CN113928342A - Train operation method based on virtual marshalling, electronic device and storage medium - Google Patents

Train operation method based on virtual marshalling, electronic device and storage medium Download PDF

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CN113928342A
CN113928342A CN202111296429.6A CN202111296429A CN113928342A CN 113928342 A CN113928342 A CN 113928342A CN 202111296429 A CN202111296429 A CN 202111296429A CN 113928342 A CN113928342 A CN 113928342A
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赵兴东
周旭
肖骁
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Traffic Control Technology TCT Co Ltd
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Abstract

The application provides a train operation method based on virtual marshalling, an electronic device and a storage medium, wherein the method comprises the following steps: determining a plurality of groups of train operation schemes according to the compilation data; respectively determining the train operation schemes of each group, selecting a preset number of train operation schemes as alternative schemes according to the value of the target function under the condition of meeting the constraint condition; and evaluating the alternative schemes, determining a final train operation scheme, and performing virtual marshalling or decompiling on the trains on the large and small road sections according to the final train operation scheme. The train operation scheme which is virtually marshalled by the method provided by the application not only meets the objective function and the constraint condition, but also ensures that the final train operation scheme meets the transport capacity matching requirements of different road sections through comprehensive evaluation, also considers the operation cost and best meets the actual requirements.

Description

Train operation method based on virtual marshalling, electronic device and storage medium
Technical Field
The present disclosure relates to the field of rail transit technologies, and in particular, to a train operation method based on virtual marshalling, an electronic device, and a storage medium.
Background
Urban rail transit is an organized and planned transportation mode, and a running chart is the basis of an operation organization. In the conventional process of compiling the operation diagram, the intersection form is generally divided into a single intersection and a large-small intersection.
With the gradual extension of subway lines to suburbs and the use of overlength lines, the space-time imbalance of passenger flows is more obvious, and the contradiction between transport capacity and passenger demand is increasingly prominent. The operation organization mode of single traffic route can not satisfy the complex riding demand of passengers, and easily causes the waste of transportation energy. The large and small traffic routes can deal with the problem of unbalanced section passenger flow, the waiting time of passengers in the small traffic route section is reduced, but the traffic interval of the large traffic route non-overlapping section is larger due to the traffic organization mode, so that the waiting time of passengers waiting in the large traffic route is increased, and the cost saving range is relatively limited. And under the existing conditions, the marshalling number of the on-line trains is basically fixed, and the trains suitable for marshalling cannot be adopted according to the passenger flow requirements.
Disclosure of Invention
In order to solve one of the technical defects, the present application provides a train operation method based on a virtual formation, an electronic device and a storage medium.
In a first aspect of the present application, a method for operating a train based on a virtual consist is provided, the method comprising:
determining a plurality of groups of train operation schemes according to the compilation data;
respectively determining the train operation schemes of each group, selecting a preset number of train operation schemes as alternative schemes according to the value of an objective function under the condition of meeting constraint conditions;
and evaluating the alternative schemes, determining a final train operation scheme, and performing virtual marshalling or decompiling on the trains on the large and small road sections according to the final train operation scheme.
Optionally, any one train operation scheme includes: the train dispatching frequency of the large traffic road section comprises a train dispatching frequency of a combined train of the large traffic road section and a train of the small traffic road section.
Optionally, the objective function is composed of a travel cost objective function, an operation fixed cost objective function, and an operation variable cost objective function.
Optionally, the travel cost objective function is a minimum product of the passenger time value and the passenger waiting total time.
Optionally, the operation fixed cost objective function is that the product of the value of the vehicle purchase cost distributed to the unit hour and the running time of the single train is the minimum.
Optionally, the operation variable cost objective function is that the product of the kilometer cost of single train per unit of travel and the kilometer number of train travel is minimum.
Optionally, the constraint condition includes a retrace station position constraint condition, an integer constraint condition, a large traffic section departure frequency constraint condition, a total number of trains passing through the route constraint condition, and a full load rate constraint condition.
Alternatively,
the retracing station position constraint condition is as follows: the first retracing point and the second retracing point of the small intersection section are positioned between the first retracing point and the second retracing point of the large intersection section;
the integer constraint condition is as follows: the departure frequency of the combined train comprising the large and small traffic section and the train comprising the small traffic section is integral multiple of the departure frequency of the train comprising the large traffic section;
the large traffic section departure frequency constraint condition is as follows: the departure frequency of the large-traffic-section train is not less than the minimum departure frequency, and the departure frequency of the large-traffic-section train is not more than the maximum departure frequency determined by the minimum tracking interval between trains and the passing interval of the train at a station;
and the total number of passing trains of the line constraint condition is as follows: the departure frequency of the combined trains at the large and small traffic sections and the trains at the small traffic sections is not more than the maximum departure frequency determined by the minimum tracking interval between the trains and the turn-back duration of a single train;
the full load rate constraint condition is as follows: the train section full load rate is not less than the design lower limit of the train section full load rate, and the train section full load rate is not more than the design upper limit of the train section full load rate.
In a second aspect of the present application, there is provided an electronic device comprising:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method according to the first aspect.
In a third aspect of the present application, there is provided a computer readable storage medium having a computer program stored thereon; the computer program is executed by a processor to implement the method according to the first aspect as described above.
The application provides a train operation method based on virtual marshalling, an electronic device and a storage medium, wherein the method comprises the following steps: determining a plurality of groups of train operation schemes according to the compilation data; respectively determining the train operation schemes of each group, selecting a preset number of train operation schemes as alternative schemes according to the value of the target function under the condition of meeting the constraint condition; and evaluating the alternative schemes, determining a final train operation scheme, and performing virtual marshalling or decompiling on the trains on the large and small road sections according to the final train operation scheme.
According to the method, the train operation scheme for virtual marshalling not only meets the target function and the constraint condition, but also ensures that the final train operation scheme meets the capacity matching requirements of different road sections through comprehensive evaluation, also considers the operation cost and best meets the actual requirements.
In addition, in one implementation, the contents of the train operation scheme are determined, the final train operation scheme is ensured to meet the capacity matching requirements of different intersection sections, the operation cost is also considered, and the actual requirements are met most.
In addition, in one implementation, the content of the objective function is determined, the selection basis of the train operation scheme can be accurately evaluated through the content, the final train operation scheme is guaranteed to meet the requirement of capacity matching of different traffic sections, the operation cost is also considered, and the actual requirement is met most.
In addition, in one implementation, a trip cost objective function is described in detail, the trip cost can be accurately evaluated through the objective function, and the final train operation scheme is guaranteed to meet the capacity matching requirements of different traffic sections and also take the operation cost into consideration, so that the final train operation scheme is most in line with the actual requirements.
In addition, in one implementation, an operation fixed cost objective function is described in detail, the operation fixed cost can be accurately evaluated through the objective function, the final train operation scheme is guaranteed to meet the requirement of capacity matching of different traffic sections, the operation cost is also considered, and the actual requirement is met most.
In addition, in one implementation, an operation variable cost objective function is described in detail, the operation variable cost can be accurately evaluated through the objective function, and the final train operation scheme is guaranteed to meet the capacity matching requirements of different traffic sections and also take the operation cost into consideration, so that the actual requirements are met most.
In addition, in one implementation, the content of the constraint condition is described in detail, and the constraint condition can ensure that the final train operation scheme not only meets the capacity matching requirements of different intersection sections, but also considers the operation cost, and best meets the actual requirements.
In addition, in one implementation, the detailed content of each constraint condition is described in detail, so that the final train operation scheme is ensured to meet the capacity matching requirements of different intersection sections and also take the operation cost into consideration, and the final train operation scheme is most in line with the actual requirements.
The electronic equipment provided by the application is characterized in that a computer program is executed by a processor to determine a plurality of groups of train operation schemes according to compilation data; respectively determining the train operation schemes of each group, selecting a preset number of train operation schemes as alternative schemes according to the value of the target function under the condition of meeting the constraint condition; and evaluating the alternative schemes, determining a final train operation scheme, and performing virtual marshalling or decompiling on the trains on the large and small road sections according to the final train operation scheme. The train operation scheme for virtual marshalling not only meets the objective function and the constraint condition, but also ensures that the final train operation scheme meets the capacity matching requirements of different road sections and also considers the operation cost through comprehensive evaluation, and the train operation scheme most meets the actual requirements.
A computer readable storage medium having a computer program executed by a processor for determining a plurality of sets of train operating scenarios from programming data; respectively determining the train operation schemes of each group, selecting a preset number of train operation schemes as alternative schemes according to the value of the target function under the condition of meeting the constraint condition; and evaluating the alternative schemes, determining a final train operation scheme, and performing virtual marshalling or decompiling on the trains on the large and small road sections according to the final train operation scheme. The train operation scheme for virtual marshalling not only meets the objective function and the constraint condition, but also ensures that the final train operation scheme meets the capacity matching requirements of different road sections and also considers the operation cost through comprehensive evaluation, and the train operation scheme most meets the actual requirements.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic diagram of train operation under a virtual grouping technique provided in an embodiment of the present application;
fig. 2 is a schematic flowchart of a train operation method based on a virtual formation according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of a large-small cross-road train operation diagram under a virtual grouping technique according to an embodiment of the present application;
fig. 4 is a schematic diagram of a decision variable provided in an embodiment of the present application.
Detailed Description
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following further detailed description of the exemplary embodiments of the present application with reference to the accompanying drawings makes it clear that the described embodiments are only a part of the embodiments of the present application, and are not exhaustive of all embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
In the process of implementing the application, the inventor finds that as the subway line is gradually prolonged to suburbs and the overlength line is put into use, the space-time imbalance of the passenger flow is more obvious, and the contradiction between the transport capacity and the passenger demand is increasingly prominent. The operation organization mode of single traffic route can not satisfy the complex riding demand of passengers, and easily causes the waste of transportation energy. The large and small traffic routes can deal with the problem of unbalanced section passenger flow, the waiting time of passengers in the small traffic route section is reduced, but the traffic interval of the large traffic route non-overlapping section is larger due to the traffic organization mode, so that the waiting time of passengers waiting in the large traffic route is increased, and the cost saving range is relatively limited. And under the existing conditions, the marshalling number of the on-line trains is basically fixed, and the trains suitable for marshalling cannot be adopted according to the passenger flow requirements.
In view of the foregoing problems, an embodiment of the present application provides a train operation method based on a virtual formation, an electronic device, and a storage medium, where the method includes: determining a plurality of groups of train operation schemes according to the compilation data; respectively determining the train operation schemes of each group, selecting a preset number of train operation schemes as alternative schemes according to the value of the target function under the condition of meeting the constraint condition; and evaluating the alternative schemes, determining a final train operation scheme, and performing virtual marshalling or decompiling on the trains on the large and small road sections according to the final train operation scheme. The train operation scheme which is virtually marshalled by the method provided by the application not only meets the objective function and the constraint condition, but also ensures that the final train operation scheme meets the transport capacity matching requirements of different road sections through comprehensive evaluation, also considers the operation cost and best meets the actual requirements.
Under the application of a virtual marshalling technology, a more flexible transportation organization mode can be provided under the form of large and small traffic routes, the running paths of the front and rear trains can be newly designed, and the large and small traffic routes can be decomposed into large and small traffic routes trains by means of the decompiling at the small traffic route retracing station; and the reconnection can also be carried out at a small traffic route return station and the reconnection operation is carried out in a small traffic route section. As shown in fig. 1, the virtual marshalling train operates in the marshalling of the small-traffic road section, the virtual marshalling train is un-marshalled before reaching the return station, and the large-traffic road train can continue to operate forwards after reaching the return station; the small traffic route train performs turn-back operation at a turn-back station to form various traffic routes; the small cross road train after being turned back can be organized into a group to run with another large cross road train which is already turned back. The transportation organization mode can adopt a small marshalling train in a small traffic section, adopts proper departure frequency, and reduces the waiting time of passengers in a large traffic section while ensuring the transportation capacity.
The train operation method based on the virtual formation provided by the embodiment determines the position of a small traffic route turning-back station and the departure frequency of a large traffic route train and a small traffic route train. The large-traffic-road train is marshalled or decompiled with the small-traffic-road train at the turn-back station, the problem that the train departure interval of the large-traffic-road section is too long can be solved, meanwhile, the small-marshalling train is adopted in the small-traffic-road section, the transport capacity matching requirements of different traffic-road sections can be better met, and the enterprise operation cost is also considered. Specifically, referring to fig. 2, the implementation flow of the train operation method based on the virtual formation provided in this embodiment is as follows:
and 101, determining a plurality of groups of train operation schemes according to the prepared data.
Wherein, any group of train operation scheme includes: a first folding point a of a small-scale traffic section, a second folding point b of a small-scale traffic section, a first folding point n1 of a large-scale traffic section, a second folding point n2 of a large-scale traffic section, and a first departure frequency f1And a second departure frequency f2，f1Frequency of departure, f, for large section trains2The departure frequency of the combined train including the large and small traffic section and the train including the small traffic section is obtained.
In addition, the compilation data is data required for compiling a train operation plan of a large-size crossing under a virtual formation, for example, the compilation of the train operation plan requires the following types of data:
1. line topology: and describing the line, the sites and the association relationship among the sites.
2. And (3) operating data: the interval running time, the station stop time, the return time and the like of the train under specific signal conditions are described.
3. Vehicle data: the available train type, maximum train count, vehicle deputy are described.
4. Passenger flow data: and describing the cross-section passenger flow data of each section of the line.
5. Operation related technical data: the maximum passing capacity of the line, the maximum full load rate of the train and the like.
In addition, when determining multiple sets of train operation schemes according to the compilation data, the compilation data can be input into a pre-trained model for solving, and the positions of the large and small intersection turning-back stations and the train departure frequency of the large and small intersection trains (namely, a first folding point a of the small intersection, a second folding point b of the small intersection, a first folding point n1 of the large intersection, a second folding point n2 of the large intersection, and a first departure frequency f1And a second departure frequency f2). Therefore, each of the models is solved as a set of a first folding point a of the small traffic segment, a second folding point b of the small traffic segment, a first folding point n1 of the large traffic segment, a second folding point n2 of the large traffic segment, a first departure frequency f1And a second departure frequency f2Namely a group of train operation schemes. And determining all solutions as a plurality of groups of train operation schemes.
It should be noted that "first" and "second" in the first folding point and the second folding point of the minor cross road section are only used for identification, and are used to distinguish 2 endpoints of the minor cross road section, and there is no other substantial meaning. For example, a small traffic route section is a route section from a station to a station b, then the station a is an end point of the small traffic route section, namely a folding point, the station b is also an end point of the small traffic route section, namely a folding point, and in order to distinguish the station a from the station b, the station a is named as a first folding point of the small traffic route section, and the station b is named as a second folding point of the small traffic route section. Namely, the first folding point of the small intersection segment is a folding point, and the second folding point of the small intersection segment is also a folding point.
Similarly, "the first" and "the second" of the first retracing point and the second retracing point of the large intersection segment are also used for identification, and are used for distinguishing 2 endpoints of the large intersection segment, and have no other substantial meanings. For example, if the large traffic route segment is a route segment from n1 station to n2 station, then n1 station is an end point of the large traffic route segment, i.e., a turning point, and n2 station is also an end point of the large traffic route segment, i.e., a turning point, and in order to distinguish between n1 station and n2 station, the n1 station is named as a first turning point of the large traffic route segment, and the n2 station is named as a second turning point of the large traffic route segment. Namely, the first folding point of the large intersection section is a folding point, and the second folding point of the large intersection section is also a folding point.
The first and second ones of the first and second departure frequencies are also used for identification only, and are used for distinguishing the departure frequencies of trains operating on different road sections, and have no other substantial meanings. That is, the first departure frequency is actually the departure frequency of a train operating on a large traffic section (note that, here, a train operating on a large traffic section means a train traveling on a large traffic section, and it is sufficient if the train is operating between the station n1 and the station n2 regardless of whether the train is currently traveling on a large traffic section or a small traffic section), that is, the departure frequency of a train on a large traffic section is named the first departure frequency so as to be distinguished from the departure frequencies of trains operating on other sections. The second departure frequency is also actually a departure frequency of a train operating on a small traffic section (note that here, the train operating on the small traffic section is not a train that only travels to and from the small traffic section, and includes trains operating on a large traffic section when the train operating on the large traffic section travels on the small traffic section, that is, a train operating only on the small traffic section (a train operating between a station and b station) and a train operating on the large traffic section but currently traveling on the small traffic section (a train operating between n1 station and n2 station but currently traveling between a station and b station)), that is, a large traffic section combination train and a small traffic section train, the departure frequency of the large traffic section combination train and the small traffic section train is named as a first departure frequency in order to distinguish the train from departure frequencies of trains operating on other sections.
And 102, respectively determining the train running schemes of each group, selecting the preset number of train running schemes as alternatives according to the value of the objective function under the condition that the constraint condition is met and the value of the objective function.
This step is divided into 2 sub-steps.
And substep 1, respectively determining the running schemes of each group of trains, and under the condition of meeting the constraint condition, determining the value of an objective function. And a substep 2 of selecting a train operation scheme of a preset number of groups as an alternative scheme according to the value of the objective function.
The following description is made separately.
And substep 1, respectively determining the running schemes of each group of trains, and under the condition of meeting the constraint condition, determining the value of an objective function.
● for objective function
The objective function is composed of a travel cost objective function, an operation fixed cost objective function and an operation variable cost objective function.
For example, the objective function is min W ═ α1·W1+α2·W2+α3·W3。
W is the total cost, W1For travel costs determined by a travel cost objective function, W2For an operational fixed cost, W, determined by an operational fixed cost function3For an operational variable cost, α, determined by an operational variable cost function1For trip cost weighting, α2To operate fixed cost weights, α3To operate variable cost weights. The constraint conditions comprise a retracing station position constraint condition, an integer constraint condition, a large traffic section departure frequency constraint condition, a total number of trains passing through the line constraint condition and a full load rate constraint condition.
1. The trip cost objective function is the minimum product of the passenger time value and the passenger waiting total time
For example: the trip cost objective function is:
minW1＝w1·Tw。
wherein the content of the first and second substances,
Figure BDA0003336724400000071
Figure BDA0003336724400000072
GDP is the national production total value in the statistical time interval, R is the employment total number in the statistical time interval, and T is the single total working time in the statistical time interval.
Tw＝tw1+tw2+tw3。
Figure BDA0003336724400000073
Figure BDA0003336724400000074
Figure BDA0003336724400000075
Figure BDA0003336724400000076
Figure BDA0003336724400000077
Figure BDA0003336724400000078
Figure BDA0003336724400000079
Figure BDA00033367244000000710
Figure BDA00033367244000000711
Figure BDA00033367244000000712
Figure BDA00033367244000000713
Figure BDA00033367244000000714
Figure BDA00033367244000000715
i1 is a first start point marker, j1 is a first end point marker, i2 is a second start point marker, j2 is a second end point marker, i3 is a third start point marker, j3 is a third end point marker, i4 is a fourth start point marker, j4 is a fourth end point marker, i5 is a fifth start point marker, j5 is a fifth end point marker, i6 is a sixth start point marker, j6 is a sixth end point marker, i7 is a seventh start point marker, j7 is a seventh end point marker, q isi1j1The traffic volume starting at i1 and ending at j1, qi2j2The traffic volume starting at i2 and ending at j2, qi3j3The traffic volume starting at i3 and ending at j3, qi4j4The traffic volume starting at i4 and ending at j4, qi5j5The traffic volume starting at i5 and ending at j5, qi6j6The traffic volume starting at i6 and ending at j6, qi7j7The traffic volume starting at i7 and ending at j 7. Beta is the probability that the passenger with the starting point on the small traffic section and the ending point on the large traffic section selects the train on the small traffic section. T iskIs a calculation cycle. T iskTypically 1 hour.
2. The fixed operation cost objective function is the minimum product of the value of the vehicle purchase cost distributed to the unit hour and the running time of the single train
For example, the operation fixed cost objective function is:
Figure BDA0003336724400000081
wherein, w2Purchase cost for single train, TkThe running time of a single train is long.
Z＝ZApplication of+ZUnder repair+ZFor standby。
Figure BDA0003336724400000082
Figure BDA0003336724400000083
Figure BDA0003336724400000084
Ln1,n2Is the total distance of the large traffic section, La,bIs the total route of the small traffic section, VdThe running speed of a single train, tFold-backThe time length of the single train is the turn-back time length.
VdUsually 35 km/h.
ZUnder repair＝δ1·ZApplication of，ZFor standby＝δ2·ZApplication of，δ1To repair train ratio, delta2Is the spare train proportion.
δ1Generally 10% to 15%, delta2About 10%.
3. The operation variable cost objective function is the minimum product of the kilometer cost of single train unit and the kilometer number of the train
For example: the operation variable cost objective function is:
minW3＝w3·L。
wherein, w3The cost of a single train per unit running kilometer.
L＝2·(Ln1,n2·f1·Nn1,n2+La,b·f2·Na,b)。
Ln1,n2Is the total distance of the large traffic section, La,bFor a total route of small traffic sections, Nn1,n2For large traffic sectionsNumber of cars in formation, Na,bThe number of grouped vehicles for a small traffic segment.
By the method, trip cost, operation fixed cost and operation variable cost can be accurately evaluated, the final train operation scheme is guaranteed to meet the capacity matching requirements of different traffic sections, the operation cost is also considered, and the method is most suitable for actual requirements.
For constraints
The constraint conditions include: the method comprises the following steps of retracing station position constraint conditions, integer constraint conditions, large traffic section departure frequency constraint conditions, total number of trains passing through a line constraint conditions and full load rate constraint conditions.
1. The retracing station position constraint conditions are as follows: the first retracing point and the second retracing point of the small intersection segment are positioned between the first retracing point and the second retracing point of the large intersection segment. For example: n1< a < b < n 2.
2. The integer constraint is: departure frequency f of combined trains at large and small traffic sections and trains at small traffic sections2Departure frequency f for large-traffic section train1Integer multiples of. For example: f. of2＝m·f1Wherein m is a positive integer.
3. The constraint conditions of the departure frequency of the large traffic section are as follows: departure frequency f of large-traffic-section train1Not less than the minimum departure frequency f0And the departure frequency of the trains at the large traffic section is not more than the maximum departure frequency determined by the minimum tracking interval between the trains and the passing interval of the trains at the station.
For example,
Figure BDA0003336724400000091
Figure BDA0003336724400000092
wherein f is0To a minimum departure frequency, IPursuingFor minimum tracking interval between trains, IVehicle with wheelsIs the passing interval of the train at the station,
Figure BDA0003336724400000093
to be minimized by trainTracking the maximum departure frequency determined by the interval,
Figure BDA0003336724400000094
the maximum departure frequency determined by the passing interval of the train at the station.
4. The constraint conditions of the total number of the trains passing through the line are as follows: departure frequency f of combined trains at large and small traffic sections and trains at small traffic sections2Not greater than the maximum departure frequency determined by the minimum tracking interval between trains and the retracing duration of a single train.
For example:
Figure BDA0003336724400000095
Figure BDA0003336724400000096
wherein, IPursuingFor minimum tracking interval between trains, tFold-backThe time length of the single train turning back is,
Figure BDA0003336724400000097
for the maximum departure frequency determined by the minimum inter-train tracking interval,
Figure BDA0003336724400000098
is the maximum departure frequency determined by the length of the turn-back time of the single train.
5. The constraint conditions of the full load rate are as follows: the train section full load rate is not less than the design lower limit of the train section full load rate, and the train section full load rate is not more than the design upper limit of the train section full load rate.
For example:
Figure BDA0003336724400000099
wherein, γminDesign the lower limit, gamma, for the train section full load ratemaxAnd designing an upper limit for the full loading rate of the train section.
Figure BDA00033367244000000910
Nn1,n2Number of vehicles in formation for large traffic sections, Na,bNumber of vehicles in formation for small traffic sections, CDeciding memberBut the train is rated for passenger capacity.
Figure BDA00033367244000000911
Figure BDA0003336724400000101
Figure BDA0003336724400000102
Figure BDA0003336724400000103
i8 is an eighth start point marker, j8 is an eighth end point marker, i9 is a ninth start point marker, j9 is a ninth end point marker, i10 is a tenth start point marker, j10 is a tenth end point marker, j11 is an eleventh end point marker, i12 is a twelfth start point marker, j12 is a twelfth end point marker, i13 is a thirteenth start point marker, j13 is a thirteenth end point marker, i14 is a fourteenth start point marker, j14 is a fourteenth end point marker, j15 is a fifteenth end point marker, qi8j8The traffic volume starting at i8 and ending at j8, qi9j9The traffic volume starting at i9 and ending at j9, qi10i10The traffic volume starting at i10 and ending at j10, qi10j11The traffic volume starting at i10 and ending at j11, qi12j12The traffic volume starting at i12 and ending at j12, qi13j13The traffic volume starting at i13 and ending at j13, qi14j14The traffic volume starting at i14 and ending at j14, qi14j15The traffic volume starting at i14 and ending at j 15. Beta is the probability that the passenger with the starting point on the small traffic section and the ending point on the large traffic section selects the train on the small traffic section.
The constraint conditions can ensure that the departure frequency, the number of trains passing through the line, the full load rate and the section passenger flow of the final train operation scheme meet the requirement of capacity matching of different traffic sections and ensure the safe and high-speed operation of the trains.
The following describes the implementation of this step by taking the operation diagram of the large-small cross-road train under the virtual formation as shown in fig. 3 as an example. Referring to fig. 3, it is assumed that only a large-traffic section train exists, a group is formed at a turning point of a small-traffic section, and in addition, a small-traffic section train can be independently driven, f2＝2·f1. When determining the value of the objective function of any group of train operation schemes, the method relates to three parts, namely a decision variable, the objective function and a constraint condition, and the relation and the position of the decision variable are shown in FIG. 4.
1. Decision variables
(1) Frequency of departure
f1Frequency of departure, f, for large section trains2The departure frequency of the combined train including the large and small traffic section and the train including the small traffic section is obtained.
(2) Ratio of departure
m, i.e. f2＝m·f1。
(3) Location of switchback station
The first retracing point a of the small intersection section, the second retracing point b of the small intersection section, the first retracing point n1 of the large intersection section and the second retracing point n2 of the large intersection section.
2. Objective function
(1) Travel time of passenger
According to the difference of the sections where the travel starting point and the travel ending point are located, the passengers are divided into three categories, and the passenger flow is calculated respectively.
1) A first type of passenger: passengers starting at large traffic sections and only able to ride the large traffic section
The first passenger traffic is the sum of the passenger traffic of the starting point in the uplink direction and the starting point in the downlink direction on the large traffic section:
Figure BDA0003336724400000111
upstream first class passenger traffic:
Figure BDA0003336724400000112
passenger traffic of the first class in the descending direction:
Figure BDA0003336724400000113
2) a second type of passenger: passengers with starting point and ending point in small traffic section can take large traffic road or small traffic road
The second type passenger flow is the sum of passenger flows of starting points and end points in the uplink direction and the downlink direction on a small traffic section:
Figure BDA0003336724400000114
upstream second-class passenger traffic:
Figure BDA0003336724400000115
second-class passenger traffic in the downward direction:
Figure BDA0003336724400000116
3) a third class of passengers: cross-road passenger with starting point on small-traffic road section and ending point on large-traffic road section
The third class of passengers have preference, and have certain probability of selecting a small train to transfer to a large train. The passenger volume is the difference value between the total passenger volume and the passenger volumes of the first class and the second class:
Figure BDA0003336724400000117
4) calculating departure intervals:
the departure interval is the ratio of the calculation period to the departure frequency, and the departure interval of the trains in the large traffic section, the departure interval of the trains in the small traffic section and the departure interval of the trains in only the small traffic section are respectively calculated.
Departure interval of trains at large traffic section:
Figure BDA0003336724400000121
departure interval of trains (including large and small traffic section combined trains and small traffic section trains) in small traffic section:
Figure BDA0003336724400000122
departure interval of only small-section trains (excluding large-and small-section combined trains):
each train of large-traffic section trains is marshalled or un-marshalled with the train of only small-traffic section at the turn-back station, and at f2In train with small cross roads f1Train marshalling with large section train, only small section train having f2-f1And (4) columns.
Figure BDA0003336724400000123
In the formula, TkTypically 1 hour.
5) Calculating waiting time:
the waiting time is the product of the passenger flow and the average waiting time, and the waiting time of three types of passengers is respectively calculated by taking one half of the departure interval as the average waiting time.
Waiting time of first type passenger:
Figure BDA0003336724400000124
waiting time of second class passenger:
Figure BDA0003336724400000125
waiting time of the third type of passengers:
Figure BDA0003336724400000126
the total waiting time of passengers is as follows:
Tw＝tw1+tw2+tw3
in the formula, β is the probability that the passenger at the starting point on the small traffic section and the ending point on the large traffic section selects the train on the small traffic section, that is, the probability that the passenger of the third class selects the train on only the small traffic section.
6) Passenger time value calculation:
the passenger trip cost fee is the total waiting time of the passenger multiplied by the unit waiting cost. The unit waiting cost is calculated by the generalized waiting time cost and is expressed by the non-working time value, and the unit waiting time cost is calculated according to the total national production value in a research year of a certain city, the total employment number in the research year of the certain city and the working time of each person in the research year.
Figure BDA0003336724400000127
Figure BDA0003336724400000131
In the formula, VOT represents the value of per-capita non-working time in a certain urban research period, and the unit is Yuan/h; GDP is the total value of national production in the research year of a certain city, and the unit is ten thousand yuan; r represents the general employment population in the research year of a certain city, and the unit is ten thousand; t represents the working time in hours for each individual study year.
7) Constructing an objective function with minimum passenger travel cost:
minW1＝w1·Tw
(2) cost of enterprise operations
1) Fixed cost (vehicle purchase cost)
The number of the applied vehicles is the ratio of the train turnover time to the departure interval, the train turnover time of the trains in the large traffic section and the train in the small traffic section is respectively calculated, and the total number of the applied vehicles is calculated. The train turnover time is the sum of the whole-course running time and the turn-back time.
Train turnaround time of the large traffic section:
Figure BDA0003336724400000132
train turnaround time of the small traffic section:
Figure BDA0003336724400000133
total number of vehicles in use:
Figure BDA0003336724400000134
in the formula, VdUsually 35 km/h;
Figure BDA0003336724400000135
the representation is rounded up, and the number of the vehicles is guaranteed to be an integer.
The vehicle purchase cost is the product of the purchase cost of the single train and the total number of the vehicle purchases. The total number of the purchased vehicles is considered to be the number of the repaired vehicles and the number of the spare vehicles, wherein the number of the repaired vehicles is generally 10-15% of the number of the operating trains, and the number of the spare vehicles is controlled to be about 10%.
Total number of vehicle purchases:
Z＝Zapplication of+ZUnder repair+ZFor standby
ZUnder repair＝δ1·ZApplication of
ZFor standby＝δ2·ZApplication of
δ1Generally 10% to 15%, delta2About 10%.
Vehicle acquisition cost:
C＝w2·Z
assuming that the service life of a train is 30 years, the daily working time of the train is 18 hours, the purchase cost of the train is divided into unit hours, and the fixed cost objective function of an operation enterprise in a research period is as follows:
Figure BDA0003336724400000141
2) variable cost (kilometers train)
The running cost of the train is the product of the running kilometers of the train and the running kilometers of the train unit, and the running kilometers of the train is calculated according to the length of a road, the departure frequency and the number of train formation vehicles.
Running kilometers:
L＝2·(Ln1,n2·f1·Nn1,n2+La,b·f2·Na,b)
the variable cost objective function for operating enterprises in the research period is:
minW3＝w3·L
(3) total objective function
The total objective function is the sum of the passenger trip cost, the fixed cost of the operation enterprise and the variable cost of the operation enterprise. The total objective function is the sum of multiple objective functions, so the weight needs to be determined for each objective function, and the weight can be determined according to expert opinions or actual needs of enterprises.
min W＝α1·W1+α2·W2+α3·W3
3. Constraint conditions
(1) Switchback station position constraints
The first folding point a of the small traffic section and the second folding point b of the small traffic section are positioned between the first folding point n1 of the traffic section and the second folding point n2 of the large traffic section.
If a is a first turning-back station of a small traffic section in the uplink direction; b is the second switchback station of the small traffic section in the up direction, n1< a < b < n2
(2) Integer constraint
f2＝m·f1
Wherein m is a positive integer.
(3) Large traffic route departure frequency constraint
Figure BDA0003336724400000142
Figure BDA0003336724400000143
(4) Total number of trains passing by line
Figure BDA0003336724400000144
Figure BDA0003336724400000151
(5) Full load rate constraint
The train section full load rate is designed to be within a reasonable range, and is not less than the minimum value and not more than the maximum value. The maximum value of the section full load rate of the interval x is the larger value of the section full load rate of the train in the large traffic section and the section full load rate of the train in the small traffic section. The section passenger flow of the large-traffic train in the small-traffic section needs to consider the sharing rate of various passenger flows. And after the train at the small traffic section turns back, virtually marshalling the train at the large traffic section, wherein the shared passenger flow is related to the marshalling quantity, the passenger flow of the second class is shared according to the total marshalling quantity proportion of the trains running per hour, and the passenger flow of the third class is shared according to the selection preference beta of the passengers.
The design range of the full load rate of the train section is as follows:
Figure BDA0003336724400000152
maximum section loading for interval x:
Figure BDA0003336724400000153
section passenger flow in section x upstream direction:
Figure BDA0003336724400000154
section passenger flow in the section x downlink direction:
Figure BDA0003336724400000155
section passenger flow in the ascending direction of the section interval x of the large traffic section:
Figure BDA0003336724400000156
section passenger flow in the x-down direction of the section interval of the large traffic section:
Figure BDA0003336724400000157
Figure BDA0003336724400000161
cross section passenger flow in x up direction between sections of the small traffic section:
Figure BDA0003336724400000162
cross section passenger flow in x downlink direction between sections of the small traffic section:
Figure BDA0003336724400000163
the starting point is a starting site, and the ending point is an ending site.
In addition, the start point is identified as the number of the start station, and the end point is identified as the number of the end station.
In addition, "first" to "tenth" and "twelfth" to "fourteenth" of the first to tenth and twelfth to fourteenth starting point identifiers are identifiers, and are used to indicate different starting identifiers without other essential meanings.
For example, the first starting point identifier is a starting point identifier, and the starting point represented by the identifier is located between the station n1 and the station a-1 (including the station n1 and the station a-1). To distinguish this origin from other origins, the identity of the origin located between the station n1 and the station a-1 (including n1 and a-1) is named the first origin identity.
The second starting point identifier is a starting point identifier, and the starting point represented by the identifier is located between the b station and the n2-1 station (including b and n 2-1). To distinguish this origin from other origins, the identity of the origin located between the b station and the n2-1 station (including b and n2-1) is named the second origin identity.
And a third starting point identifier, which is a starting point identifier, wherein the starting point represented by the identifier is located between the n1+1 station and the a station (including n1+1 and a). To distinguish this origin from other origins, the identification of the origin located between the n1+1 station to the a station (containing n1+1 and a) is named third origin identification.
……
And a fourteenth starting point identifier which is a starting point identifier, wherein the starting point represented by the identifier is positioned between the x +1 station and the b station (containing x +1 and b) (a is less than or equal to x < b). In order to distinguish this starting point from other starting points, the identification of the starting point located between the x +1 station and the b station (containing x +1 and b) is named as fourteenth starting point identification.
Similarly, "the first" to "the fifteenth" of the first to the fifteenth endpoint identifiers are used for identification, and are used for representing different endpoint identifiers without other essential meanings.
For example, the first endpoint identifier is an endpoint identifier, and the endpoint represented by the identifier is located between station i1+1 and station a (including i1+1 and a). To distinguish this endpoint from other endpoints, the identity of the endpoint located between station i1+1 to station a (including i1+1 and a) is named the first endpoint identity.
The second endpoint identifier, which is an endpoint identifier, represents an endpoint between station i2+1 and station n2 (including i2+1 and n 2). To distinguish this endpoint from other endpoints, the identity of the endpoint located between the i2+1 stop to the n2 stop (including i2+1 and n2) is named the second endpoint identity.
And a third terminal identifier, which is a terminal identifier, wherein the terminal represented by the identifier is located between the station n1 and the station i3-1 (comprising n1 and i 3-1). To distinguish this endpoint from other endpoints, the identity of the endpoint located between the station n1 and the station i3-1 (including n1 and i3-1) is named the third endpoint identity.
……
And a fifteenth endpoint identifier, which is an endpoint identifier, wherein the endpoint represented by the identifier is located between the station n1 and the station a-1 (including the stations n1 and a-1). To distinguish this endpoint from other endpoints, the identity of the endpoint located between station n1 and station a-1 (including n1 and a-1) is named the fifteenth endpoint identity.
And a substep 2 of selecting a train operation scheme of a preset number of groups as an alternative scheme according to the value of the objective function.
For example, the values of the objective function are sorted from small to large, and the train operation scheme of the preset number of train groups sorted at the top is determined as an alternative scheme.
In this step, a preset number (e.g., n) of schemes with better values of the objective function are selected as alternatives according to the values of the objective function of the train operation schemes in each group obtained in step 102.
The preferred one of these is that the value of the objective function is small.
And 103, evaluating the alternative schemes, determining a final train operation scheme, and performing virtual marshalling or decompiling on the trains of the large and small road sections according to the final train operation scheme.
This step is divided into 2 sub-steps.
And substep 1, evaluating the alternative schemes and determining a final train operation scheme. And a substep 2, performing virtual marshalling or decompiling on the trains on the large and small road sections according to the final train operation scheme.
For substep 1, the alternatives are evaluated to determine a final train operating scenario.
The n alternatives determined in substep 2 of step 102 are analyzed and evaluated, and the passenger trip time cost, the vehicle acquisition cost, the train running kilometer cost and the like are intensively studied, and a train operation scheme meeting the actual requirements is selected.
For substep 2, a virtual consist is made for the large and small section trains according to the final train operation schedule.
Namely, according to the final train operation scheme, the first retracing point a of the small-section road, the second retracing point b of the small-section road, the first retracing point n1 of the large-section road, the second retracing point n2 of the large-section road, and the first train sending frequency f1And a second departure frequency f2And performing virtual grouping.
The first returning point a of the small-section in the final train running scheme is equal to A, the second returning point B of the small-section is equal to B, the first returning point N1 of the large-section is equal to N1, the second returning point N2 of the large-section is equal to N2, and the first train-sending frequency f1F1 and a second departure frequency F2For example, F2 determines that the large traffic segment is from N1 to N2 and the small traffic segment is from a to B. And respectively distributing trains for the large traffic road section and the small traffic road section. The departure frequency of a train operating on a large traffic section is F1. In the uplink interval, the train operated in the large traffic section and the train operated in the small traffic section are marshalled at the station A, and the decompiling is carried out at the station B; in the downlink interval, the train operated in the large traffic section and the train operated in the small traffic section are marshalled in the station B, and the marshalling is performed in the station A. The departure frequency of all the trains on the minor-crossing section is F2.
The proposal provides a train operation method based on virtual marshalling, which is a train operation method based on virtual marshalling technology under a large-small traffic operation scene. Under the application of a virtual marshalling technology, a more flexible transportation organization mode can be provided under a large-small traffic way form, the running paths of the front train and the rear train can be newly designed, the virtual marshalling train can be marshalled and run in a small traffic way section, the decompiling is carried out before reaching a return station, and the large traffic way train can continue to run forwards after reaching the return station; the small traffic route train performs turn-back operation at a turn-back station to form various traffic routes; the small cross road train after being turned back can be organized into a group to run with another large cross road train which is already turned back. This transportation organizational mode can adopt less marshalling train in little traffic route district section, adopts suitable departure frequency, dwindles big traffic route district passenger's waiting time when guaranteeing the transporting capacity, better satisfies the transporting capacity matching demand of different traffic route districts to compromise enterprise operation cost.
According to the method provided by the embodiment, the train operation scheme for virtual marshalling not only meets the objective function and the constraint condition, but also ensures that the final train operation scheme meets the capacity matching requirements of different intersection sections through comprehensive evaluation, also considers the operation cost and best meets the actual requirements.
Based on the same inventive concept of a train operation method based on a virtual formation, the present embodiment provides an electronic device, including: memory, processor, and computer programs.
Wherein the computer program is stored in the memory and configured to be executed by the processor to implement a virtual consist based train operation method as shown in figure 2.
In particular, the method comprises the following steps of,
determining a plurality of groups of train operation schemes according to the compilation data;
respectively determining the train operation schemes of each group, selecting a preset number of train operation schemes as alternative schemes according to the value of the target function under the condition of meeting the constraint condition;
and evaluating the alternative schemes, determining a final train operation scheme, and performing virtual marshalling or decompiling on the trains on the large and small road sections according to the final train operation scheme.
Optionally, any one train operation scheme includes: the train dispatching frequency of the large traffic road section comprises a train dispatching frequency of a combined train of the large traffic road section and a train of the small traffic road section.
Optionally, the objective function is composed of a travel cost objective function, an operation fixed cost objective function, and an operation variable cost objective function.
Optionally, the trip cost objective function is a minimum product of the passenger time value and the passenger waiting total time.
Optionally, the fixed cost objective function is operated such that the product of the value of the vehicle purchase cost allocated to the unit hour and the time period for which the single train has been operated is minimized.
Optionally, the operation variable cost objective function is a minimum product of a cost per kilometer traveled by a single train and a number of kilometers traveled by the train.
Optionally, the constraint condition includes a retrace station position constraint condition, an integer constraint condition, a large traffic section departure frequency constraint condition, a total number of trains passing through the route constraint condition, and a full load rate constraint condition.
Alternatively,
the retracing station position constraint conditions are as follows: the first retracing point and the second retracing point of the small intersection section are positioned between the first retracing point and the second retracing point of the large intersection section;
integer constraint conditions: the train dispatching frequency of the large and small traffic section combined trains and the small traffic section trains is integral multiple of the train dispatching frequency of the large traffic section trains;
and (3) the dispatching frequency constraint condition of the large traffic section: the departure frequency of the large-traffic-section train is not less than the minimum departure frequency, and the departure frequency of the large-traffic-section train is not more than the maximum departure frequency determined by the minimum tracking interval of the train pieces and the passing interval of the train at the station;
constraint conditions of total number of trains passing through the line: the train dispatching frequency of the combined trains at the large and small traffic sections and the trains at the small traffic sections is not more than the maximum train dispatching frequency determined by the minimum tracking interval of the train pieces and the turn-back duration of a single train;
and (3) full load rate constraint conditions: the train section full load rate is not less than the design lower limit of the train section full load rate, and the train section full load rate is not more than the design upper limit of the train section full load rate.
The electronic device provided by the embodiment is characterized in that a computer program is executed by a processor to determine a plurality of groups of train operation schemes according to compilation data; respectively determining the train operation schemes of each group, selecting a preset number of train operation schemes as alternative schemes according to the value of the target function under the condition of meeting the constraint condition; and evaluating the alternative schemes, determining a final train operation scheme, and performing virtual marshalling or decompiling on the trains on the large and small road sections according to the final train operation scheme. The train operation scheme for virtual marshalling not only meets the objective function and the constraint condition, but also ensures that the final train operation scheme meets the capacity matching requirements of different road sections and also considers the operation cost through comprehensive evaluation, and the train operation scheme most meets the actual requirements.
Based on the same inventive concept of a virtual consist-based train operation method, the present embodiment provides a computer-readable storage medium having a computer program stored thereon. The computer program is executed by the processor to implement a virtual consist based train operation method as shown in fig. 2.
In particular, the method comprises the following steps of,
determining a plurality of groups of train operation schemes according to the compilation data;
respectively determining the train operation schemes of each group, selecting a preset number of train operation schemes as alternative schemes according to the value of the target function under the condition of meeting the constraint condition;
and evaluating the alternative schemes, determining a final train operation scheme, and performing virtual marshalling or decompiling on the trains on the large and small road sections according to the final train operation scheme.
Optionally, any one train operation scheme includes: the train dispatching frequency of the large traffic road section comprises a train dispatching frequency of a combined train of the large traffic road section and a train of the small traffic road section.
Optionally, the objective function is composed of a travel cost objective function, an operation fixed cost objective function, and an operation variable cost objective function.
Optionally, the trip cost objective function is a minimum product of the passenger time value and the passenger waiting total time.
Optionally, the fixed cost objective function is operated such that the product of the value of the vehicle purchase cost allocated to the unit hour and the time period for which the single train has been operated is minimized.
Optionally, the operation variable cost objective function is a minimum product of a cost per kilometer traveled by a single train and a number of kilometers traveled by the train.
Optionally, the constraint condition includes a retrace station position constraint condition, an integer constraint condition, a large traffic section departure frequency constraint condition, a total number of trains passing through the route constraint condition, and a full load rate constraint condition.
Alternatively,
the retracing station position constraint conditions are as follows: the first retracing point and the second retracing point of the small intersection section are positioned between the first retracing point and the second retracing point of the large intersection section;
integer constraint conditions: the train dispatching frequency of the large and small traffic section combined trains and the small traffic section trains is integral multiple of the train dispatching frequency of the large traffic section trains;
and (3) the dispatching frequency constraint condition of the large traffic section: the departure frequency of the large-traffic-section train is not less than the minimum departure frequency, and the departure frequency of the large-traffic-section train is not more than the maximum departure frequency determined by the minimum tracking interval of the train pieces and the passing interval of the train at the station;
constraint conditions of total number of trains passing through the line: the train dispatching frequency of the combined trains at the large and small traffic sections and the trains at the small traffic sections is not more than the maximum train dispatching frequency determined by the minimum tracking interval of the train pieces and the turn-back duration of a single train;
and (3) full load rate constraint conditions: the train section full load rate is not less than the design lower limit of the train section full load rate, and the train section full load rate is not more than the design upper limit of the train section full load rate.
The present embodiment provides a computer readable storage medium having a computer program executed by a processor to determine a plurality of sets of train operation profiles based on compilation data; respectively determining the train operation schemes of each group, selecting a preset number of train operation schemes as alternative schemes according to the value of the target function under the condition of meeting the constraint condition; and evaluating the alternative schemes, determining a final train operation scheme, and performing virtual marshalling or decompiling on the trains on the large and small road sections according to the final train operation scheme. The train operation scheme for virtual marshalling not only meets the objective function and the constraint condition, but also ensures that the final train operation scheme meets the capacity matching requirements of different road sections and also considers the operation cost through comprehensive evaluation, and the train operation scheme most meets the actual requirements.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be implemented by adopting various computer languages, such as object-oriented programming language Java and transliterated scripting language JavaScript.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

Claims (10)

1. A method of operating a train based on a virtual consist, the method comprising:
determining a plurality of groups of train operation schemes according to the compilation data;
respectively determining the train operation schemes of each group, selecting a preset number of train operation schemes as alternative schemes according to the value of an objective function under the condition of meeting constraint conditions;
and evaluating the alternative schemes, determining a final train operation scheme, and performing virtual marshalling or decompiling on the trains on the large and small road sections according to the final train operation scheme.
2. The method of claim 1, wherein any one set of train operating scenarios comprises: the train dispatching frequency of the large traffic road section comprises a train dispatching frequency of a combined train of the large traffic road section and a train of the small traffic road section.
3. The method of claim 2, wherein the objective function is comprised of a travel cost objective function, an operational fixed cost objective function, and an operational variable cost objective function.
4. The method of claim 3, wherein the travel cost objective function is a minimum product of passenger time value and total passenger waiting time.
5. The method of claim 3 wherein the operational fixed cost objective function is a minimum product of a value of vehicle acquisition cost apportioned to a unit hour and a length of time that a single train has been in operation.
6. The method of claim 3, wherein the operational variable cost objective function is a minimum product of kilometer cost per train traveled and kilometers of train traveled.
7. The method of claim 2, wherein the constraints include a foldback location constraint, an integer constraint, a large-traffic-section departure frequency constraint, a total number of trains passing by the line constraint, and a full load rate constraint.
8. The method of claim 7,
the retracing station position constraint condition is as follows: the first retracing point and the second retracing point of the small intersection section are positioned between the first retracing point and the second retracing point of the large intersection section;
the integer constraint condition is as follows: the departure frequency of the combined train comprising the large and small traffic section and the train comprising the small traffic section is integral multiple of the departure frequency of the train comprising the large traffic section;
the large traffic section departure frequency constraint condition is as follows: the departure frequency of the large-traffic-section train is not less than the minimum departure frequency, and the departure frequency of the large-traffic-section train is not more than the maximum departure frequency determined by the minimum tracking interval between trains and the passing interval of the train at a station;
and the total number of passing trains of the line constraint condition is as follows: the departure frequency of the combined trains at the large and small traffic sections and the trains at the small traffic sections is not more than the maximum departure frequency determined by the minimum tracking interval between the trains and the turn-back duration of a single train;
the full load rate constraint condition is as follows: the train section full load rate is not less than the design lower limit of the train section full load rate, and the train section full load rate is not more than the design upper limit of the train section full load rate.
9. An electronic device, comprising:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of claims 1-8.
10. A computer-readable storage medium, having stored thereon a computer program; the computer program is executed by a processor to implement the method of any one of claims 1-8.
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