Combined-mode urban rail train scheduling method

Abstract

The invention is applicable to the technical field of rail transit, and provides a combined-mode urban rail train scheduling method, which comprises the following steps of: firstly, dividing trains in a combined mode into four train types according to train stop difference and operation interval difference between a fast and slow train strategy and a large and small intersection strategy; based on four train types and a passenger transfer path selection principle, establishing a passenger travel total time calculation formula of each train type and a train total operation cost calculation formula of a combined mode, and combining the passenger travel total time calculation formula and the train total operation cost calculation formula corresponding to the combined mode; introducing constraint conditions to construct a train operation scheme optimization model for minimizing passenger travel time and enterprise operation cost, finally initializing parameters of the model, converting decision variables of the model into chromosomes for coding, and designing an algorithm to solve a global optimal solution of the train operation scheme optimization model. Therefore, the train operation cost is reduced, and the long-distance travel efficiency is improved.

Description

Technical Field

[0001] The present application belongs to the field of rail transit technology, and in particular to a combined-mode urban rail train dispatching method. Background Art

[0002] With the development of urban rail transit network, the scale of urban line laying has increased dramatically. As the main way to connect the city center with remote suburbs, urban lines have problems such as uneven passenger flow distribution and low train capacity utilization. The fast and slow train mode (including fast and slow trains) and the cross-route mode (including large cross-route and small cross-route operation strategies) have great advantages in dealing with uneven passenger flow and improving transportation efficiency, which is significantly better than the traditional stop-at-station mode.

[0003] However, adopting the fast and slow train mode or the alternate route mode alone will also lead to increased rail transit costs and low operating efficiency. Summary of the invention

[0004] The embodiment of the present application provides a combined mode urban rail train dispatching method, which can solve the problem that the current use of a fast and slow train mode or a route mode alone will also cause increased rail transit costs and low operating efficiency.

[0005] In the first aspect, an embodiment of the present application provides a combined mode urban rail train scheduling method, wherein the combined mode includes a fast and slow train mode and a route mode, including: S1, according to the differences in train stops and operating intervals between the fast and slow train strategies and the large and small route strategies, the trains in the combined mode are divided into four train types, and a passenger transfer path selection principle corresponding to the different train types is proposed; S2, based on the four train types and the passenger transfer path selection principle in S1, a calculation formula for the total passenger travel time corresponding to each train type and a calculation formula for the total train operating cost corresponding to the combined mode are established; S3, combined with S2 The total passenger travel time calculation formula and the total train operating cost calculation formula corresponding to the combination mode introduce the constraints of departure interval, stop and operating area, and construct a train operation plan optimization model that minimizes passenger travel time and enterprise operating costs; S4, initialize the parameters of the train operation plan optimization model in S3, convert the decision variables of the train operation plan optimization model in S3 into chromosomes for encoding, design a two-stage adaptive NSGA algorithm based on chaotic mapping to obtain the global optimal solution of the train operation plan optimization model, and obtain the optimal train stop plan, operating interval, and departure frequency according to the global optimal solution.

[0006] In a possible implementation of the first aspect, the above-mentioned train types are respectively a large-route slow train departing before a small-route slow train, a small-route slow train, a large-route slow train departing before a large-route express train, and a large-route express train. The large-route slow train departing before the small-route slow train is a slow train, will not pass through any station, and its service range is the entire line, and it will not turn back halfway. The small-route slow train is a slow train, will not pass through any station, and its service range is only a part of the stations on the line, and it will turn back halfway. The large-route slow train departing before the large-route express train is a slow train, will not pass through any station, and its service range is the entire line, and it will not turn back halfway. The large-route express train is an express train, will pass through a part of the stations, and its service range is the entire line, and it will not turn back halfway.

[0007] Optionally, in another possible implementation of the first aspect, the passenger transfer path selection principle is specifically as follows:

[0008] If the type of train currently arriving at the station is a large-route slow train that departs before a small-route slow train or a large-route slow train that departs before a large-route express train, the passenger will only choose to take the current train;

[0009] If the train type of the current arrival train is a small-route slow train, passengers can have the following three travel options: (1) If the first and last stations are both within the small-route slow train operation zone, choose to take the current arrival train; (2) If only the last station is outside the small-route slow train operation zone, first take the current arrival train and then transfer to a large-route slow train or a large-route slow train that departs before the large-route express train; (3) If the first station is outside the small-route slow train operation zone, wait for the next slow train;

[0010] If the train type of the current arriving train is a large-route express, passengers have the following three travel options: (1) If both the first and last stations are within the large-route express operating range, passengers can choose to take the current arriving train; (2) If the first station is not within the large-route express operating range, passengers can wait for the next slow train; (3) If only the last station is within the large-route express operating range, passengers can first take the current arriving train and then transfer to a large-route slow train that departs before a small-route slow train or a large-route slow train that departs before a large-route express.

[0011] Optionally, in another possible implementation of the first aspect, based on the four train types and the passenger transfer path selection principle in S1, a total passenger travel time calculation formula corresponding to each train type and a total train operation cost calculation formula corresponding to the combination mode are established, including:

[0012] Step S2-1: Based on the four types of trains and the principle of selecting passenger transfer routes in S1, a calculation formula for the total passenger travel time corresponding to each type of train is established, specifically:

[0013]

[0014]

[0015] T=t Case 1+t Case 2+t Case 3+t Case 4(8)

[0016] Where N is the number of stations on the line, M is the number of trains operating per hour, i, j, u, v are the indexes of the stations, and P i,j is the hourly passenger flow of the line with the starting and ending points at station i and station j respectively, r u is the running time of interval u, d is the stop time, n f is the frequency of slow trains on the entire line, y i ,y u ,y j They respectively indicate whether the express train stops at station i, whether the express train stops at station u, and whether the express train stops at station j. If the express train stops at station i, stops at station u, and stops at station j, then y i ,y u ,y j The value of is 1. If the express train does not stop at station i, does not stop at station u, and does not stop at station j, then y i ,y u ,y j The value of is 0, τ e Indicates the time that can be saved when the express train does not stop at the station, s i,j,up 、s i,j,down They represent the transfer station with the starting and ending points at stations i and j in the upward direction and the transfer station with the starting and ending points at stations i and j in the downward direction, respectively. i,j,up ,ω i,j,down They respectively indicate whether there is a transfer station with the starting and ending points of the upward direction at station i and station j respectively, and whether there is a transfer station with the starting and ending points of the downward direction at station i and station j respectively. If so, ω i,j,up ,ω i,j,down The value of is 1. If it does not exist, then ω i,j,up ,ω i,j,down The value of x is 0. -,up 、x +,up They represent the starting and ending stations of the small-route train in the upward direction, respectively. -,down 、x +,down They represent the starting and ending stations of the downlink short-distance train, respectively. ST,i Indicates whether station i is located in the short-distance train operation area. If so, then λ ST,i The value of is 1, otherwise, λ ST,i The value of t is 0, Case1 ,tCase2 ,t Case3 ,t Case4 are the passenger waiting time of four train types, and T is the total passenger waiting time;

[0017] Step S2-2: Based on the four types of trains in S1 and the principle of selecting passenger transfer paths, a calculation formula for the total train operating cost corresponding to the combination mode is established, which is:

[0018]

[0019]

[0020] C=C operation +C stop (11)

[0021] Among them, c1 and c2 are the operating cost coefficient and the average stop cost coefficient respectively, n s ,n e are the departure frequencies of short-distance trains and fast and slow trains, S i represents the distance from station i to station i+1, C operation ,C stop are the operating cost and the stop cost respectively, and C represents the total train operating cost.

[0022] Optionally, in another possible implementation of the first aspect, the total passenger travel time calculation formula in S2 and the total train operating cost calculation formula corresponding to the combination mode are combined, and the constraints of departure interval, stop and operation area are introduced to construct a train operation plan optimization model that minimizes passenger travel time and enterprise operating costs, including:

[0023] Step S3-1: Introduce the constraint condition into the total passenger travel time calculation formula in S2 and the total train operation cost calculation formula corresponding to the combination mode, specifically:

[0024]

[0025] M·R≥maxO i ,i∈[1,N]∪[N+1,2N] (14)

[0026] n f +n s +n e =M (15)

[0027] x -,up -1=2N-x +,down (16)

[0028] x +,up -1=2N-x -,down(17)

[0029]

[0030] Among them, h min ,h max are the minimum departure interval and the maximum departure interval respectively, O i is the number of people on the train from station i to station i+1, O i-1 is the number of people on the train from station i-1 to station i, P v,i is the hourly passenger flow of the line with the starting and ending points at station v and station i respectively, and R is the train capacity;

[0031] Step S3-2: Combine the total passenger travel time calculation formula in S2, the total train operating cost calculation formula corresponding to the combination mode, the total passenger travel time calculation formula with the introduction of constraints, and the total train operating cost calculation formula corresponding to the combination mode with the introduction of constraints to construct a train operation plan optimization model that minimizes passenger travel time and enterprise operating costs, specifically:

[0032]

[0033] Among them, formula (1) to formula (19) jointly construct a train operation plan optimization model that minimizes passenger travel time and enterprise operating costs.

[0034] Optionally, in another possible implementation of the first aspect, the decision variables of the train operation plan optimization model include a train stop plan and a departure frequency, the parameters of the train operation plan optimization model in S3 are initialized, the decision variables of the train operation plan optimization model in S3 are converted into chromosomes for encoding, a two-stage adaptive NSGA algorithm based on chaotic mapping is designed to obtain a global optimal solution of the train operation plan optimization model, and the optimal train stop plan, operating interval, and departure frequency are obtained according to the global optimal solution, including:

[0035] Step S4-1: Initialize the parameters of all formulas and models in S2 and S3, and divide the algorithm into two stages. In the first stage, the operation interval of the short-distance train is generated by enumeration method;

[0036] Step S4-2: The second stage is to bring the small-route train operation interval in step S4-1 into the train operation plan optimization model in S3 as known parameters, convert the train stop plan and departure frequency into chromosomes for encoding, and set the population size POP size and the maximum number of iterations g max ;

[0037] Step S4-3: Introduce the chaos mapping theory to improve the quality of algorithm population initialization, specifically:

[0038]

[0039] ξ k,g,i =ξ max,i +Γ k,g ·[0,1]·(ξ max,i -ξ min,i ). (twenty two)

[0040] Among them, Γ k,g is the chaotic mapping parameter, k and g are the indexes of individual and iteration times, K0 is the preset parameter, ξ k,g,i represents chromosome, ξ max,i ,ξ min,i are the maximum and minimum values ​​of the chromosome, respectively;

[0041] Step S4-4: Perform a fast non-dominated sort on all chromosomes, calculate the crowding distance between any two chromosomes, and select multiple optimal individuals that meet the Pareto optimal conditions from all chromosomes through the elite retention strategy, and determine the optimal values ​​and solution sets corresponding to the multiple optimal individuals;

[0042] Step S4-5: using the crossover and mutation strategies of the traditional genetic algorithm for the multiple optimal individuals selected in S4-4 to generate a new generation of population;

[0043] Step S4-6: Continue to iterate g times. If the current number of iterations is equal to the maximum number of iterations, output the optimal solution under the current number of iterations and the optimal value and solution set corresponding to the feasible solution of the operating interval in S4-1, and go to step S4-7. If the current number of iterations is less than the maximum number of iterations, return to step S4-4.

[0044] Step S4-7: Determine whether the operating range of the short-distance train in step S4-1 has traversed all feasible solutions. If not, return to step S4-1 and generate a new feasible solution; if so, select the global optimal solution from all feasible solutions generated after step S4-6 through dominance comparison, and obtain the optimal train stop plan, operating range, and departure frequency based on the global optimal solution.

[0045] In the technical solution of this application, the trains in the combination mode are first divided into four types of trains according to the differences in train stops and operating intervals between the fast and slow train strategies and the large and small route strategies. Then, based on the four types of trains and the principle of selecting passenger transfer paths, a formula for calculating the total passenger travel time of each type of train and a formula for calculating the total train operating cost of the combination mode are established. In combination with the formula for calculating the total passenger travel time and the formula for calculating the total train operating cost corresponding to the combination mode, constraints are introduced to construct a train operation plan optimization model that minimizes passenger travel time and enterprise operating costs. Finally, the parameters of the model are initialized, the decision variables of the model are converted into chromosomes for encoding, and an algorithm is designed to obtain the global optimal solution of the train operation plan optimization model. As a result, the train operating cost is reduced and the efficiency of long-distance travel is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 It is a flow chart of a combined mode urban rail train dispatching method provided in an embodiment of the present application;

[0048] Figure 2 This is a schematic diagram of various situations of passenger route selection provided by an embodiment of the present application. DETAILED DESCRIPTION

[0049] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0050] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0051] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0052] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0053] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0054] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0055] The urban rail train scheduling method of the combined mode provided in the present application is described in detail below with reference to the accompanying drawings.

[0056] Figure 1 A schematic flow chart of a combined mode urban rail train dispatching method provided in an embodiment of the present application is shown.

[0057] like Figure 1 As shown, the urban rail train scheduling method of the combined mode includes the following steps:

[0058] S1. According to the differences in train stops and operating intervals between the fast and slow train strategies and the large and small route strategies, the trains in the combined mode are divided into four types of trains, and the passenger transfer path selection principles corresponding to different train types are proposed;

[0059] S2. Based on the four types of trains and the principle of selecting passenger transfer routes in S1, a calculation formula for the total passenger travel time corresponding to each train type and a calculation formula for the total train operating cost corresponding to the combination mode are established;

[0060] S3, combining the total passenger travel time calculation formula in S2 and the total train operating cost calculation formula corresponding to the combination mode, introducing the constraints of departure interval, stop and operation area, and constructing a train operation plan optimization model that minimizes passenger travel time and enterprise operating costs;

[0061] S4. Initialize the parameters of the train operation plan optimization model in S3, convert the decision variables of the train operation plan optimization model in S3 into chromosomes for encoding, design a two-stage adaptive NSGA algorithm based on chaotic mapping to obtain the global optimal solution of the train operation plan optimization model, and obtain the optimal train stop plan, operating range, and departure frequency according to the global optimal solution.

[0062] It should be noted that the combination mode in the embodiment of the present application includes an express and slow train mode and a route mode, wherein in the express and slow train mode, an express train may refer to a train that will pass through a station, and a slow train may refer to a train that will not pass through a station; in the route mode, a large route mode refers to a mode in which the train service range covers all stations and will not turn back midway, and a small route mode refers to a mode in which the train service range covers some stations and will turn back midway.

[0063] Furthermore, in a possible implementation of the embodiment of the present application, the above-mentioned train types are respectively a large-route slow train departing before a small-route slow train, a small-route slow train, a large-route slow train departing before a large-route express train, and a large-route express train. The large-route slow train departing before the small-route slow train is a slow train, will not pass through stations, and its service range is the entire line, and it will not turn back midway. The small-route slow train is a slow train, will not pass through stations, and its service range is only a part of the stations on the line, and it will turn back midway. The large-route slow train departing before the large-route express train is a slow train, will not pass through stations, and its service range is the entire line, and it will not turn back midway. The large-route express train is an express train, will pass through a part of stations, and its service range is the entire line, and it will not turn back midway.

[0064] Furthermore, in a possible implementation of the embodiment of the present application, the above passenger transfer path selection principle is specifically as follows:

[0065] If the type of train currently arriving at the station is a large-route slow train that departs before a small-route slow train or a large-route slow train that departs before a large-route express train, the passenger will only choose to take the current train;

[0066] If the train type of the current arrival train is a small-route slow train, passengers can have the following three travel options: (1) If the first and last stations are both within the small-route slow train operation zone, choose to take the current arrival train; (2) If only the last station is outside the small-route slow train operation zone, first take the current arrival train and then transfer to a large-route slow train or a large-route slow train that departs before the large-route express train; (3) If the first station is outside the small-route slow train operation zone, wait for the next slow train;

[0067] If the train type of the current arriving train is a large-route express, passengers have the following three travel options: (1) If both the first and last stations are within the large-route express operating range, passengers can choose to take the current arriving train; (2) If the first station is not within the large-route express operating range, passengers can wait for the next slow train; (3) If only the last station is within the large-route express operating range, passengers can first take the current arriving train and then transfer to a large-route slow train that departs before a small-route slow train or a large-route slow train that departs before a large-route express.

[0068] Furthermore, in a possible implementation of the embodiment of the present application, the above step S2 may include:

[0069] Step S2-1: Based on the four types of trains and the principle of selecting passenger transfer routes in S1, a calculation formula for the total passenger travel time corresponding to each type of train is established, specifically:

[0070]

[0071]

[0072] T=t Case1 +t Case2 +t Case3 +t Case4 (8)

[0073] Where N is the number of stations on the line, M is the number of trains operating per hour, i, j, u, v are the indexes of the stations, and P i,j is the hourly passenger flow of the line with the starting and ending points at station i and station j respectively, r u is the running time of interval u, d is the stop time, n f is the frequency of slow trains on the entire line, y i ,y u ,y j They respectively indicate whether the express train stops at station i, whether the express train stops at station u, and whether the express train stops at station j. If the express train stops at station i, stops at station u, and stops at station j, then y i ,y u ,y j The value of is 1. If the express train does not stop at station i, does not stop at station u, and does not stop at station j, then y i ,y u ,y j The value of is 0, τ e Indicates the time that can be saved when the express train does not stop at the station, s i,j,up 、s i,j,downThey represent the transfer station with the starting and ending points at stations i and j in the upward direction and the transfer station with the starting and ending points at stations i and j in the downward direction, respectively. i,j,up ,ω i,j,down They respectively indicate whether there is a transfer station with the starting and ending points of the upward direction at station i and station j respectively, and whether there is a transfer station with the starting and ending points of the downward direction at station i and station j respectively. If so, ω i,j,up ,ω i,j,down The value of is 1. If it does not exist, then ω i,j,up ,ω i,j,down The value of x is 0. -,up 、x +,up They represent the starting and ending stations of the small-route train in the upward direction, respectively. -,down 、x +,down They represent the starting and ending stations of the downlink short-distance train, respectively. ST,i Indicates whether station i is located in the short-distance train operation area. If so, then λ ST,i The value of is 1, otherwise, λ ST,i The value of t is 0, Case1 ,t Case2 ,t Case3 ,t Case4 are the passenger waiting time of four train types, and T is the total passenger waiting time;

[0074] Step S2-2: Based on the four types of trains in S1 and the principle of selecting passenger transfer paths, a calculation formula for the total train operating cost corresponding to the combination mode is established, which is:

[0075]

[0076] C=C operation +C stop (11)

[0077] Among them, c1 and c2 are the operating cost coefficient and the average stop cost coefficient respectively, n s ,n e are the departure frequencies of short-distance trains and fast and slow trains, S i represents the distance from station i to station i+1, C operation ,C stop are the operating cost and the stop cost respectively, and C represents the total train operating cost.

[0078] Furthermore, in a possible implementation of the embodiment of the present application, the above step S3 may include:

[0079] Step S3-1: Introduce the constraint condition into the total passenger travel time calculation formula in S2 and the total train operation cost calculation formula corresponding to the combination mode, specifically:

[0080]

[0081] M·R≥maxO i ,i∈[1,N]∪[N+1,2N](14)

[0082] n f +n s +n e =M (15)

[0083] x -,up -1=2N-x +,down (16)

[0084] x +,up -1=2N-x -,down (17)

[0085]

[0086] Among them, h min ,h max are the minimum departure interval and the maximum departure interval respectively, O i is the number of people on the train from station i to station i+1, O i-1 is the number of people on the train from station i-1 to station i, P v,i is the hourly passenger flow of the line with the starting and ending points at station v and station i respectively, and R is the train capacity;

[0087] Step S3-2: Combine the total passenger travel time calculation formula in S2, the total train operating cost calculation formula corresponding to the combination mode, the total passenger travel time calculation formula with the introduction of constraints, and the total train operating cost calculation formula corresponding to the combination mode with the introduction of constraints to construct a train operation plan optimization model that minimizes passenger travel time and enterprise operating costs, specifically:

[0088]

[0089] Among them, formula (1) to formula (19) jointly construct a train operation plan optimization model that minimizes passenger travel time and enterprise operating costs.

[0090] Furthermore, in a possible implementation of the embodiment of the present application, the decision variables of the train operation plan optimization model include the train stop plan and the departure frequency, and the above step S4 may include:

[0091] Step S4-1: Initialize the parameters of all formulas and models in S2 and S3, and divide the algorithm into two stages. In the first stage, the operation interval of the short-distance train is generated by enumeration method;

[0092] Step S4-2: The second stage is to bring the small-route train operation interval in step S4-1 into the train operation plan optimization model in S3 as known parameters, convert the train stop plan and departure frequency into chromosomes for encoding, and set the population size POP size and the maximum number of iterations g max ;

[0093] Step S4-3: Introduce the chaos mapping theory to improve the quality of algorithm population initialization, specifically:

[0094]

[0095] ξ k,g,i =ξ max,i +Γ k,g ·[0,1]·(ξ max,i -ξ min,i ). (twenty two)

[0096] Among them, Γ k,g is the chaotic mapping parameter, k and g are the indexes of individual and iteration times, K0 is the preset parameter, ξ k,g,i represents chromosome, ξ max,i ,ξ min,i are the maximum and minimum values ​​of the chromosome, respectively;

[0097] Step S4-4: Perform a fast non-dominated sort on all chromosomes, calculate the crowding distance between any two chromosomes, and select multiple optimal individuals that meet the Pareto optimal conditions from all chromosomes through the elite retention strategy, and determine the optimal values ​​and solution sets corresponding to the multiple optimal individuals;

[0098] Step S4-5: using the crossover and mutation strategies of the traditional genetic algorithm for the multiple optimal individuals selected in S4-4 to generate a new generation of population;

[0099] Step S4-6: Continue to iterate g times. If the current number of iterations is equal to the maximum number of iterations, output the optimal solution under the current number of iterations and the optimal value and solution set corresponding to the feasible solution of the operating interval in S4-1, and go to step S4-7. If the current number of iterations is less than the maximum number of iterations, return to step S4-4.

[0100] Step S4-7: Determine whether the operating range of the short-distance train in step S4-1 has traversed all feasible solutions. If not, return to step S4-1 and generate a new feasible solution; if so, select the global optimal solution from all feasible solutions generated after step S4-6 through dominance comparison, and obtain the optimal train stop plan, operating range, and departure frequency based on the global optimal solution.

[0101] The combined mode urban rail train scheduling method provided in this application first divides the trains in the combined mode into four types of trains according to the differences in train stops and operating intervals between the fast and slow train strategies and the large and small route strategies, and then establishes a total passenger travel time calculation formula for each type of train and a total train operating cost calculation formula for the combined mode based on the four train types and the passenger transfer path selection principle. In addition, the total passenger travel time calculation formula and the total train operating cost calculation formula corresponding to the combined mode are combined to introduce constraints to construct a train operation plan optimization model that minimizes passenger travel time and enterprise operating costs. Finally, the model parameters are initialized, the decision variables of the model are converted into chromosomes for encoding, and an algorithm is designed to obtain the global optimal solution of the train operation plan optimization model. In this way, the train operating cost is reduced and the efficiency of long-distance travel is improved.

[0102] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0103] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0104] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A combined mode urban rail train dispatching method, characterized in that: The combination mode includes a fast and slow vehicle mode and an intersection mode, and the method includes: S1. According to the differences in train stops and operating intervals between the fast and slow train strategies and the large and small route strategies, the trains in the combined mode are divided into four types of trains, and the passenger transfer path selection principles corresponding to the different train types are proposed; S2. Based on the four train types and the passenger transfer path selection principle in S1, a calculation formula for the total passenger travel time corresponding to each train type and a calculation formula for the total train operation cost corresponding to the combination mode are established; S3, combining the total passenger travel time calculation formula in S2 and the total train operating cost calculation formula corresponding to the combination mode, introducing the constraints of departure interval, stop and operation area, and constructing a train operation plan optimization model that minimizes passenger travel time and enterprise operating costs; S4. Initialize the parameters of the train operation plan optimization model described in S3, convert the decision variables of the train operation plan optimization model described in S3 into chromosomes for encoding, design a two-stage adaptive NSGA algorithm based on chaotic mapping to obtain the global optimal solution of the train operation plan optimization model, and obtain the optimal train stop plan, operating range, and departure frequency according to the global optimal solution.

2. The method according to claim 1, characterized in that The train types are respectively a large-route slow train departing before a small-route slow train, a small-route slow train, a large-route slow train departing before a large-route express train, and a large-route express train. The large-route slow train departing before the small-route slow train is a slow train and will not pass through any stations, and its service range is the entire line, and it will not turn back midway. The small-route slow train is a slow train and will not pass through any stations, and its service range is only a part of the stations on the line, and it will turn back midway. The large-route slow train departing before the large-route express train is a slow train and will not pass through any stations, and its service range is the entire line, and it will not turn back midway. The large-route express train is an express train and will pass through some stations, and its service range is the entire line, and it will not turn back midway.

3. The method according to claim 2, characterized in that: The passenger transfer path selection principle is specifically as follows: If the type of the train currently arriving at the station is the large route slow train that departs before the small route slow train or the large route slow train that departs before the large route express train, the passenger will only choose to take the current train; If the train type of the current arriving train is the small-route slow train, the passenger can have the following three travel options: (1) If both the first and last stations are within the small-route slow train operation range, the passenger can choose to take the current arriving train; (2) If only the last station is outside the small-route slow train operation range, the passenger can first take the current arriving train and then transfer to the large-route slow train or the large-route slow train that departs before the large-route express train; (3) If the first station is outside the small-route slow train operation range, the passenger can wait for the next slow train; If the train type of the current arriving train is the large-route express, the passenger has the following three travel options: (1) if both the first and last stations are within the operating range of the large-route express, the passenger chooses to take the current arriving train; (2) if the first station is not within the operating range of the large-route express, the passenger waits for the next slow train; (3) if only the last station is within the operating range of the large-route express, the passenger first takes the current arriving train and then transfers to the large-route slow train that departs before the small-route slow train or the large-route slow train that departs before the large-route express.

4. The method according to claim 3, characterized in that: The formula for calculating the total passenger travel time corresponding to each train type and the formula for calculating the total train operating cost corresponding to the combination mode are established based on the four train types and the passenger transfer path selection principle in S1, including: Step S2-1: Based on the four types of trains in S1 and the passenger transfer path selection principle, a calculation formula for the total passenger travel time corresponding to each type of train is established, specifically: T=t Case1 +t Case2 +t Case3 +t Case4 (8) Where N is the number of stations on the line, M is the number of trains operating per hour, i, j, u, v are the indexes of the stations, and P i,j is the hourly passenger flow of the line with the starting and ending points at station i and station j respectively, r u is the running time of interval u, d is the stop time, n f is the frequency of slow trains on the entire line, y i ,y u ,y j They respectively indicate whether the express train stops at station i, whether the express train stops at station u, and whether the express train stops at station j. If the express train stops at station i, stops at station u, and stops at station j, then y i ,y u ,y j The value of is 1. If the express train does not stop at station i, does not stop at station u, and does not stop at station j, then y i ,y u ,y j The value of is 0, τ e Indicates the time that can be saved when the express train does not stop at the station, s i,j,up 、s i,j,down They represent the transfer station with the starting and ending points at stations i and j in the upward direction and the transfer station with the starting and ending points at stations i and j in the downward direction, respectively. i,j,up ,ω i,j,down They respectively indicate whether there is a transfer station with the starting and ending points of the upward direction at station i and station j respectively, and whether there is a transfer station with the starting and ending points of the downward direction at station i and station j respectively. If so, ω i,j,up ,ω i,j,down The value of is 1. If it does not exist, then ω i,j,up ,ω i,j,down The value of x is 0. -,up 、x +,up They represent the starting and ending stations of the small-route train in the upward direction, respectively. -,down 、x +,down They represent the starting and ending stations of the downlink short-distance train, respectively. ST,i Indicates whether station i is located in the short-distance train operation area. If so, then λ ST,i The value of is 1, otherwise, λ ST,i The value of t is 0, Case1 ,t Case2 ,t Case3 ,t Case4 are the passenger waiting time of four train types, and T is the total passenger waiting time; Step S2-2: Based on the four train types and the passenger transfer path selection principle in S1, a calculation formula for the total train operating cost corresponding to the combination mode is established, specifically: C=C operation +C stop (11) Among them, c1 and c2 are the operating cost coefficient and the average stop cost coefficient respectively, n s ,n e are the departure frequencies of short-distance trains and fast and slow trains, S i represents the distance from station i to station i+1, C operation ,C stop are the operating cost and the stop cost respectively, and C represents the total train operating cost.

5. The method according to claim 4, characterized in that The total passenger travel time calculation formula in S2 and the total train operating cost calculation formula corresponding to the combination mode are combined to introduce the constraints of departure interval, stop and operation area, and construct a train operation plan optimization model that minimizes passenger travel time and enterprise operating costs, including: Step S3-1: Introduce the constraint condition into the total passenger travel time calculation formula in S2 and the total train operation cost calculation formula corresponding to the combination mode, specifically: M·R≥max O i ,i∈[1,N]∪[N+1,2N] (14) n f +n s +n e =M (15) x -,up -1=2N-x +,down (16) x +,up -1=2N-x -,down (17) Among them, h min ,h max are the minimum departure interval and the maximum departure interval respectively, O i is the number of people on the train from station i to station i+1, O i-1 is the number of people on the train from station i-1 to station i, P v,i is the hourly passenger flow of the line with the starting and ending points at station v and station i respectively, and R is the train capacity; Step S3-2: Combine the total passenger travel time calculation formula in S2, the total train operating cost calculation formula corresponding to the combination mode, the total passenger travel time calculation formula with the constraint conditions introduced, and the total train operating cost calculation formula corresponding to the combination mode with the constraint conditions introduced, to construct the train operation plan optimization model that minimizes the passenger travel time and the enterprise operating cost, specifically: Among them, formula (1) to formula (19) jointly construct the train operation plan optimization model that minimizes passenger travel time and enterprise operating costs.

6. The method according to claim 5, characterized in that The decision variables of the train operation plan optimization model include the train stop plan and the departure frequency. The parameters of the train operation plan optimization model in S3 are initialized, the decision variables of the train operation plan optimization model in S3 are converted into chromosomes for encoding, a two-stage adaptive NSGA algorithm based on chaotic mapping is designed to obtain the global optimal solution of the train operation plan optimization model, and the optimal train stop plan, operating interval, and departure frequency are obtained according to the global optimal solution, including: Step S4-1: Initialize the parameters of all formulas and models in S2 and S3, and divide the algorithm into two stages. In the first stage, the operation interval of the short-distance train is generated by enumeration method; Step S4-2: The second stage is to bring the short-distance train operation interval described in step S4-1 into the train operation plan optimization model described in S3 as known parameters, convert the train stop plan and the departure frequency into chromosomes for encoding, and set the population size POP size and the maximum number of iterations g max ; Step S4-3: Introduce the chaos mapping theory to improve the quality of algorithm population initialization, specifically: x k,g,i =ξ max,i +C k,g ·[0,1]·(ξ max,i -x min,i (22) Among them, Γ k,g is the chaotic mapping parameter, k and g are the indexes of individual and iteration times, K0 is the preset parameter, ξ k,g,i represents chromosome, ξ max,i ,ξ min,i are the maximum and minimum values ​​of the chromosome respectively; Step S4-4: performing a fast non-dominated sort on all the chromosomes, calculating the crowding distance between any two of the chromosomes, and selecting multiple optimal individuals that meet the Pareto optimality condition from all the chromosomes through an elite retention strategy, and determining the optimal values ​​and solution sets corresponding to the multiple optimal individuals; Step S4-5: using the crossover and mutation strategies of the traditional genetic algorithm for the multiple optimal individuals selected in S4-4 to generate a new generation population; Step S4-6: Continue to iterate g times. If the current number of iterations is equal to the maximum number of iterations, output the optimal solution under the current number of iterations and the optimal value and solution set corresponding to the feasible solution of the operation interval in S4-1, and go to step S4-7. If the current number of iterations is less than the maximum number of iterations, return to step S4-4. Step S4-7: Determine whether the operating range of the short-route train in step S4-1 has traversed all feasible solutions. If not, return to step S4-1 and generate a new feasible solution; if so, select the global optimal solution from all feasible solutions generated after step S4-6 through dominance comparison, and obtain the optimal stop plan of the train, the operating range, and the departure frequency based on the global optimal solution.

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