represent the number of reserved passengers and the number of non-reserved passengers in the vehicle passengers; l iu represents the number of passengers getting off.

[0026] In another implementation of the present application, the objective function of the optimization objective is represented as:

[0027] min ω t F t +ω s F s

[0028]

[0029] wherein, F t represents the passenger waiting time; F s represents the government additional subsidy; ε uv represents the ticket price from station u to station v; φ represents the ticket price discount; and respectively represent the number of newly arrived reserved and non-reserved passengers in each departure interval.

[0030] In the demand-guided traffic train diagram and passenger flow control collaborative optimization method of the present application, by designing and implementing a ticket price discount strategy, passengers are attracted to flexibly adjust their travel time, thereby achieving the effect of peak shaving and optimizing passenger flow distribution; an efficient linkage mechanism is established between the train diagram design and the passenger flow control scheme, which effectively improves the service quality and operational efficiency on the premise of ensuring that reserved passengers directly enter the station and take the train; in order to improve the solving efficiency, an exact algorithm based on Benders decomposition is designed; the mismatch between passenger flow demand and transportation supply in the prior art is solved, and the overall service level and resource utilization efficiency of the urban rail transit system are significantly improved, and the technical bottlenecks of insufficient service quality and low operational efficiency in the traditional scheme are solved. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. By reading the detailed description of the embodiments below, the advantages and benefits of the embodiments will become clear to those skilled in the art. The drawings are only for the purpose of illustrating the preferred embodiments and are not considered as limiting the present application.

[0032] In the drawings:

[0033] Figure 1 A demand-guided traffic train diagram and passenger flow control collaborative optimization method flowchart of an embodiment of the present application.

[0034] Figure 2 A schematic diagram of a boarding rule for reserved and non-reserved passengers for an embodiment of the present application.

[0035] Figure 3 A schematic diagram of a time-equivalent method for an embodiment of the present application.

[0036] Figure 4 A schematic diagram of a traffic train operation line for an embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to make personnel in the art better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and in detail below in conjunction with the drawings of the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art should belong to the scope of protection of the embodiments of the present application.

[0038] Figure 1 A schematic diagram of a traffic train operation diagram and passenger flow control collaborative optimization method flow provided by an embodiment of the present application is shown in FIG. 1, and the embodiment mainly includes the following steps: Figure 1

[0039] S101, physical characteristics and spatial characteristics in the train operation process are acquired, and the above characteristics are expressed as a mathematical model.

[0040] S102, a decision variable is constructed based on the mathematical model of the physical characteristics and the spatial characteristics.

[0041] S103, a constraint condition is constructed according to a coupling relationship between reserved passengers and train operation in a city rail transit system, a coupling relationship between reserved passengers and non-reserved passengers, a coupling relationship between whether non-reserved passengers are willing to transfer travel time and remaining train capacity, and a fairness relationship between the number of passengers served by the train at each station and the number of waiting passengers.

[0042] S104, an initial rail transit train operation diagram and passenger flow control collaborative optimization nonlinear integer programming model is constructed with a weighted sum of passenger waiting time and government additional subsidies as an optimization objective according to the physical characteristics, the spatial characteristics, the decision variable and the constraint condition.

[0043] S105, a 0-1 variable and a large M constraint are introduced to linearize the nonlinear model, and a minimum value of the large M is derived to obtain a passenger flow control collaborative optimization linear programming model with a more compact upper bound.

[0044] ​It should be understood that the passenger flow control collaborative optimization linear programming model aims to achieve reasonable transfer of passenger travel time by introducing a demand guide strategy, and collaboratively optimize a train operation diagram and a passenger flow control scheme, thereby improving the service level and operation efficiency of the overall system.

[0045] S106, according to the structure characteristics of the optimization target original problem and the mathematical characteristics of the passenger flow control collaborative optimization model, an accurate solving algorithm based on Benders decomposition is designed, the original problem is decomposed into a main problem and a sub-problem with smaller scale, and iterative solving is performed until a standard rail transit train operation diagram, a demand guide plan and a passenger flow control strategy are obtained.

[0046] In the traffic train operation diagram and passenger flow control collaborative optimization method combined with a demand guide, a ticket price discount strategy is designed and implemented to attract passengers to flexibly adjust their travel time, thereby achieving the effect of peak shaving and optimizing passenger flow distribution; an efficient linkage mechanism is established between the train operation diagram design and the passenger flow control scheme, the service quality and operation efficiency are effectively improved on the premise of ensuring that the reserved passengers directly enter the station and take the train; in order to improve the solving efficiency, an accurate algorithm based on Benders decomposition is designed; the mismatch between passenger demand and transportation supply in the prior art is solved, and the overall service level and resource utilization efficiency of the urban rail transit system are significantly improved, and the technical bottlenecks of insufficient service quality and low operation efficiency in the traditional scheme are solved.

[0047] In another implementation manner of the present application, the physical characteristics include train operation basic parameters, the maximum passenger capacity of the train, and the ticket price along the urban rail transit line; and the spatial characteristics include the distribution of stations along the urban rail transit line and the passenger flow demand of each station in each time period.

[0048] Exemplarily, the following necessary parameters and data are determined in advance: the distribution of stations along the urban rail transit line; train operation basic parameters including inter-station running time, maximum and minimum departure interval, station stop time, etc.; the maximum passenger capacity of the train; the passenger flow demand of each station in each time period; and the ticket price along the urban rail transit line.

[0049] Specifically, the set of all stations in the line is S={1, 2,..., u,..., |S|}, wherein station 1 is the starting station and station |S| is the terminal station; the set of trains is I={1, 2,..., i,..., |I|}, wherein the capacity of each train is C; the set of discrete time periods is wherein the length of each unit time granularity is σ; the set of equivalent discrete time periods is T={1, 2,..., t,..., |T|}; the peak period is T∈T; and the dynamic passenger flow demand arriving at station u at t time and destined for station v is Duvt .

[0050] As shown in Figure 3 , the planning time domain is discretized and equivalent, and the spatial characteristics of the physical line and the physical characteristics of the train are abstracted and expressed in the form of mathematical modeling, thereby providing a basis for subsequent optimization.

[0051] In another implementation manner of the present application, the decision variables include train departure indication variables, passenger numbers transferring out of travel time, and passenger flow control related decision variables.

[0052] Exemplarily, based on the above set, parameters and index symbols, the decision variables required by the model are established. In the pre-booking travel mode, the train diagram and demand management collaborative optimization model of urban rail transit involves train departure 0-1 indication variable z it , the number of passengers traveling from station u to station v and transferring out of travel time from t to t' κ uvt't , the number of passengers controlled by the passenger flow b iuv ; wherein t represents a discrete period, t∈T, i represents a train number, i∈I, u and v represent stations, u,v∈S. Through these variables, the mutual relationship between train operation and passenger dynamic evolution can be quantitatively described, thereby laying a foundation for realizing the collaborative optimization of train diagram and passenger flow control combined with demand guidance strategy.

[0053] In another implementation manner of the present application, the constraint conditions include train diagram constraints, demand guidance strategy constraints, pre-booking passenger dynamic evolution process constraints, non-pre-booking passenger dynamic evolution process constraints, and coupling constraints between pre-booking and non-pre-booking passengers.

[0054] It should be understood that the present application focuses on the emerging operation mode of pre-booking travel in urban rail transit system, and further considers the finiteness of pre-booking quota, thereby systematically analyzing the mutual influence and dynamic coupling relationship of three groups of passengers, i.e., pre-booking successful passengers, pre-booking unsuccessful passengers and non-pre-booking passengers in time and space.

[0055] As shown in Figure 2As shown, the green passenger makes a reservation for the travel plan on February 3, 2024 after the reservation system opens on February 2, 2024, and the reservation is successful; the orange and blue passengers are also users of the reservation system, but the reservation is not successful, and they receive the recommended travel time from the system. On February 3, 2024, the green passenger arrives at the station hall according to the reservation time, directly enters the platform for waiting through the reservation special gate; the orange passenger changes the planned travel time, arrives at the station hall according to the recommended travel time from the system, enters the platform through the general gate, and obtains a ticket price discount; the blue passenger insists on the original planned travel time, arrives at the station hall, and enters the platform through the general gate with the original ticket price; the red passenger is not one of the users of the reservation system, arrives at the station hall on the operation day, and enters the platform through the general gate with the original ticket price.

[0056] In another implementation manner of the present application, the train diagram constraint is represented as:

[0057]

[0058] wherein z it represents a 0-1 indicator variable involved in the train departure in the model of the coordinated optimization of the urban rail transit train diagram and demand management in the reservation travel mode; h min and h max respectively represent the minimum and maximum departure interval limits; y ist represents whether the time t is within the departure interval time window of the trains i and i+1 at the station s; t represents a discrete time period, t∈T; i represents a train number, i∈I.

[0059] In another implementation manner of the present application, the demand guidance strategy constraint is represented as:

[0060]

[0061] wherein τ represents the maximum limit of the allowed passenger transfer time; κ uvt't represents the number of people who transfer the travel time from the time t to the time t'; D uvt represents the dynamic passenger flow demand arriving at the station u at the time t and having the station v as the destination; u and v represent stations, u,v∈S.

[0062] In another implementation manner of the present application, the reservation passenger dynamic evolution constraint is represented as:

[0063]

[0064] wherein D uvt represents the reservation quota allocation plan, that is, the number of reservation quotas from the station u to the station v at the time t; represents the number of reservations from the station u to the station v among the on-board passengers of the train i.

[0065] In another implementation form of the application, the dynamic evolution constraint of the unreserved passengers is represented as:

[0066]

[0067] where ρ iuv represents the minimum service ratio; b iuv represents the number of unreserved passengers on train i from station u to station v; w iuv represents the number of passengers waiting for train i at station u to go to station v; r iuv represents the number of passengers at station u who are detained by train i and wait for the subsequent train to go to station v.

[0068] In another implementation form of the application, the coupling constraint between the reserved and unreserved passengers is represented as:

[0069]

[0070] where o iu and o iu respectively represent the number of reserved and unreserved passengers in the train; l iu represents the number of passengers getting off.

[0071] In another implementation form of the application, the objective function of the optimization objective is represented as:

[0072] minω t F t +ω s F s (21)

[0073]

[0074] where F t represents the passenger waiting time; F s represents the government additional subsidy; ε uv represents the ticket price from station u to station v; φ represents the ticket price discount; and respectively represent the number of newly arrived reserved and unreserved passengers in each departure interval.

[0075] It should be understood that with the rise of the pre-trip technology, passengers can determine the departure time and trip before traveling, thereby to a certain extent, cope with the fluctuation and uncertainty of passenger flow demand in rail transit system. In view of this, the present application encourages passengers to transfer to off-peak travel during peak hours by optimizing the fare discount strategy, thereby realizing the effective guidance of passenger flow; through reasonable incentive policy to guide passengers to staggered travel, and considering the passenger flow demand of peak hours and non-peak hours, to ensure the balance of pre-booking and non-pre-booking passengers in service fairness, and at the same time realize the minimization of peak passenger waiting time and government additional subsidy cost.

[0076] In another implementation manner of the present application, the calculation method of the variables involved in the objective function (22) and is as follows:

[0077]

[0078] The urban rail transit train diagram and passenger flow control collaborative optimization model of the combined demand guidance strategy aiming at minimizing the weighted sum of the expected value of the operator cost and the passenger travel cost, specifically includes:

[0079]

[0080] Considering the homogeneous dynamic passenger flow demand, taking the weighted sum of the passenger waiting time and the government additional subsidy as the target, taking the number of travel time transfer, passenger flow control and train arrival and departure time as the decision, a class of integer nonlinear programming model is constructed.

[0081] Preferably, the model linearization method and the derivation method of the large M value, specifically includes:

[0082] (1) The constraint (13) is a nonlinear constraint in the form of multiplication of integer variables and 0-1 variables, and an auxiliary variable can be introduced for linearization processing.

[0083] Specifically, the auxiliary variable is defined. The constraint (13) can be equivalently converted into:

[0084]

[0085] Wherein:

[0086] Further, the calculation of the waiting number can be equivalently expressed as follows:

[0087]

[0088] (2) When linearizing the constraint (22), first define the auxiliary variable Further, its equivalent linear form is expressed as follows:

[0089]

[0090] Further, the constraint (22) can be equivalently expressed as follows:

[0091]

[0092] (3) The linear equivalent linear form of the constraint (25) can be expressed as follows:

[0093]

[0094] (4) In linearizing the constraints (26) and (27), first define the auxiliary variable θ itt' = x it x it' , the following calculation is correct:

[0095]

[0096] In view of this, the equivalent linear form of the constraint (26) can be expressed as:

[0097]

[0098] Further, define the auxiliary variable The equivalent linear form of the constraint (27) can be expressed as follows:

[0099]

[0100] In view of this, the equivalent linear form of the constraint (27) can be expressed as:

[0101]

[0102] In summary, the above integer nonlinear programming model can be equivalently reconstructed into the following integer linear programming model:

[0103]

[0104] In the present application, by optimizing the passenger flow control strategy, the service experience of non-reserved passengers is improved and the utilization rate of transport resources is maximized, based on the above idea, a class of urban rail transit train diagram and passenger flow control collaborative optimization nonlinear programming model combined with demand guidance strategy is proposed, with the minimum passenger waiting time and government additional subsidy cost as the target, considering providing convenience for reserved passengers to directly enter the station and take the train, by optimizing the ticket price discount strategy to attract peak passenger flow demand to shift to off-peak period, and by introducing 0-1 variable and large M constraint method, the above model is equivalently converted into linear form, the reasonable value of the large M parameter is derived and determined, to obtain a linear programming model with a more compact upper bound.

[0105] Preferably, the solving algorithm specifically comprises:

[0106] Step 1: Relax the model (38) into a mixed-integer programming model, where the decision variable z it ∈{0,1},κ uvt′t ≥0,b iuv ≥0.

[0107] Step 2: Decompose the relaxed model into a main problem of timetable optimization and reservation passenger assignment and a sub-problem of demand guidance and passenger flow control.

[0108] Step 3: Solve using Benders decomposition algorithm. By iteratively solving the main problem and the sub-problem until the termination condition is met, a high-quality train diagram, demand guidance plan and passenger flow control strategy are obtained. Specifically, if the sub-problem is feasible, an optimal cut is fed back to the main problem; otherwise, a feasible cut is fed back.

[0109] Step 4: Input the train diagram obtained in step 3 into the integer linear programming model (38) and use GUROBI to solve to obtain the optimal demand guidance plan and passenger flow control strategy.

[0110] In the present application, according to the model properties and structural characteristics, an exact algorithm based on Benders decomposition is designed to solve the model. The algorithm proposed improves the solving efficiency by decomposing the original problem into smaller sub-problems, and has a significant computational advantage.

[0111] Based on the above basic data, the model and solving algorithm program proposed in the present application are programmed in Java to obtain the corresponding urban rail transit train diagram, passenger flow control scheme and reservation quota allocation plan.

[0112] In another implementation manner of the present application, a line containing 6 stations is taken as an example to verify the content of the present application, as shown in Figure 4 The line contains 6 stations, which are named A, B, C, D, E and F in turn. The station stop time of the line is set to 1 minute, the minimum and maximum departure interval is 2 minutes and 6 minutes respectively, the train capacity is 500 people, the ticket price is set to 3 yuan, and if the passenger who fails to make a reservation transfers out of the departure time, the ticket price will be adjusted to 80% of the original ticket price. In addition, the research period is discretized with a discrete time granularity of 1 minute. According to the above given input data, the code is programmed in Java to construct the model and solving algorithm framework described in the present application, and the problem is solved, that is, the optimal urban rail transit train diagram, passenger flow control scheme and reservation quota allocation plan can be obtained.

[0113] As shown in Table 1, it can be seen that at least 12 trains are needed to meet all the demands when the demand management strategy of encouraging passengers to adjust the travel time through incentives is not implemented. However, if passengers are encouraged to adjust the travel time by at most 10 minutes, only 11 trains can be used to meet all the demands. It can be concluded that the demand management strategy of promoting passengers to adjust the travel time through incentives can effectively reduce the fleet size.

[0114] In addition, there is an inverse relationship between the elasticity of arrival time adjustment and the reduction of the number of stranded passengers. Specifically, as the strength of encouraging passengers to adjust the behavior increases and the maximum adjustment time extends, the number of stranded passengers significantly reduces. For example, when 11 trains are operated, as the maximum adjustment time extends from 10 minutes to 25 minutes, the number of stranded passengers reduces from 211 to 32. However, there is no linear relationship between the maximum allowed adjustment time and the average waiting time because the optimal solution is a trade-off between the waiting time and the increase of government subsidies. The research results show that the integration of the guidance policy and the timetable policy can improve the effectiveness of the rail transit system, which is embodied by significantly reducing the passenger waiting time, while there is no serious adverse impact on the operator's perspective.

[0115] Table 1 Optimal results under different maximum transfer times

[0116]

[0117] In summary, the present application researches the coordinated optimization of the train diagram and the demand management of the urban rail transit under the reservation travel mode. The core technical problem is how to realize the coordinated optimization of the train diagram, the demand guidance strategy and the passenger flow control strategy, so that they cooperate and interact with each other, thereby meeting the peak passenger flow demand while improving the service quality and the utilization efficiency of the transport capacity.

[0118] The present application constructs a coordinated optimization model of the train diagram and the passenger flow control of the urban rail transit combined with the demand guidance strategy, and proposes an accurate solving algorithm. The model effectively balances the contradiction between the passenger waiting time and the fleet size by introducing the demand management strategy and combining the optimization means of train scheduling and passenger flow control. While improving the service level, the operating cost is reduced, achieving the win-win effect of the operator and the passengers.

[0119] Therefore, the collaborative optimization model and algorithm proposed in the application has a significant effect on improving the service quality of the urban rail transit system and reducing resource waste. The collaborative optimization process not only needs to consider the demand balance between the reserved passengers and the non-reserved passengers, but also needs to reasonably design the incentive measures such as ticket price discount and entry convenience in the process of guiding the peak passengers to the off-peak period, so as to realize more effective demand management. The collaborative optimization process can provide theoretical support and practical reference for the intelligentization, demand-oriented optimization and fine management of the future urban rail transit system, and has high popularization and application value. Through the optimization model and algorithm, theoretical support and practical tools are provided for solving the peak passenger flow transportation and maximum utilization of transport resources in the urban rail transit system of a megacity, and the optimization model and algorithm have certain guiding significance in the management and planning of the smart urban rail transit system, and can provide innovative and practical solutions for realizing the sustainable and efficient operation of the urban rail transit.

[0120] The application also provides an electronic device, which can include a processor, a memory, and a communication bus, and a communications interface.

[0121] In the application, the processor, the memory, and the communications interface can communicate with each other through the communication bus.

[0122] The processor, the memory, and the communications interface can communicate with each other through the communication bus.

[0123] The communications interface is configured to communicate with other electronic devices or servers.

[0124] The processor is configured to execute a program, and specifically can execute the steps of the demand-guided train operation diagram and passenger flow control collaborative optimization method in any one of the above embodiments.

[0125] Specifically, the program can include program code, and the program code includes computer operation instructions.

[0126] The processor can be a central processing unit (CPU) or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the application. The one or more processors included in the smart device can be the same type of processor, such as one or more CPUs, or different types of processors, such as one or more CPUs and one or more ASICs.

[0127] The memory is configured to store the program. The memory can include a high-speed RAM memory, and can also include a non-volatile memory, such as at least one disk memory.

[0128] The program can be specifically used for enabling the processor to perform the steps of any one of the demand-guided traffic train working diagram and passenger flow control collaborative optimization methods described in the embodiments. The specific implementation of each step in the program can refer to the corresponding description of the steps and units performed by any one of the demand-guided traffic train working diagram and passenger flow control collaborative optimization methods described above, and will not be described here. It can be clearly understood by those skilled in the art that, for the convenience and brevity of description, the specific working processes of the devices and modules described above can refer to the corresponding process description in the foregoing method embodiments.

[0129] The application further provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the method of any one of the embodiments of the application.

[0130] The method according to the embodiments of the application described above can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium such as a CD ROM, a RAM, a floppy disk, a hard disk or a magneto-optical disk, or be downloaded through a network from a remote recording medium or a non-transitory machine-readable medium originally stored in a local recording medium and then stored in a local recording medium, so that the method described herein can be processed by such software using a general-purpose computer, a special-purpose processor or programmable or special-purpose hardware such as an ASIC or an FPGA. It can be understood that the computer, the processor, the microprocessor controller or the programmable hardware includes a storage component (for example, RAM, ROM, flash memory, etc.) that can store or receive software or computer code, when the software or computer code is accessed and executed by the computer, the processor or the hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code will convert the general-purpose computer into a special-purpose computer for executing the method shown herein.

[0131] So far, specific embodiments of the application have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0132] It should be noted that all directional indications, such as upper, lower, left, right, back, etc., in the embodiments of the application are only used to explain the relative positional relationship, movement condition, etc. between components in a certain specific posture (as shown in the drawings), and if the specific posture changes, the directional indications will also change accordingly.

[0133] In the description of the present application, the terms "first", "second" are only used for the convenience of describing different components or names, and cannot be understood as indicating or implying the order relationship, relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can be explicitly or implicitly included at least one of the features.

[0134] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0135] It should be noted that, although the specific embodiments of the present application are described in detail with reference to the accompanying drawings, it should not be understood as limiting the scope of protection of the present application. Various modifications and variations of the embodiments described in the claims are still within the scope of protection of the present application without creative labor.

[0136] The examples of the embodiments of the present application are intended to simply illustrate the technical features of the embodiments of the present application, so that those skilled in the art can intuitively understand the technical features of the embodiments of the present application, and are not intended to be an improper limitation of the embodiments of the present application.

[0137] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for coordinating optimization of a traffic train diagram with demand-oriented guidance and passenger flow control, characterized in that, The application relates to a method for constructing an initial rail transit train operation diagram and passenger flow control collaborative optimization nonlinear integer programming model. The method comprises the following steps: acquiring physical features and spatial features in a train operation process and representing the features as mathematical models; constructing decision variables based on the mathematical models of the physical features and the spatial features; constructing constraint conditions according to coupling relationships between reservation passengers and train operation, coupling relationships between reservation passengers and non-reservation passengers, coupling relationships between whether non-reservation passengers are willing to transfer travel time and train remaining capacity, and fairness relationships between the number of passengers served by trains at each station and the number of waiting passengers; constructing an initial rail transit train operation diagram and passenger flow control collaborative optimization nonlinear integer programming model with an optimization objective of minimizing the weighted sum of passenger waiting time and government additional subsidies according to the physical features, the spatial features, the decision variables and the constraint conditions; linearizing the nonlinear model by introducing 0-1 variables and large M constraints and deducing the minimum value of the large M to obtain a passenger flow control collaborative optimization linear programming model with a more compact upper bound; 2. The method of claim 1, wherein, designing an accurate solving algorithm based on Benders decomposition according to the structural features of the optimization objective original problem and the mathematical properties of the passenger flow control collaborative optimization model, solving the original problem by decomposing the original problem into a main problem and a sub-problem with smaller scale, and iteratively solving the main problem and the sub-problem until a standard rail transit train operation diagram, a demand guidance plan and a passenger flow control strategy are obtained. The physical features include train operation basic parameters, the maximum passenger capacity of a train and ticket price conditions along a rail transit line.

3. The method of claim 1, wherein, The spatial features include station distribution conditions along a rail transit line and passenger flow demand conditions of each station in each time period.

4. The method of claim 1, wherein, The decision variables include train departure indication variables, the number of passengers transferring travel time and passenger flow control related decision variables.

5. The method of claim 4, wherein, The constraint conditions include train operation diagram constraints, demand guidance strategy constraints, reservation passenger dynamic evolution process constraints, non-reservation passenger dynamic evolution process constraints and coupling constraints between reservation passengers and non-reservation passengers. where z it represents the 0-1 indicator variable involved in the train departure in the train diagram and demand management coordination optimization model under the reservation travel mode; h min and h max represent the minimum and maximum headway limits, respectively; y ist represents whether the t time is within the headway time window of trains i and i+1 at station s; t represents a discrete time period, t∈T; i represents a train number, i∈I.

6. The method of claim 4, wherein, The train operation diagram constraints are represented as: where τ represents the maximum limit of the passenger transfer time; κ uvt't represents the number of people who transfer the travel time from time t to time t'; D uvt represents the dynamic passenger flow demand arriving at station u at time t and destined for station v; u, v represent stations, u, v ∈ S.

7. The method of claim 4, wherein, The demand guidance strategy constraints are represented as: where D uvt denotes the reservation quota allocation plan, i.e., the number of reservation quotas from station u to station v at time t; denotes the number of reservations from station u to station v among the passengers on board of train i.

8. The method of claim 4, wherein, The reservation passenger dynamic evolution constraints are represented as: wherein, p iuv represents the minimum service ratio; b iuv represents the number of non-reserved passengers from station u to station v among the passengers on train i; w iuv represents the number of people waiting for train i to go to station v at station u; r iuv represents the number of people who are detained by train i at station u and wait for the subsequent train to go to station v.

9. The method of claim 4, wherein, The non-reservation passenger dynamic evolution constraints are represented as: wherein, o iu and o iu represent the number of reserved passengers and the number of unreserved passengers in the vehicle passengers, respectively; l iu represents the number of passengers who get off.

10. The method of claim 1, wherein, The coupling constraints between reservation passengers and non-reservation passengers are represented as: The objective function of the optimization objective is represented as: minω t F t +ω s F s where F t denotes the passenger waiting time; F s denotes the government subsidy; ε uv denotes the fare from station u to station v; φ denotes the fare discount; and denote the number of new arriving reserved and non-reserved passengers in each headway, respectively.

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