A train operation scheme optimization generation method, system, device and medium

Abstract

The application discloses a train operation scheme optimization generation method, comprising the following steps: based on passenger flow prediction data of a new diagram period, pushing passenger flow demand along a travel path to obtain passenger flow density of each section of a road network; according to the passenger flow density of each section, evaluating an initial train operation scheme to obtain a current train operation scheme; adjusting section division of the current train operation scheme, and constructing a neighborhood train operation scheme based on a train adjustment strategy; according to the passenger flow density and the cumulative transportation capacity distribution, calculating a supply-demand contrast of each section and performing section threshold grade division, using an operation scheme optimization model to calculate and continuously iteratively update the current train operation scheme to obtain a target value close to an optimal solution; when the iteration termination condition is met, the iteration calculation is ended, and an optimal new diagram period train operation scheme is automatically generated. The application also discloses a train operation scheme optimization generation system, equipment and medium.

Description

Technical Field

[0001] This application relates to a train operation plan design method, and more particularly to a method and system for optimizing and generating train operation plans for a new timetable. Background Technology

[0002] With the continuous expansion of the high-speed railway network, under the new operating models of networking, high speed, and high density, passenger service levels and train operation efficiency have been greatly improved, the tight railway transport capacity has been basically alleviated, and the needs of social and economic development have been basically met.

[0003] In the high-speed railway operation organization and planning system, train operation plan design is a crucial link in balancing supply and demand. Optimizing train operation plans not only requires aligning with passenger travel patterns and improving the quality of railway transportation services, but also fully utilizing transport capacity resources and increasing the network's capacity utilization rate. Its adjustment and optimization is a complex systemic task. To match the fluctuations in passenger flow demand and travel characteristics at different times, the railway system typically adjusts train operation plans within a certain period (such as a quarter), forming a new timetable and train operation plan, making capacity allocation more aligned with demand distribution. Unlike adjustments within a timetable, adjustments to the new timetable's train operation plan are more extensive, involving broader changes to operation elements. However, it is still necessary to determine the general direction of adjustment based on the current timetable's supply and demand matching and resource utilization, rather than conducting a global search.

[0004] In existing research on train operation scheme optimization, the relevant optimization methods for train operation schemes can be divided into three categories: comprehensive optimization of route design and frequency setting, comprehensive optimization of stop planning and frequency setting, and comprehensive optimization of route design, frequency setting and stop planning. In order to evaluate the degree of matching between capacity allocation and travel demand, some literatures have integrated passenger flow allocation into the train operation scheme optimization process to evaluate the train operation effect. However, existing literature research still has the following shortcomings. (1) Most literatures usually select passenger load factor as the evaluation index of supply and demand matching, only consider the overall indicators such as total passenger travel volume and deviation, ignore the distribution characteristics of demand in the road network during the adjustment and optimization process, and lack a refined evaluation standard for supply and demand fit; (2) Existing literature uses the construction of mathematical models as the optimization means, pursues global optimization, and the adjustment strategy is relatively broad, which does not conform to the actual adjustment direction on site; (3) Simply pursuing supply and demand matching, few literatures conduct in-depth research on the balance of capacity allocation, which can easily lead to road network capacity loss and actual transportation organization difficulties. Meanwhile, in the actual railway operation organization process, the adjustment and optimization of train operation plans are limited to qualitative analysis, and each railway bureau often makes adjustments independently based on its own experience, lacking a unified and systematic adjustment strategy and scientific quantitative evaluation indicators.

[0005] Therefore, in order to solve the problems existing in the current technology and improve the rationality of high-speed railway capacity allocation, it is urgent to construct a more refined supply and demand matching evaluation index, take into account both supply and demand adaptability and capacity allocation balance, improve the utilization rate of capacity resources, and at the same time, combine the actual rules on site to clarify the direction of operation plan adjustment, select adjustment strategies, and design a train operation plan optimization method that is more in line with the actual situation on site. Summary of the Invention

[0006] This application provides a method and system for optimizing train operation plans, which solves the problems existing in the prior art and improves the rationality of high-speed railway capacity allocation.

[0007] In a first aspect, embodiments of this application provide a method for optimizing and generating train operation plans, including:

[0008] Passenger demand push steps: Based on the passenger flow forecast data of the new timetable, according to the passenger flow travel characteristics and selection behavior, the passenger flow demand is pushed along the travel path, and under the condition of meeting the capacity limit constraints, the passenger flow density of each section of the road network is obtained to achieve the optimization goal of minimizing the passenger travel mileage;

[0009] Initial train operation plan evaluation steps: Based on the passenger flow density of each section, the initial train operation plan is pushed to the transportation capacity, the initial transportation capacity distribution of each section is obtained, the supply and demand contrast of each section is calculated and the section threshold is initialized, the objective function is calculated according to the operation plan optimization model, and the current train operation plan is initialized.

[0010] The steps for constructing a neighboring train operation plan are as follows: the current train operation plan is divided into adjustment sections, and a neighboring train operation plan is constructed based on the train adjustment strategy.

[0011] Evaluation and update steps for neighboring train operation plan: push the transportation capacity for the neighboring train operation plan, obtain the cumulative transportation capacity distribution of each section, calculate the supply-demand contrast of each section and classify the section threshold level according to the passenger flow density and the cumulative transportation capacity distribution, use the operation plan optimization model to calculate and continuously iterate and update the current train operation plan to obtain the target value that approaches the optimal solution.

[0012] The optimal train schedule generation steps are as follows: When the iteration termination condition is met, the iteration calculation ends and the optimal new train schedule is automatically generated.

[0013] In this embodiment of the invention, the above-mentioned passenger flow demand push step further includes:

[0014] Steps for constructing capacity constraint conditions: Construct capacity constraint conditions, which include: capacity constraint conditions, flow conservation constraint conditions, and decision variable constraint conditions;

[0015] Steps for constructing the mileage minimization optimization objective: Under the constraint of meeting capacity limits, the optimization objective for passenger flow push is to minimize the travel mileage of passengers after all passenger flow is allocated;

[0016] Passenger flow push steps: By solving the distribution ratio of each 0D passenger flow to each travel path, the passenger flow density of the interval is obtained. Under the constraint of meeting the capacity limit, the optimization goal of minimizing passenger travel mileage is achieved.

[0017] In this embodiment of the invention, the above-mentioned neighboring train operation scheme construction steps further include:

[0018] Adjustment of segment division steps: After decomposing the train network into segments, determine the supply-demand contrast ratio of each segment according to the supply-demand contrast ratio of each section within the segment, and then splice and integrate the new segments after comparing the supply-demand contrast ratios of adjacent segments.

[0019] Adjustment strategy construction steps: Based on the supply and demand contrast level, determine the train adjustment strategy, which includes: adding trains, suspending trains, adjusting train formation or car type, adjusting stops, adjusting routes, and adjusting departure times;

[0020] The steps for constructing a neighboring system are as follows: Based on the new segment after splicing and integration, and according to the supply and demand contrast level classification of each adjusted segment, the train selection and adjustment strategy for trains passing through the segment is adjusted. The operation elements of trains within the segment and trains across segments are adjusted, as well as other comprehensive adjustments and network resource capacity adjustments are made to construct a neighboring train operation plan.

[0021] In this embodiment of the invention, the above-mentioned evaluation and updating steps for neighboring train operation schemes further include:

[0022] Evaluation steps for neighboring train operation plans: push the transportation capacity of the neighboring train operation plan, obtain the transportation capacity distribution of each section, calculate the supply and demand contrast of each section and divide the section into thresholds, and calculate the objective function value of the neighboring train operation plan.

[0023] The optimization model construction steps are as follows: Construct the constraints of the operation plan optimization model, and under the constraints of the optimization model, achieve the optimization objective of minimizing the train operation cost and the supply-demand contrast deviation of the network sections;

[0024] The steps for updating the neighboring train operation plan are as follows: Calculate the difference between the objective function value of the neighboring train operation plan and the objective function value of the current train operation plan. Based on the difference and the preset update rules, update the current train operation plan and objective function value, update the optimal train operation plan and objective function value, and return to the neighboring train operation plan construction steps for repeated execution.

[0025] In this embodiment of the invention, the initial train operation plan evaluation step and the neighboring train operation plan evaluation step both further include:

[0026] Transportation capacity push steps: Based on the initial train operation plan or the current train operation plan, the transportation capacity of each train is pushed along the train operation route to obtain the cumulative transportation capacity of each section.

[0027] Steps for calculating the supply-demand contrast ratio for each interval: Calculate the supply-demand contrast ratio for each interval based on the ratio of passenger flow density to cumulative transport capacity.

[0028] Section threshold level classification steps: Based on the preset ideal threshold range of supply and demand contrast, the supply and demand contrast of each section of the train is classified into levels, including: supply and demand contrast too high, supply and demand contrast slightly high, ideal supply and demand contrast, supply and demand contrast slightly low, and supply and demand contrast too low.

[0029] In this embodiment of the invention, the above-mentioned neighborhood system construction step further includes:

[0030] Train adjustment procedures within a section: A section includes at least one interval. For sections with excessively high supply-demand contrast, additional trains will be added; for sections with moderately high supply-demand contrast, train formations will be increased; for sections with moderately low supply-demand contrast, train formations will be reduced or additional stops will be added; for sections with excessively low supply-demand contrast, trains will be suspended.

[0031] Adjustment procedures for cross-section trains: For cross-section trains, trains that need to increase capacity or reduce passenger flow will be operated by adding trains, increasing train formations, and reducing stops; trains that need to reduce capacity or increase passenger flow will be operated by suspending trains, reducing train formations, and adding stops.

[0032] Other comprehensive adjustment steps: Implement comprehensive adjustments to train sections or routes, departure times, and stops;

[0033] Road network resource capacity adjustment steps: Based on the upper limit of road network station and section transportation capacity in the constraints of the operation plan optimization model, the adjusted train set is comprehensively adjusted.

[0034] In this embodiment of the invention, the above-mentioned optimization model construction step further includes:

[0035] The steps for constructing the constraints of the optimization model are as follows: Construct the constraints of the optimization model, which include: seat kilometer constraints, section throughput constraints, origin and destination constraints of the operating section, station origin and destination capacity constraints, train formation constraints, train origin and destination time constraints, and section supply and demand contrast constraints.

[0036] The optimization objective construction steps are as follows: Under the constraints of the optimization model, the weight parameters of the model objective function are used to calculate and construct the optimization objective that minimizes the train operation cost and the supply-demand contrast deviation of the road network sections.

[0037] Secondly, embodiments of this application provide a train operation plan optimization generation system, employing the above-mentioned train operation plan optimization generation method. The system includes:

[0038] Passenger demand push module: Based on the passenger flow forecast data of the new timetable, according to the passenger flow travel characteristics and selection behavior, the module pushes the passenger flow demand along the travel path, and obtains the passenger flow density of each section of the road network under the condition of meeting the capacity limit constraints, so as to achieve the optimization goal of minimizing the passenger travel mileage.

[0039] Initial train operation plan evaluation module: Based on the passenger flow density of each section, the initial train operation plan is pushed to the transportation capacity, the initial transportation capacity distribution of each section is obtained, the supply and demand contrast of each section is calculated and the section threshold is initialized, the objective function is calculated according to the operation plan optimization model, and the current train operation plan is initialized.

[0040] Neighborhood train operation plan construction module: Divides the current train operation plan into adjustment sections and constructs a neighborhood train operation plan based on the train adjustment strategy;

[0041] Neighborhood train operation plan evaluation and update module: pushes transportation capacity for the neighborhood train operation plan, obtains the cumulative transportation capacity distribution of each section, calculates the supply-demand contrast of each section and classifies the section threshold levels according to the passenger flow density and the cumulative transportation capacity distribution, adopts the operation plan optimization model, calculates and iteratively updates the current train operation plan, and obtains the target value that approaches the optimal solution.

[0042] Optimal Train Operation Plan Generation Module: When the iteration termination condition is met, the iteration calculation ends and the optimal new train operation plan is automatically generated.

[0043] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the train operation scheme optimization generation method as described above.

[0044] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the train operation scheme optimization generation method described above.

[0045] Compared with existing technologies, it has the following outstanding advantages:

[0046] (1) The method of the present invention improves the accuracy of supply and demand matching evaluation. By using the interval as the smallest transportation unit, a supply and demand contrast index is defined to evaluate the capacity suitability, which improves the evaluation accuracy of the rationality of capacity allocation, so that the capacity allocation continuously approaches the ideal supply and demand contrast during the optimization process, and achieves higher accuracy supply and demand matching.

[0047] (2) The method of the present invention improves the level of railway transportation services. Taking full account of passengers' travel route selection and travel comfort, the method divides the threshold based on the supply and demand contrast of the interval, defines the ideal supply and demand contrast threshold range, continuously optimizes the supply and demand contrast deviation, and improves the capacity adaptability and the level of railway transportation services.

[0048] (3) The method of the present invention improves the efficiency of capacity allocation and utilization. During the adjustment process, based on the distribution of passenger load factor and stop ratio of trains in the same section, the coordination of the balance of operation between trains is fully considered. For different adjustment strategies, corresponding trains are selected to adjust the operation elements, making full use of train capacity and improving the capacity utilization rate of the line section.

[0049] (4) The method of the present invention takes into account both supply and demand adaptability and capacity allocation balance. The adjustment method simultaneously considers the supply and demand contrast classification level of the adjustment section and the operation balance of the adjusted trains, and takes into account both the overall network and individual trains, balancing supply and demand adaptability and capacity allocation balance, and realizing the coordinated adjustment and optimization of train operation in the new timetable of the network system. Attached Figure Description

[0050] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0051] Figure 1 This is a schematic diagram of the train operation plan optimization generation method of the present invention;

[0052] Figure 2 This is a schematic diagram of a train operation plan optimization generation method according to a specific embodiment of the present invention;

[0053] Figure 3 This is a schematic diagram of the train operation scheme optimization generation system of the present invention;

[0054] Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of this application.

[0055] In the above image:

[0056] 10 Passenger Flow Demand Push Module; 20 Initial Train Operation Plan Evaluation Module

[0057] 30-neighborhood train operation plan construction module; 40-neighborhood train operation plan evaluation and update module

[0058] 50 Optimal Train Deployment Scheme Generation Module. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0060] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0061] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent.

[0062] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0063] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0064] This invention aims to provide a method for adjusting the operation of high-speed trains in a new timetable based on the segment supply-demand contrast. This method is based on the distribution of passenger flow demand in the new timetable and the capacity configuration of the train operation plan in the reference timetable. It determines the adjustment strategy for each segment (between) by calculating the supply-demand contrast of the railway network, makes comprehensive adjustments to the reference operation plan, and finally obtains a train operation plan that meets the ideal supply-demand contrast.

[0065] (1) The method of this invention proposes to define the supply-demand contrast ratio and its threshold level classification rules; based on the distribution of passenger flow density and cumulative transport capacity, it defines a method for calculating the supply-demand contrast ratio of a section, and quantitatively evaluates the degree of supply-demand matching. At the same time, taking into account the benefits of both railway enterprises and passengers and on-site transport organization experience, and balancing transport capacity utilization and service level, it defines the threshold level classification rules for the supply-demand contrast ratio, providing a reference standard for subsequent section division.

[0066] (2) The method of this invention defines a refined evaluation index for the rationality of supply and demand matching; fully considers the characteristics of passenger travel choices, analyzes the supply and demand contrast of travel demand and capacity configuration from the perspective of intervals, and combines threshold level division to refine the capacity matching of each transportation unit, evaluates the rationality of capacity configuration in the form of matching degree deviation, and strives to achieve supply and demand matching of the smallest transportation unit.

[0067] (3) The method of this invention designs an adjustment section division method; based on the interval supply and demand contrast threshold division, through section decomposition and section integration, an adjustment section division method is designed to expand the scope of adjustment units and reduce adjustment complexity. At the same time, based on the adjustment section division, the adjustment direction and adjustment strategy of the relevant train capacity configuration of each section are clarified, which is more in line with the actual operation characteristics of railway sites.

[0068] (4) The present invention designs a new high-speed rail train operation adjustment method based on the segment supply and demand contrast division; based on the segment division and its corresponding supply and demand contrast level division, the corresponding adjustment strategies are selected for trains within the segment and trains across segments to adjust the operation elements. At the same time, while taking into account the constraints of the network capacity resources, the service of network elements (segments, stations) and the balance of train operation between segments are fully considered, and the operation elements are further comprehensively adjusted to achieve the balance between the overall supply and demand matching of the network and the balanced coordination of individual trains.

[0069] like Figure 1 As shown in the figure, this application provides a method for optimizing and generating train operation plans, including:

[0070] Passenger demand push step S10: Based on the passenger flow forecast data of the new timetable, according to the passenger flow travel characteristics and selection behavior, the passenger flow demand is pushed along the travel path, and under the condition of meeting the capacity limit constraints, the passenger flow density of each section of the road network is obtained to achieve the optimization goal of minimizing the passenger travel mileage.

[0071] Initial train operation plan evaluation step S20: Based on the passenger flow density of each section, the initial train operation plan is pushed to the transportation capacity, the initial transportation capacity distribution of each section is obtained, the supply and demand contrast of each section is calculated and the section threshold is initialized, the objective function is calculated according to the operation plan optimization model, and the current train operation plan is initialized.

[0072] Step S30 of constructing a neighboring train operation plan: Divide the current train operation plan into adjustment sections and construct a neighboring train operation plan based on the train adjustment strategy;

[0073] Neighborhood train operation plan evaluation and update step S40: For the neighborhood train operation plan, the transportation capacity is pushed to obtain the cumulative transportation capacity distribution of each section. Based on the passenger flow density and the cumulative transportation capacity distribution, the supply and demand contrast of each section is calculated and the section threshold level is divided. The operation plan optimization model is adopted to calculate and continuously iterate and update the current train operation plan to obtain the target value that approaches the optimal solution.

[0074] Step S50 for generating the optimal train schedule: When the iteration termination condition is met, the iteration calculation ends and the optimal train schedule for the new period is automatically generated.

[0075] In this embodiment of the invention, the above-mentioned passenger flow demand push step S10 further includes:

[0076] Steps for constructing capacity constraint conditions: Construct capacity constraint conditions, which include: capacity constraint conditions, flow conservation constraint conditions, and decision variable constraint conditions;

[0077] Steps for constructing the mileage minimization optimization objective: Under the constraint of meeting capacity limits, the optimization objective for passenger flow push is to minimize the travel mileage of passengers after all passenger flow is allocated;

[0078] Passenger flow push steps: By solving the distribution ratio of passenger flow to each travel path at each OD, the passenger flow density of the interval is obtained. Under the constraint of capacity limit, the optimization goal of minimizing passenger travel mileage is achieved.

[0079] In this embodiment of the invention, the above-mentioned neighboring train operation scheme construction step S30 further includes:

[0080] Adjustment of segment division steps: After decomposing the train network into segments, determine the supply-demand contrast ratio of each segment according to the supply-demand contrast ratio of each section within the segment, and then splice and integrate the new segments after comparing the supply-demand contrast ratios of adjacent segments.

[0081] Adjustment strategy construction steps: Based on the supply and demand contrast level, determine the train adjustment strategy, which includes: adding trains, suspending trains, adjusting train formation or car type, adjusting stops, adjusting routes, and adjusting departure times;

[0082] The steps for constructing a neighboring system are as follows: Based on the new segment after splicing and integration, and according to the supply and demand contrast level classification of each adjusted segment, the train selection and adjustment strategy for trains passing through the segment is adjusted. The operation elements of trains within the segment and trains across segments are adjusted, as well as other comprehensive adjustments and network resource capacity adjustments are made to construct a neighboring train operation plan.

[0083] In this embodiment of the invention, the aforementioned neighboring train operation plan evaluation and update step S40 further includes:

[0084] Evaluation steps for neighboring train operation plans: push the transportation capacity of the neighboring train operation plan, obtain the transportation capacity distribution of each section, calculate the supply and demand contrast of each section and divide the section into thresholds, and calculate the objective function value of the neighboring train operation plan.

[0085] The optimization model construction steps are as follows: Construct the constraints of the operation plan optimization model, and under the constraints of the optimization model, achieve the optimization objective of minimizing the train operation cost and the supply-demand contrast deviation of the network sections;

[0086] The steps for updating the neighboring train operation plan are as follows: Calculate the difference between the objective function value of the neighboring train operation plan and the objective function value of the current train operation plan. Based on the difference and the preset update rules, update the current train operation plan and objective function value, update the optimal train operation plan and objective function value, and return to the neighboring train operation plan construction steps for repeated execution.

[0087] In this embodiment of the invention, the initial train operation plan evaluation step and the neighboring train operation plan evaluation step both further include:

[0088] Transportation capacity push steps: Based on the initial train operation plan or the current train operation plan, the transportation capacity of each train is pushed along the train operation route to obtain the cumulative transportation capacity of each section.

[0089] Steps for calculating the supply-demand contrast ratio for each interval: Calculate the supply-demand contrast ratio for each interval based on the ratio of passenger flow density to cumulative transport capacity.

[0090] Section threshold level classification steps: Based on the preset ideal threshold range of supply and demand contrast, the supply and demand contrast of each section of the train is classified into levels, including: supply and demand contrast too high, supply and demand contrast slightly high, ideal supply and demand contrast, supply and demand contrast slightly low, and supply and demand contrast too low.

[0091] In this embodiment of the invention, the above-mentioned neighborhood system construction step further includes:

[0092] Train adjustment procedures within a section: A section includes at least one interval. For sections with excessively high supply-demand contrast, additional trains will be added; for sections with moderately high supply-demand contrast, train formations will be increased; for sections with moderately low supply-demand contrast, train formations will be reduced or additional stops will be added; for sections with excessively low supply-demand contrast, trains will be suspended.

[0093] Adjustment procedures for cross-section trains: For cross-section trains, operations such as adding trains, increasing train formations, and reducing stops will be implemented for trains that need to increase capacity or reduce passenger flow; operations such as suspending trains, reducing train formations, and adding stops will be implemented for trains that need to reduce capacity or increase passenger flow.

[0094] Other comprehensive adjustment steps: Implement comprehensive adjustments to train sections or routes, departure times, and stops;

[0095] The steps for adjusting the network resource capacity are as follows: Based on the upper limit of the transportation capacity of the network stations and sections in the constraints of the operation plan optimization model, the adjusted train set is comprehensively adjusted.

[0096] In this embodiment of the invention, the above-mentioned optimization model construction step further includes:

[0097] The steps for constructing the constraints of the optimization model are as follows: Construct the constraints of the optimization model, which include: seat kilometer constraints, section throughput constraints, origin and destination constraints of the operating section, station origin and destination capacity constraints, train formation constraints, train origin and destination time constraints, and section supply and demand contrast constraints.

[0098] The optimization objective construction steps are as follows: Under the constraints of the optimization model, the weight parameters of the model objective function are used to calculate and construct the optimization objective that minimizes the train operation cost and the supply-demand contrast deviation of the road network sections.

[0099] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings:

[0100] The specific steps of the new high-speed rail train operation adjustment method based on the segment supply and demand contrast division include passenger flow demand and capacity allocation push, supply and demand contrast calculation and threshold division, optimization model construction and adjustment optimization method design.

[0101] 1. Calculation of passenger demand and transportation capacity distribution

[0102] 1.1 Passenger Flow Demand Push

[0103] Given a railway network G = (V, E), where V is the set of stations, E is the set of intervals, and the total travel origin-destination (OD) set RS, based on passenger flow forecast data for the new timetable, passenger demand is pushed along travel paths according to passenger travel characteristics and selection behavior to obtain the passenger flow density of each interval in the network.

[0104] For any passenger traveling at OD(r, s) ∈ RS, the total passenger demand is denoted as q. rs Let P be the set of feasible travel paths in the road network. rs , containing N rs There are several travel routes, and each travel route has a p rsk k = 1, 2, ..., N rs Corresponding to a certain passenger flow allocation ratio ρ rsk Passenger flow push is achieved by solving the distribution ratio of passenger flow to each origin and destination (OD) and travel path to obtain the passenger flow density of each section, thereby minimizing the passenger travel distance.

[0105] 1.1.1 Constraints

[0106] (1) Capacity Constraints. When planning and designing a railway line, the maximum transport capacity is typically designed, meaning the passenger flow through the line per unit time must meet certain capacity constraints. Specifically, for the interval e∈E, the passenger flow density f... e The interval capacity limit must be met.

[0107]

[0108] f e ≤Tran(e), e∈E (2)

[0109] Wherein, δ(p) rsk e) is the path p rsk The relationship with interval e, if path p rsk If the interval e is included, then δ(p) rsk ,e)=1, otherwise,δ(p) rsk ,e)=0.

[0110] (2) Flow conservation constraint. For any OD pair, the sum of passenger flow allocated to its feasible travel paths should be equal to the total travel demand.

[0111]

[0112] (3) Decision variable constraints. Decision variable ρ rsk The value of must be between 0 and 1.

[0113] ρ rsk ∈[0, 1], (r, s)∈RS, k=1, 2,…,N rs (4)

[0114] 1.1.2 Optimization Objective

[0115] During the passenger flow push process, passengers will try to choose the shorter route. Under the constraint of meeting the capacity limit, the optimization goal of passenger flow push is to minimize the passenger travel distance after all passenger flow is allocated.

[0116]

[0117] st formulas (1) to (4)

[0118] Among them, |p rsk |For path p rsk The length of the mileage.

[0119] The above passenger flow demand push model is a linear programming model, which can be solved using the simplex method or commercial software. The final passenger flow for each interval e is the passenger flow density (cumulative passenger flow) f passing through that interval. e , e∈E.

[0120] 1.2 Transportation Capacity Push

[0121] Based on the reference operation plan, the transport capacity (i.e., passenger capacity) of each train is pushed along the train operation route to obtain the cumulative transport capacity C of each section e. e , e∈E. Referring to the diagram, the set of trains operating during the period is denoted as L={l=(V l Sl T l M l D l )}. Among them, V l Let S be the set of h(l) stations that train l passes through. l Let T be the set of stop signs for h(l) stations along the route. l For the selected train model and its passenger capacity, M l D represents the number of cars in the EMU (Electric Multiple Unit) formation of train l. l Let l be the estimated departure time of train l. Then, the formula for calculating the cumulative transport capacity of the interval e∈E is as follows:

[0122] C e =∑ l∈L T l ·M l ·∈(l,e),e∈E (6)

[0123] Wherein, ∈(l,e) represents the relationship between train l and interval e. If the route of train l includes interval e, then ∈(l,e) = 1; otherwise, ∈(l,e) = 0.

[0124] 2. Supply-demand contrast calculation and threshold division

[0125] 2.1 Supply and Demand Comparison Calculation

[0126] For each interval e∈E, calculate the supply-demand contrast ratio of passenger flow density and cumulative transport capacity distribution.

[0127] m e =f e / C e ,e∈E (7)

[0128] The supply-demand contrast ratio represents the proportion of demand and transportation capacity within a given region. When m e When m = 1, it indicates that the regional transportation capacity and travel demand are perfectly matched. e If the value is ≤1, then the transportation capacity provided by this interval is sufficient to meet passenger travel needs; otherwise, the transportation capacity provided by this interval is insufficient.

[0129] 2.2 Threshold Division

[0130] Taking into account the benefits for both railway companies and passengers, as well as experience in on-site transportation organization, the supply-demand ratio should not be too low or too high. Too low a ratio will result in a waste of transport capacity resources, while too high a ratio will reduce the level of service for passengers. Assuming an ideal supply-demand ratio of... When the supply-demand contrast within a given interval is within the ideal threshold range, it can be considered that the requirements of both supply and demand are met. If the supply-demand contrast within a given interval is not within the ideal threshold range, then the solution needs to be adjusted through certain adjustment strategies.

[0131] Based on the ideal supply-demand contrast threshold range, levels can be divided according to the magnitude of the supply-demand contrast in each interval. High-value points are defined outside the ideal threshold range. and low point m < m * Then the supply and demand contrast in the interval can be m e Divided into five levels (threshold ranges):

[0132] (1) Level 1: Supply and demand imbalance is too high. At this time... The supply-demand imbalance is very high, and the inter-regional transportation capacity provided by the operation plan may not be able to meet travel demand. There is a shortage of transportation capacity, and it is necessary to increase the allocation of transportation capacity.

[0133] (2) Level 2: Supply and demand contrast is relatively high. At this time... The supply-demand imbalance is high, transportation capacity is tight, and service levels are low, so it is necessary to appropriately increase transport capacity and improve passenger service levels.

[0134] (3) Level 3: Ideal supply and demand ratio. At this point... The supply-demand ratio within the range is within the ideal range and no adjustment is needed.

[0135] (4) Level 4: Supply and demand contrast is low. At this time, m e ∈[ m , m * The supply-demand ratio is low, transportation capacity is somewhat underutilized, and the utilization rate of transport capacity is low. It is necessary to appropriately reduce transport capacity and improve utilization rate.

[0136] (5) Level 5: Supply and demand contrast is too low. At this time, m e < m The supply-demand ratio is very low, resulting in a large waste of transportation capacity. It is necessary to significantly reduce the allocation of transportation capacity to ensure the utilization rate of transportation capacity.

[0137] 3. Optimize model construction

[0138] Adjusting the train operation plan for the new timetable not only needs to consider the transportation costs of railway enterprises, but also needs to meet the travel needs of passengers, that is, the supply-demand ratio needs to meet the ideal threshold range. When adjusting the operation plan, it is also necessary to meet the constraints of railway network resources, such as transportation capacity and transportation organization characteristics.

[0139] 3.1 Constraints

[0140] (1) Seat kilometer constraint

[0141] Train operation provides transportation capacity, and the service range is affected by the train composition and the operating section. Due to the limitations of EMU rolling stock, the new timetable operation plan has a certain upper limit on transportation capacity. Therefore, the upper limit of seat kilometers provided (SM) is used to represent the upper limit of the network's transportation capacity configuration.

[0142] ∑ l∈L T l ·M l ·d l ≤SM (8), where d l This refers to the mileage of train l.

[0143] (2) Interval transit capacity constraints

[0144] Due to railway traffic safety factors, the number of trains passing through interval e∈E per unit time (day or hour) must meet the corresponding capacity limit. Let PT be the total number of trains entering interval e in a day. e (D), its size cannot exceed the maximum number of trains that can pass through the section throughout the day (CT). e (D)

[0145] PT e (D)≤CT e (D) (9)

[0146] (3) Start and end point constraints of the operating section

[0147] The origin and destination points of a train's operating section must be stations with originating and terminating capabilities. Stations without originating and terminating capabilities can only serve as intermediate stations waiting for transport services. This also determines the spatial distribution of transport capacity in the railway network. Let the i-th station that train l passes through be defined as… The set of stations in the road network that have originating and terminating capabilities is: The origin and destination constraints of the train operating section can then be expressed as:

[0148]

[0149]

[0150] (4) Station originating and terminating capacity constraints

[0151] The capacity of a station for originating and terminating trains is influenced by various factors, including station level, nature of technical operations, number of arrival and departure tracks, and distribution of EMU depots (sections). When designing train operation plans, the number of originating and terminating trains at each station cannot exceed its maximum capacity. For station v∈V and train l∈L, define the correlation variables. κ (v, l) represent the relationship between station v and train l, indicating their origin and destination: if station v is the originating station of train l, then otherwise, If station v is the final destination of train l, then κ (v, l) = 1, otherwise, κ (v, l) = 0. If the originating and terminating capacity of station v∈V is CS. v ,but

[0152]

[0153] Σ l∈L κ (v, l) ≤ CS v , v∈V (13)

[0154] (5) Vehicle grouping constraints

[0155] The selection of high-speed train models must conform to the specific trainset models reserved by each railway bureau in the relevant railway network area; therefore, selection can only be made from a specific set of models. Assuming FT... l Given the set of train models that can be filtered for train l, the train model of train l must meet the following requirements.

[0156] T l ∈FT l , l∈L (14)

[0157] For high-speed trains, the train formation includes two types: single-unit and double-unit. Therefore, the number of cars in the train set of train l must meet the following requirements.

[0158] M l ∈{1,2} (15)

[0159] (6) Train departure and arrival time constraints

[0160] Train operation plans are the foundation of timetable planning, requiring estimated train times. Arrival and departure times at stations along the route can be derived from the estimated departure times. Assuming the high-speed train operates for the entire day in the range [T1, T2], the arrival and departure times of train l at stations along the route are as follows: Then the following constraints must be satisfied.

[0161]

[0162] (7) Interval supply and demand contrast constraint

[0163] The train operation plan needs to be configured with sufficient transport capacity to meet passenger travel demand, and also needs to achieve an ideal supply-demand match. Therefore, for the interval e∈E in the road network, it is necessary to satisfy the following:

[0164]

[0165] 3.2 Optimization Objectives

[0166] The objectives of train operation adjustments mainly include two aspects: minimizing train operating costs and meeting passenger travel demand. Since the supply-demand contrast constraint is considered, the generated train operation plan can be assumed to meet passenger travel needs; therefore, minimizing train operating costs is the primary consideration. The magnitude of train operating costs is related to capacity allocation, and therefore is represented by the number of seat kilometers provided, i.e.

[0167] Z1=∑ l∈L T l ·M l ·d l (18)

[0168] Due to the uneven spatial and temporal distribution of passenger flow in large-scale road networks, the capacity configuration in different areas of the road network will be unbalanced, resulting in an imbalance in the supply and demand matching of each segment (interval) in the road network. Therefore, it is difficult to guarantee that the supply and demand contrast of all intervals in the same segment meets the ideal threshold range. In order to improve the solution quality and solution speed, the solution space can be expanded by relaxing the constraints to obtain a satisfactory solution. Therefore, the constraint (17) can be transformed into a penalty function and relaxed as a new optimization objective. First, the degree of deviation between the supply and demand contrast of each interval e∈E and the ideal threshold range is defined as follows: Therefore, the supply-demand ratio deviation within the road network is:

[0169] Z2=∑ e∈E dd e (19)

[0170] By introducing weighting factors into the above optimization objective, it is transformed into a single objective.

[0171]

[0172] st (8)~(16)

[0173] 4. Adjust and optimize methods

[0174] 4.1 Adjustment of Section Division

[0175] A section is formed by connecting sections. Typically, trains run between fixed sections, with their origin and destination being stations capable of starting and ending at each station, thus creating a specific capacity distribution. Therefore, the supply and demand distribution of sections can be converted into sections, allowing for train adjustments to be made on a section-by-section basis.

[0176] (1) Segment decomposition

[0177] The railway network integrates stations with originating and terminating capabilities, and divides the high-speed railway network into several sections along each high-speed railway line, using stations with originating and terminating capabilities as dividing points.

[0178] (2) Segmentation of supply and demand contrast

[0179] For any segment, based on the current capacity allocation distribution (initial plan or neighboring plan), calculate the supply-demand contrast ratio for each interval within the segment and apply threshold classifications. Integrate the threshold levels for each interval, perform statistical classification, and use the threshold level with the highest proportion as the supply-demand contrast ratio classification level for that segment.

[0180] (3) Section integration

[0181] For each high-speed rail line, the supply-demand ratio of adjacent sections is compared to classify them into different levels. Adjacent sections with the same level are then combined to form a new adjusted section, and their respective levels are determined. For example, two adjacent sections of a high-speed rail line (v1, v2, v3, v4) and (v4, v6, v9, v...)... 10 v 12 If, after supply and demand comparison analysis, all segments are classified as Level 1, then they are concatenated into a new segment (v1, v2, v3, v4, v6, v9, v). 10 v 12 The classification level is determined to be Level 1.

[0182] 4.2 Adjustment Strategy and Neighborhood System Construction

[0183] 4.2.1 Adjustment Strategy

[0184] (1) Adding trains

[0185] When the supply-demand imbalance is too high and train formation cannot be adjusted, additional trains will be added to the corresponding sections. Based on the originating and terminating stations within the corresponding sections, and taking into account passenger flow distribution, train operating costs, and rolling stock turnaround, suitable operating sections and train formations will be selected to add the corresponding trains.

[0186] (2) Trains suspended

[0187] When the supply-demand ratio is too low and train formation cannot be adjusted, trains on the corresponding sections will be suspended. Trains will be suspended based on their passenger occupancy rates.

[0188] (3) Adjusting the formation and vehicle type

[0189] ① Increase train formation and passenger capacity (train type). When the supply and demand ratio is high in a section, increase the train formation and passenger capacity of high-occupancy trains passing through that section. For short-formation trains, convert them into double-unit trains if the relevant rolling stock is confirmed to be available; if the available train type is confirmed to be available, replace the corresponding trains with train types that can meet the ideal threshold.

[0190] ② Reduce train formation and passenger capacity (train type). When the supply and demand ratio is low in a section, reduce the train formation and passenger capacity of low-occupancy trains passing through that section. For long-formation trains, convert them to single-formation trains; if it is confirmed that there are available train types, replace the corresponding trains with train types that can meet the ideal threshold.

[0191] (4) Adjusting the stops

[0192] ① Add stops. For sections with low supply-demand ratios, add stops for low-occupancy trains passing through the section. When adding stops, the demand growth rate and stop ratio of each station in the section should be taken into account to determine the number of stops to be added, and trains that pass through the station and have a low stop ratio should be selected to add stops.

[0193] ② Reduce stops. For sections with a high supply-demand ratio, reduce stops for high-occupancy trains passing through the section. When reducing stops, the level of stations the train passes through and the distribution of passenger flow should be considered. Trains with a higher frequency of stops should be selected, and stops at corresponding lower-level stations should be canceled.

[0194] (5) Adjust the route

[0195] ① Extend the operating section. For trains with low passenger load factors, extend the operating section to a nearby high-grade station or a section with high passenger flow. At the same time, the capacity limitations of the line, section, and station, as well as the turnover of rolling stock, must be taken into account.

[0196] ②Shorten the route. For trains with high occupancy rates, appropriately shorten the operating route and eliminate sections with low utilization rates, while also considering capacity limitations and rolling stock turnover.

[0197] ③ Adjust routes. For trains whose passenger load factor does not meet the corresponding threshold or capacity limit, adjust their routes appropriately to ensure that the passenger load factor meets the corresponding threshold or that the corresponding network resources meet the capacity constraints.

[0198] (6) Adjust the departure time

[0199] ① Adjustment of departure times for existing train routes. Trains on existing routes are grouped by route, and the passenger load factor of trains on each route is sorted to determine the passenger load factor threshold for that route. For trains with a passenger load factor below the threshold, the departure time is adjusted. The departure time of the train is randomly determined by using the probability corresponding to the passenger load factor ratio of each time period in the current plan.

[0200] ② Adjustment of departure times for trains on newly added routes. Trains on newly added routes will be grouped by route, and their departure times will be randomly determined based on the probability corresponding to the passenger load factor ratio for each time period in the current plan, or the departure times of trains on newly added routes will be evenly distributed.

[0201] 4.2.2 Neighborhood System Structure

[0202] Based on the division of each adjustment section, a suitable adjustment strategy is selected for the trains passing through the section, and the train operation elements are adjusted. Since the adjustment directions between adjacent sections are different, the adjustment strategies for cross-section trains may be contradictory. Therefore, the relevant trains are divided into intra-section trains and cross-section trains. After the adjustment is completed, the adjusted train set is also comprehensively adjusted according to the network capacity constraints (8) to (13) to obtain the neighborhood solution.

[0203] 4.2.2.1 Trains within the section

[0204] (1) Sections with excessively high supply-demand contrast (Level 1): Additional trains will be added to the relevant sections.

[0205] Trains with high occupancy rates (above average) within the section are sorted by occupancy rate. Based on the order of occupancy rate from high to low, additional trains are added one by one along each train route, provided that the transport capacity allows. The train formation and passenger capacity are consistent with the trains with high occupancy rates on the same route. At the same time, the supply and demand contrast of the section is updated until the supply and demand contrast of the section reaches the ideal threshold or all operating routes are traversed.

[0206] (2) Sections with high supply-demand ratio (Level 2): ​​Increase train formation for relevant trains in this section.

[0207] Trains within the section are sorted by occupancy rate, from highest to lowest. For short-formation trains with a high occupancy rate (e.g., greater than 0.85), the trains are adjusted to double-unit trains or the train models are changed, if the rolling stock allows. For long-formation trains with a high occupancy rate (e.g., greater than 0.85), the train models are changed, if the rolling stock allows. At the same time, the supply-demand contrast ratio of the section is updated until the supply-demand contrast ratio of the section reaches the ideal threshold or all trains with high occupancy rates are traversed.

[0208] (3) Sections with low supply-demand ratio (Level 4): Trains in this section will be reduced in formation or have additional stops added.

[0209] ① Reduce group size

[0210] Trains within this section are sorted by occupancy rate, from lowest to highest. For short-formation trains with low occupancy rates (e.g., less than 0.7), the train type is changed to meet the corresponding occupancy rate, if the rolling stock allows. For long-formation trains with low occupancy rates (e.g., less than 0.4), they are adjusted to single-formation trains. Simultaneously, the supply-demand contrast ratio for this section is updated until it reaches the ideal threshold or all trains with low occupancy rates have been traversed.

[0211] ②Add stops

[0212] For trains with low occupancy rates in this section, if the conditions for reducing train formation are not met, additional stops can be added to attract passenger flow and improve occupancy rates. Stations in this section are sorted according to passenger arrival and departure volumes, and additional stops are determined in descending order: if the station's stop ratio is lower than the average stop ratio of stations of the same level, then the train will be added to that stop; otherwise, no stop is added. This process is repeated until the train's occupancy rate reaches the ideal threshold or all stations have been visited.

[0213] (4) Sections with excessively low supply-demand ratio (Level 5): Trains in this section will be suspended.

[0214] Trains with low occupancy rates in the section are sorted and suspended in order of occupancy rate from low to high. At the same time, the supply-demand contrast ratio of the section is updated until the supply-demand contrast ratio of the section reaches the ideal threshold or all trains with low occupancy rates are traversed.

[0215] 4.2.2.2 Cross-section trains

[0216] If the ideal threshold is not reached after the adjustment of trains within a section is completed, adjustments must be made to trains crossing sections. Since the adjustment sections are divided according to the strategy selection based on the line sections, the adjustment strategies for different sections of trains crossing sections may conflict with each other. Therefore, it is necessary to comprehensively consider the adjustment strategies for each section of the train and select strategies with the same adjustment direction for comprehensive adjustment.

[0217] Increase capacity / reduce passenger flow: Add trains, increase train formations, reduce stops

[0218] Reduce capacity / increase passenger flow: Suspend train services, reduce train formations, increase stops.

[0219] Among these measures, station adjustments do not conflict with other strategies and can be implemented simultaneously. When frequency adjustments (adding / canceling trains) and train formation adjustments coexist, if the adjustment directions are consistent, train formation adjustments take priority. At the same time, changes in train occupancy rates are considered to maximize train occupancy rates to the ideal threshold.

[0220] 4.2.2.3 Other comprehensive adjustments

[0221] After initial adjustments to frequency, train formation, and stops, it is also necessary to make comprehensive adjustments to train routes, departure times, and stops from an overall perspective to make the overall train occupancy rate, stop ratio, and other indicators more balanced and reasonable.

[0222] (1) Section / Route Adjustment

[0223] ① Extend the section. For trains with low occupancy rates, if the supply and demand contrast between adjacent sections at the terminal is high and there are conditions to increase transport capacity, the corresponding section at the terminal can be extended.

[0224] ② Shorten the section. For trains with high occupancy rates, if the supply and demand contrast between the two ends of the operating section is low and there are conditions to reduce transport capacity, the section at the end point should be shortened accordingly.

[0225] (2) Adjustment of departure time

[0226] For existing routes, if the passenger load factor is low, the departure time of the train is randomly determined based on the probability corresponding to the passenger load factor ratio of each time period of the current route. For new routes, the departure time of the train is randomly determined based on the probability corresponding to the passenger load factor ratio of each time period of the current plan, or the departure times of the new routes are evenly distributed.

[0227] (3) Comprehensive adjustment of stops

[0228] The comprehensive adjustment of stops is based on a segment-based adjustment scheme, aiming for overall balance through fine-tuning. Using the highest-grade station with the highest stop-through ratio as a benchmark, the required number of stops for each station is calculated based on passenger flow, and stations that do not meet the stop frequency requirements are screened. For stations that do not meet the stop frequency requirements, the stop balance index for each station on the line is calculated. For any station (especially lower-grade stations), the time periods with poor balance are screened based on passenger flow distribution across different time periods, and trains passing through the station without stopping during each time period are counted. Based on the segment division of the line, the stop-through ratio of the screened trains in each segment is calculated, and the train with the lowest stop-through ratio in the specified station segment is selected as the stop. This process is repeated until all stations meet the stop frequency requirements or all stations that do not meet the stop frequency requirements are traversed.

[0229] 4.2.2.4 Adjustment of Road Network Resource Capacity

[0230] After the above steps, the adjusted train set is comprehensively adjusted according to the network stations, section transport capacity and transport capacity upper limit in the constraints (8) to (13) of the optimization model. For cases where the constraints are not met, the trains on each route are reduced proportionally until the constraints are met.

[0231] 4.3 Algorithm Flow

[0232] The solution is obtained by following the simulated annealing algorithm framework, evaluating the initial scheme, constructing and evaluating neighborhood solutions based on the neighborhood system construction method, and designing a solution algorithm based on the simulated annealing schedule.

[0233] Input the high-speed rail network G = (V, E), the total demand q for each 0-D pair (r, s) ∈ RS during the entire day's operating hours [T1, T2]. rs and its travel path set P rs The interval capacity Tran(e) and throughput CT of the interval e∈E e (D), the set of originating and terminating stations SZ, and the originating and terminating capacity of station v∈V CS. vSeat-kilometer limit SM, initial operation plan L0, set of train models that can be filtered FT l Ideal threshold for supply-demand contrast and high and low value points m Model objective function weights w1, w2, algorithm initial temperature and termination temperature T0, T f The number of inner loop iterations N k The rate of temperature decrease is θ.

[0234] Output the new train operation plan L * ={l=(V l S l T l M l D l )}.

[0235] The specific calculation flowchart is as follows: Figure 2 As shown:

[0236] Step 1: Pushing passenger flow demand.

[0237] Based on the method in section 1.1, passenger flow demand is pushed to obtain the passenger flow density distribution of each section. Proceed to step 2.

[0238] Step 2: Evaluate the initial train operation plan.

[0239] Based on the method in Section 1.2, the initial train operation plan L0 is used to push the transport capacity, obtain the transport capacity distribution of each section, calculate the supply-demand contrast of each section, and divide the sections into thresholds. The objective function Z(L0) is then calculated. Let the current solution and its objective value L←L0, Z(L)←Z(L0), and the optimal solution and its objective value L * ←L0, Z(L * )←Z(L0). Inner iteration count i←1, current temperature T←T0. Go to step 3.

[0240] Step 3: Construct a train operation plan for the surrounding area.

[0241] Based on the current solution L, the segment division is adjusted according to the method in Section 4.1, and the neighboring train operation scheme L is constructed according to the method described in Section 4.2. i Proceed to step 4.

[0242] Step 4: Evaluate the train operation plan in the surrounding area.

[0243] Based on the method in section 1.2, the neighboring train operation scheme L... i The system performs transportation capacity push, obtains the transportation capacity distribution of each interval, calculates the supply-demand contrast of each interval and divides the intervals into thresholds, and calculates the objective function Z(L). i Proceed to step 5.

[0244] Step 5: Solution update.

[0245] ΔZ←Z(L i If ΔZ ≤ 0, then update the current solution and its objective value L←L. i Z(L)←Z(L) i ), and the optimal solution and its objective value L * ←L i Z(L) * )←Z(L i Otherwise, if exp[-ΔZ / T] > Random(0, 1), then update the current solution and its objective value L←L. i Z(L)←Z(L) i Update the inner iteration count i ← i+1. Go to step 6.

[0246] Step 6: Check the inner loop termination condition.

[0247] If i>N k If the current temperature T←θ·T and the inner iteration count i←1 are updated, proceed to step 7; otherwise, return to step 3.

[0248] Step 7: Check the outer loop termination condition.

[0249] If T < T f If the target value is found, the algorithm terminates by outputting the optimal solution and the objective value; otherwise, it returns to step 3.

[0250] Secondly, embodiments of this application provide a train operation plan optimization generation system, employing the above-mentioned train operation plan optimization generation method, such as... Figure 3 As shown, the system includes:

[0251] Passenger demand push module 10: Based on the passenger flow forecast data of the new timetable, according to the passenger flow travel characteristics and selection behavior, the passenger flow demand is pushed along the travel path, and under the condition of meeting the capacity limit constraints, the passenger flow density of each section of the road network is obtained to achieve the optimization goal of minimizing the passenger travel mileage;

[0252] Initial train operation plan evaluation module 20: Based on the passenger flow density of each section, the initial train operation plan is pushed to the transportation capacity, the initial transportation capacity distribution of each section is obtained, the supply and demand contrast of each section is calculated and the section threshold is initialized, the objective function is calculated according to the operation plan optimization model, and the current train operation plan is initialized.

[0253] Neighborhood train operation plan construction module 30: Divides the current train operation plan into adjustment sections and constructs a neighborhood train operation plan based on the train adjustment strategy;

[0254] Neighborhood Train Operation Plan Evaluation and Update Module 40: Pushes transportation capacity for neighborhood train operation plans, obtains the cumulative transportation capacity distribution of each section, calculates the supply-demand contrast of each section and classifies the section threshold levels based on passenger flow density and cumulative transportation capacity distribution, adopts the operation plan optimization model, calculates and continuously iterates and updates the current train operation plan to obtain the target value that approaches the optimal solution.

[0255] Optimal Train Operation Scheme Generation Module 50: When the iteration termination condition is met, the iteration calculation ends and the optimal new train operation scheme for the new timetable is automatically generated.

[0256] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the train operation scheme optimization generation method described above.

[0257] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, it implements the train operation scheme optimization generation method described above.

[0258] In addition, combined Figure 1 The train operation scheme optimization generation method described in this application embodiment can be implemented by computer equipment. Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of this application.

[0259] The computer device may include a processor 81 and a memory 82 storing computer program instructions.

[0260] In some embodiments, the computer device may further include a communication interface 83 and a bus 80. For example, Figure 4 As shown, the processor 81, memory 82, and communication interface 83 are connected through bus 80 and complete communication with each other.

[0261] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for optimizing and generating train operation plans, characterized in that, The method includes: Passenger demand push steps: Based on the passenger flow forecast data of the new timetable, according to the passenger flow travel characteristics and selection behavior, the passenger flow demand is pushed along the travel path, and under the condition of meeting the constraints, the passenger flow density of each section of the road network is obtained to achieve the optimization goal of minimizing the passenger travel mileage; Initial train operation plan evaluation steps: Based on the passenger flow density of each section, the initial train operation plan is pushed to the transportation capacity, the initial transportation capacity distribution of each section is obtained, the supply and demand contrast of each section is calculated and the section threshold is initialized, and the current train operation plan is initialized according to the operation plan optimization model. The steps for constructing a neighboring train operation plan are as follows: the current train operation plan is divided into adjustment sections, and a neighboring train operation plan is constructed based on the train adjustment strategy; the supply and demand contrast of each section of the train is classified according to the preset ideal threshold range of supply and demand contrast. The railway network is divided into several sections by using stations with originating and terminating capabilities as dividing points. Based on the distribution of supply and demand contrast levels in each section, the section with the highest proportion is used as the division level. Adjacent sections with the same division level are spliced ​​and integrated into adjustment sections. Based on the spliced ​​and integrated new sections, and according to the supply and demand contrast level division of each adjustment section, a neighboring train operation plan is constructed for trains passing through the adjustment section based on the train adjustment strategy. Evaluation and update steps for neighboring train operation plan: push the transportation capacity for the neighboring train operation plan, obtain the cumulative transportation capacity distribution of each section, calculate the supply-demand contrast of each section and classify the section threshold levels according to the passenger flow density and the cumulative transportation capacity distribution, use the operation plan optimization model to calculate and continuously iterate and update the current train operation plan to obtain the target value that approaches the optimal solution. The optimal train schedule generation steps are as follows: When the iteration termination condition is met, the iteration calculation ends and the optimal new train schedule is automatically generated.

2. The train operation plan optimization generation method according to claim 1, characterized in that, The passenger flow demand push step further includes: Constraint construction steps: Construct constraints, which include: capacity limit constraints, flow conservation constraints, and decision variable constraints; the decision variable constraints are that the values ​​of the decision variables are between 0 and 1. Steps for constructing the mileage minimization optimization objective: Under the constraints, the optimization objective for passenger flow push is to minimize the travel mileage of passengers after all passenger flow is allocated. Passenger flow push steps: By solving the distribution ratio of passenger flow to each travel path at each OD, the passenger flow density of the interval is obtained. Under the constraint of capacity limit, the optimization goal of minimizing passenger travel mileage is achieved.

3. The train operation plan optimization generation method according to claim 1, characterized in that, The steps for constructing the neighboring train operation plan further include: Adjustment of segment division steps: After decomposing the train network into segments, determine the supply-demand contrast ratio of each segment according to the supply-demand contrast ratio of each section within the segment, and then splice and integrate the new segments after comparing the supply-demand contrast ratios of adjacent segments. Adjustment strategy construction steps: Based on the supply and demand contrast level classification, determine the train adjustment strategy, which includes: adding trains, suspending trains, adjusting train formation or model, adjusting stops, adjusting routes, and adjusting departure times; The steps for constructing the neighborhood system are as follows: Based on the new segment after splicing and integration, according to the supply and demand contrast level classification of each adjusted segment, the adjustment strategy is selected for the trains passing through the segment, and the operation elements of trains within the segment and trains across segments are adjusted, as well as other comprehensive adjustments and network resource capacity adjustments are carried out respectively, to construct the neighborhood train operation scheme.

4. The train operation plan optimization generation method according to claim 1, characterized in that, The evaluation and updating steps for the neighboring train operation plan further include: Evaluation steps for the neighboring train operation plan: push the transportation capacity of the neighboring train operation plan, obtain the transportation capacity distribution of each section, calculate the supply and demand contrast of each section and divide the section into thresholds, and calculate the objective function value of the neighboring train operation plan. The optimization model construction steps are as follows: Construct the constraints of the operation scheme optimization model, and under the constraints of the optimization model, achieve the optimization objective of minimizing the train operation cost and the supply-demand contrast deviation of the network section; The neighboring train operation plan update steps are as follows: Calculate the difference between the objective function value of the neighboring train operation plan and the objective function value of the current train operation plan. Based on the difference and a preset update rule, update the current train operation plan and its objective function value, update the optimal train operation plan and its objective function value, and return to the neighboring train operation plan construction step for repeated execution. The preset update rule is: if the difference is less than or equal to zero, update the current solution and its objective value, and the optimal solution and its objective value; if the difference satisfies the Metropolis criterion, update the current solution and its objective value.

5. The train operation plan optimization generation method according to claim 1 or 4, characterized in that, The initial train operation plan evaluation step and the neighboring area train operation plan evaluation step both further include: Transportation capacity push step: Based on the initial train operation plan or the current train operation plan, the transportation capacity of each train is pushed along the train operation route to obtain the cumulative transportation capacity of each section; Steps for calculating the supply-demand contrast ratio for each interval: Calculate the supply-demand contrast ratio for each interval based on the ratio of the passenger flow density to the cumulative transport capacity for each interval; Interval threshold level classification steps: Based on the preset ideal threshold range of supply and demand contrast, the supply and demand contrast of each interval of the train is classified into levels, including: supply and demand contrast too high, supply and demand contrast slightly high, ideal supply and demand contrast, supply and demand contrast slightly low, and supply and demand contrast too low.

6. The train operation plan optimization generation method according to claim 3, characterized in that, The neighborhood system construction step further includes: Train adjustment procedures within a section: The section includes at least one of the aforementioned intervals. For sections with excessively high supply-demand contrast, additional trains will be added; for sections with moderately high supply-demand contrast, train formations will be increased; for sections with moderately low supply-demand contrast, train formations will be reduced or additional stops will be added; for sections with excessively low supply-demand contrast, trains will be suspended. Adjustment procedures for cross-section trains: For cross-section trains, trains that need to increase capacity or reduce passenger flow will be added, their formations increased, and their stops reduced; for trains that need to reduce capacity or increase passenger flow, trains will be suspended, their formations reduced, and their stops increased. Other comprehensive adjustment steps: Implement comprehensive adjustments to train sections or routes, departure times, and stops; The steps for adjusting the network resource capacity are as follows: Based on the upper limit of the network station and section transportation capacity in the constraints of the operation plan optimization model, the adjusted train set is comprehensively adjusted.

7. The train operation plan optimization generation method according to claim 4, characterized in that, The optimization model construction step further includes: The steps for constructing the constraints of the optimization model are as follows: Construct the constraints of the optimization model, which include: seat kilometer constraints, section throughput constraints, operating section origin and destination constraints, station origin and destination capacity constraints, train type formation constraints, train origin and destination time constraints, and section supply and demand contrast constraints. The optimization objective construction steps are as follows: Under the constraints of the optimization model, the weight parameters of the model objective function are used to calculate and construct the optimization objective that minimizes the train operation cost and the supply-demand contrast deviation of the road network sections.

8. A train operation plan optimization generation system, employing the train operation plan optimization generation method as described in any one of claims 1-7, characterized in that, The system includes: Passenger demand push module: Based on the passenger flow forecast data of the new timetable, according to the passenger flow travel characteristics and selection behavior, the module pushes the passenger flow demand along the travel path, and under the condition of meeting the constraints, obtains the passenger flow density of each section of the road network, so as to achieve the optimization goal of minimizing the passenger travel mileage. Initial train operation plan evaluation module: Based on the passenger flow density of each section, the initial train operation plan is pushed to the transportation capacity, the initial transportation capacity distribution of each section is obtained, the supply and demand contrast of each section is calculated and the section threshold is initialized, and the current train operation plan is initialized according to the operation plan optimization model. The neighboring train operation scheme construction module: adjusts the current train operation scheme by dividing it into sections, and constructs a neighboring train operation scheme based on the train adjustment strategy; classifies the supply-demand contrast of each section of the train according to a preset ideal threshold range of supply-demand contrast; decomposes the network into several sections by using stations with originating and terminating capabilities as dividing points; selects the section with the highest proportion of supply-demand contrast levels based on the distribution of supply-demand contrast levels within each section; merges adjacent sections with the same classification level into adjustment sections; and constructs a neighboring train operation scheme for trains passing through the adjustment section based on the train adjustment strategy, according to the supply-demand contrast level classification of each adjustment section. Neighborhood train operation plan evaluation and update module: pushes the transportation capacity for the neighborhood train operation plan, obtains the cumulative transportation capacity distribution of each section, calculates the supply-demand contrast of each section and divides the section threshold levels according to the passenger flow density and the cumulative transportation capacity distribution, adopts the operation plan optimization model, calculates and iteratively updates the current train operation plan, and obtains the target value that approaches the optimal solution. Optimal Train Operation Plan Generation Module: When the iteration termination condition is met, the iteration calculation ends and the optimal new train operation plan is automatically generated.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the train operation scheme optimization generation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the train operation scheme optimization generation method as described in any one of claims 1 to 7.

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