CN108536965B - Urban rail transit line operation service reliability calculation method - Google Patents

Urban rail transit line operation service reliability calculation method Download PDF

Info

Publication number
CN108536965B
CN108536965B CN201810321042.3A CN201810321042A CN108536965B CN 108536965 B CN108536965 B CN 108536965B CN 201810321042 A CN201810321042 A CN 201810321042A CN 108536965 B CN108536965 B CN 108536965B
Authority
CN
China
Prior art keywords
train
reliability
calculating
station
trains
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.)
Active
Application number
CN201810321042.3A
Other languages
Chinese (zh)
Other versions
CN108536965A (en
Inventor
王艳辉
李曼
林帅
崔逸如
张冬雪
李阳
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.)
BEIJING TELESOUND ELECTRONICS CO LTD
Original Assignee
Beijing Jiaotong University
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 Beijing Jiaotong University filed Critical Beijing Jiaotong University
Priority to CN201810321042.3A priority Critical patent/CN108536965B/en
Publication of CN108536965A publication Critical patent/CN108536965A/en
Application granted granted Critical
Publication of CN108536965B publication Critical patent/CN108536965B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • 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
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Strategic Management (AREA)
  • General Physics & Mathematics (AREA)
  • Economics (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Computer Hardware Design (AREA)
  • Game Theory and Decision Science (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Train Traffic Observation, Control, And Security (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention provides a method for calculating the reliability of urban rail transit line operation service, which relates to the technical field of urban rail train operation control, and the method determines the number of delayed trains and the delayed train delay time according to train operation simulation and a train operation diagram so as to calculate the reliability of train right-point; meanwhile, according to the delay time, determining a line operation conveying capacity co-scheduling solving model, and calculating line operation conveying capacity co-scheduling and the position density of passengers in the train; and finally, according to the gain type weighted fusion model, combining the positive point reliability, the line operation transmission capacity co-scheduling and the position density, constructing an operation service reliability model and calculating the operation service reliability. The invention utilizes a gain type weighting fusion method to establish a line operation service reliability model of a 'reward merit and penalty' function, considers the weight influence of each operation parameter and realizes the comprehensive evaluation of the urban rail transit line operation service reliability.

Description

Urban rail transit line operation service reliability calculation method
Technical Field
The invention relates to the technical field of urban rail train operation control, in particular to a method for calculating the reliability of urban rail transit line operation service.
Background
In recent years, along with the rapid development of the urbanization process of China, the urban scale is continuously enlarged, the urban population is increased, the travel demand of residents is increased, the urban traffic service quality is gradually concerned by urban residents, and the problem of solving the urban population is at the forefront. The urban rail transit is the first transportation means of residents in super-large or medium-sized cities, has the advantages of land conservation, large transportation capacity, comfort, convenience, accuracy, reliability, greenness, safety and the like, and becomes one of the traffic modes for rapid urban development.
While the urban rail transit is rapidly developed, the attention of urban residents to the operation service quality is increased day by day, and the operation service quality also becomes one of the focus problems. The reliability of urban rail line operation service is one of key factors for measuring and improving the quality of urban rail operation service, and is the comprehensive reflection of the train operation state and the passenger flow state along the line. Many factors can influence the normal operation of urban rail transit, such as passenger flow volume increase suddenly, passenger gets on or off the bus, subsystem trouble etc. these factors all can lead to the delay of train, and the train is as passenger's carrier, and the train appears the delay phenomenon and not only can influence the daily operation of city rail, also can lead to station platform passenger flow backlog for operation service reliability reduces. Therefore, how to improve the operation quality of urban rail transit on the premise of ensuring safety is a core problem concerned by government administration departments, traffic administration departments, the public and even scientific research workers.
At present, the research on urban rail transit service level, reliability and service reliability is more, a service level evaluation index system is constructed and a corresponding evaluation method is provided, but most of the established index systems have stronger subjectivity and are not combined with the actual operation condition; meanwhile, in terms of urban rail transit reliability, most researches are carried out on operation reliability from the perspectives of connectivity, survivability and the like of a road network of an urban rail transit system, and the fundamental problem of road network operation reliability is ignored because the operation process of a train and the traffic flow are not combined; the reliability of the operation service of the urban rail transit line is rarely researched, and the reliability of the operation service of urban public transit is mostly researched.
Disclosure of Invention
The invention aims to provide a calculation method capable of accurately evaluating the reliability of urban rail transit operation service from multiple angles by combining actual operation conditions, so as to solve the technical problems that the traditional urban rail transit service evaluation index system in the background technology is strong in subjectivity, does not combine the actual operation conditions and is limited in evaluation angle.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a method for calculating the reliability of operation service of an urban rail transit line, which comprises the following steps:
step S110: determining the number of delayed trains in a delayed state and the delay time of each delayed train according to the train operation simulation and the train operation diagram;
step S120: calculating the train right-point reliability according to the delay train number and the delay time;
step S130: determining a line operation conveying capacity co-scheduling solving model according to the delay time, and calculating line operation conveying capacity co-scheduling and the position density of passengers in the train;
step S140: and according to a gain type weighted fusion model, combining the positive point reliability, the line operation transmission capacity co-scheduling and the position density to construct an operation service reliability model and calculate the operation service reliability.
The method utilizes the gain type weighting fusion model to construct the operation service reliability model, not only retains the advantages of linear correlation of index values, but also can better embody the evaluation principle of the awards, the benefits and the penalties, and is a very good effective means for processing the evaluation problems.
Further, the determining the number of delayed trains in the delayed state and the delay time of each delayed train according to the train operation simulation and the train operation diagram includes:
obtaining the number n of delayed trains of the line in the delayed state based on the operation rule and the delay propagation characteristic of the trainsDDelay time at station i associated with each delayed train j
Figure GDA0002620900880000031
Further, the calculating the train punctual reliability according to the delay train number and the delay time comprises:
according to the delay time of the train running on the line L in the T time period
Figure DA00026209008858017
Grouping trains according to different delay time, and dividing into p groups, xkRepresenting the number of trains owned by the k-th trainkRepresenting the delay time corresponding to the kth train;
delay time of running each train on the line according to T time period
Figure GDA0002620900880000033
Sorting according to the sequence from small to large, and correspondingly sorting trains corresponding to different delay times to obtain x1＞x2＞...＞xp；
Calculating the ratio X of the number of the kth group of trains to the sum of the number of each group of trainskAnd cumulative percent X'k；
Figure GDA0002620900880000034
Figure GDA0002620900880000035
Calculating the ratio Y of the delay time of the kth group to the sum of the delay time of each groupkAnd cumulative percentage Yk′；
Figure GDA0002620900880000036
Figure GDA0002620900880000037
The cumulative percentage Y of each group of delay time to the total delay timek' is a vertical axis, and takes the cumulative percentage X of the number of each group of trains to the total number of the trains in each groupk' As a horizontal axis, each set of data (X) in the coordinate axisk′,Yk') is represented by a dot, depicting the position of all valid data in the coordinate axis during the T period;
fitting curve Y based on least square principlek′＝f(Xk', a), where a denotes the fitting parameters, the difference between the values of the fitted function curve and the actual values is found so that the sum of squares thereof is minimal:
Figure GDA0002620900880000041
calculating the delay effect coefficient lambda according to the Gini coefficient and Lorenz curve theory*；
Figure GDA0002620900880000042
According to the formula
Figure GDA0002620900880000043
And calculating the train punctual reliability.
Further, determining a line operation transportation capacity co-scheduling solution model according to the delay time, and calculating line operation transportation capacity co-scheduling and passenger position density in the train comprises:
step S131: combining the train fixed member with the train marshalling and the passenger arrival rate of the station to iteratively calculate the arrival rate of the train j at the station siNumber of waiting passengers
Figure GDA0002620900880000044
Figure GDA0002620900880000045
Wherein t isFThe departure interval is shown as the interval between the two departure cars,
Figure GDA0002620900880000046
indicating that the train j-1 is at the station siThe remaining passenger flow volume.
Step S132: arrival of train j at station siResidual carrying capacity of
Figure GDA0002620900880000047
And (3) calculating:
when the value of i is 1, the value of i,
Figure GDA0002620900880000048
when i ≠ 1, it is,
Figure GDA0002620900880000049
wherein, C0Representing the rated passenger carrying capacity of the train, which can be quantified by the product of the train's fixed member and the train consist;
Figure GDA00026209008800000410
indicating arrival of train j at station siThe number of alighting passengers.
Step S133: calculating to obtain the arrival station s of the train j according to a formulaiIs in the passenger flow
Figure GDA00026209008800000411
Figure GDA00026209008800000412
Calculating to obtain the departure station s of the train jiNumber of passengers in train
Figure GDA0002620900880000051
When the value of i is 1, the value of i,
Figure GDA0002620900880000052
when i ≠ 1, it is,
Figure GDA0002620900880000053
if i is equal to i +1, if i is equal to or less than m, returning to step S131 to calculate the state of the train j reaching the next station, otherwise, making j equal to j +1, if j is equal to or less than n, making i equal to 1, going to step S131 to continue calculating the passenger carrying condition of the train j +1 at each station until all trains are calculated, and obtaining the remaining carrying capacity of all trains reaching the station, the retained passenger flow and the retained passenger flow of the train leaving the station; calculating the capacity co-scheduling and the seat density as follows:
Figure GDA0002620900880000054
Figure GDA0002620900880000055
wherein:
Figure GDA0002620900880000056
indicating departure of train j from station siThe density of the mat in the carriage is increased,
Figure GDA0002620900880000057
indicating departure of train j from station siThe number of passengers in the front compartment; cyWhich represents the passenger seating area in the train car.
Further, according to the gain-type weighted fusion model, combining the positive point reliability, the line operation transmission capacity co-scheduling and the position density, constructing an operation service reliability model, wherein calculating the operation service reliability comprises:
co-scheduling the line operation transport capacity
Figure GDA0002620900880000058
And the density of the mat
Figure GDA0002620900880000059
Carrying out dimensionless treatment to obtain the construction parameters of the operation service reliability model as follows:
Figure GDA00026209008800000510
Figure GDA0002620900880000061
according to the gain type weighting fusion model, the constructed line operation service reliability model is as follows:
Figure GDA0002620900880000062
wherein, κp,κc,κdWeight values respectively representing the positive point reliability, the line operation transmission capacity co-scheduling and the seat density, and satisfying kappap+κc+κd＝1。
In the constructed reliability model of the line operation service, the following are defined: assuming s > 0, if:
1) the function u (x) is continuous, piecewise derivable;
2) if x1≥x2→u(x1)≥u(x2),u′(x1)≥′u(x2)；
3)u(0.5)＜0.5；
Then when s > 1, the mapping u [0,1] → [0, s ] is called a gain function;
when s belongs to (0,1), the mapping u: [0,1] → [0, s ] is called a break function;
when s is 1, the mapping u is called [0,1] → [0, s ] as a non-breaking and non-gain function;
i.e., function u (x) is also referred to as a "reward and penalty" function.
The invention has the beneficial effects that: the method comprises the steps of considering the line operation condition from the view point of train operation, the view point of passengers and the view point of coordination of the train and the passengers, establishing a line operation service reliability model with a function of 'rewarding, good and bad' by utilizing a gain type weighting fusion method of 'rewarding, good and bad', considering the influence of the weight of each operation parameter, conforming to the characteristic of reliability evaluation of urban rail transit operation service, and realizing comprehensive evaluation of the reliability of the urban rail transit line operation service.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a method for calculating reliability of operation service of an urban rail transit line according to an embodiment of the present invention.
Fig. 2 is a lorentz curve illustrating the train delay imbalance of the train urban rail transit according to the embodiment of the invention.
Fig. 3 is a flow of a line operation transport capacity co-scheduling solution algorithm according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a simulation result of an eight-way train operating in Beijing subway according to the embodiment of the present invention.
Fig. 5 is a schematic diagram of delay time of a line train at each station in a delay state of the beijing subway octuplex according to the embodiment of the present invention.
Fig. 6 is a lorentz curve diagram of the beijing subway octree line in the delay state according to the embodiment of the present invention.
Fig. 7 is a schematic diagram of the remaining bearing capacity after arrival of the train according to the embodiment of the present invention.
FIG. 8 shows a train j at a station s according to an embodiment of the present inventioniSchematic diagram of coordination degree of line operation conveying capacity.
Fig. 9 is a schematic diagram of the coordination degree of the average operation transportation capacity of the train after no delay and delay.
Fig. 10 is a schematic diagram of the density of seats in each operating train car before and after a delay condition in accordance with an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or modules having the same or similar functionality throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or modules, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, modules, and/or groups thereof.
It should be noted that, unless otherwise explicitly stated or limited, the terms "connected" and "fixed" and the like in the embodiments of the present invention are to be understood in a broad sense and may be fixedly connected, detachably connected, or integrated, mechanically connected, electrically connected, directly connected, indirectly connected through an intermediate medium, connected between two elements, or in an interaction relationship between two elements, unless explicitly stated or limited. The specific meanings of the above terms in the embodiments of the present invention can be understood by those skilled in the art according to specific situations.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding of the embodiments of the present invention, the following description will be further explained by taking specific embodiments as examples with reference to the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
It will be understood by those of ordinary skill in the art that the figures are merely schematic representations of one embodiment and that the elements or devices in the figures are not necessarily required to practice the present invention.
Example one
As shown in fig. 1, a method for calculating reliability of operation service of an urban rail transit line according to an embodiment of the present invention includes the following steps:
step 1: obtaining the number n of delayed trains in the delayed state by simulation calculationDDelay time associated with each delayed train j
Figure GDA0002620900880000081
Assuming that m train stations and n trains are shared in the line operation;
step 2: calculating a formula according to the train punctual reliability:
Figure GDA0002620900880000091
(wherein λ)*As a delay effect coefficient), the train correct point is calculatedReliability; according to the number n of delayed trains obtained in the step 1DAnd delay time of train at each station
Figure DA00026209008858072
According to the delay time of running trains on the line L in the T time period
Figure GDA0002620900880000093
Grouping trains according to different delay time, and dividing into p groups, xkRepresenting the number of components owned by the kth group of all running trains on the line on the horizontal axis (here, the trains are grouped according to different delay time); y iskRepresenting the delay time corresponding to the first train. And drawing a Lorenz curve of the delay imbalance of the urban rail transit train according to the Gini coefficient and the Lorenz curve, wherein the line A represents an absolute equal line, and the line B represents the Lorenz curve, as shown in figure 2.
Specifically, the method comprises the following steps:
step 2.1: delay time of running each train on the line according to T time period
Figure GDA0002620900880000094
Sorting according to the sequence from small to large, and correspondingly sorting trains corresponding to different delay times to obtain x1＞x2＞...＞xp。
Step 2.1: calculating the ratio X of the number of the kth group of trains to the sum of the number of each group of trainskAnd cumulative percent X'k。
Figure GDA0002620900880000095
Figure GDA0002620900880000096
Step 2.3: calculating the ratio Y of the delay time of the kth group to the sum of the delay time of each groupkAnd cumulative percentage Yk′。
Figure GDA0002620900880000097
Figure GDA0002620900880000098
Step 2.4: the cumulative percentage Y of each group of delay time to the total delay timek' is a vertical axis, and takes the cumulative percentage X of the number of each group of trains to the total number of the trains in each groupk' As a horizontal axis, each set of data (X) in the coordinate axisk′,Yk') is represented by a dot, depicting the position of all valid data in the p time period in the coordinate axis.
Step 2.5: fitting curve Y based on least square principlek′＝f(Xk', A), wherein
Figure GDA0002620900880000101
For a particular parameter, the difference between the values of the fitted function curve and the actual values is found to minimize the sum of the squares thereof:
Figure GDA0002620900880000102
step 2.6: calculating a kini coefficient, namely a train punctual operation service delay effect coefficient on an urban rail transit line
Figure GDA0002620900880000103
Step 2.7: according to the formula
Figure GDA0002620900880000104
Calculating the delay effect coefficient lambda*；
Step 2.8: according to the formula
Figure GDA0002620900880000105
And calculating the train punctual reliability.
And step 3: calculating the line operation transmission capacity co-scheduling algorithm flow according to the established line operation transmission capacity co-scheduling solving algorithm flow based on the passenger seat density and the line operation transmission capacity in the carriage
Figure GDA0002620900880000106
Density of passenger seats in carriage
Figure GDA0002620900880000107
As shown in fig. 3, the line operation transport capacity co-scheduling solving algorithm flow includes the following steps:
step 3.1: through the simulation of line operation, the train operation diagram of the line and the operation time of the train j at the adjacent station can be obtained
Figure GDA0002620900880000108
The train j arriving at the station s can be obtained through calculationiDelay time of
Figure GDA0002620900880000109
Inputting the basic data as basic data of a subsequent process; inputting train set members and train marshalling, and passenger arrival rate of each station;
step 3.2: and (5) initializing operation. Let i equal 1 and j equal 1.
Step 3.3: calculating the arrival of the train j at the station siAccording to the formula
Figure GDA0002620900880000111
Calculating and outputting the arrival of the train j at the station siNumber of waiting passengers
Figure GDA0002620900880000112
When i is 1, j is 1,
Figure GDA0002620900880000113
when i ≠ 1, j ≠ 1,
Figure GDA0002620900880000114
when i is 1, j is not equal to 1,
Figure GDA0002620900880000115
when i is not equal to 1, j is not equal to 1,
Figure GDA0002620900880000116
step 3.4: residual bearing capacity of train j arriving at station i
Figure GDA0002620900880000117
And (3) calculating:
when the value of i is 1, the value of i,
Figure GDA0002620900880000118
when i ≠ 1, it is,
Figure GDA0002620900880000119
step 3.5: calculating the passenger flow staying at the platform according to a formula
Figure GDA00026209008800001110
Figure GDA00026209008800001111
Step 3.6: calculating to obtain the train leaving stationsiNumber of passengers in the vehicle compartment
Figure GDA00026209008800001112
When the value of i is 1, the value of i,
Figure GDA0002620900880000121
when i ≠ 1, it is,
Figure GDA0002620900880000122
step 3.7: if i is equal to i +1, go to step 3.3, and calculate the state of the next station of train j, otherwise go to step 3.7.
Step 3.8: if j is equal to j +1, if j is equal to or less than n, the step i is equal to 1, the step 3.3 is carried out, the passenger carrying condition of the train j +1 at each station is continuously calculated, and otherwise, the step 3.8 is carried out.
Step 3.9: after all the trains are calculated, outputting results including: 1) the remaining bearing capacity, the waiting passenger flow and the remaining passenger flow of the train leaving the station of all the trains arriving at the station are calculated; 2) the capacity co-dispatching of all trains at each station is calculated and output; 3) calculating the density of the seats of the train and grading; the algorithm terminates.
And 4, step 4: the parameters are classified between [0,1], and the non-quantitative toughening treatment is carried out, wherein the basic expression is as follows:
Figure GDA0002620900880000123
in the formula:
Xl-the actual value of the parameter;
min Xj-the minimum value of the data relating to the train j operating on the line l, in particular according to a normalized parameter.
max Xj-maximum value of data relating to train j operating on line l, in particular toA normalization parameter.
The specific form of each parameter normalization formula is as follows:
1) the train punctual reliability is dimensionless and is a dimensionless value between [0,1], and the normalization is not needed.
2) Therefore, line transport capacity co-scheduling θd lThe expression is as follows:
Figure GDA0002620900880000131
3) the passenger seat density in the carriage is represented by applying a normalization formula, wherein the normalization formula is as follows:
Figure GDA0002620900880000132
defining: assuming s > 0, if:
1) the function u (x) is continuous, piecewise derivable;
2) if x1≥x2→u(x1)≥u(x2),u′(x1)≥′u(x2)；
3)u(0.5)＜0.5；
Then when s > 1, the mapping u [0,1] → [0, s ] is called a gain function;
when s belongs to (0,1), the mapping u: [0,1] → [0, s ] is called a break function;
when s is 1, the mapping u: [0,1] → [0, s ] is called a non-folding, non-gain function.
According to the gain type weighting fusion model, the constructed line operation service reliability model is as follows:
Figure GDA0002620900880000133
wherein, each fused parameter has its own weight value, which is kp,κc,κdSatisfy kp+κc+κ d1 is ═ 1; and calculating the reliability of the operation service of the urban rail transit line in the delay state.
Example two
The second embodiment of the invention takes a Beijing urban rail transit system as an example, and provides a method for calculating the reliability of urban rail transit line operation service in a delay state, which comprises the following steps:
step 1: the simulation research is carried out on the line delay condition based on the existing data of the eight-way line station entering and exiting quantity of the subway in Beijing city, the passenger OD data researched on site, the station entering and exiting quantity and the station entering speed of each station.
(1) The assumption is that:
1) the Beijing subway eight-way line is taken as a simulation background, and the simulation time T7200 s T is two hours of the peak time of 7:00-9: 00. And the station spacing of subway octuples is assumed to be 2000 m. The influence of the distance between stations length factor on the reliability of the line operation service is not considered.
2) In the simulation time period of T7200 s, the number j of the train with the initial delay on the subway line is 12, and the train is at the station s9Delay time of (double-bridge subway station with eight-way line)
Figure GDA0002620900880000141
3) The maximum bearing capacity of the simulated B-type subway train running at 6 marshalls of early peak is C0×120％；
4) In the simulation, the initial passenger flow retention of the station is 0;
5) in the embodiment, only the operation of a unidirectional line (from an earth bridge to a four-benefit direction) is considered, the total amount of the early-peak passenger flow of the eight-way line is certain, the inbound waiting passenger flow of each station uniformly enters the station and waits according to the investigated OD data, and the short-time large passenger flow impact condition does not exist;
6) the value a is generally determined according to the production technology level and general experience, and since the non-dimensionalized parameters are all [0,1], this embodiment is studied by preselecting a to 0.5, and the "lower limit" of each parameter threshold is constrained by 0.5.
(2) Inputting data:
the number of passengers getting off at each station and the basic data of the simulation are input, and the data are shown in table 1 and are an important parameter initial value table for train simulation.
TABLE 1
Figure GDA0002620900880000142
Figure GDA0002620900880000151
Fig. 4 is a schematic diagram of simulation results of a subway octuple operation train, wherein a) in fig. 4 is an operation diagram of a train without delay on the line, and b) in fig. 4 is a simulation operation diagram of a subway line with delay time of 10min, namely 600 s.
From the simulation results, it can be seen that the delay time of the line train in the delay state at each station is as shown in fig. 5, where the initial delay phenomenon occurs at the station double bridge at train j-12, the delay time is 600s at the media university, high monument shop, four-benefit and four-benefit-east stations, the tie delay occurs due to the influence of the initial delay train j-12 at train j-13, 14 and 15, the delay time is 435s, 207s and 105s at the double-bridge station, the delay time is 13,14 and 15 is 435s, 207s and 105s at the media university, high monument shop, four-benefit and four-benefit-east stations, respectively.
And selecting the delay time of the train with the initial delay or the continuous delay on the line as a research object within a certain T time period. The horizontal coordinate is the cumulative percentage of the number of trains, the vertical coordinate is the cumulative percentage of the delay time, and the Lorentz curve of the total delay time of the line operation in a certain T time period can be obtained by drawing a smooth curve, as shown in FIG. 6, wherein A represents an absolute balance line, and B represents the Lorentz curve when the initial delay time is 10 min.
Step 2: initial delay train j-12 at station s9Delay time of
Figure GDA0002620900880000161
The cumulative percentage of the number of trains and the cumulative percentage of the delay time under different conditions are shown in table 2:
TABLE 2
Figure GDA0002620900880000162
As can be seen from fig. 6, in the time period T during the line operation process, the total number of trains running on the line is 40, and the number of delayed trains is nDThe number of trains without delay is n-nDTrain j-12 at station s12Initial delay occurs and delay time of train is initially delayed
Figure GDA0002620900880000163
The number of delayed trains on the line is greatly different from the number of trains without delay, the delay time of the delayed trains is more, and the Lorenz curve is far from the absolute equal curve, so that the imbalance of the delay on the line is proved, and a dispatcher needs to dispatch a small number of delayed trains, possibly spreading to other trains, so that the influence area of the delay condition on the line is larger, and the imbalance of the delay on the line in the example is larger.
The delay effect coefficient can be obtained by the calculation formula of FIG. 6 and the delay effect coefficient
Figure GDA0002620900880000164
Train j being 12 at station s9An initial delay occurs with an initial delay time of
Figure GDA0002620900880000165
The calculation results of the delay effect coefficient and the reliability of the line service under the circumstances are shown in table 3 below.
TABLE 3
Figure GDA0002620900880000171
And step 3: fig. 7 shows the data of the occurrence of the delay and the remaining load capacity of the train after the occurrence of the delay, in which the horizontal axis represents the train and the vertical axis represents the remaining load capacity of the train. Wherein (a) is the simulation result of the residual bearing capacity of the train after arrival of the train after no delay phenomenon and delay occurrence; (b) the residual bearing capacity of the train reaching the four Huidong stations after the delay phenomenon occurs.
According to the line conveying capacity coordination solving algorithm flow, the train j at the station s is obtained through simulating the obtained data and combining the calculated line operation conveying capacity coordination schedulingiThe capacity co-scheduling of (2) is shown in fig. 8.
Calculating according to a formula to obtain the transport capacity co-scheduling of n trains, wherein the following curve is the average transport capacity co-scheduling of the train j without delay phenomenon and after delay phenomenon
Figure GDA0002620900880000172
See fig. 9, in which the broken line marked by the circle at the vertex is the capacity coordinated schedule of the train when no delay occurs on the line, and the broken line marked by the star is the capacity coordinated schedule of the train when the delay occurs on the line.
The density of seats in the passenger compartment before the train j arrives at the station i can be obtained according to the formula, as shown in fig. 10, when the train j on the described line is delayed at 12,13,14,15, the train j arrives at the station siThe density of the seats of the front passengers in the carriage.
And 4, step 4: according to the calculation of the reliability of the line operation service, the reliability of the line operation service without delay and with delay is calculated, and the calculation results are compared and analyzed.
The parameter determination steps are as follows:
(1) parameter setting
1)
Figure GDA0002620900880000181
In this document, each parameter is treated equally, and the importance of each parameter is considered equally importantFirstly, mixing;
2) and calculating the numerical value of the index, wherein the numerical value is related to the determination of the magnitude of the s value, if s is larger, the value range of the value is enlarged, and the increase trend change of the calculated index value is larger. Let s be 2 in this text, i.e.
Figure GDA0002620900880000182
(2) Determining k value
The process comprises the steps of (1), (u), (x) x and (u), (x) 2xkComparative analysis was performed. It can be seen that when s > 1, there is an intersection point (a, a), where a is 0.5 and k is 2 to ensure that the three parameter indexes can be treated equally.
(3) The reliability of the line operation service of the delay phenomenon on the line is calculated according to a formula as follows:
Figure GDA0002620900880000183
the reliability of the line operation service of the second embodiment of the invention is 0.7326, and meanwhile, the reliability of the train punctuality is 0.9006 in the result analysis, and the seating density is ensured to be 8 persons/m2Since passengers are not comfortable in the line and can feel congestion, it is known that the early peak operation of the eight-way line is a state in which a train is delayed, a passenger flow is stopped, and some passengers travel the train with a delay, and thus the level of line operation service and the reliability of line operation service need to be improved.
In summary, the embodiment of the invention considers the line operation condition from the view point of train operation, the view point of passengers and the view point of coordination of the train and the passengers, a method for calculating the service reliability of an urban rail train operation line is constructed, the line operation punctuality reliability, the passenger seat density in a carriage and the line transmission capacity co-scheduling index are comprehensively considered, determining corresponding weight according to the influence of each on the reliability of the line operation service, and using the gain type weighting fusion method of the characteristics of 'rewarding, good price and bad price', thereby realizing the comprehensive evaluation of the reliability of the line operation service, the method conforms to the characteristic of the reliability evaluation of the urban rail transit operation service, and a line operation service reliability model with a function of 'rewarding, good and bad' is established according to the principle of 'rewarding, good and bad', so that the principle of averaging of the traditional linear weighted fusion method is avoided.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (5)

1. A method for calculating the reliability of operation service of an urban rail transit line is characterized by comprising the following steps:
step S110: determining the number of delayed trains in a delayed state and the delay time of each delayed train according to the train operation simulation and the train operation diagram;
step S120: calculating the train right-point reliability according to the delay train number and the delay time;
step S130: determining a line operation conveying capacity co-scheduling solving model according to the delay time, and calculating line operation conveying capacity co-scheduling and the position density of passengers in the train;
step S140: according to a gain type weighting fusion model, combining the positive point reliability, the line operation transmission capacity co-scheduling and the position density, constructing an operation service reliability model, and calculating operation service reliability; wherein, the gain type weighting fusion model is as follows:
assuming s > 0, if:
1) function u (x) sxvContinuous, piecewise conductive, where s, v are parameters;
2) if x1≥x2→u(x1)≥u(x2),u′(x1)≥′u(x2)；
3)u(0.5)＜0.5；
Then when s > 1, the mapping u [0,1] → [0, s ] is called a gain function;
when s belongs to (0,1), the mapping u: [0,1] → [0, s ] is called a break function;
when s is 1, the mapping u: [0,1] → [0, s ] is called a non-folding, non-gain function.
2. The method for calculating the reliability of the operation service of the urban rail transit line according to claim 1, wherein the step of determining the number of delayed trains in a delayed state and the delay time of each delayed train according to the train operation simulation and the train operation diagram comprises the steps of:
obtaining the number n of delayed trains of the line in the delayed state based on the operation rule and the delay propagation characteristic of the trainsDWith each delayed train j at station siDelay time of
Figure FDA0002633300950000011
Wherein m represents m stations in the line operation, and n represents n trains in the line operation.
3. The method for calculating the reliability of the operation service of the urban rail transit line according to the claim 2, wherein the calculating the reliability of the train on-schedule according to the number of delayed trains and the delay time comprises:
according to the delay time of the train running on the line L in the T time period
Figure FDA0002633300950000021
Grouping trains according to different delay time, and dividing into p groups, xkRepresenting the number of trains owned by the k-th trainkRepresenting the delay time corresponding to the kth train;
delay time of running each train on the line according to T time period
Figure FDA0002633300950000022
Sorting according to the sequence from small to large, and correspondingly sorting trains corresponding to different delay times to obtain x1＞x2＞...＞xp；
Calculating the ratio X of the number of the kth group of trains to the sum of the number of each group of trainskAnd cumulative percent X'k；
Figure FDA0002633300950000023
Figure FDA0002633300950000024
Calculating the ratio Y of the delay time of the kth group to the sum of the delay time of each groupkAnd cumulative percentage Yk′；
Figure FDA0002633300950000025
Figure FDA0002633300950000026
The cumulative percentage Y of each group of delay time to the total delay timek' is a vertical axis, and takes the cumulative percentage X of the number of each group of trains to the total number of the trains in each groupkIs a crossAxis, each set of data (X) in the coordinate axisk′,Yk') is represented by a dot, depicting the position of all valid data in the coordinate axis during the T period;
fitting curve Y based on least square principlek′＝f(Xk', a), where a denotes the fitting parameters, the difference between the values of the fitted function curve and the actual values is found so that the sum of squares thereof is minimal:
Figure FDA0002633300950000027
calculating the delay effect coefficient lambda according to the Gini coefficient and Lorenz curve theory*；
Figure FDA0002633300950000031
According to the formula
Figure FDA0002633300950000032
And calculating the train punctual reliability.
4. The method for calculating the reliability of the urban rail transit line operation service according to claim 3, wherein the determining a line operation transportation capability co-scheduling solution model according to the delay time comprises:
step S131: combining the train fixed member with the train marshalling and the passenger arrival rate of the station to iteratively calculate the arrival rate of the train j at the station siNumber of waiting passengers
Figure FDA0002633300950000033
Wherein the content of the first and second substances,
Figure FDA0002633300950000034
indicating passenger presence at station si(ii) arrival rate of;
Figure FDA0002633300950000035
indicating train j slave station si-1To station siRun time of tdRepresenting a planned sojourn time of the train at the station;
Figure FDA0002633300950000036
wherein, tFThe departure interval is shown as the interval between the two departure cars,
Figure FDA0002633300950000037
indicating that the train j-1 is at the station siThe retention passenger flow volume of (2);
step S132: arrival of train j at station siResidual carrying capacity of
Figure FDA0002633300950000038
And (3) calculating:
when the value of i is 1, the value of i,
Figure FDA0002633300950000039
when i ≠ 1, it is,
Figure FDA00026333009500000310
wherein, C0Representing the rated passenger carrying capacity of the train, quantified by the product of the train's fixed member and the train consist;
Figure FDA00026333009500000311
indicating arrival of train j at station siThe number of alighting passengers;
step S133: calculating to obtain the leaving station s of the train j according to a formulaiHour, station siVolume of remaining passenger
Figure FDA00026333009500000312
Figure FDA0002633300950000041
Calculating to obtain the departure station s of the train jiNumber of passengers in train
Figure FDA0002633300950000042
When the value of i is 1, the value of i,
Figure FDA0002633300950000043
when i ≠ 1, it is,
Figure FDA0002633300950000044
if i is equal to i +1, if i is equal to or less than m, returning to the step S131 to calculate the state of the train j reaching the next station, otherwise, making j equal to j +1, if j is equal to or less than n, making i equal to 1, and going to the step S131 to continue calculating the passenger carrying condition of the train j +1 at each station until all the trains are calculated, so as to obtain the residual bearing capacity and the waiting passenger flow of all the trains reaching the station and the reserved passenger flow of the trains leaving the station; calculating the line operation transmission capacity co-scheduling and the position density as follows:
Figure FDA0002633300950000045
Figure FDA0002633300950000046
wherein:
Figure FDA0002633300950000047
indicating departure of train j from station siThe density of the mat in the carriage is increased,
Figure FDA0002633300950000048
indicating departure of train j from station siThe number of passengers in the front compartment; cyThe standing area of passengers in the train compartment is shown, C is the total area of the train compartment,
Figure FDA0002633300950000049
the area of the seats in the train car is shown.
5. The method according to claim 4, wherein an operational service reliability model is constructed according to a gain-type weighted fusion model by combining the positive point reliability, the line operational transport capacity co-scheduling and the position density, and the calculating of the operational service reliability comprises:
co-scheduling the line operation transport capacity
Figure FDA0002633300950000051
And the density of the mat
Figure FDA0002633300950000052
Carrying out dimensionless treatment to obtain:
Figure FDA0002633300950000053
Figure FDA0002633300950000054
and according to a gain type weighted fusion model, taking the punctuality reliability, the line operation transmission capacity co-scheduling and the position density after the dimensionless processing as construction parameters, wherein the constructed line operation service reliability model is as follows:
Figure FDA0002633300950000055
wherein, κp,κc,κdWeight values respectively representing the positive point reliability, the line operation transmission capacity co-scheduling and the seat density, and satisfying kappap+κc+κd1, wherein s, v are parameters.
CN201810321042.3A 2018-04-11 2018-04-11 Urban rail transit line operation service reliability calculation method Active CN108536965B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810321042.3A CN108536965B (en) 2018-04-11 2018-04-11 Urban rail transit line operation service reliability calculation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810321042.3A CN108536965B (en) 2018-04-11 2018-04-11 Urban rail transit line operation service reliability calculation method

Publications (2)

Publication Number Publication Date
CN108536965A CN108536965A (en) 2018-09-14
CN108536965B true CN108536965B (en) 2020-12-01

Family

ID=63480788

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810321042.3A Active CN108536965B (en) 2018-04-11 2018-04-11 Urban rail transit line operation service reliability calculation method

Country Status (1)

Country Link
CN (1) CN108536965B (en)

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109409578B (en) * 2018-09-29 2021-07-27 北京市地铁运营有限公司 Urban rail transit road network flow-limiting organization scheme optimization method
CN109767075B (en) * 2018-12-17 2023-07-07 同济大学 Urban rail transit network train operation reliability assessment method
CN110135629B (en) * 2019-04-24 2021-07-02 中车青岛四方机车车辆股份有限公司 Method and device for predicting average fault period of train
CN111198894B (en) * 2020-01-03 2023-09-22 中国铁道科学研究院集团有限公司通信信号研究所 Method for realizing operation line data fusion and consistency
CN111324853B (en) * 2020-03-09 2023-06-27 西南交通大学 Method and system for calculating passage capacity of channel type high-speed railway
CN113256188A (en) * 2021-07-15 2021-08-13 广州地铁设计研究院股份有限公司 Regional track cross-line operation transport capacity evaluation interaction system
CN115465336A (en) * 2022-08-29 2022-12-13 通号万全信号设备有限公司 Tramcar operation diagram-based method and device for counting driver and passenger driving data

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101826200A (en) * 2010-04-02 2010-09-08 北京交通大学 Method for evaluating operating effect of urban track traffic hub
CN105740606A (en) * 2016-01-22 2016-07-06 北京交通大学 High speed train reliability analysis method based on reliability GERT (Graphical Evaluation and Review Technique) model
CN105760589A (en) * 2016-02-03 2016-07-13 北京交通大学 Reliability analyzing method based on high-speed train system action relation network
US20170171834A1 (en) * 2015-11-30 2017-06-15 Veniam, Inc. Systems and methods for improving coverage and throughput of mobile access points in a network of moving things, for example including a network of autonomous vehicles
US20170352202A1 (en) * 2015-11-10 2017-12-07 Veniam, Inc. Systems and methods for multi-vehicle adaptive data collection in a network of moving things, for example including autonomous vehicles

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101826200A (en) * 2010-04-02 2010-09-08 北京交通大学 Method for evaluating operating effect of urban track traffic hub
US20170352202A1 (en) * 2015-11-10 2017-12-07 Veniam, Inc. Systems and methods for multi-vehicle adaptive data collection in a network of moving things, for example including autonomous vehicles
US20170171834A1 (en) * 2015-11-30 2017-06-15 Veniam, Inc. Systems and methods for improving coverage and throughput of mobile access points in a network of moving things, for example including a network of autonomous vehicles
CN105740606A (en) * 2016-01-22 2016-07-06 北京交通大学 High speed train reliability analysis method based on reliability GERT (Graphical Evaluation and Review Technique) model
CN105760589A (en) * 2016-02-03 2016-07-13 北京交通大学 Reliability analyzing method based on high-speed train system action relation network

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
Research and Implementation on Connectivity Reliability Calculation Algorithm of Urban Rail Transit Network Operation;Man Li,et al.;《11th World Congress on Intelligent Control and Automation》;20150305;第5886-5890页 *
urban rail transit types and single wire on the propagation law of delays;Zhang D X,et al.;《Applied Mechanics and Materials》;20160501;第835卷;第763-766页 *
基于BP神经网络的城市轨道交通车辆可靠性预测;李建伟等;《中南大学学报（自然科学版）》;20130731;第44卷;第42-46页 *
增益型加权综合法在城轨交通运营安全评价中的应用;王艳辉等;《铁道学报》;20130331;第35卷(第3期);第9-17页 *

Also Published As

Publication number Publication date
CN108536965A (en) 2018-09-14

Similar Documents

Publication Publication Date Title
CN108536965B (en) Urban rail transit line operation service reliability calculation method
EP4030365A1 (en) Multi-mode multi-service rail transit analog simulation method and system
CN105427605B (en) A kind of public transportation lane considering travel mode transfer arranges Benefit Calculation
Li et al. A dynamic simulation model of passenger flow distribution on schedule-based rail transit networks with train delays
CN102867408A (en) Method and system for selecting bus trip route
CN106651728A (en) Determination method for advantageous haul distances of passenger transport modes in comprehensive transport system
CN110228507A (en) A kind of automatic train regulation control method based on event triggering
Long et al. Prioritizing future funding and construction of the planned high-speed rail corridors of China–According to regional structure and urban land development potential indices
CN111797283A (en) Air-rail transit method based on undirected weighted graph
Binglei et al. An evaluation on coordinated relationship between urban rail transit and land-use under TOD mode
Feng et al. Choices of intercity multimodal passenger travel modes
Orth et al. Calibration of a public transport performance measurement system for Switzerland
Zhang et al. Study on evaluation indicators system of crowd management for transfer stations based on pedestrian simulation
CN107239851A (en) A kind of method that power consumption index towards track traffic is predicted
Justin et al. Regional air mobility market study
MacDonald The future of high capacity PRT
CN112348341A (en) Method for calculating real-time competition index of ground bus
Li et al. Service quality evaluation of bus lines based on improved momentum back‐propagation neural network model: A study of Hangzhou in China
CN117745108B (en) Passenger flow demand prediction method and system for advanced air traffic
Martinod et al. Decision support algorithm for intermodal transport networks: Urban aerial cableway systems
Liang et al. Assignment performance at Beijing south railway station based on Anylogic simulation
Hu et al. A sensitivity-based approach for identification of flexible design opportunities in engineering system design
Li et al. Energy-saving methods for urban travel and public transportation in smart cities
Cao et al. The adjustment method of bus lines along new subway after operation
Wang Design of passenger transport modes between cities with environmental sustainability

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
TR01 Transfer of patent right

Effective date of registration: 20240606

Address after: 100094 building 11, yard 9, FengHao East Road, Haidian District, Beijing

Patentee after: BEIJING TELESOUND ELECTRONICS Co.,Ltd.

Country or region after: China

Address before: 100044 Beijing city Haidian District Xizhimen Shangyuan Village No. 3

Patentee before: Beijing Jiaotong University

Country or region before: China