CN115743247B - Virtual marshalling train formation structure decision-making method for double-line converging line - Google Patents

Abstract

The invention belongs to the field of rail transit train dispatching decision, and the technical scheme of the method is suitable for a double-line converging line scene. A virtual marshalling train formation structure decision-making method facing a double-line converging line is characterized by responding to transportation plan requirements and system safety constraints in time. Designing a group coupling coordination strategy taking a pilot vehicle as a core to screen all formation structures meeting system limitations, comparing the merits of different formation structures as the basis of formation structure updating through design preference rules by calculating system utility function values under each formation structure, performing traversal updating on all feasible formation structures until all the feasible formation structures are traversed, outputting the formation structure with optimal system efficiency, and fully playing the advantages of the virtual formation technology in engineering practice.

Description

A virtual train formation structure decision method for double-track merging lines

Technical Field

The present invention relates to the field of rail transit train dispatching decision methods, and is specifically directed to emerging virtual marshaling technologies, and in particular to a virtual marshaling train formation structure decision method under a double-track merging line.

Background Art

In recent years, with the spatial expansion and structural integration of metropolitan areas, rail transit faces the problem of sustainable development. With the continuous increase in transportation demand, the problem of oversaturation of railway network utilization has emerged. However, traditional measures such as optimizing line conditions, updating vehicles or updating infrastructure are difficult to meet the requirements in terms of economic expenditure and land consumption. Virtual marshaling technology is an advanced technology to improve system efficiency based on the progress of computer, communication and control technology. Trains can be coupled and decoupled on double-line converging lines, thereby improving the operating frequency of branch lines and the service quality in remote areas.

Virtual marshaling technology is a key technology that needs to be broken through in the field of rail transit in recent years. So far, relevant studies have shown that virtual marshaling technology can provide capacity improvements on double-line merging lines, so it has great application potential. However, the implementation of virtual marshaling technology needs to consider the multiple influences of constraints such as external environment and internal system. The restrictions of transportation plan and operation environment lead to different structural coupling combinations of train formations, which will produce different operating benefits. Therefore, a reasonable train formation structure has the opportunity to give full play to the great advantages of virtual marshaling technology. Furthermore, considering that virtual marshaling requires fleet stability control technology to coordinate the behavior between trains in the coupled fleet, so that the leading train in the fleet receives system control, and the following train receives speed and braking commands from the preceding train to establish tracking and braking behavior, therefore, the marshaling structure decision needs to obtain clear instructions before the coupling mode is started. In the prior art, the patent "Virtual marshaling unit train protection control method and system based on multiple braking modes (application (patent) number: CN202210579685.4)" and the patent "A dynamic scheduling spatiotemporal decision-making method based on virtual coupling mode (application (patent) number: CN201910019669.8)" and other technologies as well as research papers "Cao Y, Wen J, Ma L. Tracking and collision avoidance of virtual coupling train control system [J]. Future Generation Computer Systems, 2021, 120: 76-90." and "Felez J, Kim Y, Borrelli F. A model predictive control approach for virtual coupling in railways [J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20 (7): 2728-2739.", etc., mainly focus on the transportation planning decision at the scheduling layer, or through variable parameters and optimal control strategies to ensure the safe operation of the virtual marshaling fleet, but have not discussed the virtual marshaling fleet structure decision method. When trains form a convoy, they need to go through a series of dynamic coordination processes. The group coupling process is the key process that affects the efficiency of train virtual formation. Therefore, it is necessary to determine the virtual coupling train formation structure decision method based on the characteristics of double-track merging lines to solve the efficiency problem of virtual formation technology.

Summary of the invention

The purpose of the present invention is to overcome the gap in the prior art and disclose a virtual marshaling train formation structure decision method for double-track merging lines. When multiple trains pass through a double-track merging line, the traditional system needs to set a larger operating cycle on the branch line to ensure that the trains enter the merging section at a safe interval. Virtual marshaling trains can form a small-scale fleet, which can greatly compress the driving interval and improve the throughput capacity of existing lines. Under different marshaling structures, the running distance and coordination time required in the coupling process of small-scale fleet groups are also different. Depending on the coupling objects, the different formation structures formed will present differentiated system operation benefits. The formation structure decision method improves the throughput capacity of existing lines by optimizing the departure time of each train entering the double-track merging line, and calculating the sequence number of each train's formation and the train's position in the formation as the output value of the formation structure.

Technical solution:

The technical solution of the present invention is applicable to the scenario of double-track merging line. Features of double-track merging line: In this field, double-track merging line refers to the situation where the direction of the path is controlled by the turnout, so that trains from two directions merge into the same line and share a track section.

A virtual marshaling train formation structure decision method for double-line merging lines is characterized by timely responding to transportation plan requirements and system safety constraints. A group coupling coordination strategy with a pilot car as the core is designed to screen all formation structures that meet system constraints. By calculating the system utility function value under each formation structure, the advantages and disadvantages of different formation structures are compared by designing preference rules as the basis for updating the formation structure. All feasible formation structures are traversed and updated until all feasible formation structures are traversed. The formation structure that optimizes the system efficiency is output, giving full play to the advantages of virtual marshaling technology in engineering practice.

A virtual marshaling train formation structure decision method for a double-track merging line, characterized in that it specifically includes the following steps:

(1) Obtain train attributes, transportation demand information, and route information to provide to step (2) and start the formation decision process.

(2) Formulate a coupling coordination strategy based on the characteristics of the double-line merging route, and then calculate the synergy relationship between the coordination distance and time within the small-scale fleet to provide (3);

(3) Taking the departure time and the sequence number of each train in the formation and the position of the train in the formation as the decision variables of the formation structure, a quantitative utility optimization model obtained by the train formation through the double-track merging line is established to provide (6);

(4) Design preference rules to compare the advantages and disadvantages of different formation structures and provide (7);

(5) Filter out all formation structures that meet the formation length limit and provide (6) and (7);

(6) Starting from the initialization formation structure, calculate the total utility value under different formation structures and provide (7);

(7) Traverse all feasible formation structures, update the formation structure according to the formation structure utility value and preference rules, and feedback the optimized departure time, and enter (8);

(8) Output the optimal platoon structure and optimized departure time.

Furthermore, a virtual train formation formation method for double-track merging lines is provided. Through the decision-making process from step (1) to step (8), the specific data flow is shown in Figure 2, and the optimal train formation structure attributes (including the train formation number and the formation position information) and train formation operation attributes (the time when the train enters the intersection) are output as the final result.

At the same time, the definitions of the relevant data variables are as follows:

I is the train set I = {i|i = 1, 2, ..., N}, N is the number of trains entering the bottleneck section in a certain period;

J is the line direction set J = {j|j = 1, 2}, 1 represents the lateral access direction of the turnout, and 2 represents the straight access direction;

S _vc is the ideal coupling interval S _vc ;

is the coordination distance of the ith train;

L _train is the train length;

L _bottleneck is the length of the intersection segment;

_ti is the departure time of the i-th train entering the double-track merging line;

ti _+n is the departure time of the i+nth train entering the double-track merging line

is the coordination time of the ith train, which is the time from when the train enters the bottleneck section to when it reaches the coupling state;

is the time it takes for the i-th train to accelerate to cruising speed

t ^bra is the braking time of the train

t _work is the turnout working time

H _safe is the safe headway, which is the headway under relative braking protection when the train is a following train, and the headway under absolute braking protection when the train is a leading train or operating in an uncoupled mode;

τ _j is the set of departure time windows on route j;

v _cru represents the orbital cruising speed;

v _lim represents the speed limit of the turnout section;

a _tra is the common traction acceleration of the train;

a _bra is the train emergency braking acceleration;

Π ^k is a formation structure k of trains passing through the bottleneck section within a certain period of time;

Π is the set of all possible formation structures;

Π ^f is the set of all formation structures that meet the system formation length limit after screening;

is a small group in the formation structure Π ^k , and its small group length is

Furthermore, in step (1):

The algorithm input needs to be obtained, including:

Train attributes such as the train's traction acceleration, braking acceleration, and design cruising speed;

Transport demand information requires information such as operation cycle and dispatch ratio;

The line information needs to obtain information such as the speed limit of the turnout section, the maximum formation length that the system can carry, and the length of the merging section;

The above three types of data are passed as input to the formation structure decision step.

Furthermore, the virtual marshaling train formation method for double-track merging,

The coupling coordination strategy described in step (2) is a speed trajectory planning and adjustment strategy that enables independently running trains to reach coupling conditions (the speeds of trains within the coupling queue are equal and the intervals between trains are the ideal train intervals). Specifically, the last car in the coupling group, after clearing the turnout section and entering the co-linear section, accelerates to the cruising speed v _cru using the optimal speed curve to improve the passing efficiency of the entire fleet. The leading car in the fleet maintains the turnout limit speed v _lim , delays acceleration for a period of time, and selects the appropriate time to accelerate in turn until the last accelerated pilot train accelerates to the ideal coupling speed (designed cruising speed), and the entire vehicle reaches the coupling state (that is, the speeds of all trains are equal to the cruising speed, and the intervals within the fleet are the ideal intervals). It is characterized in that if the i-th train is the first train in the fleet and the i+n-th train is the last train in the fleet, the coordinated relationship between the coordination distance and time within the small fleet is:

Furthermore, in step (3), the optimization model of the quantitative formation utility v(Π ^k , Π) that can be obtained by forming a train under a given formation structure Π ^k is:

The constraints that the train formation described in the optimization model needs to satisfy are as follows:

Departure time constraints:

Safety interval constraint: t _i -t _i-1 -H _safe ≥ 0, i∈I;

Turnout working time constraint: t _i -t _i-1 -t _work ≥ 0, i∈I;

Coupling process constraints:

Allowed formation length constraints:

Furthermore, the structural comparison utilitarian preference rule as the iterative update of the formation structure described in step (4)> _utili is:

Given two alliance structures and If and only if v(Π ¹ )＜v(Π ² ), the formation structure Π ¹ ＞ _utili Π ² holds, that is, the formation method of Π ¹ is utilitarian and superior to Π ² , denoted as ＞ _utili . When the newly traversed formation structure satisfies the preference ＞ _utili , the current formation structure is updated.

Furthermore, the process of screening the feasible formation structures that meet the system formation length constraint described in step (5) is specifically to list all the formation structures that can be combined when all adjacent trains can form a team, and according to the restriction that each train can only become a leading train or a following train, screen out all the formation structures that meet the formation length constraint n _max to form a feasible formation structure set Π ^f .

Furthermore, in step (6), the optimization model of the formation utility v(Π ^k ,Π) described in step (4) is solved. By way of example and not limitation, a mature existing algorithm in the field may be used. For example, a CPLEX solver or a Groubi solver may be directly called to calculate and solve the problem after linearization; or a heuristic method such as a particle swarm or a genetic algorithm may be directly applied to solve the problem.

Furthermore, the optimal formation update process in step (7) is as follows: start from the initialized formation structure as the optimal formation; select formation structures from Π ^f in turn, and determine the optimal departure time and formation utility under each formation structure; if the traversed formation structure satisfies the designed utilitarian preference rule > _utili , then update the optimal formation structure and the time to enter the intersection section; continuously iterate and update through step (8) until all feasible formation structures are traversed, output the optimal structure, output the optimal departure time under the optimal formation structure, and output the position of the train in each formation and the number of the formation to which the train belongs.

Compared with the prior art, the present invention has the following beneficial effects:

(1) In view of the characteristics of double-track intersection lines, a coupling process coordination decision-making method with better efficiency and benefit is disclosed. Based on this strategy, a calculation method for the benefits obtained by trains passing through double-track intersection lines in a coupled manner is disclosed, so that the benefit value of virtual coupling teams can be quantitatively obtained in engineering practice.

(2) A feasible formation structure search method that meets the system's longest formation restriction is disclosed. A time optimization method that maximizes the optimal coupling benefit of coupled formations is disclosed. All feasible formation structures are traversed, and the optimization method of formation structure and entry time of coupling benefit is disclosed. This enables the virtual formation program to be started in engineering practice and to issue reasonable coupling formation instructions, thus giving full play to the advantages of virtual formation technology.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

FIG1 shows a general flow chart of a virtual marshaling train formation structure decision method for a double-track intersection line according to the present invention;

FIG2 shows a data flow diagram in a virtual marshaling train formation structure for a double-track intersecting line according to the present invention;

FIG. 3 shows an illustration of a virtual marshaling operation scenario on a double-line intersection line according to an embodiment of the present invention.

FIG. 4 shows a diagram of a coupling coordination process for two-line intersections according to an embodiment of the present invention.

FIG. 5 shows the recommended optimal formation structure results under different allowed coupling queue lengths according to an embodiment of the present invention.

FIG. 6 shows the recommended optimal platoon structure and entry time results under different departure ratios according to an embodiment of the present invention.

FIG. 7 shows the efficiency improvement effect of the output formation structure according to an embodiment of the present invention and the traditional uncoupled mode system.

DETAILED DESCRIPTION

The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Example 1

This embodiment provides a virtual marshaling train formation decision method for a double-track intersection line, as shown in FIG1 , the method includes the following steps:

(1) Obtain train attributes, transportation demand information, and route information to provide to the initial formation decision process.

(2) Formulate a coupling coordination strategy based on the characteristics of the double-track merging line and calculate the coordination distance and time coordination relationship of trains within the formation;

(3) Establish a quantitative utility model for train formations through double-track merging lines, using departure time and formation structure decision variables to resolve conflicts between transportation demand and safety constraints and the formation process;

(4) Designing preference rules for utility models to compare the advantages and disadvantages of different formation structures;

(5) Screening all formation structures that meet the formation length limit;

(6) Starting from the initialization of the formation structure, calculate the total utility value under different formation structures;

(7) Traverse all feasible formation structures, update the formation structure according to the preference rules, update the formation structure and correct the departure time;

(8) Output the final formation structure and optimized departure time.

Through the above steps, the following are specifically determined: the recommended time t _i for each train to enter the double-track intersection line, the sequence number m of the coupled formation where the train is located, and the position γ _i of the train in the formation.

The double-track intersection line is a cross-convergence line, which means that two or more lines from different directions share a track section through the control of the switch. Exemplarily, Figure 3 shows the scene of virtual marshaling in the double-track intersection line formation operation compared with the traditional mode. In the traditional mode, when multiple trains enter the merging intersection, buffer time must be reserved for planned deceleration and/or waiting so that the train in front can clear the switch section. Virtual marshaling technology can enable train groups to be combined into high-density, small-interval virtual marshaling fleets to improve efficiency by reducing the necessary headway.

The initial information of the train group that needs to be obtained includes the train's traction acceleration, braking acceleration, and designed cruising speed; the line information includes the line length from the turnout section of the double-track intersection to the first station of the common line section, the turnout section speed limit, and the maximum formation length that the system can carry; the transportation demand includes the required service cycle of each branch line, and the adjustable train departure time range data as method input. The relevant basic parameters in the implementation example are defined as follows:

I is the train set I = {i|i = 1, 2, ..., N}, N is the number of trains entering the bottleneck section in a certain period;

J is the line direction set J = {j|j = 1, 2}, 1 represents the lateral access direction of the turnout, 2 represents the straight access direction

direction;

S _vc is the ideal coupling interval S _vc ;

is the i-th train from Run to The coordination distance;

L _train is the train length;

L _bottleneck is the length of the intersection segment;

_ti is the time when the ith train enters the line section of the junction;

is the coordination time of the ith train, which is the time from when the train enters the bottleneck section to when it reaches the coupling state;

is the time it takes for the i-th train to accelerate to cruising speed

t ^bra is the braking time of the train

t _work is the turnout working time

H _safe is the safe headway, which is the headway under relative braking protection when the train is a following train, and the headway under absolute braking protection when the train is a leading train or operating in an uncoupled mode;

T _j represents the running cycle of the train in direction j;

τ _j is the set of departure time windows on route j;

v _cru represents the orbital cruising speed;

v _lim represents the speed limit of the turnout section;

a _tra is the common traction acceleration of the train;

a _bra is the train emergency braking acceleration;

Π ^k is a formation structure k of trains passing through the bottleneck section within a certain period of time;

Π is the set of all possible formation structures;

Π ^f is the set of all formation structures that meet the system formation length limit after screening;

When trains from different directions approach the co-linear section on an exemplary double-track intersection line, the initial state often cannot directly meet the coupling state, so a coordination process is required to make the trains reach the coupling condition. The coordination strategy in step (2) is a speed trajectory planning method that shortens the driving interval between the independently running trains and the trains that need to be coupled to the relative braking protection range, and reduces the speed difference to achieve the coupling condition (the train speeds within the coupled queue are equal and the interval between trains is the ideal train interval). Specifically, as shown in Figure 4, the last car in the coupling group, after clearing the turnout section and entering the co-linear section, accelerates to the cruising speed v _cru with the optimal speed curve to improve the passing efficiency of the entire fleet. The leading car in the fleet maintains the turnout limit speed v _lim , delays acceleration for a period of time, and selects the appropriate time to accelerate in turn until the last accelerated pilot train accelerates to the ideal coupling speed (designed cruising speed), and the entire vehicle reaches the coupling state (that is, the speed of all trains is equal to the cruising speed, and the interval within the fleet is the ideal interval). According to the coordination strategy, when the train speed approaches the cruising speed, the distance between trains must be close to the coupling distance, and all trains in the fleet will be at For example, assuming that the i-th train is the lead train (i.e., the first train in the queue), and the i+n-th train is the last train in the queue, the coordination distance relationship is:

is a small group in the formation structure Π ^k , and its small group length is Based on the distance traveled by each vehicle in the coupling coordination process, the time it takes for all trains to pass through the double-track intersection can be calculated. However, it is difficult for all trains to form a coupled fleet and operate. It is necessary to form a small fleet according to the needs. Specifically, one of the coupled fleets Under a given formation structure Π ^k , the coupled queue Utility value obtained through a two-line intersection for:

Specifically, in step (3), all trains adopt the formation structure Π ^k, and the optimization target of the system benefit v(Π ^k , Π) of all trains passing through the double-track intersection line within a certain period of time is:

In order to ensure driving safety in the actual system, each train must enter the double-track intersection line at least once a safe headway H _safe , that is, t _i+1 -t _i ≥H _safe . When the train runs independently in a conventional manner, H _safe must ensure the safe headway TH ₀ under absolute braking protection. When the train runs in virtual coupling mode, the running coupling headway H _safe within the team is guaranteed to be the coupling headway TH _vc with relative braking protection. Therefore, the transportation and safety constraints in the formation process are as follows:

t _i -t _i-1 -((1-c _i )TH _vc +c _i TH ₀ )≥0, i∈I;

t _i -t _i-1 -t _work ≥0, i∈I, t _work <TH ₀ ;

Where, _ci is the position weight of the train. If train i is the pilot train, _ci = 1, otherwise _ci = 0, and c ₁ = 1. The constraints indicate that the time _ti of the train entering the double-track intersection must be within the allowed time window τ _j , the time interval between adjacent trains entering _ti -ti _-1 must be greater than the necessary headway, the time interval between adjacent trains entering _ti -ti _-1 must be greater than the turnout working time, and the coordination distance of any train i It must be less than the length of the double-line intersection line; the length of each coupling pair must not be greater than the maximum length n _max allowed by the system. τ _j is mainly determined by the required operating cycle T _j and the first train departure time α.

In order to compare the advantages and disadvantages of different formation structures, according to the formation structure Π ^k system benefit value obtained in the root step (3), the utilitarian preference relationship is applied in step (4) to design the preference rule as the basis for iterative update of the formation structure. The coupling formation utilitarian preference rule is as follows:

Given two formation structures and If and only if When , the formation structure Π ¹ ＞ _utili Π ² holds, that is, the formation method utilitarianism of Π ¹ is better than that of Π ² , denoted as ＞ _utili . This relationship is complete, reflexive and transitive.

The formation structure Π ¹ > _utili Π ² means that the formation structure Π ² can be updated to the formation structure Π ¹ , otherwise it remains unchanged.

Considering the system and environmental conditions, the small grouping allowed by virtual grouping is limited. The length of the length n _max . Therefore, in step (5), all formation structures that meet the system small formation length limit are first screened, and the screening method is as follows:

Define a binary function _bi , if the i-th train is the lead train, then _bi = 0, otherwise _bi = 1. Except for the first train, which must be the lead train, _bi = 0, each train may become the lead train or the follower train. When the maximum formation length allowed in the system is n _max , all feasible formation structures must meet the following conditions:

All formation structures that meet the above conditions are recorded as a feasible formation structure set Π ^f . The subsequent traversal process only selects this feasible formation structure set Π ^f for traversal.

Step (6) starts from the initial formation structure where all trains run independently, selects formation structures in turn in Π ^f , solves the utility function time linear time programming model established in step (2), solves the optimal departure time and the optimal system throughput efficiency under each formation structure, and uses the triplet G _k ∈ G, respectively record the traversed alliance structure The optimal utility value of the train coupling formation passing system under Optimal time to enter the junction route and formation coupling structure In steps (7) to , all possible formation structures are traversed in turn. If the designed utilitarian preference is > _utili , the optimal formation structure is updated and the time is optimized. The traversed formation structure is continuously updated according to the preference rule until all feasible formation structures are traversed. The formation structure finally determined in step (8) is selected in G The smallest structure is obtained, that is, the formation structure with the shortest time for the convoy to pass through the double-line intersection is the recommended output structure. The optimal time under this structure is It is the recommended time for each train to enter the junction line.

Examples and effect verification

For example, a certain urban railway is selected, whose double-track intersection line length is 7km, the design minimum headway of the common line section is 150s, the design track cruising speed is 160km/h, the turnout speed limit is 50km/h, the turnout conversion operation time is 38s, and the ideal coupling interval is 140m. The commonly used traction acceleration is 0.8m/ ^s2 , and the emergency braking acceleration is 1.25m/ ^s2 . Select the appropriate first train departure time deviation, and the optimal recommended formation structure under different maximum allowable formation structures is shown in Figure 5. The optimal formation results in the calculation results show that there will be no more than 4 trains. When the branch line train runs with the minimum interval of the main line railway as the cycle, when _nmax = 2, _nmax = 3, the train group mainly chooses to pass the bottleneck section without coupling. The optimal formation structure is formed when the first train offset α = 20s and the branch line operation cycle is 170s. At this time, the initial train interval deviates from the basic operating cycle by 40s in order to ensure the safe working time of the turnout.

Analyze the impact of the departure ratio of unconnected branch lines on the recommended formation and effect improvement. As shown in Figure 6, when branch line trains enter the double-track intersection line at different ratios, the recommended formation structure time finally converges to the optimal formation structure when n _max = 3. As shown, when the ratio of branch line trains in different directions entering the bottleneck section is more uniform, the virtual marshaling mode model has a better effect on improving system efficiency. The time it takes for trains to pass through the double-track intersection in an uncoupled manner is used as a control. When trains enter the collinear section at a departure ratio of 1:2, the average running time of all trains can be increased by 23.4% compared with the uncoupled method; when entering the collinear section at a departure ratio of 2:3, the average running time of all trains can be increased by 27.4% compared with the uncoupled method.

The influence of branch line service cycle and first car deviation on different branch lines on recommended formation and effect improvement is analyzed, as shown in Figure 7. When branch line trains enter the Y-shaped intersection line in a 1:1 manner, the recommended formation structure time finally converges to the optimal formation structure when n _max = 4. The recommended branch line service cycle is 160s, and the first car deviation is about 30s. Compared with the traditional mode, the efficiency can be improved by 30.7%. In many cases, trains are operated in small coupled formations.

It is known in the art that by generating a formation structure decision according to the method of the present invention, a reasonable coupling object and an optimal time strategy can be determined according to transportation demand and system constraints. If the decision method provided by the present invention is used in the engineering implementation of virtual marshaling, a coupling group of trains that are more suitable and have more potential can be formed, and different formation structures can be converged according to environmental changes, thereby improving the passing efficiency of the double-track intersection line and giving full play to the technical advantages of virtual marshaling.

It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims (8)

1. A virtual train formation structure decision method for a double-track merging line, characterized in that it specifically includes the following steps: (1) Obtain train attributes, transportation demand information, and route information to provide to step (2) and start the formation decision process; (2) Formulate a coupling coordination strategy based on the characteristics of the double-line merging route, and then calculate the synergy relationship between the coordination distance and time within the small-scale fleet to provide (3); (3) Taking the departure time and the sequence number of each train in the formation and the position of the train in the formation as the decision variables of the formation structure, a quantitative utility optimization model obtained by the train formation through the double-track merging line is established to provide (6); A coupled fleet Under a given formation structure Π ^k , the coupled queue Utility value obtained through a two-line intersection for: The optimization model of the quantitative formation utility v(Π ^k ,Π) obtained by platooning under a given formation structure Π ^k is: The constraints that the train formation described in the optimization model needs to satisfy are as follows: Departure time constraints: Safety interval constraint: t _i -t _i-1 -H _safe ≥ 0, i∈I; Turnout working time constraint: t _i -t _i-1 -t _work ≥0, i∈I; Coupling process constraints: Allowed formation length constraints: (4) Design preference rules to compare the advantages and disadvantages of different formation structures and provide (7); (5) Filter out all formation structures that meet the formation length limit and provide (6) and (7); (6) Starting from the initialization formation structure, calculate the total utility value under different formation structures and provide (7); (7) Traverse all feasible formation structures, update the formation structure according to the formation structure utility value and preference rules, and feedback the optimized departure time, and enter (8); (8) Output the optimal platoon structure and optimized departure time; The data flow of the decision-making process from step (1) to step (8) is as follows: outputting the optimal train formation structure attributes and train formation operation attributes as the final result; The train formation structure attributes include the train formation number and the train formation position information; the train formation operation attribute is the time when the train enters the intersection; At the same time, the relevant data variables involved in the decision-making process are defined as follows: I is the train set I = {i|i = 1, 2, ..., N}, N is the number of trains entering the bottleneck section in a certain period; J is the line direction set J = {j|j = 1, 2}, 1 represents the lateral access direction of the turnout, and 2 represents the straight access direction; S _vc is the ideal coupling interval S _vc ; is the coordination distance of the ith train; L _train is the train length; L _bottleneck is the length of the intersection segment; _ti is the departure time of the i-th train entering the double-track merging line; t _i+n is the departure time of the i+nth train entering the double-track merging line; is the coordination time of the ith train, which is the time from when the train enters the bottleneck section to when it reaches the coupling state; is the time it takes for the i-th train to accelerate to the cruising speed; t ^bra is the braking time of the train; t _work is the turnout working time; H _safe is the safe headway, which is the headway under relative braking protection when the train is a following train, and the headway under absolute braking protection when the train is a leading train or operating in an uncoupled mode; τ _j is the set of departure time windows on route j; v _cru represents the orbital cruising speed; v _lim represents the speed limit of the turnout section; a _tra is the common traction acceleration of the train; a _bra is the train emergency braking acceleration; Π ^k is a formation structure k of trains passing through the bottleneck section within a certain period of time; Π is the set of all possible formation structures; Π ^f is the set of all formation structures that meet the system formation length limit after screening; is a small group in the formation structure Π ^k , and its small group length is 2. The method according to claim 1, characterized in that in step (1): The algorithm input needs to be obtained, including: Train attributes include the train's traction acceleration, braking acceleration, and design cruising speed; Transportation demand information refers to the need to obtain the operation cycle and dispatch ratio; The line information includes the speed limit of the turnout section, the maximum formation length that the system can carry, and the length of the merging section. The above three types of data are passed as input to the formation structure decision step. 3. The method as claimed in claim 1 is characterized in that the coupling coordination strategy in step (2) is a speed trajectory planning adjustment strategy, which enables independently operating trains to reach coupling conditions, and the trains reaching the coupling conditions include that the speeds of the trains within the coupling queue are equal and the intervals between the trains are ideal train intervals. 4. The method according to claim 1, characterized in that the coupling coordination strategy is specifically that the last vehicle in the coupling group, after clearing the turnout section and entering the co-linear section, accelerates to the cruising speed v _cru using the optimal speed curve to improve the passing efficiency of the entire convoy; The leading train in the convoy maintains the switch limit speed v _lim , delays acceleration for a period of time, and selects appropriate times to accelerate in sequence, until the last accelerated lead train accelerates to the ideal coupling speed, and the entire convoy reaches the coupling state. 5. The method according to claim 1, characterized in that the coupling coordination strategy is characterized as follows: if the i-th train is the first train in the convoy and the i+n-th train is the last train in the convoy, the coordination relationship between the coordination distance and time within the small-scale convoy is: 6. The method according to claim 1, characterized in that the structural comparison utilitarian preference rule _used as the iterative update of the formation structure in step (4) is: Given two alliance structures and If and only if v(Π ¹ )<v(Π ² ), the formation structure Π ¹ ＞ _utili Π ² holds, that is, the formation method of Π ¹ is utilitarian better than that of Π ² , denoted as ＞ _utili ; when the newly traversed formation structure satisfies the preference ＞ _utili , the current optimal formation structure is updated. 7. The method as claimed in claim 1 is characterized in that the process of screening feasible formation structures that meet the system formation length constraint in step (5) is specifically: listing all formation structure combinations when all adjacent trains can form a fleet, and based on the restriction that each train can only become a leading train or a following train, screening out all formation structures that meet the formation length constraint n _max to form a feasible formation structure set Π ^f . 8. The method as claimed in claim 1 is characterized in that the optimal formation updating process in step (7) is as follows: starting from the initialized formation structure as the optimal formation; selecting formation structures from Π ^f in turn, and determining the optimal departure time and formation utility under each formation structure; if the traversed formation structure satisfies the designed utilitarian preference rule > _utili , then updating the optimal formation structure and the time of entering the intersection section; continuously iterating and updating through step (8) until all feasible formation structures are traversed, and outputting the optimal departure time under the optimal formation structure, as well as the position of the train in each formation and the number of the formation to which the train belongs.