Multi-type-train operation optimization method for multi-network integration

Abstract

The present application provides a multi-type-train operation optimization method for multi-network integration, comprising: analyzing attribute features of trains; analyzing the influence on a train diagram when the operation is performed in different combination modes; designing train path value calculation methods for the trains according to the attribute features; designing a train path combined operation scheme optimization model according to the train path values; solving the combined operation scheme optimization model by a branch and bound method; and inputting model parameters into the train path combined operation scheme optimization model, and solving to obtain an optimization scheme. According to the multi-type-train operation optimization method for multi-network integration of the present application, a train operation optimization scheme with the goal of maximizing the total train path value is constructed while meeting diversified transportation demands and trafficability of line networks, which improves the utilization rate of the line operation capacity. In addition, the solving algorithm based on a branch and bound method provided by the present application can be directly used in actual operation of municipal railways, so as to avoid the situation of local optimization of solving results.

Description

Multi-type train operation optimization method for multi-network integration Technical Field

[0001] The present application relates to a method for optimizing the operation of multiple types of trains for multi-network integration. Background Art

[0002] In the current context of regional integration, as urban rail networks gradually become operational, they are also facing new demands for interconnection with national railways and cross-line operations with other urban lines. To this end, the urban rail network will feature national railway off-line trains, cross-line trains from other urban lines, direct trains on the same line, express trains at major stations on the same line, and trains stopping at every station on the same line. Different types of trains, due to their varying speeds and stopping schedules, will have varying impacts on the line.

[0003] In the context of regional integration, passenger travel is characterized by long distances, cross-line travel, and multiple transfers. The existing urban rail transit network can no longer meet the demand for such long-distance and rapid access. As a transportation solution to the problem of regional rapid travel, urban rail needs to optimize the operation plan of urban rail with a combination of multiple types of trains from the perspective of passenger travel and line capacity utilization.

[0004] In existing research, there are many studies on the formulation and optimization of urban rail operation plans, such as train operation routes and marshaling plans, but most of them consider single-line situations. There is a lack of research on the types of trains, number of trains and stop plans from the perspective of regional multi-modal rail transit coordinated operation.

[0005] In existing research, the calculation methods for the capacity of existing lines and urban rail transit are relatively complete, but in-depth research and analysis is still needed on the impact of the combined operation of national railway offline trains, urban cross-line trains, and direct / major station / stop-at-every-station trains on the line capacity on urban rail.

[0006] The Chinese patent "A method for formulating a set of alternative operation plans for regional rail transit network operation" (publication number: CN110796332A) discloses a planning method for formulating an alternative operation plan set from a single-line stop-at-station, multi-system and multi-line cross-line stop-at-station operation plan set to a skip-stop operation plan set, and finally to a regional rail transit network operation plan set. The regional rail transit network operation plan set is designed from the perspective of the stop plan. However, it does not consider the capacity impact and passenger flow service of various types of trains under the combined operation strategy, and has certain limitations in the optimization of urban rail operation plans. Summary of the Invention

[0007] The purpose of the present invention is to provide a method for optimizing the operation of multiple types of trains for multi-network integration, which has the advantage of high line utilization.

[0008] To achieve the above objectives, the present invention provides a method for optimizing the operation of multiple types of trains for multi-network integration, which comprises:

[0009] S10. Analyze the attribute characteristics of different types of trains;

[0010] S20. Quantitatively analyze the impact of different types of trains running in different combinations on the operation diagram;

[0011] S30. Designing a method for calculating the running line value of the train according to the attribute characteristics of the train;

[0012] S40, designing a train line combination operation plan optimization model based on the line value;

[0013] S50, solving the combined operation plan optimization model using the branch and bound method;

[0014] S60: Input the actual operation model parameters into the train line combination operation plan optimization model and solve to obtain the optimization plan.

[0015] Preferably, in step S30, the method for calculating the running line value specifically includes:

[0016] S301. The value of a train's operating route = (the train's passenger flow in the s period * the travel distance corresponding to this type of train route) * the train's operating grade coefficient / (the number of train runs in the s period * the travel time corresponding to this type of train route).

[0017] Preferably, after step S30, the method further includes:

[0018] S31. Convert the running line value according to the train grade and the line running scale.

[0019] Preferably, the attribute characteristics of the train include: the train's operating level, stop plan, travel time and load factor.

[0020] Preferably, the determining factors of the train's operating grade include train load factor, operating speed and number of trains running.

[0021] Preferably, step S50, solving the combined operation plan optimization model using a branch and bound method, includes:

[0022] S51. Decompose the original problem into multiple sub-problems;

[0023] S52, solving each of the sub-problems respectively;

[0024] S53, and comparing the solutions obtained for each of the sub-problems to obtain the optimal solution to the original problem.

[0025] Preferably, step S51 specifically includes:

[0026] S511. Define the branch set as X={M} (M represents the original problem, Pk represents the kth subproblem), initialize the upper bound U=+∞, and the optimal integer solution in the current case is the empty set.

[0027] Preferably, step S52 specifically includes:

[0028] S521, determine whether the branch set X = {M} is an empty set, if so, go to S53; if not, select the subproblem P in the set X k , delete the selected subproblem Pk from the set X and go to S522;

[0029] S522. Solve subproblem P k For the corresponding relaxed linear programming problem, if there is no solution, go back to S521; if there is a solution, go to S523;

[0030] S523, judgment sub-problem P k The optimal solution of the relaxed linear programming problem X k Does it meet the integer requirement? If it does, then go to S524. If not, then X k A non-integer component of generates P k The two new nodes after the branch are generated, and the new nodes are added to the branch set X and then the process returns to S521;

[0031] S524, determine the target function value Z k Does Z satisfy k <U, if it satisfies, then the current integer solution Xk is taken as the current optimal integer solution, and the current objective function value Z k Assign the value to the upper bound U, and return to S521 after completion.

[0032] Preferably, step S53 specifically includes:

[0033] S531. If the optimal integer solution is empty and the upper bound U = +∞, then the original problem has no solution; otherwise, take the current optimal integer solution as the optimal solution, and take the current U as the optimal value of the objective function.

[0034] Preferably, the goal of the optimal solution is to maximize the total running line value of the train on the basis of meeting passenger flow demand and route map throughput capacity.

[0035] Preferably, the different types of trains include national railway trains, urban cross-line trains, urban main line through trains, urban main line trains with major stations and urban main line trains that stop at every station.

[0036] Preferably, after step S20, the method further includes the following steps:

[0037] S21. Based on the trains that stop at every station on the urban main line, the influence coefficients of the national railway trains, the urban cross-line trains, the urban main line through trains and the urban main line trains at major stations on the operation diagram are converted.

[0038] Preferably, the constraints of the train line combination operation plan optimization model include: tracking interval, operation time, minimum load factor and operation ratio requirements.

[0039] In summary, compared with the prior art, the multi-type train operation optimization method for multi-network integration provided by the present invention has the following beneficial effects:

[0040] The multi-type train operation optimization method for multi-network integration in this application, on the basis of meeting the diversified transportation needs and through-capacity of the line network, constructs a train operation optimization plan with the goal of maximizing the total value of the train operation line, thereby improving the utilization rate of the line operation capacity; in addition, the solution algorithm based on the branch and bound method proposed in this application can be directly used in the actual operation of urban rail, and can avoid the situation where the solution result is locally optimal. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] FIG1 is a flow chart of a method for optimizing the operation of multiple types of trains for multi-network integration according to the present invention.

[0042] FIG2 is a logic diagram of an algorithm for solving a train line combination operation plan optimization model according to the present invention. DETAILED DESCRIPTION

[0043] The technical solutions, structural features, achieved objectives and effects of the embodiments of the present invention will be described in detail below with reference to FIG1 and FIG2 of the embodiments of the present invention.

[0044] It should be noted that the drawings are in a very simplified form and use non-precise proportions. They are only used to conveniently and clearly assist in explaining the embodiments of the present invention, and are not used to limit the conditions for the implementation of the present invention. Therefore, they have no substantive technical significance. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in the present invention without affecting the efficacy and purpose that can be achieved by the present invention.

[0045] It should be noted that, in the present invention, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only the elements explicitly listed, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0046] The present invention provides a method for optimizing the operation of multiple types of trains for multi-network integration, comprising the following steps:

[0047] S10. Analyze the attribute characteristics of different types of trains. Specifically, the attribute characteristics of a train include: the train's operating grade, stop plan, and travel time. Travel time refers to the total time required for a train to travel from its starting station to its terminal station, including the interval operating time and the stop time at the stations it passes through. The operating grade of a train is determined by the train's full load factor, the train's operating speed, and the number of trains in operation. Different types of trains include national railway trains, urban cross-line trains, urban main line through trains, urban main line trains with major stops, and urban main line trains that stop at every station. National railway trains refer to trains that run on state-owned railways on a daily basis. State-owned railways refer to railways managed by the railway authority of the State Council of China; urban cross-line trains refer to trains that run across other urban lines; urban main-line direct trains refer to trains on the urban line that depart from the starting station and go directly to the terminal station without stopping at any stations along the way; urban main-line major station trains refer to trains on the urban line that only stop at major stations along the way and skip general small stations; urban main-line stop-at-every-station trains refer to trains that stop at every station.

[0048] S20. Quantitatively analyze the impact of different types of trains running in different combinations on the timetable. Quantitative analysis is performed on the different types of trains running on the urban rail system in different combinations, which will result in different timetable capacity occupancy (including the number of station tracks and section occupancy time, etc.), thereby obtaining the impact coefficients of different trains on the timetable, which facilitates the subsequent evaluation of the impact of different trains on the timetable.

[0049] In addition, in this embodiment, after step S20, step S21 is further included;

[0050] S21. Using trains that stop at every station on the main line of the urban area as a benchmark, the impact coefficients of national railway trains, urban cross-line trains, urban main line through trains, and urban main line trains with major stops on the timetable are converted using the coefficient deduction method. By converting the impact coefficients of different types of trains on the timetable capacity, the impact of different types of trains on the timetable capacity can be compared on the same scale, avoiding the incomparable impact of different types of trains on the timetable capacity. Taking the Shanghai Urban Rail Airport Link Line as an example, from Sanlin South to Shanghai East Station, the travel time for major-station express trains and trains that stop at every station is 33 minutes and 37 minutes, respectively. The total travel time difference between the two trains is 4 minutes. This results in the train departure interval increasing from the current tracking interval of 3 minutes to 7 minutes, which is equivalent to losing the capacity of an entire line.

[0051] The value of a train's operating route refers to the passenger volume that each operating route can transport per unit time under certain passenger demand conditions. This value is calculated using the passenger turnover achieved by each operating route and converted based on factors such as the train class and operating scale to determine the value contributed by each route under different types of train combinations. Specifically, in this embodiment, the method for calculating the value of a train's operating route is shown in step S30.

[0052] S30. Design a method for calculating the train's route value based on the train's attributes. In this embodiment, the route value calculation method is as follows: Basic Route Value = (Train Passenger Flow in Time S * Travel Distance for This Type of Train Route) * Train's Operating Grade Coefficient / (Number of Train Departures in Time S * Travel Time for This Type of Train Route).

[0053] In this embodiment, after step S30, the method further includes step S31, where the basic operating route value is converted based on the train class and line operating scale to determine the operating route value contributed by each line under the conditions of multiple train combinations. Because different trains have different operating classes, stop schedules, and travel times, it is necessary to design operating route value calculation methods for different types of trains based on their different attributes.

[0054] S40. Design an optimization model for train line combination operation plans based on line values. Different types of trains include national railway trains, intercity cross-line trains, through trains on the city's main line, trains on the city's main line that stop at every station, and trains on the city's main line that stop at every station. Establishing the optimization model requires considering the constraints imposed on different types of trains on the same line. Constraints in the optimization model for train line combination operation plans include tracking intervals, operating times, minimum load factors, and operating ratio requirements.

[0055] S50: Solve the combined operation plan optimization model using a branch-and-bound method. Based on the constructed linear mixed integer programming model, a branch-and-bound algorithm is designed to avoid local optimality. Step S50 specifically includes S51-S53, and each step of S51-S53 is as follows:

[0056] S51, decompose the original problem into multiple sub-problems; wherein, step S51 specifically includes: S511, define the branch set as X={M} (M represents the original problem, P k represents the kth subproblem. The initial upper bound is U = +∞. In this case, the optimal integer solution is the empty set. To avoid obtaining a locally optimal solution instead of a globally optimal solution, first list all the subproblems within the original problem set, solve each subproblem separately, and finally compare the solutions to each subproblem to find the best solution. This way, the globally optimal solution is obtained.

[0057] S52, solving each sub-problem separately; wherein step S52 specifically includes:

[0058] S521, determine whether the branch set X = {M} is an empty set, if so, go to S53; if not, select the subproblem P in the set X k , the selected subproblem P k , delete from the set X and enter S522. S521 is used to determine the remaining sub-problems P in the branch set X={M} k Quantity, if each subproblem P k All have been calculated, then it is an empty set. If the branch set X={M} is not an empty set, it means that there are still remaining subproblems P in the branch set X={M}. k Unsolved, take out a subproblem P k Solve the problem and extract the subproblem P k Delete from the branch set X={M} to avoid subsequent repeated calculations.

[0059] S522. Solve subproblem P k If there is no solution for the corresponding relaxed linear programming problem, go back to S521, and if there is a solution, go to S523. k If there is no solution, then go back to the branch set to get other sub-problems. If the sub-problem P k If there is a solution, continue to judge the sub-problem P k Is the solution satisfactory to reality?

[0060] S523, judgment sub-problem P k The optimal solution of the relaxed linear programming problem X k Does it meet the integer requirement? If it does, then go to S524. If not, then X kA non-integer component of generates P k The two new nodes after that are added to the branch set X and then return to S521. k When a solution exists, if P k The directly solved solution is not an integer solution, which means that the current solution cannot be used directly in the actual model. k The two new subproblems nearby are added to the branch set X={M}, and the new subproblems generated are subsequently solved.

[0061] S524, determine the target function value Z k Does Z satisfy k <U, if satisfied, then solve the current integer X k As the current optimal integer solution, and the current objective function value Z k Assign the value to the upper bound U and return to S521 after completion. Compare each sub-problem P k The criterion for judging whether the solution is better than the previous solution is to compare the subproblems P that meet the integer conditions. k The objective function value Z k Is it smaller than the current upper bound U? If the current subproblem P k The objective function value Z k is smaller than the current upper bound U, indicating that the subproblem P k The solution is better than the previous solution, and the current subproblem P k The integer solution of the current subproblem P is stored as the optimal solution and k The objective function value Z k Stored in the current upper bound value U.

[0062] S53, and compare the solutions obtained from each sub-problem to obtain the optimal solution of the original problem. Step S53 specifically includes: S531, if the optimal integer solution is empty, the upper bound U=+∞, then the original problem has no solution; otherwise, take the current optimal integer solution as the optimal solution, and take the current U as the optimal value of the objective function. When all sub-problems P in the branch set X={M} are calculated k After that, if the upper bound U=+∞, it means that all subproblems P in the original branch set X={M} k There is no integer solution, and the train line combination operation plan optimization model needs to be re-planned. If all sub-problems P in the branch set X={M} are calculated k Then, the upper bound U is a subproblem P k The objective function value Z k , which means that the objective function value Z k The corresponding subproblem P k The integer solution is to traverse all subproblems P kThe optimal solution after , takes the integer solution as the final driving plan. In this embodiment, the goal of the optimal solution is to maximize the total running line value of the train on the basis of meeting the passenger flow demand and the line map through capacity.

[0063] S60: Input the actual operation model parameters into the train line combination operation plan optimization model and solve to obtain the optimization plan. Put the train combination that needs to be optimized in the actual situation into the optimized model for solution, and obtain the solution result under the actual constraint conditions.

[0064] Although the present invention has been described in detail through the above preferred embodiments, it should be understood that the above description is not intended to limit the present invention. After reading the above description, various modifications and substitutions of the present invention will become apparent to those skilled in the art. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A multi-type train operation optimization method for multi-network integration, characterized in that: The optimization method comprises: S10, analyzing the attribute characteristics of different types of trains; S20, quantitatively analyzing the impact of different types of trains running in different combinations on the operation diagram; S30, designing a method for calculating the running line value of the train according to the attribute characteristics of the train; S40, designing a train running line combination operation plan optimization model according to the running line value; S50, solving the combined operation scheme optimization model by using the branch and bound method; S60, inputting the model parameters of actual operation into the train line combination operation plan optimization model and solving to obtain the optimization plan.

2. The multi-type train operation optimization method for multi-network integration as claimed in claim 1, characterized in that: In step S30, the method for calculating the running line value specifically includes: S301. The running line value of a train = (the passenger flow of the train in the time period s * the travel distance corresponding to this type of train route) * the running grade coefficient of the train / (the number of train runs in the time period s * the travel time corresponding to this type of train route).

3. The multi-type train operation optimization method for multi-network integration as claimed in claim 1, characterized in that: After step S30, the method further includes: S31. Convert the running line value according to the train grade and the running scale of the line.

4. The multi-type train operation optimization method for multi-network integration as claimed in claim 1, characterized in that: The attribute characteristics of the train include: the train's operating level, stop plan, travel time and full load rate.

5. The multi-type train operation optimization method for multi-network integration as claimed in claim 4, characterized in that: The determining factors of the operation grade of the train include train load factor, operation speed and number of trains.

6. The multi-type train operation optimization method for multi-network integration as claimed in claim 1, characterized in that: S50, solving the combined operation scheme optimization model by using the branch and bound method includes: S51. Decompose the original problem into multiple sub-problems; S52, solving each of the sub-problems respectively; S53, and comparing the solutions obtained for each of the sub-problems to obtain the optimal solution to the original problem.

7. The multi-type train operation optimization method for multi-network integration as claimed in claim 6, characterized in that: Step S51 specifically includes: S511. Define the branch set as X={M} (M represents the original problem, P k represents the kth subproblem,), initialize the upper bound U=+∞, and the optimal integer solution in the current case is the empty set.

8. The multi-type train operation optimization method for multi-network integration as claimed in claim 7, characterized in that: Step S52 specifically includes: S521, determine whether the branch set X={M} is an empty set. If so, proceed to S53; if not, select the subproblem P in the set X. k , the selected subproblem P k， Delete from set X and go to S522; S522. Solve subproblem P k For the corresponding relaxed linear programming problem, if there is no solution, return to S521, and if there is a solution, enter S523; S523, judgment sub-problem P k The optimal solution of the relaxed linear programming problem X k Does it meet the integer requirement? If it does, then go to S524. If it does not, then X k A non-integer component of generates P k The two new nodes after that are added to the branch set X and then return to S521; S524, determine the objective function value Z k Does Z satisfy k <U, if it satisfies, the current integer solution Xk is taken as the current optimal integer solution, and the current objective function value Z k Assign the value to the upper bound U, and return to S521 after completion.

9. The multi-type train operation optimization method for multi-network integration as claimed in claim 8, characterized in that: Step S53 specifically includes: S531. If the optimal integer solution is empty and the upper bound U=+∞, then the original problem has no solution; otherwise, take the current optimal integer solution as the optimal solution, and take the current U as the optimal value of the objective function.

10. The multi-type train operation optimization method for multi-network integration according to claim 6, characterized in that: The goal of the optimal solution is to maximize the total running line value of the train on the basis of meeting the passenger flow demand and the line map's throughput capacity.

11. The multi-type train operation optimization method for multi-network integration according to claim 1, characterized in that: The different types of trains include national railway trains, urban cross-line trains, urban main line direct trains, urban main line trains with major stations, and urban main line trains that stop at every station.

12. The multi-type train operation optimization method for multi-network integration according to claim 11, characterized in that: After step S20, the method further includes the following steps: S21. Taking the trains that stop at every station on the urban main line as a benchmark, the influence coefficients of the national railway trains, the urban cross-line trains, the urban main line through trains and the urban main line trains at major stations on the operation diagram are converted.

13. The multi-type train operation optimization method for multi-network integration according to claim 1, characterized in that: The constraints of the train line combination operation plan optimization model include: tracking interval, operation time, minimum full load factor and operation ratio requirements.

Images