CN115593471A - Method and device for optimizing operation scheme of rail transit virtual marshalling train - Google Patents

Method and device for optimizing operation scheme of rail transit virtual marshalling train Download PDF

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CN115593471A
CN115593471A CN202211062510.2A CN202211062510A CN115593471A CN 115593471 A CN115593471 A CN 115593471A CN 202211062510 A CN202211062510 A CN 202211062510A CN 115593471 A CN115593471 A CN 115593471A
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游婷
杨中平
林飞
钟志宏
孙湖
方晓春
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Abstract

The invention discloses an optimization method and a device for a rail transit virtual marshalling train operation scheme, wherein the method comprises the following steps: the method comprises the steps of obtaining line information, train operation information, train vehicle parameters and passenger flow information, inputting the line information, the train operation information, the train vehicle parameters and the passenger flow information into a virtual marshalling train optimization model, and generating a virtual marshalling train operation optimization scheme. The method can effectively improve the line transportation efficiency, meet the transportation requirements of the urban rail transit in different periods, effectively shorten the passenger travel time, improve the rail transit service quality, and has guiding significance for planning and construction of urban rail transit lines.

Description

Method and device for optimizing operation scheme of rail transit virtual marshalling train
Technical Field
The invention relates to the technical field of rail transit, in particular to a method and a device for optimizing a rail transit virtual marshalling train operation scheme.
Background
The Virtual marshalling technology (also called Virtual Coupling, virtual connection, etc., english Virtual Coupling), because there is no physical coupler Coupling between vehicles, can realize more flexible transportation mode to adapt to the real-time changing passenger flow of urban rail transit, the typical transportation organization mode of urban rail transit includes "fast and slow car", big and small traffic routes, cross-line operation, flexible marshalling, etc., through adopting one or several combination of them, can form different operation schemes, effectively solve the problem of the unbalanced distribution of passenger flow in time, space, improve the transport efficiency of the line.
At present, fixed marshalling trains are mostly adopted in urban rail transit, the passenger carrying capacity of the fixed marshalling trains cannot change in the operation process, the transport capacity can be improved to a certain extent by adjusting the train operation scheme, but the passenger comfort level is reduced due to severe congestion in the peak period, and energy waste is caused due to the obvious mismatching of the transport capacity and the passenger carrying rate in the peak period. The flexible marshalling train adapts to the change of passenger flow by adjusting the marshalling quantity of the train, but the marshalling needs to be carried out empty operation at the starting station or the intermediate station, the flexibility is not high, and the scheme has small improvement on the travel time of passengers.
The virtual marshalling train can realize more efficient and flexible transportation service through online dynamic passenger carrying marshalling operation; the research of the virtual marshalling train operation scheme is beneficial to optimizing the marshalling number of the virtual marshalling trains aiming at special scenes by using a mathematical optimization model so as to avoid unnecessary transport capacity, but the used model does not consider time cost, transportation modes of train cross-station running and the like, so that the advantages of a virtual marshalling technology are limited; the comparison analysis of a virtual marshalling train adopting 'fast and slow trains' with the traditional operation scheme under a specific stop scheme is also available, but the formulation rule of the virtual marshalling train operation scheme cannot be given; in addition, an algorithm for generating a virtual marshalling train operation scheme is provided in the prior art, but in the algorithm, a train stops according to a periodic rule, the stop rule of the train does not consider the influence of passenger flow volume difference of different stations, and the algorithm has no practicability.
Disclosure of Invention
Therefore, the technical problem to be solved by the present invention is to overcome the defect that the existing virtual marshalling train operation scheme does not consider the time cost, the transportation mode of train cross-station driving, etc., and the influence of passenger flow difference of different stations, and has no practicability, thereby providing an optimization method and device for the rail transit virtual marshalling train operation scheme.
The embodiment of the invention provides an optimization method of a track traffic virtual marshalling train operation scheme, which comprises the following steps:
acquiring line information, train operation information, train vehicle parameters and passenger flow information;
and inputting the line information, the train operation information, the train vehicle parameters and the passenger flow information into the virtual marshalling train optimization model to generate a virtual marshalling train operation optimization scheme.
According to the method for optimizing the operation scheme of the virtual marshalling train of the rail transit, provided by the invention, the line information, the train operation information, the train vehicle parameters and the passenger flow information are all input into the virtual marshalling train optimization model by acquiring the line information, the train operation information, the train vehicle parameter information and the passenger flow information, so that the virtual marshalling train optimization model can fully consider the influence of the passenger flow, the train and the line on the virtual marshalling train operation scheme, and the generated virtual marshalling train operation optimization scheme is based on the travel rule of passengers, so that the line transportation efficiency can be effectively improved, the transportation requirements of the urban rail transit in different periods can be met, the passenger travel time can be effectively shortened, the rail transit service quality is improved, and the method has guiding significance for planning and construction of the urban rail transit lines.
Optionally, determining passenger travel time, train operation time and train full-line average occupancy based on the line information, train operation information, train vehicle parameters and passenger flow information comprises:
extracting inter-station distance between two adjacent stations in the line information, train departure interval in the train running information, a train stop variable value and train stop time, average train running speed in train parameters and the number of passengers from the departure station to the destination station in the passenger flow information, and determining passenger travel time by using a virtual marshalling train optimization model based on the train departure interval, the train stop variable value, the train stop time, the inter-station distance between two adjacent stations, the average train running speed and the number of passengers from the departure station to the destination station;
determining the train running time by using a virtual marshalling train optimization model based on the distance between two adjacent stations, the average running speed of the train, the train stopping time and the train stopping variable value;
extracting the number of train passengers getting on the station and the number of train passengers getting off the station in the passenger flow information from the train parameters, and determining the average passenger carrying rate of the whole train line by using a virtual marshalling train optimization model based on the number of train stop variables, the number of train passengers getting on the station and the number of train passengers getting off the station;
a virtual consist train operation optimization scheme is generated based on passenger travel time, train operation time, and train full-line average occupancy.
Optionally, determining the passenger travel time by using the virtual marshalling train optimization model based on the train departure interval, the train stop variable value, the train stop time, the inter-station distance between two adjacent stations, the average train running speed and the number of passengers from the departure station to the destination station, includes:
determining passenger waiting time based on the train stop variable value and the train departure interval;
determining passenger loss time based on the train stop variable value and the train stop time;
determining passenger riding time based on the distance between two adjacent stations and the average running speed of the train;
the passenger travel time is generated based on the waiting time of the passengers, the passenger loss time, the passenger riding time and the number of passengers from the departure station to the destination station.
The time for waiting for passengers, the time for losing passengers and the time for taking passengers are taken as part of the travel time of the passengers, so that the influence of passenger flow on the train operation scheme is fully considered, and the operation efficiency of the virtual marshalling train is improved.
Optionally, the passenger travel time is generated based on the passenger waiting time, the passenger loss time, the passenger riding time and the number of passengers from the departure station to the destination station, and the calculation formula of the passenger travel time is as follows:
Figure BDA0003826849720000031
in the above formula, t p Representing passenger travel time, N representing the number of stations on the full line, x representing the departure station, y representing the destination station, ζ i (x) Indicates the stop variable value, ζ, of the train i at the departure station i (y) a stop variable value h of the train i at the destination station x,y (i, j)/2 represents the waiting time of the passenger, h x,y (i, j) represents a train departure interval,
Figure BDA0003826849720000032
indicates time lost, ζ of passenger i (r) a stop variable value, t, of the train i at the station r s The time at which the train is stopped is indicated,
Figure BDA0003826849720000033
the time of the ride of the passenger is indicated,
Figure BDA0003826849720000034
indicates the distance between two adjacent stations, v avg Represents the average running speed of the train, m x,y Indicating the number of passengers from the departure station to the destination station.
Optionally, the determining the average passenger carrying rate of the whole train line by using the virtual marshalling train optimization model based on the train stop variable value, the number of train passengers getting on the station and the number of train passengers getting off the station comprises:
determining the passenger capacity of the train when the train departs based on the number of passengers getting on the train and the number of passengers getting off the train;
and determining the average passenger carrying rate of the whole train line based on the stop variable value of the train, the passenger carrying capacity of the train when the train starts and the number of the passengers of the train.
Optionally, generating a virtual marshalling train operation optimization based on the passenger travel time, the train operation time, and the train full-line average load factor, comprises:
constructing a virtual marshalling train operation optimization function based on passenger travel time, train operation time and train full-line average passenger carrying rate;
taking the travel demand of all-line passengers and the average passenger carrying rate of each train as constraint conditions of a virtual marshalling train operation optimization function;
and solving the virtual marshalling train operation optimization function by using a genetic algorithm to generate a virtual marshalling train operation optimization scheme.
Optionally, a virtual marshalling train operation optimization function is constructed based on the passenger travel time, the train operation time and the train full-line average passenger carrying capacity, and the calculation formula of the virtual marshalling train operation optimization function is as follows:
Figure BDA0003826849720000041
in the above formula, t p Indicating passenger travel time, t c The running time of the train is represented,
Figure BDA0003826849720000042
the train full-line average load factor is represented, and the minZ represents a virtual marshalling train operation optimization function.
The virtual marshalling train operation optimization function is suitable for designing the existing line operation scheme and planning the line, has high application value, and has guiding significance for planning and constructing the urban rail transit line.
In a second aspect of the present application, there is also provided an operation scheme optimization apparatus for a rail transit virtual consist train, including:
the acquisition module is used for acquiring line information, train operation information, train vehicle parameters and passenger flow information;
and the generating module is used for inputting the line information, the train operation information, the train vehicle parameters and the passenger flow information into the virtual marshalling train optimization model to generate a virtual marshalling train operation optimization scheme.
Optionally, the generating module includes:
the first determining submodule is used for extracting inter-station distance between two adjacent stations in the line information, train departure interval, train stop variable value and train stop time in the train running information, average train running speed in train vehicle parameters and the number of passengers from the departure station to the target station in the passenger flow information, and determining passenger travel time based on the train departure interval, the train stop variable value, the train stop time, the inter-station distance between two adjacent stations, the average train running speed and the number of passengers from the departure station to the target station;
the second determining submodule is used for determining the train running time based on the distance between two adjacent stations, the average train running speed, the train stopping time and the train stopping variable value;
the third determining submodule is used for extracting the number of train passengers and the number of passengers getting on the station and the number of passengers getting off the station in the passenger flow information from the train parameters, and determining the whole-line average passenger carrying rate of the train based on the number of the train stop variables, the number of the train passengers, the number of the passengers getting on the station and the number of the passengers getting off the station;
and the generation submodule is used for generating a virtual marshalling train operation optimization scheme based on the passenger travel time, the train operation time and the train full-line average passenger carrying rate.
Optionally, the first determining sub-module includes:
the first determining unit is used for determining the waiting time of passengers on the basis of the stop variable value of the train and the departure interval of the train;
the second determining unit is used for determining passenger loss time based on the train stop variable value and the train stop time;
and the third determining unit is used for determining the passenger riding time based on the inter-station distance between two adjacent stations and the average running speed of the train, and generating the passenger travel time based on the passenger waiting time, the passenger loss time, the passenger riding time and the number of passengers from the departure station to the destination station.
Optionally, the third determining unit includes:
the formula for the passenger travel time is as follows:
Figure BDA0003826849720000051
in the above formula, t p Representing passenger travel time, N representing the number of stations on the full line, x representing the departure station, y representing the destination station, ζ i (x) Indicates the stop variable value, ζ of the train i at the departure station i (y) a stop variable value h of the train i at the destination station x,y (i, j)/2 represents the waiting time of the passenger, h x,y (i, j) represents a train departure interval,
Figure BDA0003826849720000052
indicates time lost, ζ, of passengers i (r) a stop variable value, t, of the train i at the station r s The time at which the train is stopped is indicated,
Figure BDA0003826849720000053
the time of the ride of the passenger is indicated,
Figure BDA0003826849720000054
indicates the distance between two adjacent stations, v avg Represents the average running speed of the train, m x,y Indicating the number of passengers from the departure station to the destination station.
Optionally, the third determining sub-module includes:
the fourth determining unit is used for determining the passenger capacity of the train when the train departs based on the number of passengers getting on the train at the station and the number of passengers getting off the train at the station;
and the fifth determining unit is used for determining the average passenger carrying rate of the whole train line based on the stop variable value of the train, the passenger carrying capacity when the train starts and the number of the passengers of the train.
Optionally, generating a sub-module comprising:
the building unit is used for building a virtual marshalling train operation optimization function based on passenger travel time, train operation time and train full-line average passenger capacity;
the restraint unit is used for taking the travel demand of all-line passengers and the average passenger carrying rate of each train as restraint conditions of a virtual marshalling train operation optimization function;
and the solving unit is used for solving the virtual marshalling train operation optimization function by using a genetic algorithm to generate a virtual marshalling train operation optimization scheme.
Optionally, the building unit comprises:
the calculation formula of the virtual marshalling train operation optimization function is as follows:
Figure BDA0003826849720000061
in the above formula, t p Indicating passenger travel time, t c The running time of the train is represented,
Figure BDA0003826849720000062
the train full-line average load factor is represented, and the minZ represents a virtual marshalling train operation optimization function.
In a third aspect of the present application, a computer device is also presented, which includes a processor and a memory, where the memory is used for storing a computer program, and the computer program includes a program, and the processor is configured to invoke the computer program to execute the above-mentioned method for optimizing the operation scheme of the virtual consist train of rail transit.
In a fourth aspect of the present application, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and the computer program is executed by a processor to implement the above-mentioned method for optimizing a running scheme of a rail transit virtual consist train.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a method for optimizing a running scheme of a rail transit virtual marshalling train in embodiment 1 of the present invention;
fig. 2 is a schematic diagram of a dynamic formation of a virtual marshalling train in embodiment 1 of the present invention;
fig. 3 is a schematic view of dynamic de-compilation of a virtual marshalling train in embodiment 1 of the present invention;
fig. 4 is a schematic view of a virtual consist operation scheme in embodiment 1 of the present invention;
fig. 5 is a schematic diagram of a virtual marshalling train formation status in embodiment 1 of the present invention;
FIG. 6 is a flowchart of step S102 in embodiment 1 of the present invention;
FIG. 7 is a flowchart illustrating step S1021 in embodiment 1 of the present invention;
fig. 8 is a flowchart of step S1023 in embodiment 1 of the invention;
fig. 9 is a flowchart of step S1024 in embodiment 1 of the present invention;
FIG. 10 is a schematic diagram of an eight-way line of Beijing subway in embodiment 1 of the present invention;
fig. 11 is a schematic view of the coding of the train operation scheme in embodiment 1 of the present invention;
FIG. 12 is a schematic diagram of a pareto front in accordance with embodiment 1 of the present invention;
fig. 13 is a schematic view of the operation of a virtual consist train in embodiment 1 of the present invention;
fig. 14 is a schematic block diagram of an apparatus for optimizing a running scheme of a rail transit virtual consist train in embodiment 2 of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "first", "second", and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Example 1
The embodiment provides an optimization method for a rail transit virtual marshalling train operation scheme, as shown in fig. 1, including:
s101, obtaining line information, train running information, train vehicle parameters and passenger flow information, and determining passenger travel time, train running time and train whole-line average passenger carrying rate based on the line information, the train running information, the train vehicle parameters and the passenger flow information.
The line information comprises data such as section length, station number, line layout and the like; the train operation information comprises data such as inter-district driving time, train stop time, train departure interval and the like; the train parameters comprise data such as average train running speed, train number of passengers and the like; the passenger flow information comprises data such as station names of the station entering and the station exiting, time of the station entering and the station exiting, passenger number and the like.
And S102, inputting the line information, the train operation information, the train vehicle parameters and the passenger flow information into a virtual marshalling train optimization model to generate a virtual marshalling train operation optimization scheme.
As shown in fig. 2-3, the vehicles of the virtual marshalling train are organized in a high-performance vehicle-to-vehicle wireless communication manner, and no physical connection exists, so that real-time dynamic marshalling and uncompiling operations can be performed; in order to better adapt to the problem of uneven passenger flow distribution at different stations, a fleet consisting of a plurality of vehicles is taken as a research object, and one fleet is sent out at an initial station in a virtual marshalling mode, wherein each vehicle has different stop schemes, the stop schemes of all vehicles in the fleet form an operation scheme, and the next virtual marshalling fleet repeats the operation scheme of the previous fleet; as shown in fig. 4, the operation schemes of the fleet B1-B2-B3 correspond to the operation schemes of the previous fleet A1-A2-A3 one-to-one, for each vehicle in the fleet, the stop scheme of the whole line is generated by the above-mentioned virtual marshalling train operation optimization function, and for each station, the number of trains stopping in a period of time is different, and in order to meet the overtaking demand of the trains, some stations need to have avoidance lines.
Furthermore, the virtual marshalling motorcade which is sent from the starting station forms vehicles, and the number of the vehicles is determined by the passenger flow requirement and the platform length; at present, the length of most subway platforms is about 140m, for example, A-type vehicles are used, the length of the subway platforms is 22m, when the subway platforms are in a passenger flow peak period, the subway platforms are grouped according to the maximum limit of the platform length, and a virtual grouping fleet is 6 groups; when the train is in the peak leveling period, grouping is carried out according to the passenger flow requirements, and 1-6 trains can be formed; in the running process of the virtual marshalling fleet, the number of the marshalling vehicles is dynamically changed due to different stop schemes; each vehicle is an independent transportation unit in the process of operation, and grouping and compiling operations can be carried out according to respective operation schemes to form different combinations. As shown in fig. 5, a 6-consist train is taken as an example to show a partial consist state that may exist when a train is operating on a line.
Assuming that the stop time of all vehicles is the same, the train can carry out marshalling operation when entering or leaving the station, the operation time is ignored, assuming that the time of passengers arriving at each station is uniformly distributed, all the passengers take the nearest direct train, and transfer does not exist, because the vehicles can virtually marshal and run at the same speed on the interval line and can carry out overtaking in the platform area, the vehicles on the line do not interfere with each other under normal conditions, and the stop scheme of each vehicle can be independently designed; however, different stop schemes of the vehicles greatly affect passenger taking, for example, if there are fewer stop vehicles at a certain station, passengers at the station or passengers heading to the station will spend more waiting time, so the stop schemes of each vehicle need to be comprehensively considered to meet the taking demands of different passengers, and therefore, a virtual marshalling train operation optimization function is constructed based on line information, train operation information, train vehicle parameters and passenger flow information.
According to the method for optimizing the operation scheme of the virtual marshalling train of the rail transit, the line information, the train operation information, the train parameters and the passenger flow information are acquired and input into the virtual marshalling train optimization model, so that the virtual marshalling train optimization model can fully consider the influence of passenger flow, trains and lines on the operation scheme of the virtual marshalling train, and the generated virtual marshalling train operation optimization scheme takes the travel rule of passengers as the basis, so that the line transportation efficiency can be effectively improved, the transportation requirements of the urban rail transit in different periods can be met, the passenger travel time can be effectively shortened, the rail transit service quality can be improved, and the method has guiding significance for planning and construction of the urban rail transit line.
Preferably, as shown in fig. 6, the step S102 of inputting the route information, the train operation information, the train vehicle parameters, and the passenger flow information into a virtual train formation optimization model to generate a virtual train formation operation optimization plan includes:
and S1021, extracting inter-station distance between two adjacent stations in the line information, train departure interval, train stop variable value and train stop time in the train operation information, average train running speed in the train vehicle parameters and the number of passengers from the departure station to the destination station in the passenger flow information, and determining passenger travel time by using the virtual marshalling train optimization model based on the train departure interval, the train stop variable value, the train stop time, the inter-station distance between the two adjacent stations, the average train running speed and the number of passengers from the departure station to the destination station.
And S1022, determining the train operation time by using the virtual marshalling train optimization model based on the distance between the two adjacent stations, the average train operation speed, the train stop time and the train stop variable value.
The virtual marshalling train has the operations of crossing stations and the like, so that the train running time is greatly shortened, the running cost is measured by adopting the train running time, and the calculation formula of the train running time is as follows:
Figure BDA0003826849720000091
in the above formula, t c Representing train running time, n t Representing the number of virtual consist vehicles at the origin station,
Figure BDA0003826849720000092
indicates the distance between two adjacent stations, v avg Representing the average running speed, t, of the train s Representing train stop time, N representing number of stations of the whole line, ζ i (r) represents the stop variable value of the train i at the station r.
And S1023, extracting the number of train passengers getting on the train station and the number of train passengers getting off the train station in the passenger flow information, and determining the average passenger carrying rate of the whole train line by using the virtual marshalling train optimization model based on the train stop variable value, the number of train passengers getting on the train station and the number of train passengers getting off the train station.
And S1024, generating the virtual marshalling train operation optimization scheme based on the passenger travel time, the train operation time and the train overall-line average passenger carrying rate.
Preferably, as shown in fig. 7, the determining the passenger travel time using the virtual train formation optimization model based on the departure interval of the train, the stop variable value of the train, the stop time of the train, the inter-station distance between the two adjacent stations, the average train traveling speed, and the number of passengers from the departure station to the destination station in step S1021 includes:
s10211, determining the waiting time of passengers based on the train stop variable value and the train departure interval.
Specifically, the waiting time of the passenger is expressed as:
h x,y (i,j)/2
in the above formula, h x,y And (i, j) represents the departure interval between two adjacent vehicles departing from the same departure station and arriving at the same destination station.
S10212, determining the time of passenger loss based on the train stop variable value and the train stop time.
Specifically, the passenger loss time is expressed as:
Figure BDA0003826849720000101
in the above formula, ζ i (r) a stop variable value, t, of the train i at the station r s Indicating the train stop time.
S10213, determining passenger riding time based on the distance between two adjacent stations and the average running speed of the train.
Specifically, the passenger riding time is expressed as:
Figure BDA0003826849720000102
in the above formula, the first and second carbon atoms are,
Figure BDA0003826849720000103
indicates the distance between two adjacent stations, v avg Representing the average train speed of travel.
The passenger travel time is generated based on the passenger waiting time, the passenger loss time, the passenger riding time, and the number of passengers from the departure station to the destination station.
Specifically, the formula for calculating the travel time of the passenger is as follows:
Figure BDA0003826849720000104
in the above formula, t p Representing passenger travel time, N representing the number of stations on the full line, x representing the departure station, y representing the destination station, ζ i (x) Indicates the stop variable value, ζ of the train i at the departure station i (y) stop variable value, h, of train i at destination station x,y (i, j)/2 represents the waiting time of the passenger, h x,y (i, j) represents departure interval of train (i.e., departure interval of two adjacent trains departing from the same departure station and arriving at the same destination station), m x,y The number of passengers from the departure station to the destination station is shown, and r is one of the stations between the departure station x and the destination station y.
In which the passenger departs from the same departure station and arrives at the departure interval h of two adjacent vehicles at the same destination station x,y The calculation formula of (i, j) is as follows:
h x,y (i,j)＝t i,x -t j,x ,i＝2,…,n t ,x＝1,2,…,N,y＝1,2,…,N.
in the above formula, t i,x Indicating the time, t, at which the train i departs from the departure station x j,x Indicating the time when train j exits the departure station x, where train j is the train that is previous to train i and train i is the same as the destination station of train j.
h x,y (i,j)＝h 0 ,i＝1
In the above formula, h 0 Representing the departure time interval of the train at the origin station.
Further, as the number of stops of the train increases, the average waiting time of the passengers decreases, but the riding time of the passengers also increases, so that it is necessary to comprehensively optimize the train operation plan and shorten the passenger travel time as much as possible.
Preferably, as shown in fig. 8, the step S1023 of determining the average train occupancy of the entire train line by using the virtual consist optimization model based on the train stop variable value, the number of train determiners, the number of boarding persons and the number of disembarking persons in the station includes:
and S10231, determining the passenger capacity of the train when the train departs based on the number of passengers getting on the train and the number of passengers getting off the train.
Wherein the number p of the trains i getting on the station r i,r The calculation formula of (a) is as follows:
Figure BDA0003826849720000111
in the above formula, m r,y Indicates the number of passengers from station r to destination station y, h r,y (i, j) represents the departure time interval between the train i departing from the station r and arriving at the destination station y and the train j departing from the station r and arriving at the destination station y.
Further, the number q of passengers getting off the train i at the station r i,r The calculation formula of (a) is as follows:
Figure BDA0003826849720000112
in the above formula, m x,r Indicating the number of passengers, h, from departure station x to station r x,r (i, j) represents the departure time interval between the train i departing from the departure station x and arriving at the station r and the train j departing from the departure station x and arriving at the station r.
Specifically, for the same train, the utilization rate of the high average passenger carrying rate is higher; reducing the number of stops of the train can effectively reduce the time lost by passengers, but at the same time, the transportation efficiency of the train can be reduced because the number of stops of the train is too small to load more passengers, therefore, the transportation efficiency of the train is ensured by limiting the average passenger carrying rate of the train, and the calculation formula of the passenger carrying capacity of a train when the train departs at the station is as follows:
C i,r ＝C i,r-1 +p i,r -q i,r ,r＝2,3,…,N,i＝1,2,…,n t
in the above formula, C i,r Indicating the passenger capacity of train i at station r, C i,r-1 Indicating the passenger capacity of train i at station r-1, n t Representing the number of virtual consist vehicles at the origin station.
Wherein the passenger capacity of the train i at the starting station is represented as C i,r ＝0,r＝1,i＝1,2,…,n t 。
And S10232, determining the average passenger carrying rate of the whole train line based on the stop variable value of the train, the passenger carrying capacity when the train departs and the number of the train passengers.
Specifically, the calculation formula of the all-line average load factor of the train i is as follows:
Figure BDA0003826849720000121
in the above-mentioned formula, the compound has the following structure,
Figure BDA0003826849720000122
represents the average load factor of the whole train line, C 0 Indicating the number of train passengers, omega i Represents the number of stops of the train i on the whole line, wherein, omega i The calculation formula of (a) is as follows:
Figure BDA0003826849720000123
wherein, when the train i stops at the station r, ζ i (r) has a value of 1, else ζ i The value of (r) is 0.
Preferably, as shown in fig. 9, the generating of the virtual formation train operation optimization plan based on the passenger travel time, the train operation time, and the all-train-line average load factor in step S1024 includes:
s10241, and constructing a virtual marshalling train operation optimization function based on the passenger travel time, the train operation time and the train full-line average passenger carrying rate.
Specifically, the calculation formula of the objective function is as follows:
Figure BDA0003826849720000131
in the above formula, t p Indicating passenger travel time, t c The running time of the train is represented,
Figure BDA0003826849720000132
and the minZ represents an objective function of a virtual marshalling train operation optimization model.
S10242, and taking the travel demand of the passengers on the whole line and the average passenger carrying rate of each train as constraint conditions of the operation optimization function of the virtual marshalling train.
Specifically, the travel demand of the passengers on the whole line and the average passenger carrying rate of each train are taken as constraint conditions of the objective function; wherein, guarantee that two arbitrary stations all have at least same train to berth to satisfy whole line passenger's trip demand, and then the constraint condition of whole line passenger's trip demand expresses as:
Figure BDA0003826849720000133
the constraint condition of the average load factor of each train is expressed as:
Figure BDA0003826849720000134
s10243, solving the virtual marshalling train operation optimization function by using a genetic algorithm to generate the virtual marshalling train operation optimization scheme.
Specifically, the objective function decision variable is the stop variable value of each vehicle, namely ζ i (1)，ζ i (2)，……，ζ i And (N) solving the objective function by using a genetic algorithm to generate a full-line stop scheme of different vehicles, namely a virtual marshalling train operation optimization scheme.
The following describes an optimization method of a track traffic virtual consist train operation scheme by a specific embodiment:
(1) As shown in fig. 10, taking the eight-way line of the beijing subway as an example, the total line has 15 stations, most of the trains in the currently adopted train driving plan operate in the quail-earth bridge section, and table 1 is an Origin-Destination (OD) table of the peak time period (8.
Table 1:
Figure BDA0003826849720000135
Figure BDA0003826849720000141
from the above table 1, it can be seen that most of the passenger flows move from suburbs to the urban center direction (the direction of the four-way station), and then the following steps are mainly used for researching and designing the train operation scheme in the subway eight-way line early peak period earth bridge-four-way direction, so that the number of trains can be reduced in the peak period of passenger flow leveling, and optimization is performed according to the number.
(2) Solving a virtual marshalling train optimized operation scheme based on an NSGA-II algorithm (genetic algorithm); since the train has only two states of stop and pass at each station, the train states can be represented by 1 and 0 respectively, and the stop scheme is coded for each train, i.e. each running scheme can be represented by a binary number, the number of bits of the binary number is the number of stations, and the eight-way current train running scheme (scheme one) is shown in table 2 below.
Table 2:
Figure BDA0003826849720000142
six marshalling trains are dispatched successively at 2min dispatch intervals, and 39.45min is required for one train to run the full line.
Because the stop scheme of the train in the operation scheme presents an obvious combined explosion trend along with the increase of the number of trains and the number of stations through which the train passes, and a plurality of optimization targets exist, the stop scheme of the train is difficult to obtain by using a traditional optimization method, a rapid non-dominated sorting genetic algorithm (NSGA-II) is adopted for solving, the algorithm has lower calculation complexity and can ensure population diversity, the specific solving process is shown in figure 11, the stop scheme of 6 trains in one driving cycle is regarded as a chromosome, and the stop scheme of each train is a gene forming the chromosome; fig. 12 shows the pareto frontier after 100 iterations of the initial population number, the passenger travel time in fig. 12 is the ratio of the passenger travel time of the virtual consist optimization operation scheme to the time required by the current scheme (scheme one), and as can be seen from the pareto frontier, the train transportation efficiency will increase as the passenger average travel time increases, and the railway operator can select a compromise between the passenger travel time and the train transportation efficiency according to the requirements, and the set of optimization results in the operation scheme is shown in table 3 below.
Table 3:
Figure BDA0003826849720000151
as can be seen from table 3 above, in the set of optimization results, the operation schemes of each train in the virtual marshalling fleet are different, and the transstation operation is performed at different stations, and at the same time, the scheme can still meet the riding requirements of all passengers on the whole line, and at this time, the average train load rate of the whole line of the train is 49.05%, and compared with the existing station parking scheme, the average travel time of all passengers on the whole line can be reduced by 6.61%.
Assuming that the trains with corresponding numbers (such as A1 and B1) in the two virtual marshalling have the same operation scheme, the trains can be dynamically organized into different small marshalling fleets according to the stop scheme in the operation process, and at the moment, the fastest running line of one train is only 36.45min, so that the method saves 3min compared with the existing scheme, and can effectively reduce the operation cost and improve the line utilization rate.
Fig. 13 shows the scenes that the vehicles leave the stations in sequence, taking the booby station (station 12) as an example, 6 vehicles form three formation together, wherein the vehicle 1 runs alone, the vehicles 5 and 2, and the vehicles 6, 3 and 4 are formed into two virtual formation fleets respectively, and from the whole line, the octg line only needs to be provided with six avoidance stations to meet the requirement of the optimized operation scheme, so that the line modification of the octg line can be guided to be suitable for the operation of the virtual formation trains according to the optimized result.
(3) And (3) comparing and analyzing the optimization scheme: to compare the advantages of the designed virtual consist train operating scheme, five train operating schemes are given in table 4 below, respectively: the first scheme is a stop-by-stop parking scheme taking 2min as departure interval; in the second scheme, a virtual marshalling train cross-station operation scheme taking 2min as departure interval; a scheme III, namely a fixed marshalling train station-crossing scheme taking 2min as departure intervals; a scheme IV, namely a stop-by-stop parking scheme taking 1min as departure interval; and a fifth scheme, namely a virtual marshalling train station-crossing operation scheme taking 1min as a departure interval, wherein in order to meet the requirement of normal getting on and off of passengers, the station-stopping time of all trains is considered to be 30s, and the same scheme as the scheme 2 is adopted for crossing of all schemes.
Table 4:
Figure BDA0003826849720000161
the following conclusions can be drawn by comparison:
A. compared with the existing fixed marshalling station parking scheme, the virtual marshalling train transportation mode can shorten the passenger travel time by 6.61% and shorten the passenger travel time by 1.5min. Meanwhile, the running time of the train is greatly shortened, and the transportation efficiency is improved.
B. As can be seen from the schemes 2 and 3, although the fixed marshalling train offside scheme can shorten the travel time of the fast passengers, overall, if the scheme is not optimized reasonably, the waiting time of the slow passengers is too long, and the overall average travel time of the passengers is increased. In the scheme 2, trains of different stop schemes can be dispatched simultaneously through a virtual marshalling technology without waiting for a certain dispatching interval, so that the problem of overlong waiting time of passengers can be effectively solved, and the requirement of passengers for quick trip can be met.
C. As can be seen from the comparison between the scheme 2 and the scheme 5, when the departure interval of the train is shortened from 2min to 1min, the passenger travel time can be further shortened to 91.69%.
Example 2
The present embodiment provides an optimization apparatus for a track traffic virtual marshalling train operation scenario, as shown in fig. 14, including:
the obtaining module 141 is configured to obtain the route information, the train operation information, the train parameters, and the passenger flow information.
And a generating module 142, configured to input the line information, the train operation information, the vehicle parameters, and the passenger flow information into a virtual train configuration optimization model, so as to generate a virtual train configuration operation optimization scheme.
The virtual marshalling train realizes marshalling among all vehicles in a high-performance vehicle-vehicle wireless communication mode without physical connection, so real-time dynamic marshalling and marshalling operation can be carried out; in order to better adapt to the problem of uneven passenger flow distribution at different stations, a fleet consisting of a plurality of vehicles is taken as a research object, and one fleet sends vehicles at an initial station in a virtual marshalling mode, wherein each vehicle has different stop schemes, the stop schemes of all vehicles in the fleet form an operation scheme, and the next virtual marshalling fleet repeats the operation scheme of the previous fleet; as shown in fig. 4, the operation schemes of the fleet B1-B2-B3 correspond to the operation schemes of the previous fleet A1-A2-A3 one-to-one, for each vehicle in the fleet, the stop scheme of the whole line is generated by the virtual marshalling train operation optimization function, and for each station, the number of trains stopping in a period of time is different, and in order to meet the overtaking requirement of the train, some stations need to have avoidance lines.
Furthermore, the virtual marshalling motorcade which is sent from the starting station forms vehicles, and the number of the vehicles is determined by the passenger flow requirement and the platform length; at present, the length of most subway platforms is about 140m, for example, A-type vehicles are used, the length of the subway platforms is 22m, when the subway platforms are in a peak period of passenger flow, grouping is carried out according to the maximum limitation of the platform length, and a virtual grouping fleet is 6 groups; when the passenger flow is in the peak leveling period, grouping is carried out according to the passenger flow requirements, and 1-6 groups can be formed; in the running process of the virtual marshalling fleet, the number of the marshalling vehicles is dynamically changed due to different stop schemes; each vehicle is an independent transportation unit in the process of operation, and grouping and compiling operations can be carried out according to respective operation schemes to form different combinations. As shown in fig. 5, a 6-marshalling train is taken as an example to show the partial marshalling status that may exist when the train is operating on the track.
Assuming that the stop time of all vehicles is the same, the train can carry out marshalling operation when entering or leaving the station, the operation time is ignored, assuming that the time of passengers arriving at each station is uniformly distributed, all the passengers take the nearest direct train, and transfer does not exist, because the vehicles can virtually marshal and run at the same speed on the interval line and can carry out overtaking in the platform area, the vehicles on the line do not interfere with each other under normal conditions, and the stop scheme of each vehicle can be independently designed; however, different stop schemes of the vehicles greatly affect passenger taking, for example, if there are fewer stop vehicles at a certain station, passengers at the station or passengers heading to the station will spend more waiting time, so the stop schemes of each vehicle need to be comprehensively considered to meet the taking demands of different passengers, and therefore, a virtual marshalling train operation optimization function is constructed based on line information, train operation information, train vehicle parameters and passenger flow information.
According to the optimization device for the operation scheme of the virtual marshalling train of the rail transit, the line information, the train operation information, the train parameters and the passenger flow information are all input into the virtual marshalling train optimization model through obtaining the line information, the train operation information, the train parameters and the passenger flow information, so that the virtual marshalling train optimization model can fully consider the influence of passenger flow, trains and lines on the operation scheme of the virtual marshalling train, and the generated virtual marshalling train operation optimization scheme takes the travel rule of passengers as a basis, so that the line transportation efficiency can be effectively improved, the transportation requirements of the urban rail transit in different periods can be met, the passenger travel time can be effectively shortened, the rail transit service quality can be improved, and the device has guiding significance for planning and construction of the urban rail transit line.
Preferably, the generating module 142 includes:
a first determining sub-module 1421, configured to extract an inter-station distance between two adjacent stations in the route information, a train departure interval, a train stop variable value, and train stop time in the train operation information, an average train operation speed in the train vehicle parameters, and the number of passengers from the departure station to the destination station in the passenger flow information, and determine a passenger travel time using the virtual marshalling train optimization model based on the train departure interval, the train stop variable value, the train stop time, the inter-station distance between two adjacent stations, the average train operation speed, and the number of passengers from the departure station to the destination station.
A second determining sub-module 1422, configured to determine the train operation time by using the virtual formation train optimization model based on the inter-station distance between the two adjacent stations, the average train operation speed, the train stop time, and the train stop variable value.
The virtual marshalling train has the operations of station crossing and the like, so that the train running time is greatly shortened, the running cost is measured by adopting the train running time, and a calculation formula of the train running time is as follows:
Figure BDA0003826849720000191
in the above formula, t c Representing train running time, n t Representing the number of virtual consist vehicles at the origin station,
Figure BDA0003826849720000192
indicates the distance between two adjacent stations, v avg Represents the average running speed of the train, t s Indicating train stopStation time, N represents the number of stations of the whole line, ζ i (r) represents the stop variable value of the train i at the station r.
The third determining sub-module 1423 is configured to extract the number of train owners in the train parameters, the number of train owners getting on the station and the number of train owners getting off the station in the passenger flow information, and determine the average train-line passenger carrying rate by using the virtual marshalling train optimization model based on the train stop variable value, the number of train owners getting on the station and the number of train owners getting off the station.
A generating sub-module 1424 is configured to generate the virtual consist operation optimization scheme based on the passenger travel time, the train operation time, and the all-line average load factor.
Preferably, the first determining submodule 1421 includes:
a first determining unit 14211, configured to determine the waiting time of the passenger based on the train stopping variable value and the train departure interval.
Specifically. The waiting time of the passenger is expressed as:
h x,y (i,j)/2
in the above formula, h x,y And (i, j) represents the departure interval between two adjacent vehicles departing from the same departure station and arriving at the same destination station.
A second determining unit 14212, configured to determine the passenger loss time based on the train stop variable value and the train stop time.
Specifically, the passenger lost time is expressed as:
Figure BDA0003826849720000193
in the above formula, ζ i (r) stop variable value t representing the stop of train i at station r s Indicating the train stop time.
A third determining unit 14213, configured to determine a passenger riding time based on the distance between two adjacent stations and the average train running speed.
Specifically, the passenger riding time is expressed as:
Figure BDA0003826849720000201
in the above-mentioned formula, the compound has the following structure,
Figure BDA0003826849720000202
indicates the distance between two adjacent stations, v avg Representing the average train speed of travel.
The passenger travel time is generated based on a passenger waiting time, the passenger loss time, the passenger boarding time, and the number of passengers from the departure station to the destination station.
Specifically, the formula for calculating the travel time of the passenger is as follows:
Figure BDA0003826849720000203
in the above formula, t p Represents passenger travel time, N represents the number of stations on the whole route, x represents departure station, y represents destination station, ζ i (x) Indicates the stop variable value, ζ of the train i at the departure station i (y) stop variable value, h, of train i at destination station x,y (i, j)/2 represents the waiting time of the passenger, h x,y (i, j) represents departure interval of two adjacent vehicles departing from the same departure station and arriving at the same destination station, m x,y The number of passengers from the departure station to the destination station is indicated, and r indicates any station between the departure station x and the destination station y.
In which the departure interval h between two adjacent cars departing from the same departure station and arriving at the same destination station x,y The calculation formula of (i, j) is as follows:
h x,y (i,j)＝t i,x -t j,x ,i＝2,…,n t j =1,2, …, i-1, x =1,2, …, N, y =1,2, …, N i,x Indicating the time, t, at which the train i departs from the departure station x j,x Indicating that the previous train is the same as the destination station of the train iWhen the train j exits the departure station x.
h x,y (i,j)＝h 0 ,i＝1
In the above formula, h 0 Representing the departure time interval of the train at the origin station.
Further, as the number of stops of the train increases, the average waiting time of the passengers decreases, but the riding time of the passengers also increases, so that it is necessary to comprehensively optimize the train operation plan and shorten the passenger travel time as much as possible.
Preferably, the third determining sub-module 1423 includes:
a fourth determining unit 14231, configured to determine the passenger capacity of the train when the train is dispatched based on the number of passengers getting on the train at the station and the number of passengers getting off the train at the station.
Wherein the number p of the trains i getting on the station r i,r The calculation formula of (a) is as follows:
Figure BDA0003826849720000211
in the above formula, m r,y Indicates the number of passengers from station r to destination station y, h r,y (i, j) represents a departure time interval, i.e., a train departure interval, between a train i departing from the station r and arriving at the destination station y and a train j departing from the station r and arriving at the destination station y.
Further, the number q of passengers getting off the train i at the station r i,r The calculation formula of (a) is as follows:
Figure BDA0003826849720000212
in the above formula, m x,r Indicates the number of passengers from the departure station x to the station r, h x,r (i, j) represents the departure time interval between the train i departing from the departure station x and arriving at the station r and the train j departing from the departure station x and arriving at the station r.
Specifically, for the same train, the utilization rate of high average passenger carrying rate is higher; reducing the number of stops of the train can effectively reduce the time lost by passengers, but at the same time, the transportation efficiency of the train can be reduced because the number of stops of the train is too small to load more passengers, therefore, the transportation efficiency of the train is ensured by limiting the average passenger carrying rate of the train, and the calculation formula of the passenger carrying capacity of a train when the train departs at the station is as follows:
C i,r ＝C i,r-1 +p i,r -q i,r ,r＝2,3,…,N,i＝1,2,…,n t
in the above formula, C i,r Indicating the passenger capacity of train i at station r, C i,r-1 Indicating the passenger capacity of train i at station r-1, n t Indicating the number of virtual marshalling cars at the origination station.
Wherein the passenger capacity of the train i at the origin station is represented as C i,r ＝0,r＝1,i＝1,2,…,n t 。
A fifth determining unit 14232, configured to determine an average total train occupancy based on the train stop variable value, the passenger capacity at the departure of the train, and the number of the train passengers.
Specifically, the calculation formula of the all-line average load factor of the train i is as follows:
Figure BDA0003826849720000213
in the above formula, the first and second carbon atoms are,
Figure BDA0003826849720000214
represents the average load factor of the whole train line, C 0 Indicating the number of train passengers, omega i Represents the number of stops of the train i on the whole line, wherein, omega i The calculation formula of (a) is as follows:
Figure BDA0003826849720000221
wherein ζ is obtained when the train i stops at the station r i (r) has a value of 1, else ζ i The value of (r) is 0.
Preferably, the generating sub-module 1424 includes:
a construction unit 14241, configured to construct a virtual train operation optimization function based on the passenger travel time, the train operation time, and the train line-wide average load factor.
Specifically, the calculation formula of the objective function is as follows:
Figure BDA0003826849720000222
in the above formula, t p Indicating the time of travel, t, of the passenger c The running time of the train is represented,
Figure BDA0003826849720000223
and the minZ represents an objective function of a virtual marshalling train operation optimization model.
And the constraint unit 14242 is used for taking the travel demand of the all-line passengers and the average passenger carrying rate of each train as constraint conditions of the virtual marshalling train operation optimization function.
Specifically, the travel demand of the passengers on the whole line and the average passenger carrying rate of each train are taken as constraint conditions of the objective function; wherein, guarantee that two arbitrary stations all have at least same train to berth to satisfy whole line passenger's trip demand, and then the constraint condition of whole line passenger's trip demand expresses as:
Figure BDA0003826849720000224
the constraint condition of the average load factor of each train is expressed as:
Figure BDA0003826849720000225
the solving unit 14243 is configured to solve the virtual train configuration operation optimization function by using a genetic algorithm, and generate the virtual train configuration operation optimization scheme.
Specifically, the objective function decision variable is the station stopping variable value of each vehicle, namely zeta i (1)，ζ i (2)，……，ζ i And (N) solving the objective function by using a genetic algorithm to generate a full-line stop scheme of different vehicles, namely a virtual marshalling train operation optimization scheme.
Example 3
The present embodiment provides a computer device, which includes a memory and a processor, where the processor is configured to read instructions stored in the memory to execute a method for optimizing a running scheme of a virtual rail transit consist train in any of the above method embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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.
Example 4
The present embodiment provides a computer-readable storage medium storing computer-executable instructions, which can execute a method for optimizing an operation scheme of a virtual rail transit consist train in any of the above method embodiments. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications can be made without departing from the scope of the invention.

Claims (10)

1. A method for optimizing a track traffic virtual marshalling train operation scheme is characterized by comprising the following steps:
acquiring line information, train operation information, train vehicle parameters and passenger flow information;
and inputting the line information, the train operation information, the train vehicle parameters and the passenger flow information into a virtual marshalling train optimization model to generate a virtual marshalling train operation optimization scheme.
2. The method for optimizing a track traffic virtual train formation operation scheme according to claim 1, wherein the step of inputting the route information, the train operation information, the train vehicle parameters and the passenger flow information into a virtual train formation optimization model to generate a virtual train formation operation optimization scheme comprises:
extracting inter-station distance between two adjacent stations in the line information, train departure interval, train stop variable value and train stop time in the train operation information, average train running speed in train vehicle parameters and the number of passengers from a departure station to a destination station in the passenger flow information, and determining passenger travel time by using the virtual marshalling train optimization model based on the train departure interval, the train stop variable value, the train stop time, the inter-station distance between the two adjacent stations, the average train running speed and the number of passengers from the departure station to the destination station;
determining the train running time by using the virtual marshalling train optimization model based on the distance between the two adjacent stations, the average running speed of the train, the train stop time and the train stop variable value;
extracting the number of train passengers getting on the station and the number of train passengers getting off the station in the passenger flow information, and determining the average passenger carrying rate of the whole train line by using the virtual marshalling train optimization model based on the variable numerical value of the train stopping station, the number of the train passengers getting on the station and the number of the train passengers getting off the station in the passenger flow information;
generating the virtual consist train operation optimization plan based on the passenger travel time, the train operation time, and the on-train average load factor.
3. The method as claimed in claim 2, wherein the determining the passenger travel time using the virtual marshalling train optimization model based on the departure interval of the train, the stop variable value of the train, the stop time of the train, the inter-station distance between two adjacent stations, the average train running speed and the number of passengers from the departure station to the destination station comprises:
determining passenger waiting time based on the train stop variable value and the train departure interval;
determining passenger loss time based on the train stop variable value and the train stop time;
determining passenger riding time based on the distance between two adjacent stations and the average running speed of the train;
and generating the passenger travel time based on the passenger waiting time, the passenger loss time, the passenger riding time and the passenger number from the departure station to the destination station.
4. The method for optimizing the operation scheme of the virtual rail transit consist train according to claim 3, wherein the passenger travel time is generated based on the waiting time of passengers, the passenger loss time, the passenger taking time and the number of passengers from the departure station to the destination station, and the calculation formula of the passenger travel time is as follows:
Figure FDA0003826849710000021
in the above formula, t p Representing passenger travel time, N representing the number of stations on the full line, x representing the departure station, y representing the destination station, ζ i (x) Indicates the stop variable value, ζ of the train i at the departure station i (y) a stop variable value h of the train i at the destination station x,y (i, j)/2 represents the waiting time of the passenger, h x,y (i, j) represents a train departure interval,
Figure FDA0003826849710000022
indicates time lost, ζ of passenger i (r) a stop variable value, t, of the train i at the station r s The time at which the train is stopped is indicated,
Figure FDA0003826849710000023
the time of the ride of the passenger is indicated,
Figure FDA0003826849710000024
indicating the distance between two adjacent stations, v avg Represents the average running speed of the train, m x,y Indicating the number of passengers from the departure station to the destination station.
5. The method as claimed in claim 2, wherein the determining the average train occupancy of the whole train line using the virtual marshalling train optimization model based on the stop variable value, the number of train passengers, the number of passengers getting on the station and the number of passengers getting off the station comprises:
determining the passenger capacity of the train when the train departs based on the number of passengers getting on the train and the number of passengers getting off the train;
and determining the average passenger carrying rate of the whole train line based on the train stop variable value, the passenger carrying capacity when the train departs and the number of the passengers of the train.
6. The method of claim 2, wherein the generating the virtual consist train operation optimization scheme based on the passenger travel time, the train operation time and the all-train average load factor comprises:
constructing a virtual consist train operation optimization function based on the passenger travel time, the train operation time and the on-line average load factor of the train;
taking the travel demand of passengers on the whole line and the average passenger carrying rate of each train as constraint conditions of the operation optimization function of the virtual marshalling train;
and solving the virtual marshalling train operation optimization function by using a genetic algorithm to generate the virtual marshalling train operation optimization scheme.
7. The method as claimed in claim 6, wherein the virtual train operation optimization function is constructed based on the passenger travel time, the train operation time and the on-line average load factor of the train, and the virtual train operation optimization function is calculated as follows:
Figure FDA0003826849710000031
in the above formula, t p Indicating the time of travel, t, of the passenger c The running time of the train is represented,
Figure FDA0003826849710000032
the train full-line average load factor is represented, and the minZ represents a virtual marshalling train operation optimization function.
8. An operation scheme optimization device for a rail transit virtual marshalling train is characterized by comprising the following components:
the acquisition module is used for acquiring line information, train operation information, train vehicle parameters and passenger flow information;
and the generating module is used for inputting the line information, the train operation information, the train vehicle parameters and the passenger flow information into a virtual marshalling train optimization model to generate a virtual marshalling train operation optimization scheme.
9. A computer device comprising a processor and a memory, wherein the memory is configured to store a computer program and the processor is configured to invoke the computer program to perform the steps of the method according to any one of claims 1-7.
10. A computer-readable storage medium having stored thereon computer instructions, which when executed by a processor, perform the steps of the method according to any one of claims 1-7.
CN202211062510.2A 2022-08-31 2022-08-31 Method and device for optimizing operation scheme of rail transit virtual marshalling train Pending CN115593471A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117485398A (en) * 2024-01-02 2024-02-02 成都交控轨道科技有限公司 Method, equipment and storage medium for calculating train number based on virtual marshalling

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117485398A (en) * 2024-01-02 2024-02-02 成都交控轨道科技有限公司 Method, equipment and storage medium for calculating train number based on virtual marshalling
CN117485398B (en) * 2024-01-02 2024-04-05 成都交控轨道科技有限公司 Method, equipment and storage medium for calculating train number based on virtual marshalling

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