Traffic train working diagram and passenger flow control collaborative optimization method combined with demand guidance

Abstract

The invention provides a demand-guided traffic train working diagram and passenger flow control collaborative optimization method, which comprises the following steps of: acquiring physical characteristics and spatial characteristics in a train running process, and representing the physical characteristics and the spatial characteristics as mathematical models; constructing decision variables and constraint conditions; according to the physical features, the spatial features, the decision variables and the constraint conditions, constructing an initial rail transit train working diagram and passenger flow control collaborative optimization nonlinear integer programming model; introducing a 0-1 variable and a large M constraint to linearize the nonlinear model, and deriving a minimum value of the large M to obtain a passenger flow control collaborative optimization linear programming model with a more compact upper bound; and according to the structural characteristics of the original problem of the optimization target and the mathematical characteristics of the passenger flow control collaborative optimization model, designing an accurate solution algorithm based on Benders decomposition, and obtaining a standard rail transit train working diagram, a demand guidance plan and a passenger flow control strategy. According to the method, the service quality and the operation efficiency of the urban rail transit system are improved, and meanwhile resource waste is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of urban public transportation operation management, and in particular to a collaborative optimization method for a traffic train operation diagram and passenger flow control combined with demand guidance. Background Art

[0002] With the rapid development of metropolitan rail transit systems, the growing peak passenger demand has brought great challenges to the system's carrying capacity. Especially during peak hours, the passenger flow far exceeds the maximum carrying capacity of the rail transit system, resulting in train congestion and prolonged waiting time for passengers. How to effectively guide passengers with flexible travel needs to avoid peak hours and transfer to non-peak hours to relieve the passenger flow pressure during peak hours and improve the train capacity utilization rate during off-peak hours, so as to achieve the optimal distribution of overall passenger flow, is a key issue that needs to be urgently solved in the current smart city rail transit system. However, existing academic research and engineering practice have not yet fully explored this issue. Although demand management and passenger flow control strategies have gradually received attention in the field of rail transit, there is still a lack of systematic research methods and practical solutions in the coordinated 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 cope with this complex travel demand management problem.

[0003] By analyzing the existing relevant literature, most studies or patents tend to study the timetable optimization or passenger flow control optimization problems respectively. At present, no research has focused on the demand management and train timetable coordinated optimization problem considering service fairness under the reservation travel mode, which makes it difficult to improve operational efficiency while ensuring service quality. Summary of the invention

[0004] In view of this, the present invention provides a collaborative optimization method for a traffic train operation diagram and passenger flow control combined with demand guidance to solve the above problems.

[0005] The present invention provides a method for collaborative optimization of a traffic train operation diagram and passenger flow control combined with demand guidance, comprising: obtaining physical characteristics and spatial characteristics during train operation, and expressing the above characteristics as a mathematical model; constructing decision variables based on the mathematical model of the physical characteristics and spatial characteristics; constructing constraint conditions according to the coupling relationship between reserved passengers and train operation, the coupling relationship between reserved and unreserved passengers, the coupling relationship between whether unreserved passengers are willing to transfer travel time and the remaining capacity of the train, and the fairness relationship between the number of passengers served by the train at each station and the number of waiting passengers; constructing a constraint condition according to the physical characteristics, the spatial characteristics, the decision variables and the constraint conditions. An initial rail transit train operation diagram and passenger flow control collaborative optimization nonlinear integer programming model with the optimization objective of minimizing the weighted sum of passenger waiting time and government additional subsidies is established; 0-1 variables and large M constraints are introduced to linearize the nonlinear model, and the minimum value of large M is derived to obtain a passenger flow control collaborative optimization linear programming model with a tighter upper bound; according to the structural characteristics of the original problem of the optimization objective and the mathematical characteristics of the passenger flow control collaborative optimization model, an exact solution algorithm based on Benders decomposition is designed, which decomposes the original problem into smaller main problems and sub-problems, and gradually iterates the solution until a standard rail transit train operation diagram, demand guidance plan and passenger flow control strategy are obtained.

[0006] In another implementation of the present invention, the physical characteristics include basic train operation parameters, the maximum passenger capacity of the train, and ticket prices along the urban rail transit line; 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.

[0007] In another implementation of the present invention, the decision variables include a train departure indicator variable, the number of passengers who transfer travel time, and decision variables related to passenger flow control.

[0008] In another implementation of the present invention, the constraints include train operation diagram constraints, demand guidance strategy constraints, dynamic evolution process constraints of reserved passengers, dynamic evolution process constraints of unreserved passengers, and coupling constraints between reserved and unreserved passengers.

[0009] In another implementation of the present invention, the train operation diagram constraint is expressed as:

[0010]

[0011]

[0012] Among them, z itrepresents a 0-1 indicator variable involving train departure in the collaborative optimization model of urban rail transit train operation diagram and demand management under the reservation travel mode; h min and h max Respectively represent the minimum and maximum departure interval limits; y ist Indicates whether time t is within the departure interval window of trains i and i+1 at station s; t represents the discrete time period, t∈T; i represents the train number, i∈I.

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

[0014]

[0015] Among them, τ represents the maximum allowed passenger transfer time; κ uvt't represents the number of people whose travel time is transferred from time t to time t'; D uvt represents the dynamic passenger flow demand arriving at station u and whose destination is station v at time t; u, v represent stations, u, v∈S.

[0016] In another implementation of the present invention, the dynamic evolution constraint of the reserved passengers is expressed as:

[0017]

[0018] Among them, D uvt represents the reservation quota allocation plan, that is, the number of reservation quotas from station u to station v at time t; It represents the number of passengers on train i who have made reservations to travel from station u to station v.

[0019] In another implementation of the present invention, the dynamic evolution constraint of the unbooked passengers is expressed as:

[0020]

[0021]

[0022] Among them, ρ iuv Indicates the minimum service ratio; b iuv represents the number of passengers on train i who have not made a reservation from station u to station v; w iuv represents the number of people at station u waiting for train i to station v; r iuv It represents the number of people who are stranded by train i at station u and are waiting for the subsequent train to go to station v.

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

[0024]

[0025] Among them, iu and iu Respectively represent the number of passengers with reservations and the number of passengers without reservations in the car; l iu Indicates the number of people getting off the bus.

[0026] In another implementation of the present invention, the objective function of the optimization target is expressed as:

[0027] minω t F t +ω s F s

[0028]

[0029] Among them, F t Indicates the passenger waiting time; F s represents additional government subsidies; uv represents the fare from station u to station v; φ represents the fare discount; and Respectively represent the number of newly arrived reserved and unreserved passengers in each departure interval.

[0030] In the collaborative optimization method of the demand-guided traffic train operation diagram and passenger flow control of the present invention, by designing and implementing a fare discount strategy, passengers are attracted to flexibly adjust their travel time, thereby achieving the effect of shaving peaks and filling valleys and optimizing passenger flow distribution; an efficient linkage mechanism is established between the train operation diagram design and the passenger flow control scheme, and the service quality and operational efficiency are effectively improved under the premise of ensuring that the reserved passengers can enter the station and board the train directly; in order to improve the solution efficiency, an accurate algorithm based on Benders decomposition is designed; the mismatch problem between passenger flow demand and transportation supply in the prior art is solved, 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 traditional solutions are solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. By reading the detailed description of the following implementations, the advantages and benefits of the solutions become clear to those skilled in the art. The drawings are only used to illustrate the preferred implementations and are not to be considered as limiting the present invention.

[0032] In the attached picture:

[0033] Figure 1 The present invention is a flowchart of a collaborative optimization method for demand-guided transportation train operation diagram and passenger flow control according to an embodiment of the present invention.

[0034] Figure 2 This is a schematic diagram of the entry rules for reserved and non-reserved passengers according to an embodiment of the present invention.

[0035] Figure 3 FIG. 4 is a schematic diagram of a time equivalence method according to an embodiment of the present invention.

[0036] Figure 4 The figure is a schematic diagram of a traffic train operation route according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be described clearly and in detail below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in the field based on the embodiments in the embodiments of the present invention should fall within the scope of protection of the embodiments of the present invention.

[0038] Figure 1 A flow chart of a method for collaborative optimization of a traffic train operation diagram and passenger flow control combined with demand guidance provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, this embodiment mainly includes the following steps:

[0039] S101. Acquire physical characteristics and spatial characteristics during train operation, and express the above characteristics as a mathematical model.

[0040] S102: Constructing decision variables based on the mathematical model of the physical characteristics and spatial characteristics.

[0041] S103. Construct constraint conditions based on the coupling relationship between reserved passengers and train operation, the coupling relationship between reserved and unreserved passengers, the coupling relationship between whether unreserved passengers are willing to shift their travel time and the remaining capacity of the train, and the fairness relationship between the number of passengers served by the train at each station and the number of waiting passengers.

[0042] S104. Based on the physical characteristics, the spatial characteristics, the decision variables and the constraints, a nonlinear integer programming model for collaborative optimization of the initial rail transit train operation diagram and passenger flow control is constructed with the optimization objective of minimizing the weighted sum of passenger waiting time and government additional subsidies.

[0043] S105 , introducing 0-1 variables and large M constraints to linearize the nonlinear model, and deriving the minimum value of large M, to obtain a passenger flow control collaborative optimization linear programming model with a tighter upper bound.

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

[0045] S106. According to the structural characteristics of the original problem of the optimization objective and the mathematical properties of the passenger flow control collaborative optimization model, an accurate solution algorithm based on Benders decomposition is designed. The original problem is decomposed into smaller main problems and sub-problems, and the solution is gradually iterated until a standard rail transit train operation diagram, demand guidance plan and passenger flow control strategy are obtained.

[0046] In the collaborative optimization method of the demand-guided traffic train operation diagram and passenger flow control of the present invention, by designing and implementing a fare discount strategy, passengers are attracted to flexibly adjust their travel time, thereby achieving the effect of shaving peaks and filling valleys and optimizing passenger flow distribution; an efficient linkage mechanism is established between the train operation diagram design and the passenger flow control scheme, and the service quality and operational efficiency are effectively improved under the premise of ensuring that the reserved passengers can enter the station and board the train directly; in order to improve the solution efficiency, an accurate algorithm based on Benders decomposition is designed; the mismatch problem between passenger flow demand and transportation supply in the prior art is solved, 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 traditional solutions are solved.

[0047] In another implementation of the present invention, the physical characteristics include basic train operation parameters, the maximum passenger capacity of the train, and ticket prices along the urban rail transit line; 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 predetermined: distribution of stations along the urban rail transit line; basic parameters of train operation, including running time between stations, maximum and minimum departure intervals, station stop time, etc.; maximum passenger capacity of the train; passenger demand at each station in each time period; and ticket prices along the urban rail transit line.

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

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

[0051] In another implementation of the present invention, the decision variables include a train departure indicator variable, the number of passengers who transfer travel time, and decision variables related to passenger flow control.

[0052] Exemplarily, based on the above-mentioned sets, parameters and index symbols, the decision variables required for the model are established. it , among the passengers departing from station u and whose destination is station v, the number of passengers whose travel time is transferred from time t to time t' uvt't , passenger flow control number b iuv ; where t represents a discrete time period, t∈T, i represents a train number, i∈I, u,v represent a station, u,v∈S. Through these variables, the relationship between train operation and passenger dynamic evolution can be quantitatively described, laying the foundation for realizing the coordinated optimization of train operation diagram and passenger flow control combined with demand guidance strategy.

[0053] In another implementation of the present invention, the constraints include train operation diagram constraints, demand guidance strategy constraints, dynamic evolution process constraints of reserved passengers, dynamic evolution process constraints of unreserved passengers, and coupling constraints between reserved and unreserved passengers.

[0054] It should be understood that the present invention focuses on the emerging operation mode of reservation travel in urban rail transit systems, and further takes into account the limited number of reservation places, and systematically analyzes the mutual influence and dynamic coupling relationship in time and space among three groups of passengers with successful reservations, passengers with unsuccessful reservations, and passengers without reservations.

[0055] like Figure 2As shown in the figure, after the reservation system was opened on February 2, 2024, the green passenger made a reservation for the travel plan on February 3, and the reservation was successful; the orange and blue passengers are also users of the reservation system, but they did not make a reservation successfully and received the recommended travel time feedback from the system. On February 3, 2024, the green passenger arrived at the station hall according to the reservation time and directly entered the platform to wait for the train through the reservation-only gate; the orange passenger changed the planned travel time, arrived at the station hall according to the travel time recommended by the system, entered the platform through the ordinary gate, and received a fare discount; the blue passenger insisted on the original planned travel time, swiped the card at the original fare after arriving at the station hall, and entered the platform through the ordinary gate; the red passenger was not one of the users of the reservation system, arrived at the station hall on the day of operation, and entered the platform through the ordinary gate at the original fare.

[0056] In another implementation of the present invention, the train operation diagram constraint is expressed as:

[0057]

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

[0059] In another implementation of the present invention, the demand guidance strategy constraint is expressed as:

[0060]

[0061] Among them, τ represents the maximum allowed passenger transfer time; κ uvt't represents the number of people whose travel time is transferred from time t to time t'; D uvt represents the dynamic passenger flow demand arriving at station u and whose destination is station v at time t; u, v represent stations, u, v∈S.

[0062] In another implementation of the present invention, the dynamic evolution constraint of the reserved passengers is expressed as:

[0063]

[0064] Among them, D uvt represents the reservation quota allocation plan, that is, the number of reservation quotas from station u to station v at time t; It represents the number of passengers on train i who have made reservations to travel from station u to station v.

[0065] In another implementation of the present invention, the dynamic evolution constraint of the unbooked passengers is expressed as:

[0066]

[0067] Among them, ρ iuv Indicates the minimum service ratio; b iuv represents the number of passengers on train i who have not made a reservation from station u to station v; w iuv represents the number of people at station u waiting for train i to station v; r iuv It represents the number of people who are stranded by train i at station u and are waiting for the subsequent train to go to station v.

[0068] In another implementation of the present invention, the coupling constraint between the reserved and unreserved passengers is expressed as:

[0069]

[0070] Among them, iu and iu Respectively represent the number of passengers with reservations and the number of passengers without reservations in the car; l iu Indicates the number of people getting off the bus.

[0071] In another implementation of the present invention, the objective function of the optimization target is expressed as:

[0072] minω t F t +ω s F s (twenty one)

[0073]

[0074] Among them, F t Indicates the passenger waiting time; F s represents additional government subsidies; uv represents the fare from station u to station v; φ represents the fare 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 travel reservation technology, passengers can book departure times and itineraries before traveling, thereby coping with the volatility and uncertainty of passenger flow demand in the rail transit system to a certain extent. In view of this, the present invention optimizes the fare discount strategy to encourage peak-period passengers to shift to off-peak travel, thereby achieving effective guidance of passenger flow; guiding passengers to travel during off-peak hours through reasonable incentive policies, and comprehensively considering peak and non-peak passenger flow demand to ensure a balance in service fairness for reserved and non-reserved passengers, while minimizing the waiting time of peak-period passengers and the cost of additional government subsidies.

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

[0077]

[0078] The coordinated optimization model of urban rail transit train operation diagram and passenger flow control combined with demand guidance strategy is aimed at minimizing the expected value of the weighted sum of operator cost and passenger travel cost, including:

[0079]

[0080] Considering homogeneous dynamic passenger flow demand, with the goal of minimizing the weighted sum of passenger waiting time and government additional subsidy, and taking the number of people transferred during travel time, passenger flow control and train arrival and departure times as decision-making, 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 include:

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

[0083] Specifically, define the auxiliary variables Constraint (13) can be equivalently transformed into:

[0084]

[0085] in:

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

[0087]

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

[0089]

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

[0091]

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

[0093]

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

[0095]

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

[0097]

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

[0099]

[0100] In view of this, the equivalent linear form of 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 invention, the service experience of non-reserved passengers is improved and the utilization rate of transport resources is maximized by optimizing the passenger flow control strategy. Based on the above idea, a type of urban rail transit train operation diagram and passenger flow control collaborative optimization nonlinear programming model combining demand guidance strategy is proposed with the goal of minimizing passenger waiting time and government additional subsidy costs. Considering providing convenience for reserved passengers to enter the station directly and take the train, the fare discount strategy is optimized to attract peak passenger flow demand to shift to off-peak period, and the above model is equivalently converted into a linear form by introducing 0-1 variables and large M constraints. The reasonable value of the large M parameter is derived and determined to obtain a linear programming model with a tighter upper bound.

[0105] Preferably, the solution algorithm specifically includes:

[0106] Step 1: Relax model (38) into a mixed integer programming model, where the decision variables

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

[0108] Step 3: Use the Benders decomposition algorithm to solve. By iteratively solving the main problem and subproblems until the termination condition is met, a high-quality train operation diagram, demand guidance plan, and passenger flow control strategy are obtained. Specifically, if the subproblem 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 operation diagram obtained in step 3 into the integer linear programming model (38) and use GUROBI to solve the optimal demand guidance plan and passenger flow control strategy.

[0110] According to the properties and structural characteristics of the model, the present invention designs an exact algorithm based on Benders decomposition to solve the model. The proposed algorithm improves the solution efficiency by decomposing the original problem into smaller sub-problems and has significant computational advantages.

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

[0112] In another implementation of the present invention, a line including 6 stations is taken as an example to verify the content of the present invention. Figure 4 As shown. The line contains 6 stations, which are named A, B, C, D, E, and F in sequence. The station stop time of this line is set to 1 minute, the minimum and maximum departure intervals are 2 minutes and 6 minutes respectively, the train capacity is 500 people, and the fare is set to 3 yuan. If the passengers who have not made a successful reservation transfer the departure time, their fare will be adjusted to 80% of the original fare. In addition, the study period is discretized with 1 minute as the discrete time granularity. According to the given input data above, write code in Java to construct the model and solution algorithm framework of the present invention, solve the problem, and obtain a better urban rail transit train operation diagram, passenger flow control plan and reservation quota allocation plan.

[0113] As shown in Table 1, it can be seen that when the demand management strategy of encouraging passengers to adjust their travel time through incentives is not implemented, at least 12 trains are needed to meet all demand. However, if passengers are encouraged to adjust their travel time by up to 10 minutes, only 11 trains can be used to meet all demand. It can be concluded that the demand management strategy of encouraging passengers to adjust their 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 in the number of stranded passengers. Specifically, the number of stranded passengers decreases significantly as the intensity of incentives for passengers to adjust their behavior increases and the maximum adjustment time increases. For example, when operating 11 trains, the number of stranded passengers decreases from 211 to 32 as the maximum adjustment time increases from 10 minutes to 25 minutes. However, there is no linear relationship between the maximum allowed adjustment time and the average waiting time, because the optimal solution trades off between waiting time and increased government subsidies. The results show that the integration of guidance policies and timetable policies can improve the effectiveness of rail transit systems, and this improvement is reflected in a significant reduction in passenger waiting time, without serious adverse effects from the operator's perspective.

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

[0116]

[0117] To sum up, the present invention studies the problem of coordinated optimization of urban rail transit train operation diagram and demand management under the reservation travel mode. The core technical problem lies in how to achieve coordinated optimization of train operation diagram, demand guidance strategy and passenger flow control strategy, so that they can cooperate and work together, thereby improving service quality and capacity utilization efficiency while meeting peak passenger flow demand.

[0118] The present invention constructs a collaborative optimization model of urban rail transit train operation diagram and passenger flow control combined with demand guidance strategy, and proposes an accurate solution algorithm. The model effectively balances the contradiction between passenger waiting time and fleet size by introducing demand management strategy and combining the optimization means of train scheduling and passenger flow control. While improving the service level, it reduces operating costs and achieves a win-win effect for operators and passengers.

[0119] Therefore, the collaborative optimization model and algorithm proposed in the present invention have significant effects in improving the service quality of urban rail transit systems and reducing resource waste. This collaborative optimization process not only needs to consider the demand balance between reserved passengers and non-reserved passengers, but also needs to reasonably design incentives such as fare discounts and convenient entry to achieve more effective demand management in the process of guiding peak-period passengers to divert to off-peak periods. It can provide theoretical support and practical reference for the intelligent, demand-oriented optimization and refined management of future urban rail transit systems, and has high promotion and application value. Through this optimization model and algorithm, theoretical support and practical tools are provided to solve the problems of peak-period passenger transportation and maximization of transportation resources in megacities' rail transit systems. It has certain guiding significance in the management and planning of smart city rail transit systems, and can provide innovative and practical solutions for achieving sustainable and efficient operation of urban rail transit.

[0120] The present invention further provides an electronic device, which may include: a processor, a memory, a communication bus, and a communication interface.

[0121] in:

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

[0123] Communication interface, used to communicate with other electronic devices or servers.

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

[0125] Specifically, the program may include program codes including computer operation instructions.

[0126] The processor may 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 present application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0127] The memory is used to store programs. The memory may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0128] The program can be specifically used to enable the processor to execute to implement any of the steps described in the embodiments of the method for collaborative optimization of the traffic train operation diagram and passenger flow control combined with demand guidance. The specific implementation of each step in the program can refer to the corresponding descriptions in the steps and units executed by any of the above steps in the method for collaborative optimization of the traffic train operation diagram and passenger flow control combined with demand guidance, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described equipment and modules can refer to the corresponding process description in the aforementioned method embodiment.

[0129] The present invention also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the methods of the various embodiments of the present application.

[0130] The above-described method according to an embodiment of the present invention may be implemented in hardware, firmware, or as software or computer code that may be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein may be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, processor, or 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 converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0131] Thus far, specific embodiments of the present invention have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired results. Additionally, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing may be advantageous.

[0132] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0133] In the description of the present invention, the terms "first" and "second" are only used to facilitate the description of different components or names, and cannot be understood as indicating or implying a sequential relationship, relative importance, or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features.

[0134] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0135] It should be noted that although the specific embodiments of the present invention are described in detail in conjunction with the accompanying drawings, it should not be understood as limiting the scope of protection of the present invention. Within the scope described in the claims, various modifications and variations that can be made by those skilled in the art without creative work still belong to the scope of protection of the present invention.

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

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A collaborative optimization method for traffic train operation diagram and passenger flow control based on demand guidance, characterized in that: include: Obtaining the physical and spatial characteristics of the train during operation and expressing the above characteristics as a mathematical model; constructing decision variables based on a mathematical model of the physical and spatial characteristics; According to the coupling relationship between reserved passengers and train operation, the coupling relationship between reserved and unreserved passengers, the coupling relationship between whether unreserved passengers are willing to shift their travel time and the remaining capacity of the train, and the fairness relationship between the number of passengers served by the train at each station and the number of waiting passengers, the constraints are constructed. According to the physical characteristics, the spatial characteristics, the decision variables and the constraints, a nonlinear integer programming model for collaborative optimization of an initial rail transit train operation diagram and passenger flow control is constructed with the optimization objective of minimizing the weighted sum of passenger waiting time and government additional subsidies; The nonlinear model is linearized by introducing 0-1 variables and large M constraints, and the minimum value of large M is derived to obtain a passenger flow control collaborative optimization linear programming model with a tighter upper bound. According to the structural characteristics of the original problem of the optimization objective and the mathematical properties of the passenger flow control collaborative optimization model, an exact solution algorithm based on Benders decomposition is designed. The original problem is decomposed into smaller main problems and sub-problems, and the solution is gradually iterated until the standard rail transit train operation diagram, demand guidance plan and passenger flow control strategy are obtained.

2. The method according to claim 1, characterized in that The physical characteristics include basic train operation parameters, maximum passenger capacity of the train, and ticket prices along the urban rail transit line; 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.

3. The method according to claim 1, characterized in that: The decision variables include a train departure indicator variable, the number of passengers who transfer travel time, and decision variables related to passenger flow control.

4. The method according to claim 1, characterized in that The constraints include train operation diagram constraints, demand guidance strategy constraints, dynamic evolution process constraints of reserved passengers, dynamic evolution process constraints of unreserved passengers, and coupling constraints between reserved and unreserved passengers.

5. The method according to claim 4, characterized in that The train operation diagram constraint is expressed as: Among them, z it represents a 0-1 indicator variable involving train departure in the collaborative optimization model of urban rail transit train operation diagram and demand management under the reservation travel mode; h min and h max Respectively represent the minimum and maximum departure interval limits; y ist Indicates whether time t is within the departure interval window of trains i and i+1 at station s; t represents the discrete time period, t∈T; i represents the train number, i∈I.

6. The method according to claim 4, characterized in that The demand-guided strategy constraint is expressed as: Among them, τ represents the maximum allowed passenger transfer time; κ uvt't represents the number of people whose travel time is transferred from time t to time t'; D uvt represents the dynamic passenger flow demand arriving at station u and whose destination is station v at time t; u, v represent stations, u, v∈S.

7. The method according to claim 4, characterized in that The dynamic evolution constraint of the reserved passengers is expressed as: Among them, D uvt represents the reservation quota allocation plan, that is, the number of reservation quotas from station u to station v at time t; It represents the number of passengers on train i who have made reservations to travel from station u to station v.

8. The method according to claim 4, characterized in that The dynamic evolution constraint of the unbooked passengers is expressed as: Among them, ρ iuv Indicates the minimum service ratio; b iuv represents the number of passengers on train i who have not made a reservation from station u to station v; w iuv represents the number of people at station u waiting for train i to station v; r iuv It represents the number of people who are stranded by train i at station u and are waiting for the subsequent train to go to station v.

9. The method according to claim 4, characterized in that The coupling constraint between the reserved and unreserved passengers is expressed as: Among them, iu and iu Respectively represent the number of passengers with reservations and the number of passengers without reservations in the car; l iu Indicates the number of people getting off the bus.

10. The method according to claim 1, characterized in that The objective function of the optimization objective is expressed as: I am t F t +oh s F s Among them, F t Indicates the passenger waiting time; F s represents additional government subsidies; uv represents the fare from station u to station v; φ represents the fare discount; and Respectively represent the number of newly arrived reserved and unreserved passengers in each departure interval.

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