A method for railway train operation auxiliary decision-making

Abstract

The application discloses a railway train operation auxiliary decision-making method, and relates to the field of railway transportation organization. The method first acquires train operation and window setting related parameters such as passenger and freight train traffic volume, train travel time, train operation parameters, window setting time length, window form and the like; secondly, an optimization model with coordination matching degree as the target is constructed, and multiple types of constraints such as train interval, station arrival-departure line capacity, train and window mutual exclusion are comprehensively considered in balancing train travel time and window setting time length; finally, the model is solved through a heuristic search algorithm based on the hierarchical idea of optimization process, and a coordination scheme of train operation and window setting is output. The method realizes the collaborative optimization of train operation and window setting, improves the safety and efficiency of train operation under the premise of guaranteeing passenger and freight transportation demand, simultaneously considers the window operation quality, and can provide efficient auxiliary decision-making support for railway transportation organization.

Description

Technical Field

[0001] This application relates to the field of railway transportation organization, and more specifically, to a method for auxiliary decision-making in railway train operation. Background Technology

[0002] The train timetable is a comprehensive plan for railway transportation work and the basis for railway traffic organization. It is a tool for coordinating the production activities of various railway departments and units according to certain procedures. A track window, as an indispensable component of the train timetable, refers to the time reserved for construction and maintenance work by not marking train lines or adjusting / reducing train lines. It is a fundamental strategy for properly handling the contradictions between transportation organization and construction / maintenance.

[0003] Currently, railway departments typically use an iterative, step-by-step approach to handle train timetable and track maintenance window settings. This involves first creating the train timetable, then setting up maintenance windows within the remaining available time and space, or first setting up maintenance window plans, then creating the train timetable. When conflicts arise, the process is iterated repeatedly. This method often requires continuous iterative adjustments, making it difficult to obtain an optimal coordination solution within a reasonable timeframe. This can easily lead to a low degree of matching between train operations and maintenance window settings, affecting transportation efficiency and the quality of maintenance window operations, and potentially causing safety hazards. Therefore, an auxiliary decision-making method is needed that can collaboratively optimize train operations and maintenance window settings, improving their coordination and ensuring the safety and efficiency of railway transportation. Summary of the Invention

[0004] The purpose of this invention is to provide a railway train operation auxiliary decision-making method to improve the above-mentioned problems, realize the coordinated optimization of train operation and track maintenance window settings, ensure train operation safety and efficiency while meeting transportation needs, and take into account track maintenance window efficiency.

[0005] To achieve the above objectives, in one aspect, this application provides a railway train operation auxiliary decision-making method, the method comprising:

[0006] S1. Obtain relevant parameters for railway train operation and track maintenance window settings.

[0007] The relevant parameters for railway train operation include passenger and freight train traffic volume, train travel time, train operation parameters, station arrival and departure line capacity parameters, train interval time parameters, passenger train origination and termination range parameters, train technical station stopping parameters, and train overtaking level parameters.

[0008] The parameters related to sunroof settings include sunroof setting duration, sunroof type, sunroof planning time range parameters, and sunroof maintenance operation parameters.

[0009] S2. Based on the acquired information, construct a coordinated optimization model for railway train operation and track maintenance window settings.

[0010] The coordination and optimization model for railway train operation and track maintenance window settings includes an objective function and constraints.

[0011] The objective function is a maximum function with the goal of achieving a high degree of coordination and matching.

[0012] The coordination and matching degree is obtained by weighting the normalized results of train travel time, the normalized results of sunroof setting time, and the satisfaction of passenger and freight transport needs.

[0013] The constraints include node connection and flow balance constraints, track closure constraints, train interval time constraints, mutual exclusion constraints between trains and track closures, station arrival and departure line capacity constraints, reasonable origin and destination range constraints for passenger trains, train section operation intersection constraints, train technical station stopping demand constraints, and train overtaking level constraints.

[0014] S3. Solve the model using a heuristic search algorithm based on the hierarchical optimization process idea, and output a coordination scheme for train operation and sunroof setting.

[0015] The heuristic search algorithm based on the hierarchical optimization process idea includes the following steps:

[0016] S31. Without considering the time window, solve the timetable for medium and long-distance passenger trains based on expert experience.

[0017] S32. Traverse the sunroofs at different locations within the sunroof search area and search for the impact of sunroofs at different locations on train operation;

[0018] S33. Based on the deep greedy search heuristic algorithm, solve the medium and long-distance freight train operation lines in sequence, with the shortest freight travel time as the objective function. When the train operation line conflicts with the track opening window, adopt the coordination optimization strategy of adjusting the track opening window time.

[0019] S34. Solving short-distance passenger train operation lines based on a deep greedy search heuristic algorithm;

[0020] S35. Solving short-distance freight train operation lines based on a deep greedy search heuristic algorithm;

[0021] S36. Calculate the coordination degree and return to S32 until the traversal ends.

[0022] The coordination and optimization strategies for conflicts between train operating lines and maintenance windows include:

[0023] When a freight train in front of a track window conflicts with a track window in a section, the opening time of the track window in the section will be postponed until the freight train arrives at the station without having to stop to avoid the track window, and the postponement of the track window will not affect the operation of subsequent trains.

[0024] When a freight train conflicts with a track maintenance window after a track maintenance window, the track maintenance window opening time will be brought forward until the freight train can reach the station without stopping to avoid the track maintenance window, and the advance opening time will not affect the operation of the preceding train.

[0025] The beneficial effects of this invention are as follows:

[0026] (1) The method of the present invention realizes the coordinated optimization of train operation and sunroof setting. By constructing a coordination matching objective function, the method simultaneously considers the train travel time and the sunroof setting time, balances the transportation efficiency and the sunroof operation requirements, and improves the coordination matching degree between the two.

[0027] (2) A heuristic search algorithm based on the hierarchical optimization process is adopted to solve the complex collaborative optimization problem in layers, which reduces the computational complexity and can obtain a better coordination scheme within a reasonable time. It is suitable for large-scale railway transportation scenarios.

[0028] (3) By using flexible coordination and optimization strategies to handle the conflict between trains and the sunroof window, the sunroof window time can be adjusted according to the actual situation, reducing the waiting time of trains, improving transportation efficiency, and ensuring the normal operation of the sunroof window.

[0029] (4) Decision-makers can adjust the weight coefficients in the objective function to flexibly adapt to different transportation needs and track window operation requirements, thereby improving the applicability of the method. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart of the heuristic algorithm designed based on the hierarchical optimization process idea in the railway train operation auxiliary decision-making method described in this embodiment of the invention;

[0032] Figure 2 This is a flowchart of the depth-greedy search algorithm in the railway train operation auxiliary decision-making method described in this embodiment of the invention. Detailed Implementation

[0033] Please see Figure 1 and Figure 2 This embodiment provides a railway train operation auxiliary decision-making method, which includes the following steps:

[0034] S1. Obtain relevant parameters for railway train operation and track maintenance window settings.

[0035] Railway operators can obtain the parameters mentioned above through historical statistical data, monitoring equipment data analysis, and operation planning documents, and use these parameters as inputs to this method.

[0036] S2. Based on the information obtained above, construct a coordinated optimization model for railway train operation and track maintenance window settings.

[0037] The model includes an objective function and constraints. The objective function is a maximization function with a coordination matching degree as the objective. The coordination matching degree is obtained by weighting the normalized results of train travel time, the normalized results of sunroof setting time, and the satisfaction of passenger and freight transport demand. The specific expression is as follows:

[0038] (1)

[0039] in, This represents the sum of travel times for all trains under the sunroof setting scheme. This represents the optimal travel time for all trains under the scenario where no sunroof is set. This represents the sum of the worst-case travel times for all trains under the maximum duration of the sunroof scheme. Indicates the duration of the sunroof installation plan. This indicates the minimum standard duration for setting a sunroof; This indicates the maximum allowable duration of fluctuation under the standard sunroof setting. , This is a weighting coefficient used to adjust the weight of train travel time and sunroof setting time in the target. It is a 0-1 variable, which is 1 when the passenger and freight demand is met, and 0 otherwise.

[0040] The constraints include node connection and flow balance constraints, track maintenance window constraints, train interval time constraints, mutual exclusion constraints between trains and track maintenance windows, station arrival and departure line capacity constraints, reasonable origin and destination range constraints for passenger trains, train section operation intersection constraints, train technical station stop demand constraints, and train overtaking level constraints. The specific constraints are as follows:

[0041] (2)

[0042] (3)

[0043] (4)

[0044] (5)

[0045] (6)

[0046] (7)

[0047] (8)

[0048] (9)

[0049] (10)

[0050] (11)

[0051] (12)

[0052] (13)

[0053] (14)

[0054] (15)

[0055] Formulas (2) and (3) are node connection and flow balance constraints. Formula (2) is a connection constraint, ensuring that the train maintains consistency with the spatiotemporal nodes it passes through when selecting a time arc. Formula (3) is a flow balance constraint, ensuring the conservation of the arrival and departure spatiotemporal arcs in the path selection at spatiotemporal nodes. Formula (4) is a skylight constraint (this constraint requirement does not need to be met for V-shaped skylights). The opening time of railway skylights must be selected within the planned time range, therefore the requirement is met. ,in For the time when the sunroof is open, The time range for planning the maintenance window. Formulas (5) and (6) are the time constraints for train intervals. There are different types of trains with different operating speeds on the railway. In order to ensure the safety of the trains, the minimum safe distance between different and the same type of trains must be met. Formula (5) is the departure interval constraint for EMU trains. Formula (6) is the arrival interval constraint for EMU trains. Formula (7) is the mutual exclusion constraint between trains and maintenance windows. When a maintenance window is being carried out in a section, there is an incompatibility constraint between the train and the maintenance. No train can enter the section. In the spatiotemporal network, this constraint can be expressed through the train departure node. Formulas (8) and (9) are the capacity constraints for station arrival and departure lines. The maximum number of trains in each direction in a station should not exceed the capacity of the station. When EMU trains overtake and freight trains are waiting for a maintenance window, the number of trains that the station avoids the maintenance window is not allowed to exceed the number of arrival and departure lines in each direction, that is, it should not exceed the maximum storage capacity of trains in each direction. Formula (8) is the station capacity constraint in the down direction. Formula (9) is the station capacity constraint in the up direction. Formulas (10) and (11) are constraints on the reasonable origin and destination range of passenger trains. Considering that the railway needs to operate overnight passenger trains and daytime passenger trains, the passenger boarding and alighting time should be within a reasonable time period in order to provide high-quality passenger services. Formula (10) is the origin constraint of passenger trains. Formula (11) is the destination constraint of passenger trains. Formula (12) is the crossover constraint of train operation in the section. Trains in the same direction are prohibited from crossing the operating line in the section to avoid train collisions and rear-end collisions. Formula (13) is the stop requirement constraint of train technical stations. Railway freight trains need to carry out technical operations when stopping at technical stations. Formulas (14) and (15) are the overtaking level constraints of trains. The railway operates EMU trains, ordinary passenger trains and freight trains, and their operating speed levels are different, which will inevitably lead to the situation where trains stop to give way. The highest overtaking level is EMU trains, followed by ordinary passenger trains and freight trains. In order to strictly abide by this overtaking level requirement, trains with lower speed levels overtake trains with higher speed levels. Formula (14) is the constraint for passenger trains overtaking freight trains. Formula (15) is the constraint for EMU trains overtaking regular passenger trains.

[0056] S3. Solve the model using a heuristic search algorithm based on the hierarchical optimization process idea, and output a coordination scheme for train operation and sunroof setting. The algorithm includes the following steps:

[0057] S31. Without considering the timetable for long-distance passenger trains, the timetable for medium- and long-distance passenger trains is solved based on expert experience, and the framework of the passenger train operation diagram is obtained.

[0058] S32, The search area within the sunroof is... ,by The search begins at the start time, traversing the entire skylight search area, with a search step size of [missing information]. The impact of sunroofs at different locations on train operation was investigated sequentially.

[0059] S33. Based on a deep greedy search heuristic algorithm, the medium- and long-distance freight train routes are solved sequentially, with the shortest freight travel time as the objective function. When the train route conflicts with the maintenance window, a coordination optimization strategy is adopted:

[0060] When a freight train's scheduled time conflicts with a track maintenance window in a given section, the opening time of the track maintenance window will be postponed until the freight train can reach the station without stopping to allow it to pass through the window, and the postponement period will be [not specified]. Must meet ,in This refers to the departure time of subsequent trains;

[0061] When a freight train's runway window conflicts with a track maintenance window in a given section, the opening time of the track maintenance window will be brought forward until the freight train can reach the station without stopping to allow it to pass through the maintenance window, and the advance opening time will be [not specified]. Must meet ,in This refers to the arrival time of the train before the sunroof window.

[0062] If the medium- and long-distance freight train lines are not fully laid out, skip to S36.

[0063] S34. Solve for short-distance passenger train routes using a deep greedy search heuristic algorithm. If the short-distance passenger train routes are not fully mapped, skip to S36.

[0064] S35. Solve the short-distance freight train operation line based on the deep greedy search heuristic algorithm. If the short-distance freight train operation line is not fully drawn, skip to S36.

[0065] S36. Calculate the coordination degree and return to S32, until the traversal ends.

[0066] The key steps of the depth-greedy search heuristic algorithm are as follows:

[0067] Step 1: Trains will proceed in sequence according to the line sections. If the train stops at the departure station, the additional time for train departure must be added. If the train requires a technical stop upon arrival at the station, an additional train stop time must be added. The train's arrival time at the station is recalculated, and the train arrives at the next station. The time is .

[0068] Step 2: The train is at the station. Departure time and location Minimum departure interval must be met. Combined with the already planned trains Comparison, train departure time Combined with the already laid-out trains Departure time The difference is greater than or equal to the departure interval time. For time points that do not meet the departure requirements, i.e. Check if the station requires a technical stop. If a technical stop is required, the train should depart at [time]. With arrival time The difference should be greater than or equal to the technical downtime. ,Right now Within the time frame for train search, train departure points will be arranged to bypass stations without stopping. This is the default departure point. If there is a subsequent operational conflict, the train needs to depart from the station. The train stopped at the station to coordinate the conflict on the operating line, and the original train arrived at the station. time Additional train stopping time needs to be added. According to the search step size The system iteratively calculates the subsequent train operation at different departure positions until the minimum constraint requirement is met. If the constraint requirement cannot be met after traversing the search, the calculation terminates, indicating that the line capacity is full and cannot meet the operational demand.

[0069] Step 3: Check the number of departure tracks and reconcile them with the already marked train schedules. Comparison of train stop times Meet with the train At the station Stopping time The number of numbers that can produce an intersection is ,station The number of arrival and departure lines is ,like If there are sufficient arrival and departure tracks, otherwise if there are insufficient arrival and departure tracks, the train needs to return to the original departure point.

[0070] Step 4: Check the arrival time interval and match it with the already planned train schedule. Comparison, train collection The train in Station arrival time With train arrival time The difference must be greater than or equal to the arrival time interval. ,Right now Otherwise, if the arrival time interval constraint is not met, return to Step 2.

[0071] Step 5: Check for overtaking conflicts and assemble with the already planned trains. Comparison, if the train is at the station Departure time and location Smaller than train set Train departure times ,Right now Then the train arrives Station Time Also smaller than the train set Train arrival times ,Right now Or the train is at the station Departure time and location Larger than train set Train departure times ,Right now Then the train arrives Station Time Also larger than the train set Train arrival times ,Right now Otherwise, there is a crossover conflict, which needs to be resolved. Return to Step 2 until the requirements are met.

[0072] Step 6: Check for sunroof conflicts. Section window start time In comparison, if the train is at the station Departure time and location Less than the start time of the interval window ,Right now Then the train arrives The station's time should be less than or equal to the start time of the interval window, i.e. If the train is at the station Departure time and location Greater than or equal to the end time of the interval window ,Right now Then the train arrives The station's time should be greater than or equal to the end time of the interval window, that is... Otherwise, there will be a conflict with the track maintenance window, which needs to be resolved by using the algorithm strategy analyzed earlier for coordination and optimization. When the train is at the station... Departure time and location Less than the start time of the interval window ,Right now The train arrived The station's time falls within the opening period of the track maintenance window, i.e. Then Section skylight start time Delay The duration is checked, and it is also checked whether the already laid-out trains conflict with the extended sunroof. If a conflict occurs, return to Step 1 until the requirements are met; when the train is at the station The departure time is within the start of the designated time window for the section, i.e. If the previous window coordination strategy was to postpone it, then... Section skylight start time Delay Duration. Otherwise, Section skylight start time forward The time limit is set, and it is checked whether the planned train schedule conflicts with the earlier opening time of the daylight window. If a conflict occurs, return to Step 2 until the requirements are met.

[0073] Step 7 returns to Step 1, and the cycle continues until the train reaches its final destination station.

Claims

1. A railway train operation auxiliary decision-making method, characterized in that, Includes the following steps: S1. Obtain relevant parameters for railway train operation and track maintenance window settings; S2. Construct a coordinated optimization model for railway train operation and track maintenance window settings based on the parameters; S3. Solve the model using a heuristic search algorithm based on the hierarchical optimization process idea, and output a coordination scheme for train operation and sunroof setting.

2. The railway train operation auxiliary decision-making method according to claim 1, characterized in that, The relevant parameters for railway train operation include passenger and freight train traffic volume, train travel time, train operation parameters, station arrival and departure line capacity parameters, and train interval time parameters; the relevant parameters for track maintenance window settings include track maintenance window duration, track maintenance window type, and track maintenance window planning time range parameters.

3. The railway train operation auxiliary decision-making method according to claim 1, characterized in that, The coordination and optimization model for railway train operation and track maintenance window settings includes an objective function and constraints. The objective function is a maximum function with the goal of achieving a high degree of coordination and matching. The coordination and matching degree is obtained by weighting the normalized results of train travel time, the normalized results of sunroof setting time, and the satisfaction of passenger and freight transport needs. The constraints include node connection and flow balance constraints, track closure constraints, train interval time constraints, mutual exclusion constraints between trains and track closures, station arrival and departure line capacity constraints, reasonable origin and destination range constraints for passenger trains, train section operation intersection constraints, train technical station stopping demand constraints, and train overtaking level constraints.

4. The railway train operation auxiliary decision-making method according to claim 1, characterized in that, The heuristic search algorithm based on the hierarchical optimization process idea includes the following steps: S31. Without considering the time window, solve the timetable for medium and long-distance passenger trains based on expert experience. S32. Traverse the sunroofs at different locations within the sunroof search area and search for the impact of sunroofs at different locations on train operation; S33. Based on the deep greedy search heuristic algorithm, solve the medium and long-distance freight train operation lines in sequence, with the shortest freight travel time as the objective function. When the train operation line conflicts with the track opening window, adopt the coordination optimization strategy of adjusting the track opening window time. S34. Solving short-distance passenger train operation lines based on a deep greedy search heuristic algorithm; S35. Solving short-distance freight train operation lines based on a deep greedy search heuristic algorithm; S36. Calculate the coordination degree and return to S32 until the traversal ends.

5. The railway train operation auxiliary decision-making method according to claim 4, characterized in that, The coordination and optimization strategies for conflicts between train operating lines and maintenance windows include: When a freight train in front of a track window conflicts with a track window in a section, the opening time of the track window in the section will be postponed until the freight train arrives at the station without having to stop to avoid the track window, and the postponement of the track window will not affect the operation of subsequent trains. When a freight train conflicts with a track maintenance window after a track maintenance window, the track maintenance window opening time will be brought forward until the freight train can reach the station without stopping to avoid the track maintenance window, and the advance opening time will not affect the operation of the preceding train.

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