CN111932034A - Regional multi-standard rail transit train operation scheme compiling method and system - Google Patents

Regional multi-standard rail transit train operation scheme compiling method and system Download PDF

Info

Publication number
CN111932034A
CN111932034A CN202010998691.4A CN202010998691A CN111932034A CN 111932034 A CN111932034 A CN 111932034A CN 202010998691 A CN202010998691 A CN 202010998691A CN 111932034 A CN111932034 A CN 111932034A
Authority
CN
China
Prior art keywords
train
rail transit
station
section
passenger
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010998691.4A
Other languages
Chinese (zh)
Other versions
CN111932034B (en
Inventor
王莹
刘岭
刘军
张波
李擎
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
CRSC Research and Design Institute Group Co Ltd
Original Assignee
CRSC Research and Design Institute Group Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by CRSC Research and Design Institute Group Co Ltd filed Critical CRSC Research and Design Institute Group Co Ltd
Priority to CN202010998691.4A priority Critical patent/CN111932034B/en
Publication of CN111932034A publication Critical patent/CN111932034A/en
Application granted granted Critical
Publication of CN111932034B publication Critical patent/CN111932034B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Marketing (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Development Economics (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Educational Administration (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Train Traffic Observation, Control, And Security (AREA)

Abstract

The invention discloses a regional multi-standard rail transit train operation scheme compiling method and a system thereof, wherein the operation scheme compiling method comprises the steps of constructing an objective function taking a passenger congestion coefficient and train operation cost as double targets; determining decision variables and one or more of the following constraints: the method comprises the following steps of passenger travel demand constraint, regional multi-standard rail transit overload rate constraint, train driving frequency range constraint, interval capacity constraint, station capacity constraint and parameter variable constraint. The passenger crowding coefficient and the train running cost are jointly used as dual targets of a running method compiling model to be optimized, and the difference characteristics of regional multi-system rail transit transportation are accurately reflected, so that the running scheme of regional multi-system rail transit is more refined.

Description

Regional multi-standard rail transit train operation scheme compiling method and system
Technical Field
The invention belongs to the field of rails, and particularly relates to a method and a system for compiling a running scheme of a regional multi-standard rail transit train.
Background
In recent years, urban rail transit operation mileage and traffic volume are in a high-speed development stage in China, and the conventional rail transit operation method compilation research mainly comprises operation method compilation based on a mathematical method, operation method compilation based on an alternative selection set and operation method compilation of a station-stopping scheme.
And the Yu-HernChang establishes a multi-target linear programming model optimized by the train operation method, the objective function is the minimization of railway operation cost and passenger total time loss, and a fuzzy mathematical programming method is adopted for solving. The indexes that the model can solve comprise an optimal train running method set (comprising a station stopping scheme), train running frequency, the number of motor train units capable of meeting passenger traffic requirements, and the number of passengers between stations of each scheme (see Yu-Hern Chang, Chung-Hsing Yeh, Ching-Cheng Shen. A multiple objective model for passenger train service development: application to a high-speed train line. transportation science.2000.34 (2). 91-106). Chi-Kang LEE pays more attention to The autonomous selection behavior of passengers for transportation Service, and authors establish a double-layer planning model aiming at The design of a train operation method and apply The double-layer planning model to a certain high-speed Railway system (see Chi-Kang LEE, Wen-jin HSIEH.A Demand Oriented Service planning. Process [ A ]. The World Congress On Rail research.2001.55-89.). The method for driving the passenger train related to the passenger special line is researched by the Schwark, the dual benefits of railway enterprises and passengers are considered in the research, and a model is constructed to optimize the driving method (see Schwark, Dengdong wave, Ri Xinhua, Fangqi root. the method for driving the passenger train related to the passenger special line is researched [ J ]. railway bulletin, 2004, (02), 16-20); besides, the historical peak also constructs an evaluation index system of an operation method (Dengdong wave, Scheak. passenger train operation method evaluation index system [ J ] China railway science, 2006, (03), 106-; the objective function of the underlying planning is to minimize the travel time or cost of passengers, and the assignment of passengers to service networks according to utility functions is described as a nonlinear planning problem (see double-layer planning model and algorithm [ J ] of the schmutn, dungeon, holly.
Foreign railway networks are small in scale, and trains mostly adopt a high-frequency and periodic operation mode. Therefore, the most typical method in the research of foreign railway operation methods is a train operation method alternative set generation method, and the method generates an alternative set of operation methods for a research route according to the information of the shortest path and selects an optimal operation method through a certain research target and constraint. Scholl and Schobel studied the Optimization method of the train driving method for the shortest travel Time and the smallest passenger transfer (see Scholl S. Customer-oriented Line Planning. PHD the. university of Kaiserslauter.2005, 23-56, Scholl Bel A, Scholl S. Line Planning with minor conveying Time [ J ]. 2005. and Schobel A. Scholl S. Line Planning with minor conveying Time. in 5th Workshop on integrating methods and Models for timing of Railways, number 06901 in Stuhl train progress, 2006.).
Aiming at the starting method compilation of the side-weight station-stopping scheme, Qi X and Xiong J provide a train starting method optimization method based on a stopping plan under the condition of a passenger special line, and factors such as a train starting station/terminal station, a route, a train grade, the number of trains to be started, the station-stopping plan and the like are considered in a model. The difference between the operating income and the operating cost is taken as an objective function (see Xin Q, Jian X. Optimization method of passer train bed on stop schedule plant for passer determined line [ C ]. International Conference on availability reading & Knowledge Engineering, 2012.). Yang L, Qi J, Li S and Gao Y establish a multi-target mixed integer linear programming model. The model is intended to minimize the total dwell time and total delay between the actual departure time and the predicted departure time for all trains on the high-speed rail corridor (see Yang L, Qi J, Li S, et al. collagen optimization for train scheduling and train stop planning on high-speed-railway railroads [ J ]. Omega, 2016, 64: 57-76.). Luo Q, Hou Y, Li W, Zhang XF propose an integer planning model for train stop plans. A genetic algorithm is used to solve the model aiming at minimizing the total travel time of passengers (see Luo Q, Hou Y, Li W, et al. Stop plan of express and local train for a regional train transfer line [ J ]. Journal of Advanced transfer, 2018, 2018: 1-11.).
The solution of the existing track traffic driving method mainly takes driving cost and trip cost as solution targets, comfort level of passenger trip is considered in trip cost in part of relevant researches, and punishment coefficients are mostly adopted as part of trip cost calculation. The existing operation method can only be well suitable for the single-system track traffic operation method, but the operation method for integrating the multiple-system track traffic operation in the area lacks of differential portraits. Meanwhile, the congestion degree which represents the most important comfort is different in different traveling processes in the area, and the passenger cannot perceive the congestion in multiple traveling links by a single punishment coefficient.
Therefore, how to provide an implementation method that takes into account the congestion factor is becoming an urgent technical problem to be solved.
Disclosure of Invention
Aiming at the problems, the invention discloses a regional multi-standard rail transit train operation scheme compiling method and a system thereof, wherein the operation scheme compiling method gives consideration to both passenger comfort and operation benefit, optimizes the operation scheme and has universality.
The invention aims to provide a method for compiling a running scheme of a regional multi-standard rail transit train, which comprises the following steps of,
constructing an objective function taking the passenger congestion coefficient and the train running cost as double targets;
determining decision variables and one or more of the following constraints:
the method comprises the following steps of passenger travel demand constraint, regional multi-standard rail transit overload rate constraint, train driving frequency range constraint, interval capacity constraint, station capacity constraint and parameter variable constraint.
Further, the running scheme compiling method further comprises the step of inputting the regional rail transit networked candidate set and the inter-block section passenger flow as basic data, wherein the running scheme compiling method comprises the following steps of,
based on the regional rail transit networked alternative collection, a train set, an interval set and a station set of regional multi-standard rail transit are constructed, wherein,
the train set is represented by Q
Figure 6634DEST_PATH_IMAGE001
Representing the first in a train set
Figure 517250DEST_PATH_IMAGE001
The device is similar to a train in the prior art,
Figure 562566DEST_PATH_IMAGE001
belonging to the group of Q, wherein the train set comprises L elements;
the interval set is represented by E, the element i represents an interval, i belongs to E, and the interval set comprises M elements;
the station set is represented by S, the element j represents a station, j belongs to S, and the station set has N elements.
Further, the constructing of the objective function with the passenger congestion coefficient and the train running cost as the two targets comprises the objective function with the minimum passenger congestion coefficient as the target and the objective function with the minimum train running cost as the target.
Further, the implementation scheme compiling method further comprises the following steps,
dividing the regional multi-standard rail transit into a type 1 rail transit, a type 2 rail transit and a type 3 rail transit;
calculating the congestion coefficient of any type of rail transit in the regional multi-standard rail transit in an interval i and the congestion coefficient of a station j in the regional multi-standard rail transit based on the regional multi-standard rail transit division;
and acquiring an objective function with the minimum passenger congestion coefficient as a target based on the calculated congestion coefficient of any type of rail transit in the regional multi-system rail transit in the section i and the congestion coefficient of the station j in the regional multi-system rail transit.
Further, the calculating of the congestion coefficients of the type 1 rail traffic and the type 2 rail traffic in the regional multi-standard rail traffic in the section i includes,
acquiring the average effective area of the train and the average passenger carrying number of the train in the interval i;
calculating the per-passenger occupied area of the section i based on the average effective area of the train and the average passenger carrying number of the train in the section i;
and acquiring the congestion coefficients of the type 1 rail traffic and the type 2 rail traffic in the section i based on the occupied area of the passenger in the section i.
Further, the calculating of the congestion coefficient of the type 3 rail transit in the regional multi-standard rail transit in the section i includes,
acquiring passenger volume in the interval i and passenger capacity which can be provided by all trains in the interval i;
acquiring the average full load rate of all trains in the interval i based on the passenger flow in the interval i and the passenger capacity which can be provided by all trains in the interval i;
and averaging the average full load rates of all the trains in the interval i to obtain the congestion coefficient of the 3 rd type track traffic in the interval i.
Further, the calculating of the congestion coefficient of the station j in the regional multi-standard rail transit comprises the following steps,
acquiring a difference value of the passenger flow of the section of the adjacent section of the station j and an exchange passenger flow coefficient of the station j;
solving the product of the difference of the passenger flow of the section of the adjacent section of the station j and the exchange passenger flow coefficient of the station j, and averaging the product to each train passing through the station j to obtain the average exchange passenger flow of the train at the station j;
acquiring the effective area of a station j platform;
the ratio of the effective area of the platform of the station j to the average exchange passenger flow of the train at the station j is obtained, and the occupied area of passengers at the platform of the station j is obtained
And acquiring the congestion coefficient of the station j in the regional multi-standard rail transit based on the occupied area of passengers at the station j.
Further, the objective function targeting the minimum passenger congestion coefficient is:
Figure 426617DEST_PATH_IMAGE002
（1）
wherein M is the total number of sections in the regional multi-standard rail transit, N is the total number of stations in the regional multi-standard rail transit, i represents an section, j represents a station,
Figure 568885DEST_PATH_IMAGE003
indicates the congestion coefficient of the section i,
Figure 860189DEST_PATH_IMAGE004
the cross-sectional passenger flow volume of the section i is shown,
Figure 392802DEST_PATH_IMAGE005
represents the average running time of the train in the section i,
Figure 185177DEST_PATH_IMAGE006
a congestion coefficient indicating a station j,
Figure 322898DEST_PATH_IMAGE007
representing the average exchange passenger flow for each train in station j,
Figure 644158DEST_PATH_IMAGE008
represents the average stop time of the train at station j;
the objective function aiming at the minimum train running cost is as follows:
Figure 867328DEST_PATH_IMAGE009
（2）
wherein,
Figure 869920DEST_PATH_IMAGE001
the first in the multi-standard rail transit of the presentation area
Figure 721201DEST_PATH_IMAGE001
The device is similar to a train in the prior art,
Figure 416624DEST_PATH_IMAGE010
representing trains
Figure 127091DEST_PATH_IMAGE001
The running frequency of the mobile phone is set,
Figure 730111DEST_PATH_IMAGE011
representing trains
Figure 842424DEST_PATH_IMAGE001
The cost of implementation of (c).
Further, the congestion coefficient of the section i
Figure 771065DEST_PATH_IMAGE012
Satisfies the following conditions:
Figure 968828DEST_PATH_IMAGE013
（3）
wherein,
Figure 313222DEST_PATH_IMAGE014
the congestion coefficient of the k-th type rail transit in the regional multi-standard rail transit in the section i is represented,
Figure 139096DEST_PATH_IMAGE015
the number of the variables is 0, 1,
Figure 379584DEST_PATH_IMAGE016
indicating that the section i belongs to the kth type of track traffic,
Figure 595802DEST_PATH_IMAGE017
and the section i does not belong to the kth type track traffic, and k is 1, 2 and 3.
Further, the congestion coefficients of the type 1 rail traffic and the type 2 rail traffic in the section i satisfy:
Figure 71783DEST_PATH_IMAGE018
（4）
wherein,
Figure 893108DEST_PATH_IMAGE019
the passenger flow passenger per capita occupation area of the section i is expressed in m2The number of people/person is greater than the number of people,
Figure 163552DEST_PATH_IMAGE020
，
Figure 70329DEST_PATH_IMAGE021
a parameter representing a linear function of the section congestion coefficient; and the passenger flow in the interval i occupies the area per capita
Figure 84421DEST_PATH_IMAGE022
Is the ratio of the average effective area of the train to the average number of passengers of the train in the section i:
Figure 822570DEST_PATH_IMAGE023
（5）
wherein,
Figure 404861DEST_PATH_IMAGE024
represents the average effective area of the train in the section i and has the unit of m2；
Figure 657988DEST_PATH_IMAGE025
Represents the average number of passengers of the train in the section i, and satisfies the following conditions:
Figure 351137DEST_PATH_IMAGE026
（6）
wherein L represents the number of elements in the train set,
Figure 678213DEST_PATH_IMAGE027
representing trains
Figure 556039DEST_PATH_IMAGE001
The running frequency of the mobile phone is set,
Figure 171829DEST_PATH_IMAGE028
the variables are 0 and 1, and the variables are,
Figure 793303DEST_PATH_IMAGE029
representing trains
Figure 240465DEST_PATH_IMAGE001
The operating interval includes an interval i in which,
Figure 898979DEST_PATH_IMAGE030
representing trains
Figure 126698DEST_PATH_IMAGE001
The operating interval does not include the interval i,
Figure 161650DEST_PATH_IMAGE031
representing the section passenger flow of the interval i;
the congestion coefficient of the 3 rd type rail traffic in the section i satisfies the following conditions:
Figure 728898DEST_PATH_IMAGE032
（7）
wherein,
Figure 682947DEST_PATH_IMAGE033
representing the average full load rate of all trains in the interval i; and average full load rate of all trains in section i
Figure 538908DEST_PATH_IMAGE033
For the ratio of the passenger volume in section i to the passenger capacity that can be provided by all trains in section i:
Figure 236605DEST_PATH_IMAGE034
（8）
wherein L represents the number of elements in the train set,
Figure 596043DEST_PATH_IMAGE027
representing trains
Figure 658677DEST_PATH_IMAGE001
The running frequency of the mobile phone is set,
Figure 860988DEST_PATH_IMAGE028
the variables are 0 and 1, and the variables are,
Figure 237742DEST_PATH_IMAGE035
representing trains
Figure 514003DEST_PATH_IMAGE001
The operating interval includes an interval i in which,
Figure 75434DEST_PATH_IMAGE036
representing trains
Figure 374829DEST_PATH_IMAGE001
The operating interval does not include the interval i,
Figure 352012DEST_PATH_IMAGE037
representing trains
Figure 810675DEST_PATH_IMAGE001
The order of (1);
the congestion coefficient of a station j in the regional multi-standard rail transit meets the following conditions:
Figure 152795DEST_PATH_IMAGE038
（9）
wherein,
Figure 329698DEST_PATH_IMAGE039
the passenger's per-capita area, m, of the station platform j2The number of people/person is greater than the number of people,
Figure 844993DEST_PATH_IMAGE040
，
Figure 33529DEST_PATH_IMAGE041
parameters representing a linear function of the station congestion coefficient; and the passenger of station platform j occupies the area
Figure 936763DEST_PATH_IMAGE039
The ratio of the effective area of the platform at station j to the average exchange passenger flow of the train at station j is:
Figure 273066DEST_PATH_IMAGE042
（10）
wherein,
Figure 529735DEST_PATH_IMAGE043
the effective area of the platform of the station j is expressed in m2；
Figure 635095DEST_PATH_IMAGE044
The average exchange passenger flow quantity of the trains at the station j is represented, the average exchange passenger flow quantity of the trains at the station j is obtained by multiplying the passenger flow quantity difference of the section of the adjacent section of the station j by the exchange passenger flow coefficient of the station j and averaging the passenger flow quantity difference to each train passing through the station, and the average exchange passenger flow quantity of the trains at the station j meets the following conditions:
Figure 720949DEST_PATH_IMAGE045
（11）
wherein,
Figure 216652DEST_PATH_IMAGE046
representing the exchange passenger flow coefficient of the station j, M is the number of elements in the interval set, L is the number of elements in the train set,
Figure 339329DEST_PATH_IMAGE047
the cross-sectional passenger flow volume of the section i is shown,
Figure 627091DEST_PATH_IMAGE048
represents the variables of 0, 1,
Figure 747493DEST_PATH_IMAGE049
the starting station of the section i is denoted by j,
Figure 120706DEST_PATH_IMAGE050
indicating that the starting station of section i is not j,
Figure 781494DEST_PATH_IMAGE051
represents the variables of 0, 1,
Figure 799129DEST_PATH_IMAGE052
the end station of the section i is denoted as j,
Figure 215067DEST_PATH_IMAGE053
indicating that the end station of the section i is not j,
Figure 747679DEST_PATH_IMAGE054
represents the variables of 0, 1,
Figure 415421DEST_PATH_IMAGE055
representing trains
Figure 146617DEST_PATH_IMAGE001
The travel route of (a) includes a station j,
Figure 874401DEST_PATH_IMAGE056
indicating that the travel path of train l does not include station j,
Figure 894310DEST_PATH_IMAGE027
representing trains
Figure 959218DEST_PATH_IMAGE001
The running frequency of (c).
Further, the passenger travel demand constraint is as follows:
Figure 748182DEST_PATH_IMAGE057
（12）
wherein,
Figure 646868DEST_PATH_IMAGE027
representing trains
Figure 950810DEST_PATH_IMAGE001
The running frequency of the mobile phone is set,
Figure 757092DEST_PATH_IMAGE058
the variables are 0 and 1, and the variables are,
Figure 869405DEST_PATH_IMAGE059
representing trains
Figure 798047DEST_PATH_IMAGE001
The operating interval includes an interval i in which,
Figure 730231DEST_PATH_IMAGE060
indicates that the train l operation section does not include the section i,
Figure 402520DEST_PATH_IMAGE037
representing trains
Figure 166077DEST_PATH_IMAGE001
The order of the person(s) to be assigned,
Figure 875407DEST_PATH_IMAGE061
representing trains
Figure 685100DEST_PATH_IMAGE001
The maximum rate of overload that is allowed to occur,
Figure 833185DEST_PATH_IMAGE047
and represents the cross-sectional passenger flow volume of the section i.
Further, the regional multi-standard rail transit overload rate constraint is as follows:
Figure 388931DEST_PATH_IMAGE062
（13）
wherein,
Figure 924955DEST_PATH_IMAGE063
represents the rail transit to which the train belongs,
Figure 628468DEST_PATH_IMAGE064
。
further, the train operation frequency range constraint is as follows:
Figure 252348DEST_PATH_IMAGE065
（14）
wherein,
Figure 318393DEST_PATH_IMAGE027
representing trains
Figure 431842DEST_PATH_IMAGE001
The running frequency of the mobile phone is set,
Figure 560335DEST_PATH_IMAGE066
representing trains
Figure 378119DEST_PATH_IMAGE001
The minimum running frequency at which the running can be done,
Figure 970774DEST_PATH_IMAGE067
representing trains
Figure 723966DEST_PATH_IMAGE001
Maximum run frequency at which a run can be run.
Further, the interval capability constraint is:
Figure 464389DEST_PATH_IMAGE068
（15）
wherein,
Figure 695650DEST_PATH_IMAGE027
representing trains
Figure 142812DEST_PATH_IMAGE001
The running frequency of the mobile phone is set,
Figure 925960DEST_PATH_IMAGE028
the variables are 0 and 1, and the variables are,
Figure 91363DEST_PATH_IMAGE035
representing trains
Figure 860735DEST_PATH_IMAGE001
The operating interval includes an interval i in which,
Figure 490300DEST_PATH_IMAGE069
representing trains
Figure 585295DEST_PATH_IMAGE001
The operating interval does not include the interval i,
Figure 237993DEST_PATH_IMAGE037
representing trains
Figure 670111DEST_PATH_IMAGE001
The order of the person(s) to be assigned,
Figure 91865DEST_PATH_IMAGE061
representing trains
Figure 92183DEST_PATH_IMAGE001
The maximum rate of overload that is allowed to occur,
Figure 966598DEST_PATH_IMAGE047
the cross-sectional passenger flow volume of the section i is shown,
Figure 733565DEST_PATH_IMAGE070
representing the maximum transport capacity of the interval i.
Further, the station capacity constraint is as follows:
Figure 947509DEST_PATH_IMAGE071
（16）
wherein,
Figure 181044DEST_PATH_IMAGE027
the frequency of operation of the train l is indicated,
Figure 605072DEST_PATH_IMAGE072
the number of the variables is 0, 1,
Figure 519939DEST_PATH_IMAGE073
representing trains
Figure 916285DEST_PATH_IMAGE001
The travel route of (a) includes a station j,
Figure 383038DEST_PATH_IMAGE074
representing trains
Figure 435308DEST_PATH_IMAGE001
Does not include the station j in the travel route,
Figure 950603DEST_PATH_IMAGE075
representing the maximum transport capacity of station j.
Further, the parameter variable constraint is:
Figure 529352DEST_PATH_IMAGE076
（17）
and N is the total number of stations in the regional multi-standard rail transit.
Further, the decision variable is
Figure 42373DEST_PATH_IMAGE001
Frequency of operation of train-like vehicle
Figure 706572DEST_PATH_IMAGE027
Wherein
Figure 25558DEST_PATH_IMAGE027
the value range of (A) is the whole natural number,
Figure 68601DEST_PATH_IMAGE077
and if not, the type I train is started in the research time period.
Further, the implementation scheme compiling method further comprises the following steps,
respectively solving an objective function with the minimum passenger congestion coefficient as a target and an objective function with the minimum train running cost as a target to respectively obtain corresponding expected values
Figure 814840DEST_PATH_IMAGE078
、
Figure 700756DEST_PATH_IMAGE079
；
Optimizing an objective function structure, and acquiring a dual-objective mathematical model of comprehensive driving cost and congestion coefficient based on an objective function taking the minimum passenger congestion coefficient as a target and an objective function taking the minimum train driving cost as a target:
Figure 26695DEST_PATH_IMAGE080
（18）
wherein p is the p-th priority, q is the q-th objective function,
Figure 48878DEST_PATH_IMAGE081
a priority factor representing the p-th priority,
Figure 966018DEST_PATH_IMAGE082
Figure 480176DEST_PATH_IMAGE083
weight coefficients representing positive and negative bias variables of different objective functions in the same priority,
Figure 203282DEST_PATH_IMAGE084
、
Figure 17654DEST_PATH_IMAGE085
a target excess value and a target deficiency value, which are respectively compared with corresponding expected values by a target function with the minimum passenger congestion coefficient as a target and a target function with the minimum train running cost as a target;
giving a priority factor and a weight coefficient to the dual-target mathematical model, and optimizing the dual-target mathematical model as follows:
Figure 574537DEST_PATH_IMAGE086
（19）
wherein,
Figure 435046DEST_PATH_IMAGE087
an objective function and an expectation value for minimizing the passenger congestion coefficient
Figure 571629DEST_PATH_IMAGE088
Target deficit value of comparison;
Figure 833983DEST_PATH_IMAGE089
objective function and expected value with minimum train running cost as target
Figure 358505DEST_PATH_IMAGE090
Target deficit value of comparison;
building a set of optimization objectives
Figure 316097DEST_PATH_IMAGE091
The optimization target set respectively meets an objective function taking the minimum passenger congestion coefficient as a target and an objective function taking the minimum train running cost as a target:
Figure 646584DEST_PATH_IMAGE092
（20）
Figure 435549DEST_PATH_IMAGE093
（21）
wherein,
Figure 803076DEST_PATH_IMAGE094
an objective function and an expectation value for minimizing the passenger congestion coefficient
Figure 638177DEST_PATH_IMAGE078
Target excess value of comparison;
Figure 444459DEST_PATH_IMAGE095
objective function and expected value with minimum train running cost as target
Figure 291192DEST_PATH_IMAGE079
Target excess value of comparison;
Figure 219834DEST_PATH_IMAGE096
、
Figure 152018DEST_PATH_IMAGE097
respectively optimizing the running cost and the congestion coefficient of the double-target mathematical model;
and taking the formulas (3) - (11), (12) - (17) and (19) - (21) as the constraints of the optimized dual-target mathematical model, and solving the optimal solution of the optimized dual-target mathematical model by adopting Global Sever in Lingo.
Further, the starting compilation system comprises,
the construction module is used for constructing an objective function taking the passenger congestion coefficient and the train running cost as double targets;
a determination module for determining a decision variable and one or more of the following constraints:
the method comprises the following steps of passenger travel demand constraint, regional multi-standard rail transit overload rate constraint, train driving frequency range constraint, interval capacity constraint, station capacity constraint and parameter variable constraint.
The method for compiling the regional multi-standard rail transit train running scheme comprehensively considers the benefits of passengers and operators, optimizes the passenger crowding coefficient and the train running cost which are used as double targets of a running method compiling model, accurately reflects the difference characteristics of regional multi-standard rail transit transportation, and enables the running scheme of the regional multi-standard rail transit to be more refined.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
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 introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 shows a flow chart of a method for compiling a regional multi-standard rail transit train operation scheme in an embodiment of the invention;
FIG. 2 is a diagram illustrating a relationship between a passenger's per-capita occupancy area and a passenger congestion factor according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a passenger flow difference between immediate zones in an embodiment of the present invention
Figure 761991DEST_PATH_IMAGE098
Schematic analysis of (a);
fig. 4 shows a line schematic diagram of a regional multi-standard rail transit formed by a Chongqing subway No. 5 line south section, a river jumper and a Yukun high-speed Chongqing section in the embodiment of the invention;
fig. 5 is a schematic diagram illustrating a regional multi-standard rail transit train operation scheme compilation system according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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.
As shown in fig. 1, the embodiment of the present invention introduces a method for compiling a regional multi-standard rail transit train operation scheme, where the method for compiling the operation scheme includes: firstly, constructing an objective function taking a passenger congestion coefficient and train running cost as double targets; then, decision variables and one or more of the following constraints are determined: the method comprises the following steps of passenger travel demand constraint, regional multi-standard rail transit overload rate constraint, train driving frequency range constraint, interval capacity constraint, station capacity constraint and parameter variable constraint. The congestion coefficient is quantized according to the difference of different rail transit standards in intervals, the passenger congestion degree and the rail transit standard characteristics, the passenger congestion coefficient is used as a main factor influencing the rail transit service level, and the difference characteristics of regional multi-standard rail transit are accurately grasped, so that the benefits of passengers and an operator are comprehensively considered, the passenger congestion coefficient and the train operation cost are jointly used as two targets of a model establishment method for optimizing, the difference characteristics of regional multi-standard rail transit transportation are accurately reflected, and the operation scheme of regional multi-standard rail transit is refined.
In this embodiment, the running scheme compiling method further includes inputting the regional rail transit networked candidate set and the inter-block section passenger flow volume as basic data. Specifically, in the embodiment of the present invention, the elements of the candidate set include: train origin-destination, train path, train speed grade, train consist, etc. And constructing a train set, an interval set and a station set of the regional multi-standard rail transit based on the regional rail transit networked alternative collection. Specifically, each element in the train set represents a class 1 train, each class of train includes a train path (an originating station, a terminating station and all intermediate stations), all sections where the train runs, and a transit time at each station and a running time of each section, and the train set can be represented by Q, wherein a class i train in the train set is represented by an element L, and the set has L elements in total. The interval set is represented by E, wherein an element i represents an interval, i belongs to E, and the set has M elements; the station set is represented by S, the element j represents a station, j belongs to S, and the set has N elements. Preferably, one section in the section set has one type of rail transit corresponding to the section set, and the station in the station set has one type of rail transit corresponding to the section set.
In this embodiment, the constructing of the objective function with the passenger congestion coefficient and the train operation cost as the two objectives includes an objective function with the minimum passenger congestion coefficient as the objective and an objective function with the minimum train operation cost as the objective.
In this embodiment, the running scheme compilation method further includes obtaining an objective function targeting the minimum passenger congestion coefficient, specifically, first, dividing the regional multi-system rail transit into a type 1 rail transit, a type 2 rail transit, and a type 3 rail transit; the regional multi-standard rail transit comprises subways, light rails, trams, urban (suburban) railways, inter-city railways, high-speed railways, ordinary-speed railways and the like, and can be divided into 3 types according to transportation organization characteristics, specifically, as shown in table 1:
TABLE 1 Rail traffic classifications and characteristics for each System
Figure DEST_PATH_IMAGE100A
The class 1 rail transit provides high-frequency transportation service, the train running speed is low, the speed per hour is generally not higher than 100km/h (kilometer/hour), passengers do not use a train schedule as guidance when selecting the class of rail transit to go out, and follow the going-to-go travel rule, the class mainly comprises rail transit systems such as subways, light rails and tramcars, the trains are allowed to overtake, and the overtake in partial sections of partial cities even exceeds 20% according to actual operation experience.
The class 2 rail transit mainly comprises urban (suburban) railways and ordinary speed railways, the train running frequency is high, the designed speed is lower than 200km/h, passengers travel according to a train schedule, partial overtaking conditions of the trains are allowed to exist, and the overtaking conditions can be strictly controlled according to a ticketing link.
The 3 rd type rail transit mainly refers to intercity railways and high-speed railways, the train running frequency is high, the speed per hour of the train can reach more than 200km/h, passengers are strictly scheduled to run according to a train schedule, and the condition of overtaking is generally not allowed.
Then, calculating the congestion coefficient of any type of rail transit in the regional multi-standard rail transit in the interval i and the congestion coefficient of the station j in the regional multi-standard rail transit based on the regional multi-standard rail transit division; the passenger crowding coefficient comprises an interval crowding coefficient and a station crowding coefficient, and therefore certain difference exists in crowding perception of different types of rail transit in the traveling process of passengers. Specifically, for trains in the 1 st type rail transit and the 2 nd type rail transit, overtaking is allowed, passengers have strong correlation between the congestion perception in the train and the passenger flow density, and the congestion perception is reflected by the passenger flow density, so that the classification threshold value of uncongested train and congestion in the train is 3.6 persons/m2The threshold for classification of congestion to heavy congestion is 6.2 persons/m2And further, converting the grading threshold value into a passenger per-person occupied area threshold value, wherein 1/3.6=0.278 and 1/6.2=0.161, so that the threshold values of the passenger per-person occupied areas which are not crowded and extremely crowded in the vehicle are respectively 0.278m20.161m and/human2Then, the congestion coefficient can be set as a piecewise function according to the passenger-average occupied area, and as shown in fig. 2, the passenger congestion coefficient satisfies: the average occupied area of passengers is more than or equal to 0.278m2When the passenger is in person, the passenger crowding coefficient is 1, and the average occupied area of the passengers is less than or equal to 0.161m2When people are in person, the crowding coefficient is 0, and passengers all have peopleAnd if the occupied area is between the two, expressing by adopting a linear relation, so that the congestion coefficients of the type 1 rail traffic and the type 2 rail traffic in the section i meet the following conditions:
Figure 536717DEST_PATH_IMAGE101
(1)
wherein,
Figure 511626DEST_PATH_IMAGE102
the passenger-average occupied area of the section i is expressed in m2The number of people/person is greater than the number of people,
Figure 993423DEST_PATH_IMAGE103
，
Figure 469404DEST_PATH_IMAGE104
and parameters representing a linear function of the section congestion coefficients. It should be noted that, in the following description,
Figure 290729DEST_PATH_IMAGE105
and (1) (2) denotes k =1 or k = 2.
More specifically, the passenger flow passenger per capita occupation area of the section i
Figure 295594DEST_PATH_IMAGE106
Is the ratio of the average effective area of the train to the average number of passengers of the train in the section i:
Figure 202370DEST_PATH_IMAGE107
(2)
wherein,
Figure 482042DEST_PATH_IMAGE108
represents the average effective area of the train in the section i and has the unit of m2；
Figure 954612DEST_PATH_IMAGE109
The average number of passengers of the train in the section i, and the average load of the train in the section iThe number of passengers
Figure 271324DEST_PATH_IMAGE109
Satisfies the following conditions:
Figure 790030DEST_PATH_IMAGE110
(3)
wherein L represents the number of elements in the train set,
Figure 279917DEST_PATH_IMAGE111
representing trains
Figure 810255DEST_PATH_IMAGE112
The running frequency of the mobile phone is set,
Figure 688081DEST_PATH_IMAGE113
the variables are 0 and 1, and the variables are,
Figure 100608DEST_PATH_IMAGE114
representing trains
Figure 331869DEST_PATH_IMAGE112
The operating interval includes an interval i in which,
Figure 106927DEST_PATH_IMAGE115
representing trains
Figure 827759DEST_PATH_IMAGE112
The operating interval does not include the interval i,
Figure 930844DEST_PATH_IMAGE116
and represents the cross-sectional passenger flow volume of the section i.
In this embodiment, the train in the 3 rd type rail transit is not allowed to exceed the number, which means that passengers all have seats, the congestion coefficient of the passengers going out is significantly lower than that of the 1 st and 2 nd type rail transit, and the range of the congestion coefficient of the seats in the 3 rd type rail transit is defined as 0-0.5, that is, in the embodiment of the present invention, the maximum congestion coefficient of the 3 rd type rail transit is set to 0.5, the minimum congestion coefficient is set to 0, and the congestion coefficient is in direct proportion to the average full load rate of the train, so that the congestion coefficient of the 3 rd type rail transit in the section i satisfies:
Figure 90430DEST_PATH_IMAGE117
(4)
wherein,
Figure 595360DEST_PATH_IMAGE118
representing the average loading rate of all trains in interval i. Average full load rate of all trains in interval i
Figure 487093DEST_PATH_IMAGE118
For the ratio of the passenger volume in section i to the passenger capacity that can be provided by all trains in section i:
Figure 936529DEST_PATH_IMAGE119
(5)
wherein L represents the number of elements in the train set,
Figure 775172DEST_PATH_IMAGE111
representing trains
Figure 196926DEST_PATH_IMAGE112
The running frequency of the mobile phone is set,
Figure 321877DEST_PATH_IMAGE113
the variables are 0 and 1, and the variables are,
Figure 196292DEST_PATH_IMAGE120
representing trains
Figure 573047DEST_PATH_IMAGE112
The operating interval includes an interval i in which,
Figure 911624DEST_PATH_IMAGE121
representing trains
Figure 348422DEST_PATH_IMAGE112
Operating areaThe interval between the first and second frames does not include the interval i,
Figure 710133DEST_PATH_IMAGE122
representing trains
Figure 749633DEST_PATH_IMAGE112
The member of (1).
In the embodiment, the station is most obviously crowded with the platform, the crowding perceptions of passengers at different rail transit platforms are basically similar, and the occupied area of each passenger reflects the service level, so that the crowding coefficients are all represented by the occupied area of each passenger at the platform. The congestion coefficient of the station can be set as a piecewise function according to the passenger average occupancy area of the platform, the congestion coefficient corresponding to the passenger average occupancy area of the platform at the service level E is defined as 1, and the congestion coefficient corresponding to the passenger average occupancy area of the platform at the service level A is defined as 0, namely the passenger average occupancy area of the platform is greater than or equal to 3.247m2When people are in the platform, the crowding coefficient is 0, and the occupied area of passengers in the platform is less than or equal to 0.464m2When people are in use, the congestion coefficient is 1, and if the congestion coefficient is between the two, the linear relation is adopted for expression, so that the congestion coefficient of a station j in the regional multi-standard rail transit meets the following conditions:
Figure 145979DEST_PATH_IMAGE123
(6)
wherein,
Figure 753678DEST_PATH_IMAGE124
the passenger occupying area of the station platform j is expressed in m2The number of people/person is greater than the number of people,
Figure 665002DEST_PATH_IMAGE125
，
Figure 383560DEST_PATH_IMAGE126
and parameters representing a linear function of the station congestion coefficient.
Further, the passenger occupation area of the station platform j is
Figure 962309DEST_PATH_IMAGE124
The ratio of the effective area of the platform at station j to the average exchange passenger flow of the train at station j is:
Figure 537646DEST_PATH_IMAGE127
(7)
wherein,
Figure 811633DEST_PATH_IMAGE128
the effective area of the platform of the station j is expressed in m2；
Figure 192936DEST_PATH_IMAGE129
Representing the average number of exchanged traffic for the train at station j. The average exchange passenger flow of the train at the station j is that the difference value of the passenger flow of the section of the adjacent section of the station j is multiplied by the exchange passenger flow coefficient of the station j, and then the average exchange passenger flow of the train at the station j is averaged to each train passing through the station, so that the average exchange passenger flow of the train at the station j meets the following requirements:
Figure 501557DEST_PATH_IMAGE130
(8)
wherein,
Figure 575693DEST_PATH_IMAGE131
representing the exchange passenger flow coefficient of the station j, M is the number of elements in the interval set, L is the number of elements in the train set,
Figure 602554DEST_PATH_IMAGE132
the cross-sectional passenger flow volume of the section i is shown,
Figure 459652DEST_PATH_IMAGE133
represents the variables of 0, 1,
Figure 216255DEST_PATH_IMAGE134
the starting station of the section i is denoted by j,
Figure 133396DEST_PATH_IMAGE135
indicating that the starting station of section i is not j,
Figure 506608DEST_PATH_IMAGE136
represents the variables of 0, 1,
Figure 370659DEST_PATH_IMAGE137
the end station of the section i is denoted as j,
Figure 512927DEST_PATH_IMAGE138
indicating that the end station of the section i is not j,
Figure 804232DEST_PATH_IMAGE139
represents the variables of 0, 1,
Figure 336844DEST_PATH_IMAGE140
representing trains
Figure 129220DEST_PATH_IMAGE112
The travel route of (a) includes a station j,
Figure 1361DEST_PATH_IMAGE141
representing trains
Figure 588200DEST_PATH_IMAGE112
Does not include the station j in the travel route,
Figure 608108DEST_PATH_IMAGE111
representing trains
Figure 813962DEST_PATH_IMAGE112
The running frequency of (c).
In the present embodiment, as shown in fig. 3, the passenger flow volume exchanged at station j in a certain period of time
Figure 602926DEST_PATH_IMAGE142
When the temperature of the water is higher than the set temperature,
Figure 95087DEST_PATH_IMAGE142
difference in passenger flow through immediate vicinity
Figure 805554DEST_PATH_IMAGE143
In connection with, among others,
Figure 611836DEST_PATH_IMAGE143
expressed as:
Figure 317624DEST_PATH_IMAGE144
（9）
wherein M is the number of elements in the interval set, L is the number of elements in the train set,
Figure 387211DEST_PATH_IMAGE132
the cross-sectional passenger flow volume of the section i is shown,
Figure 116133DEST_PATH_IMAGE145
represents the variables of 0, 1,
Figure 788423DEST_PATH_IMAGE134
the starting station of the section i is denoted by j,
Figure 286400DEST_PATH_IMAGE135
indicating that the starting station of section i is not j,
Figure 526889DEST_PATH_IMAGE146
represents the variables of 0, 1,
Figure 71003DEST_PATH_IMAGE137
the end station of the section i is denoted as j,
Figure 156770DEST_PATH_IMAGE138
the end station indicating section i is not j.
Then, based on the calculated congestion coefficient of any one of the regional multi-system rail traffics in the section i and the congestion coefficient of the station j in the regional multi-system rail traffic, an objective function with the minimum passenger congestion coefficient as a target is obtained, specifically, the objective function with the minimum passenger congestion coefficient as a target is as follows:
Figure 774833DEST_PATH_IMAGE002
(10)
wherein M is the total number of sections in the regional multi-standard rail transit, N is the total number of stations in the regional multi-standard rail transit, i represents an section, j represents a station,
Figure 310857DEST_PATH_IMAGE147
indicates the congestion coefficient of the section i,
Figure 952054DEST_PATH_IMAGE148
the cross-sectional passenger flow volume of the section i is shown,
Figure 903829DEST_PATH_IMAGE149
represents the average running time of the train in the section i,
Figure 704295DEST_PATH_IMAGE150
a congestion coefficient indicating a station j,
Figure 21007DEST_PATH_IMAGE151
representing the average exchange passenger flow for each train in station j,
Figure 211817DEST_PATH_IMAGE152
representing the average stop time of the train at station j.
Finally, the minimized passenger congestion coefficient can be obtained based on the objective function of the minimized passenger congestion coefficient.
In this embodiment, the minimizing the congestion coefficient of the regional multi-system rail transit passengers further includes obtaining the congestion coefficient of the section i in the regional multi-system rail transit based on the congestion coefficient of any type of rail transit in the regional multi-system rail transit in the section i
Figure 29600DEST_PATH_IMAGE147
Satisfies the following conditions:
Figure 559939DEST_PATH_IMAGE153
(11)
wherein,
Figure 844289DEST_PATH_IMAGE154
the congestion coefficient of the k-th type rail transit in the regional multi-standard rail transit in the section is represented,
Figure 850292DEST_PATH_IMAGE155
the number of the variables is 0, 1,
Figure 81553DEST_PATH_IMAGE156
indicating that the section i belongs to the kth type of track traffic,
Figure 528715DEST_PATH_IMAGE157
and the section i does not belong to the kth type track traffic, and k is 1, 2 and 3.
The method comprises the steps of respectively obtaining congestion coefficients of the section i aiming at different types of rail transit, comprehensively considering the difference of congestion perception of passengers on different types of rail transit in the traveling process, and finally obtaining the congestion coefficients of any section in the regional multi-system rail transit, so that the calculation of the regional multi-system rail transit congestion coefficients is more universal.
In this embodiment, when the section congestion coefficient and the station congestion coefficient are calculated, the passenger flow of the section is evenly distributed to each train, instead of accurately matching the passenger flow of the section to each train.
In this embodiment, an objective function targeting the minimum train operation cost is as follows:
Figure 577442DEST_PATH_IMAGE158
(12)
wherein,
Figure 414948DEST_PATH_IMAGE112
the first in the multi-standard rail transit of the presentation area
Figure 574534DEST_PATH_IMAGE112
The device is similar to a train in the prior art,
Figure 141782DEST_PATH_IMAGE159
representing trains
Figure 971197DEST_PATH_IMAGE112
The running frequency of the mobile phone is set,
Figure 358316DEST_PATH_IMAGE160
representing trains
Figure 56014DEST_PATH_IMAGE112
The cost of implementation of (c).
In this embodiment, the decision variable is
Figure 681030DEST_PATH_IMAGE112
Frequency of operation of train-like vehicle
Figure 743664DEST_PATH_IMAGE159
Wherein
Figure 692115DEST_PATH_IMAGE159
the value range of (A) is the whole natural number,
Figure 865607DEST_PATH_IMAGE161
when the train is not in operation, otherwise, the train is in operation
Figure 345130DEST_PATH_IMAGE112
The quasi-train is driven during the study period.
In this embodiment, the passenger travel demand constraint is:
Figure 640982DEST_PATH_IMAGE162
(13)
wherein,
Figure 205956DEST_PATH_IMAGE111
representing trains
Figure 183139DEST_PATH_IMAGE112
The running frequency of the mobile phone is set,
Figure 376223DEST_PATH_IMAGE163
the variables are 0 and 1, and the variables are,
Figure 983922DEST_PATH_IMAGE164
representing trains
Figure 160825DEST_PATH_IMAGE112
The operating interval includes an interval i in which,
Figure 941700DEST_PATH_IMAGE165
representing trains
Figure 864656DEST_PATH_IMAGE112
The operating interval does not include the interval i,
Figure 439994DEST_PATH_IMAGE122
representing trains
Figure 104194DEST_PATH_IMAGE112
The order of the person(s) to be assigned,
Figure 626442DEST_PATH_IMAGE166
representing trains
Figure 59697DEST_PATH_IMAGE112
The maximum rate of overload that is allowed to occur,
Figure 9199DEST_PATH_IMAGE132
and represents the cross-sectional passenger flow volume of the section i.
Introducing trains in passenger travel demand constraints
Figure 160694DEST_PATH_IMAGE112
The allowable maximum overload rate fully considers the rail transit with various different standards in the area, and the large difference exists between the ratio of the possibility of the overtaking and the ratio of the overtaking of each type of rail transit train, so that the maximum overload rate is ensuredThe running scheme compiling method is more in line with the regional multi-standard rail traffic characteristics, and the accuracy is higher.
The regional multi-standard rail transit overload rate constraint is as follows:
Figure 221054DEST_PATH_IMAGE167
(14)
wherein,
Figure 243237DEST_PATH_IMAGE168
represents the rail transit to which the train belongs,
Figure 629219DEST_PATH_IMAGE169
。
the train operation frequency range constraint is as follows:
Figure 674535DEST_PATH_IMAGE170
(15)
wherein,
Figure 397640DEST_PATH_IMAGE111
representing trains
Figure 680854DEST_PATH_IMAGE112
The running frequency of the mobile phone is set,
Figure 768896DEST_PATH_IMAGE171
representing trains
Figure 363825DEST_PATH_IMAGE112
The minimum running frequency at which the running can be done,
Figure 828305DEST_PATH_IMAGE172
representing trains
Figure 966025DEST_PATH_IMAGE112
Maximum run frequency at which a run can be run.
The interval capability constraint is:
Figure 287285DEST_PATH_IMAGE173
(16)
wherein,
Figure 307194DEST_PATH_IMAGE111
representing trains
Figure 247468DEST_PATH_IMAGE112
The running frequency of the mobile phone is set,
Figure 364328DEST_PATH_IMAGE113
the variables are 0 and 1, and the variables are,
Figure 59752DEST_PATH_IMAGE174
representing trains
Figure 504640DEST_PATH_IMAGE112
The operating interval includes an interval i in which,
Figure 45342DEST_PATH_IMAGE175
indicates that the train l operation section does not include the section i,
Figure 282289DEST_PATH_IMAGE176
a member of the train l is shown,
Figure 820717DEST_PATH_IMAGE177
representing trains
Figure 143114DEST_PATH_IMAGE112
The maximum rate of overload that is allowed to occur,
Figure 690770DEST_PATH_IMAGE132
the cross-sectional passenger flow volume of the section i is shown,
Figure 454327DEST_PATH_IMAGE178
representing the maximum transport capacity of the interval i.
The station capacity constraint is as follows:
Figure 553870DEST_PATH_IMAGE179
(17)
wherein,
Figure 973350DEST_PATH_IMAGE111
representing trains
Figure 449331DEST_PATH_IMAGE112
The running frequency of the mobile phone is set,
Figure 270656DEST_PATH_IMAGE180
the number of the variables is 0, 1,
Figure 806680DEST_PATH_IMAGE181
representing trains
Figure 713456DEST_PATH_IMAGE112
The travel route of (a) includes a station j,
Figure 399652DEST_PATH_IMAGE182
representing trains
Figure 200118DEST_PATH_IMAGE112
Does not include the station j in the travel route,
Figure 782409DEST_PATH_IMAGE183
representing the maximum transport capacity of station j.
The parameter variable constraints are:
Figure 973219DEST_PATH_IMAGE184
(18)
wherein,
Figure 525423DEST_PATH_IMAGE111
representing trains
Figure 852499DEST_PATH_IMAGE112
The running frequency of (A) is the total number of stations (namely, the number of elements in a station set) in the regional multi-standard rail transitA number),
Figure 340112DEST_PATH_IMAGE185
the number of the variables is 0, 1,
Figure 346114DEST_PATH_IMAGE186
indicating that the section i belongs to the kth type of track traffic,
Figure 639693DEST_PATH_IMAGE187
indicating that the section i does not belong to the kth class of rail traffic,
Figure 290117DEST_PATH_IMAGE133
represents the variables of 0, 1,
Figure 807686DEST_PATH_IMAGE134
the starting station of the section i is denoted by j,
Figure 973088DEST_PATH_IMAGE135
indicating that the starting station of section i is not j,
Figure 742461DEST_PATH_IMAGE163
the variables are 0 and 1, and the variables are,
Figure 637604DEST_PATH_IMAGE164
indicates that the train l running section includes a section i,
Figure 263758DEST_PATH_IMAGE188
representing trains
Figure 854139DEST_PATH_IMAGE112
The operating interval does not include the interval i,
Figure 817416DEST_PATH_IMAGE139
represents the variables of 0, 1,
Figure 973591DEST_PATH_IMAGE189
the travel path representing the train l includes a station j,
Figure 973908DEST_PATH_IMAGE141
representing trains
Figure 176219DEST_PATH_IMAGE112
Does not include the station j in the travel route,
Figure 615291DEST_PATH_IMAGE190
represents the variables of 0, 1,
Figure 94814DEST_PATH_IMAGE137
the end station of the section i is denoted as j,
Figure 656245DEST_PATH_IMAGE138
the end station indicating section i is not j.
Because the regional multi-standard rail transit train operation scheme compilation method is an objective function taking the passenger congestion coefficient and the train operation cost as double targets, namely a double-target planning model, the operation scheme compilation method also comprises the step of solving the objective function taking the passenger congestion coefficient and the train operation cost as the double targets based on a target planning method, and specifically comprises the following steps:
respectively solving an objective function with the minimum passenger congestion coefficient as a target and an objective function with the minimum train running cost as a target to respectively obtain corresponding expected values
Figure 221218DEST_PATH_IMAGE191
、
Figure 526298DEST_PATH_IMAGE192
. Firstly, the single objective function is optimized and solved to obtain an expected value, namely the optimal target value of each objective function in the starting scheme compiling method under the single objective function.
Optimizing an objective function structure, and acquiring a dual-objective mathematical model of comprehensive driving cost and congestion coefficient based on an objective function taking the minimum passenger congestion coefficient as a target and an objective function taking the minimum train driving cost as a target:
Figure 594748DEST_PATH_IMAGE193
（19）
wherein p is the p-th priority, q is the q-th objective function,
Figure 999184DEST_PATH_IMAGE194
a priority factor representing the p-th priority,
Figure 176088DEST_PATH_IMAGE195
、
Figure 629066DEST_PATH_IMAGE196
weight coefficients representing positive and negative bias variables of different objective functions in the same priority,
Figure 207815DEST_PATH_IMAGE197
、
Figure 986415DEST_PATH_IMAGE198
a target excess value and a target deficiency value, which are respectively compared with corresponding expected values by a target function with the minimum passenger congestion coefficient as a target and a target function with the minimum train running cost as a target;
giving a priority factor and a weight coefficient to the dual-target mathematical model, and optimizing the dual-target mathematical model as follows:
Figure 650615DEST_PATH_IMAGE199
（20）
wherein,
Figure 969600DEST_PATH_IMAGE200
an objective function and an expectation value for minimizing the passenger congestion coefficient
Figure 12643DEST_PATH_IMAGE201
Target deficit value of comparison;
Figure 86778DEST_PATH_IMAGE202
objective function and expected value with minimum train running cost as target
Figure 644798DEST_PATH_IMAGE203
Target deficit value of comparison; in the embodiment of the invention, the priority factor is 1, and the priority factor is 1 because the operation cost and the congestion coefficient are very important in regional multi-system rail transit operation
Figure 970737DEST_PATH_IMAGE196
、
Figure 992920DEST_PATH_IMAGE195
Also take 1.
Building a set of optimization objectives
Figure 910061DEST_PATH_IMAGE204
The optimization target set respectively meets an objective function taking the minimum passenger congestion coefficient as a target and an objective function taking the minimum train running cost as a target:
Figure 752115DEST_PATH_IMAGE205
（21）
Figure 221359DEST_PATH_IMAGE206
（22）
wherein,
Figure 35732DEST_PATH_IMAGE207
an objective function and an expectation value for minimizing the passenger congestion coefficient
Figure 592615DEST_PATH_IMAGE208
Target excess value of comparison;
Figure 453124DEST_PATH_IMAGE209
objective function and expected value with minimum train running cost as target
Figure 855286DEST_PATH_IMAGE210
Target excess value of comparison;
Figure 852061DEST_PATH_IMAGE211
、
Figure DEST_PATH_IMAGE212
respectively optimizing the running cost and the congestion coefficient of the double-target mathematical model;
and taking the formulas (1) - (8), (11), (13) - (18) and (20) - (22) as the constraints of the optimized dual-target mathematical model, and solving the optimal solution of the optimized dual-target mathematical model by adopting Global Server in Lingo.
Obtaining the optimal solution of the model by adopting Global Server in Lingo; the solution obtained at this time is an optimal solution comprehensively considering the running cost and the congestion coefficient of the running scheme and the guidance of the actual running scheme. Preferably, it can be increased appropriately when solving
Figure 845425DEST_PATH_IMAGE207
、
Figure 927650DEST_PATH_IMAGE209
Is limited by the value range of (a).
For example, as shown in fig. 4, a regional multi-system rail transit network formed by a south section of a 5th line of a Chongqing subway, a river jumper and a Yukun high-speed Chongqing section is taken as an exemplary illustration.
Specifically, the south section of the Chongqing subway No. 5 line is a type 1 rail transit, the total length is 11.2km (kilometers), the river crossing line is a type 2 rail transit, the total length is 28.22km and the Yukun high-speed railway Chongqing section is a type 3 rail transit total length 100km, wherein the regional multi-standard rail transit network formed by the three sections has 18 stations and 30 sections (up-down lines), and the Chongqing west station and the hop station are multi-standard transfer stations.
The regional multi-standard rail transit network corresponds to 20 trains, wherein 8 trains are stopped at a station and 12 trains are stopped at a large station. The passenger flow in the model adopts peak hour section passenger flow data predicted in the initial stage of each line, namely the section passenger flow in each interval of the peak hour.
The model data is structured based on the selected road network, and the open-line scheme is obtained by respectively adopting Global solution of Lingo with the lowest open-line cost, the lowest congestion coefficient and the optimal solution of double targets, wherein the Lingo is a Solver, and the optimal solution can be obtained by converting model expressions into the language of the Lingo one by one and selecting the Global Solver in the Lingo. Further, the dual-target planning can iterate within 2s to obtain a global optimal solution. At this time, the minimum value of the running cost is 2133.6, and the minimum value of the congestion coefficient is 1384.1, but when one target minimum value is controlled, the opposite one is greatly increased. After the double-target optimization is adopted, the cost and the congestion coefficient are increased, but the cost fluctuation is controlled within 25%, and the congestion coefficient fluctuation is controlled within 35%.
When the minimum cost is taken as a target, the interval loading rate of 13 percent exceeds 1, and the interval loading rate of 40 percent is more than or equal to 0.7. The train obtained by solving when the congestion coefficient is minimum is close to the maximum departure frequency, and the full load rate among most of the trains is lower than 0.5. After the double-target optimization is adopted, the interval full load rate of 13% of the peak hour is between 0.7 and 1, and the interval full load rate of more than half is lower than 0.5.
After the train running cost and the congestion coefficient are comprehensively optimized, 85 trains are run in the rush hour, the average running distance of the trains is reduced to 33.3km, the running frequency is increased, and the train turnover speed is accelerated.
As shown in fig. 5, an embodiment of the present invention further introduces a regional multi-standard rail transit train operation scheme compilation system, where the operation scheme compilation system is capable of performing the regional multi-standard rail transit train operation scheme compilation, and specifically, the operation scheme compilation system includes a construction module and a determination module, where the construction module is configured to construct a target function with a passenger congestion coefficient and a train operation cost as two targets; the determination module is configured to determine a decision variable and one or more of the following constraints: the method comprises the following steps of passenger travel demand constraint, regional multi-standard rail transit overload rate constraint, train driving frequency range constraint, interval capacity constraint, station capacity constraint and parameter variable constraint. The specific content, the decision variables and the one or more constraint conditions for constructing the objective function are all consistent with the content in the above-mentioned implementation compilation, and are not repeated herein.
The method for compiling the regional multi-standard rail transit train running scheme comprehensively considers the benefits of passengers and operators, optimizes the passenger crowding coefficient and the train running cost which are used as double targets of a running method compiling model, accurately reflects the difference characteristics of regional multi-standard rail transit transportation, and enables the running scheme of regional multi-standard rail transit to be more refined.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (19)

1. A method for compiling a running scheme of a regional multi-standard rail transit train is characterized by comprising the following steps of,
constructing an objective function taking the passenger congestion coefficient and the train running cost as double targets;
determining decision variables and one or more of the following constraints:
the method comprises the following steps of passenger travel demand constraint, regional multi-standard rail transit overload rate constraint, train driving frequency range constraint, interval capacity constraint, station capacity constraint and parameter variable constraint.
2. The method for compiling the regional multi-standard rail transit train operation scheme according to claim 1, further comprising inputting regional rail transit networked candidate sets and section cross-section passenger flow as basic data, wherein the method comprises the following steps of,
based on the regional rail transit networked alternative collection, a train set, an interval set and a station set of regional multi-standard rail transit are constructed, wherein,
the train set is represented by Q
Figure 862240DEST_PATH_IMAGE001
Representing the first in a train set
Figure 107277DEST_PATH_IMAGE001
The device is similar to a train in the prior art,
Figure 355855DEST_PATH_IMAGE001
belonging to the group of Q, wherein the train set comprises L elements;
the interval set is represented by E, the element i represents an interval, i belongs to E, and the interval set comprises M elements;
the station set is represented by S, the element j represents a station, j belongs to S, and the station set has N elements.
3. The method for making a regional multi-standard rail transit train operation scheme according to claim 2, wherein the constructing of the objective function with the passenger congestion coefficient and the train operation cost as two objectives includes an objective function with the minimum passenger congestion coefficient as an objective and an objective function with the minimum train operation cost as an objective.
4. The method for making a regional multi-standard rail transit train operation scheme according to claim 3, further comprising,
dividing the regional multi-standard rail transit into a type 1 rail transit, a type 2 rail transit and a type 3 rail transit;
calculating the congestion coefficient of any type of rail transit in the regional multi-standard rail transit in an interval i and the congestion coefficient of a station j in the regional multi-standard rail transit based on the regional multi-standard rail transit division;
and acquiring an objective function with the minimum passenger congestion coefficient as a target based on the calculated congestion coefficient of any type of rail transit in the regional multi-system rail transit in the section i and the congestion coefficient of the station j in the regional multi-system rail transit.
5. The method for making a train operation plan for regional multi-standard rail transit according to claim 4, wherein calculating the congestion coefficients of the type 1 rail transit and the type 2 rail transit in the regional multi-standard rail transit at the section i comprises,
acquiring the average effective area of the train and the average passenger carrying number of the train in the interval i;
calculating the per-passenger occupied area of the section i based on the average effective area of the train and the average passenger carrying number of the train in the section i;
and acquiring the congestion coefficients of the type 1 rail traffic and the type 2 rail traffic in the section i based on the occupied area of the passenger in the section i.
6. The method for making a train operation plan for regional multi-standard rail transit according to claim 5, wherein calculating the congestion coefficient of class 3 rail transit in regional multi-standard rail transit in section i comprises,
acquiring passenger volume in the interval i and passenger capacity which can be provided by all trains in the interval i;
acquiring the average full load rate of all trains in the interval i based on the passenger flow in the interval i and the passenger capacity which can be provided by all trains in the interval i;
and averaging the average full load rates of all the trains in the interval i to obtain the congestion coefficient of the 3 rd type track traffic in the interval i.
7. The method for making a train operation plan for regional multi-standard rail transit according to claim 6, wherein calculating the congestion coefficient of a station j in regional multi-standard rail transit comprises,
acquiring a difference value of the passenger flow of the section of the adjacent section of the station j and an exchange passenger flow coefficient of the station j;
solving the product of the difference of the passenger flow of the section of the adjacent section of the station j and the exchange passenger flow coefficient of the station j, and averaging the product to each train passing through the station j to obtain the average exchange passenger flow of the train at the station j;
acquiring the effective area of a station j platform;
obtaining the ratio of the effective area of the platform of the station j to the average exchange passenger flow of the train at the station j, and obtaining the occupied area of passengers at the platform of the station j;
and acquiring the congestion coefficient of the station j in the regional multi-standard rail transit based on the occupied area of passengers at the station j.
8. The method for making a regional multi-standard rail transit train operation scheme according to claim 7, wherein an objective function targeting the minimum passenger congestion coefficient is as follows:
Figure 16644DEST_PATH_IMAGE002
（1）
wherein M is the total number of sections in the regional multi-standard rail transit, N is the total number of stations in the regional multi-standard rail transit, i represents an section, j represents a station,
Figure 158912DEST_PATH_IMAGE003
indicates the congestion coefficient of the section i,
Figure 450216DEST_PATH_IMAGE004
the cross-sectional passenger flow volume of the section i is shown,
Figure 982829DEST_PATH_IMAGE005
represents the average running time of the train in the section i,
Figure 775204DEST_PATH_IMAGE006
a congestion coefficient indicating a station j,
Figure 647345DEST_PATH_IMAGE007
representing the average exchange passenger flow for each train in station j,
Figure 234184DEST_PATH_IMAGE008
represents the average stop time of the train at station j;
the objective function aiming at the minimum train running cost is as follows:
Figure 254093DEST_PATH_IMAGE009
（2）
wherein,
Figure 459946DEST_PATH_IMAGE001
the first in the multi-standard rail transit of the presentation area
Figure 311228DEST_PATH_IMAGE001
The device is similar to a train in the prior art,
Figure 6651DEST_PATH_IMAGE010
representing trains
Figure 717118DEST_PATH_IMAGE001
The running frequency of the mobile phone is set,
Figure 320138DEST_PATH_IMAGE011
representing trains
Figure 432450DEST_PATH_IMAGE001
The cost of implementation of (c).
9. The method as claimed in claim 8, wherein the congestion coefficient of the section i is calculated by using the track-bound train driving scheme
Figure 361092DEST_PATH_IMAGE012
Satisfies the following conditions:
Figure 558855DEST_PATH_IMAGE013
（3）
wherein,
Figure 903249DEST_PATH_IMAGE014
the congestion coefficient of the k-th type rail transit in the regional multi-standard rail transit in the section i is represented,
Figure 994702DEST_PATH_IMAGE015
the number of the variables is 0, 1,
Figure 969611DEST_PATH_IMAGE016
indicating that the section i belongs to the kth type of track traffic,
Figure 185829DEST_PATH_IMAGE017
and the section i does not belong to the kth type track traffic, and k is 1, 2 and 3.
10. The method for making a train operation scheme for regional multi-standard rail transit according to claim 9, wherein the congestion coefficients of the type 1 rail transit and the type 2 rail transit in the section i satisfy:
Figure 661809DEST_PATH_IMAGE018
（4）
wherein,
Figure 483135DEST_PATH_IMAGE019
the passenger flow passenger per capita occupation area of the section i is expressed in m2The number of people/person is greater than the number of people,
Figure 753579DEST_PATH_IMAGE020
，
Figure 660355DEST_PATH_IMAGE021
a parameter representing a linear function of the section congestion coefficient; and the passenger flow in the interval i occupies the area per capita
Figure 674448DEST_PATH_IMAGE022
Is the ratio of the average effective area of the train to the average number of passengers of the train in the section i:
Figure 412597DEST_PATH_IMAGE023
（5）
wherein,
Figure 994888DEST_PATH_IMAGE024
represents the average effective area of the train in the section i and has the unit of m2；
Figure 513594DEST_PATH_IMAGE025
Represents the average number of passengers of the train in the section i, and satisfies the following conditions:
Figure 941164DEST_PATH_IMAGE026
（6）
wherein L represents the number of elements in the train set,
Figure 268240DEST_PATH_IMAGE027
representing trains
Figure 880487DEST_PATH_IMAGE001
The running frequency of the mobile phone is set,
Figure 761855DEST_PATH_IMAGE028
the variables are 0 and 1, and the variables are,
Figure 55434DEST_PATH_IMAGE029
indicates that the train l running section includes a section i,
Figure 830491DEST_PATH_IMAGE030
representing trains
Figure 223427DEST_PATH_IMAGE001
The operating interval does not include the interval i,
Figure 388829DEST_PATH_IMAGE031
representing the section passenger flow of the interval i;
the congestion coefficient of the 3 rd type rail traffic in the section i satisfies the following conditions:
Figure 548415DEST_PATH_IMAGE032
（7）
wherein,
Figure 318925DEST_PATH_IMAGE033
representing the average full load rate of all trains in the interval i; and average full load rate of all trains in section i
Figure 272974DEST_PATH_IMAGE033
For the ratio of the passenger volume in section i to the passenger capacity that can be provided by all trains in section i:
Figure 863355DEST_PATH_IMAGE034
（8）
wherein L represents the number of elements in the train set,
Figure 826632DEST_PATH_IMAGE027
representing trains
Figure 186069DEST_PATH_IMAGE001
The running frequency of the mobile phone is set,
Figure 248703DEST_PATH_IMAGE028
the variables are 0 and 1, and the variables are,
Figure 185435DEST_PATH_IMAGE035
indicates that the train l running section includes a section i,
Figure 827769DEST_PATH_IMAGE036
representing trains
Figure 104030DEST_PATH_IMAGE001
The operating interval does not include the interval i,
Figure 399882DEST_PATH_IMAGE037
representing trains
Figure 964855DEST_PATH_IMAGE001
The order of (1);
the congestion coefficient of a station j in the regional multi-standard rail transit meets the following conditions:
Figure 942039DEST_PATH_IMAGE038
（9）
wherein,
Figure 400702DEST_PATH_IMAGE039
the passenger's per-capita area, m, of the station platform j2The number of people/person is greater than the number of people,
Figure 742822DEST_PATH_IMAGE040
，
Figure 591829DEST_PATH_IMAGE041
parameters representing a linear function of the station congestion coefficient; and the passenger of station platform j occupies the area
Figure 169441DEST_PATH_IMAGE039
The ratio of the effective area of the platform at station j to the average exchange passenger flow of the train at station j is:
Figure 623556DEST_PATH_IMAGE042
（10）
wherein,
Figure 526790DEST_PATH_IMAGE043
the effective area of the platform of the station j is expressed in m2；
Figure 863093DEST_PATH_IMAGE044
The average exchange passenger flow quantity of the trains at the station j is represented, the average exchange passenger flow quantity of the trains at the station j is obtained by multiplying the passenger flow quantity difference of the section of the adjacent section of the station j by the exchange passenger flow coefficient of the station j and averaging the passenger flow quantity difference to each train passing through the station, and the average exchange passenger flow quantity of the trains at the station j meets the following conditions:
Figure 119762DEST_PATH_IMAGE045
（11）
wherein,
Figure 553018DEST_PATH_IMAGE046
representing the exchange passenger flow coefficient of the station j, M is the number of elements in the interval set, L is the number of elements in the train set,
Figure 299257DEST_PATH_IMAGE047
the cross-sectional passenger flow volume of the section i is shown,
Figure 60539DEST_PATH_IMAGE048
represents the variables of 0, 1,
Figure 257252DEST_PATH_IMAGE049
the starting station of the section i is denoted by j,
Figure 420380DEST_PATH_IMAGE050
indicating that the starting station of section i is not j,
Figure 665416DEST_PATH_IMAGE051
represents the variables of 0, 1,
Figure 976312DEST_PATH_IMAGE052
the end station of the section i is denoted as j,
Figure 574784DEST_PATH_IMAGE053
indicating that the end station of the section i is not j,
Figure 717052DEST_PATH_IMAGE054
represents the variables of 0, 1,
Figure 273935DEST_PATH_IMAGE055
representing trains
Figure 806548DEST_PATH_IMAGE001
The travel route of (a) includes a station j,
Figure 333344DEST_PATH_IMAGE056
indicating that the travel path of train l does not include station j,
Figure 205485DEST_PATH_IMAGE027
representing trains
Figure 730007DEST_PATH_IMAGE001
The running frequency of (c).
11. The method for compiling regional multi-standard rail transit train operation scheme according to any one of claims 1 to 10, wherein the passenger travel demand constraint is as follows:
Figure 812233DEST_PATH_IMAGE057
（12）
wherein,
Figure 18086DEST_PATH_IMAGE027
representing trains
Figure 807051DEST_PATH_IMAGE001
The running frequency of the mobile phone is set,
Figure 830370DEST_PATH_IMAGE058
the variables are 0 and 1, and the variables are,
Figure 9679DEST_PATH_IMAGE059
representing trains
Figure 143857DEST_PATH_IMAGE001
The operating interval includes an interval i in which,
Figure 52907DEST_PATH_IMAGE060
representing trains
Figure 591336DEST_PATH_IMAGE001
The operating interval does not include the interval i,
Figure 913733DEST_PATH_IMAGE061
representing trains
Figure 258126DEST_PATH_IMAGE001
The order of the person(s) to be assigned,
Figure 959366DEST_PATH_IMAGE062
representing trains
Figure 58909DEST_PATH_IMAGE001
The maximum rate of overload that is allowed to occur,
Figure 540706DEST_PATH_IMAGE047
and represents the cross-sectional passenger flow volume of the section i.
12. The method for compiling a regional multi-standard rail transit train running scheme according to claim 11, wherein the regional multi-standard rail transit overload rate constraint is as follows:
Figure 626474DEST_PATH_IMAGE063
（13）
wherein,
Figure 244537DEST_PATH_IMAGE064
represents the rail transit to which the train belongs,
Figure 780561DEST_PATH_IMAGE065
。
13. the method for compiling a train operation scheme of regional multi-standard rail transit according to claim 12, wherein the train operation frequency range constraint is as follows:
Figure 421758DEST_PATH_IMAGE066
（14）
wherein,
Figure 435850DEST_PATH_IMAGE027
representing trains
Figure 173999DEST_PATH_IMAGE001
The running frequency of the mobile phone is set,
Figure 490711DEST_PATH_IMAGE067
representing trains
Figure 9417DEST_PATH_IMAGE001
The minimum running frequency at which the running can be done,
Figure 436987DEST_PATH_IMAGE068
representing trains
Figure 357538DEST_PATH_IMAGE001
Maximum run frequency at which a run can be run.
14. The method for compiling a regional multi-standard rail transit train running scheme according to claim 13, wherein the section capacity constraint is as follows:
Figure 907468DEST_PATH_IMAGE069
（15）
wherein,
Figure 523258DEST_PATH_IMAGE027
representing trains
Figure 879153DEST_PATH_IMAGE001
The running frequency of the mobile phone is set,
Figure 326314DEST_PATH_IMAGE028
the variables are 0 and 1, and the variables are,
Figure 984829DEST_PATH_IMAGE035
representing trains
Figure 150231DEST_PATH_IMAGE001
The operating interval includes an interval i in which,
Figure 44238DEST_PATH_IMAGE070
representing trains
Figure 549168DEST_PATH_IMAGE001
The operating interval does not include the interval i,
Figure 440901DEST_PATH_IMAGE037
representing trains
Figure 421495DEST_PATH_IMAGE001
The order of the person(s) to be assigned,
Figure 728980DEST_PATH_IMAGE062
representing trains
Figure 150734DEST_PATH_IMAGE001
The maximum rate of overload that is allowed to occur,
Figure 275685DEST_PATH_IMAGE047
the cross-sectional passenger flow volume of the section i is shown,
Figure 353362DEST_PATH_IMAGE071
representing the maximum transport capacity of the interval i.
15. The method for compiling a regional multi-standard rail transit train operation scheme according to claim 14, wherein the station capability constraint is as follows:
Figure 792434DEST_PATH_IMAGE072
（16）
wherein,
Figure 131011DEST_PATH_IMAGE027
representing trains
Figure 567809DEST_PATH_IMAGE001
The running frequency of the mobile phone is set,
Figure 929520DEST_PATH_IMAGE073
the number of the variables is 0, 1,
Figure 703441DEST_PATH_IMAGE074
representing trains
Figure 99787DEST_PATH_IMAGE001
The travel route of (a) includes a station j,
Figure 441907DEST_PATH_IMAGE075
representing trains
Figure 618810DEST_PATH_IMAGE001
Does not include the station j in the travel route,
Figure 134105DEST_PATH_IMAGE076
representing the maximum transport capacity of station j.
16. The method for compiling a regional multi-standard rail transit train running scheme according to claim 15, wherein the parameter variable constraints are as follows:
Figure 588220DEST_PATH_IMAGE077
（17）
and N is the total number of stations in the regional multi-standard rail transit.
17. The method for making a regional multi-standard rail transit train operation scheme according to claim 16, wherein the decision variable is the operation frequency of class I trains
Figure 225875DEST_PATH_IMAGE027
Wherein
Figure 765441DEST_PATH_IMAGE027
the value range of (A) is the whole natural number,
Figure 84427DEST_PATH_IMAGE078
and if not, the type I train is started in the research time period.
18. The method for making a regional multi-standard rail transit train operation scheme according to claim 17, further comprising,
respectively solving an objective function with the minimum passenger congestion coefficient as a target and an objective function with the minimum train running cost as a target to respectively obtain corresponding expected values
Figure 252103DEST_PATH_IMAGE079
、
Figure 998342DEST_PATH_IMAGE080
；
Optimizing an objective function structure, and acquiring a dual-objective mathematical model of comprehensive driving cost and congestion coefficient based on an objective function taking the minimum passenger congestion coefficient as a target and an objective function taking the minimum train driving cost as a target:
Figure 759625DEST_PATH_IMAGE081
（18）
wherein p is the p-th priority, q is the q-th objective function,
Figure 944618DEST_PATH_IMAGE082
a priority factor representing the p-th priority,
Figure 904484DEST_PATH_IMAGE083
Figure 24887DEST_PATH_IMAGE084
weight coefficients representing positive and negative bias variables of different objective functions in the same priority,
Figure 398099DEST_PATH_IMAGE085
、
Figure 262150DEST_PATH_IMAGE086
a target excess value and a target deficiency value, which are respectively compared with corresponding expected values by a target function with the minimum passenger congestion coefficient as a target and a target function with the minimum train running cost as a target;
giving a priority factor and a weight coefficient to the dual-target mathematical model, and optimizing the dual-target mathematical model as follows:
Figure 76522DEST_PATH_IMAGE087
（19）
wherein,
Figure 758039DEST_PATH_IMAGE088
an objective function and an expectation value for minimizing the passenger congestion coefficient
Figure 228335DEST_PATH_IMAGE089
Target deficit value of comparison;
Figure 755131DEST_PATH_IMAGE090
objective function and expected value with minimum train running cost as target
Figure 689589DEST_PATH_IMAGE091
Target deficit value of comparison;
building a set of optimization objectives
Figure 417374DEST_PATH_IMAGE092
The optimization target set respectively meets an objective function taking the minimum passenger congestion coefficient as a target and an objective function taking the minimum train running cost as a target:
Figure 234020DEST_PATH_IMAGE093
（20）
Figure 502190DEST_PATH_IMAGE094
（21）
wherein,
Figure 228838DEST_PATH_IMAGE095
an objective function and an expectation value for minimizing the passenger congestion coefficient
Figure 986578DEST_PATH_IMAGE096
Target excess value of comparison;
Figure 697045DEST_PATH_IMAGE097
objective function and expected value with minimum train running cost as target
Figure 503327DEST_PATH_IMAGE098
Target excess value of comparison;
Figure 474694DEST_PATH_IMAGE099
、
Figure 278702DEST_PATH_IMAGE100
respectively optimizing the running cost and the congestion coefficient of the double-target mathematical model;
and taking the formulas (3) - (11), (12) - (17) and (19) - (21) as the constraints of the optimized dual-target mathematical model, and solving the optimal solution of the optimized dual-target mathematical model by adopting Global Sever in Lingo.
19. A regional multi-standard rail transit train operation scheme compilation system is characterized by comprising a running compilation system,
the construction module is used for constructing an objective function taking the passenger congestion coefficient and the train running cost as double targets;
a determination module for determining a decision variable and one or more of the following constraints:
the method comprises the following steps of passenger travel demand constraint, regional multi-standard rail transit overload rate constraint, train driving frequency range constraint, interval capacity constraint, station capacity constraint and parameter variable constraint.
CN202010998691.4A 2020-09-22 2020-09-22 Regional multi-system rail transit train running scheme compiling method and system Active CN111932034B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010998691.4A CN111932034B (en) 2020-09-22 2020-09-22 Regional multi-system rail transit train running scheme compiling method and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010998691.4A CN111932034B (en) 2020-09-22 2020-09-22 Regional multi-system rail transit train running scheme compiling method and system

Publications (2)

Publication Number Publication Date
CN111932034A true CN111932034A (en) 2020-11-13
CN111932034B CN111932034B (en) 2024-03-29

Family

ID=73333542

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010998691.4A Active CN111932034B (en) 2020-09-22 2020-09-22 Regional multi-system rail transit train running scheme compiling method and system

Country Status (1)

Country Link
CN (1) CN111932034B (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112434969A (en) * 2020-12-10 2021-03-02 西南交通大学 Regional multi-type rail transit transport capacity resource allocation method
CN112598331A (en) * 2021-01-06 2021-04-02 株洲中车时代电气股份有限公司 Dynamic scheduling method, system, computer equipment and storage medium for rail transit
CN114266010A (en) * 2022-03-01 2022-04-01 华东交通大学 Method and system for calculating congestion coefficient of regional multi-standard rail traffic interval
CN114781881A (en) * 2022-04-26 2022-07-22 北京全路通信信号研究设计院集团有限公司 Analysis system and analysis method for regional rail transit system
CN116523166A (en) * 2023-07-03 2023-08-01 中铁第四勘察设计院集团有限公司 High-speed rail train running path optimization method and device based on path distribution passenger flow
CN116882714A (en) * 2023-09-07 2023-10-13 中国铁路设计集团有限公司 Multi-year intersection integrated scheme programming method considering line network construction time sequence

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2395144A1 (en) * 1999-12-30 2001-07-12 John M. Belcea Optimal locomotive assignment for a railroad network
AU2925801A (en) * 1999-12-30 2001-07-16 Ge Transportation Systems Global Signaling, Llc A train corridor scheduling process including various cost functions associated with railway operations
CN105857350A (en) * 2016-03-17 2016-08-17 中南大学 High-speed rail train driving method based on section profile passenger flow
CN107330547A (en) * 2017-06-15 2017-11-07 重庆交通大学 A kind of city bus dynamic dispatching optimization method and system
US20180047124A1 (en) * 2016-08-12 2018-02-15 Hatch Ltd. System and method for optimizing a rail system
CN111353639A (en) * 2020-02-26 2020-06-30 北京交通大学 Urban rail transit peak current limiting optimization method for coordinating train timetable

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2395144A1 (en) * 1999-12-30 2001-07-12 John M. Belcea Optimal locomotive assignment for a railroad network
AU2925801A (en) * 1999-12-30 2001-07-16 Ge Transportation Systems Global Signaling, Llc A train corridor scheduling process including various cost functions associated with railway operations
CN105857350A (en) * 2016-03-17 2016-08-17 中南大学 High-speed rail train driving method based on section profile passenger flow
US20180047124A1 (en) * 2016-08-12 2018-02-15 Hatch Ltd. System and method for optimizing a rail system
CN107330547A (en) * 2017-06-15 2017-11-07 重庆交通大学 A kind of city bus dynamic dispatching optimization method and system
CN111353639A (en) * 2020-02-26 2020-06-30 北京交通大学 Urban rail transit peak current limiting optimization method for coordinating train timetable

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
王莹;刘军;: "铁路行包快运专列开行方案优化编制方法的研究", 交通运输系统工程与信息, no. 03, 15 June 2007 (2007-06-15) *
苏焕银;史峰;邓连波;单杏花;: "面向时变需求的高速铁路列车开行方案优化方法", 交通运输系统工程与信息, no. 05, 15 October 2016 (2016-10-15) *

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112434969A (en) * 2020-12-10 2021-03-02 西南交通大学 Regional multi-type rail transit transport capacity resource allocation method
CN112598331A (en) * 2021-01-06 2021-04-02 株洲中车时代电气股份有限公司 Dynamic scheduling method, system, computer equipment and storage medium for rail transit
CN112598331B (en) * 2021-01-06 2024-01-23 株洲中车时代电气股份有限公司 Dynamic scheduling method, system, computer equipment and storage medium for rail transit
CN114266010A (en) * 2022-03-01 2022-04-01 华东交通大学 Method and system for calculating congestion coefficient of regional multi-standard rail traffic interval
CN114781881A (en) * 2022-04-26 2022-07-22 北京全路通信信号研究设计院集团有限公司 Analysis system and analysis method for regional rail transit system
CN116523166A (en) * 2023-07-03 2023-08-01 中铁第四勘察设计院集团有限公司 High-speed rail train running path optimization method and device based on path distribution passenger flow
CN116523166B (en) * 2023-07-03 2023-09-15 中铁第四勘察设计院集团有限公司 High-speed rail train running path optimization method and device based on path distribution passenger flow
CN116882714A (en) * 2023-09-07 2023-10-13 中国铁路设计集团有限公司 Multi-year intersection integrated scheme programming method considering line network construction time sequence
CN116882714B (en) * 2023-09-07 2023-11-28 中国铁路设计集团有限公司 Multi-year intersection integrated scheme programming method considering line network construction time sequence

Also Published As

Publication number Publication date
CN111932034B (en) 2024-03-29

Similar Documents

Publication Publication Date Title
CN111932034A (en) Regional multi-standard rail transit train operation scheme compiling method and system
Wang et al. Multi-train trajectory optimization for energy-efficient timetabling
CN111859718B (en) Method and system for calculating congestion coefficient of regional multi-standard rail transit station
CN108090668B (en) Time-varying passenger flow driven urban rail transit passenger flow matching and schedule adjusting method
CN110782079B (en) Method for adjusting energy-saving high-speed railway train running chart based on stop scheme
CN109774750B (en) Dynamic scheduling space-time decision method based on virtual coupling mode
CN107330539A (en) Train schedule chart and the scheme cooperative optimization method that stops based on bus traveler assignment
CN112132551B (en) Urban rail transit emergency passenger flow cooperative distribution method
CN111882156B (en) Train schedule robust optimization method for random dynamic passenger flow and energy-saving operation
CN112668101A (en) Method for compiling high-speed railway train running chart
CN113988371B (en) Urban rail transit cross-station stop-start scheme optimization method based on passenger flow direct
CN114925909A (en) Urban rail transit passenger flow and traffic flow coupling optimization method and system
CN112446648B (en) Urban rail transit mixed transportation method and system based on off-peak hours
CN111324853A (en) Method and system for calculating passing capacity of channel type high-speed railway
CN114707709A (en) Safety early warning method and system for comprehensive passenger transport hub of railway
CN111985814A (en) Method and system for optimizing inter-city train operation scheme with intermittent power supply
CN112580866A (en) Bus route bunching optimization method based on whole-course vehicle and inter-vehicle combined scheduling
CN111931386B (en) Method and system for calculating congestion coefficient of regional multi-standard rail traffic interval
CN111859717B (en) Method and system for minimizing regional multi-standard rail transit passenger congestion coefficient
CN112434969B (en) Regional multi-type rail transit transport capacity resource allocation method
Jafarian-Moghaddam Economical speed for optimizing the travel time and energy consumption in train scheduling using a fuzzy multi-objective model
CN111861290A (en) Constraint method and system for regional multi-standard rail transit operation scheme
Tian A short-turning strategy for the management of bus bunching considering variable spatial-temporal running time
CN113641727B (en) Method for calculating transport speed of buses on different lines based on same time and space
Lu et al. Integrated Optimization of Train Diagrams and Rolling Stock Circulation with Full-Length and Short-Turn Routes of Virtual Coupling Trains in Urban Rail Transit

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant