CN113793042A - Passenger flow control scheme compilation method for rail traffic line station - Google Patents

Passenger flow control scheme compilation method for rail traffic line station Download PDF

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CN113793042A
CN113793042A CN202111095624.2A CN202111095624A CN113793042A CN 113793042 A CN113793042 A CN 113793042A CN 202111095624 A CN202111095624 A CN 202111095624A CN 113793042 A CN113793042 A CN 113793042A
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station
passenger flow
flow control
passengers
train
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王宇
张宇鹏
李季涛
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Dalian Jiaotong University
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Abstract

A passenger flow control scheme compiling method for a rail traffic line station comprises the following steps: (1) collecting and arranging urban rail transit passenger flow data and related operation parameters; (2) establishing a passenger flow control model by using the data; (3) bringing the data into a model, and solving by using an improved genetic algorithm, wherein the method comprises the following steps: (4) and after the model and the algorithm are verified to be reasonable and effective, generating a passenger flow control scheme by utilizing a Matlab software writing program. The method for compiling the passenger flow control scheme of the rail transit line station can solve the problem that the passenger flow control effect cannot be ensured when the existing urban rail transit system carries out the passenger flow control in the peak period by depending on manual experience. The method can also improve the compiling efficiency of the passenger flow control scheme, further strengthen the passenger flow control effect of the scheme, and provide a feasible solution for the problem of passenger flow control in peak hours.

Description

Passenger flow control scheme compilation method for rail traffic line station
Technical Field
The invention relates to the field of rail transit passenger flow control.
Background
The rail transit is the first choice for most urban residents, but the accompanying problems of passenger flow congestion and operation safety in the peak period need to be solved urgently. The urban rail transit system has large passenger flow in peak period, centralized distributed time and obvious tide phenomenon. When the existing transport capacity cannot meet the increasing passenger flow demand, passenger flow congestion and passenger detention can be caused, and hidden troubles are caused to the operation safety. The transport capacity of the urban rail transit system depends on the existing equipment passing capacity, and the theoretical maximum transport capacity is relatively fixed when no extension or new line construction is carried out. Under the condition that the transportation capacity cannot be increased in a short time, the passenger flow management is enhanced, and a passenger flow control scheme is optimized, so that the method is the first choice for improving the transportation efficiency.
The passenger flow control scheme aims to control the arrival speed of passengers and guarantee safe and efficient operation of the rail transit system. The existing passenger flow control scheme is mainly implemented from three levels of a station level, a line level and a network level. The passenger flow control scheme of the single station level can only solve the problem of passenger flow congestion in the peak period of the station; the passenger flow control schemes of the line level and the network level are more complex due to the fact that more conditions need to be considered, and the compiling difficulty is greatly improved.
The existing compiling method is not mature, and has great research value and space. At present, most urban rail transit systems adopt manual experience to formulate passenger flow control schemes, have no unified standard and are not objective enough, so the compilation level of the passenger flow control schemes determines the transportation efficiency of rail transit to a great extent.
Disclosure of Invention
In order to solve the problems of the existing rail transit passenger flow control scheme, the invention provides a method for compiling a passenger flow control scheme of a rail transit line station.
The technical scheme adopted by the invention for realizing the purpose is as follows: a passenger flow control scheme compiling method for a rail traffic line station comprises the following steps:
(1) collecting and arranging urban rail transit passenger flow data and related operation parameters;
(2) the data is used to establish a passenger flow control model,
before model construction, making assumptions about influencing factors of the passenger flow control model: a. assuming that the passenger flow state in the research period is stable, the arrival number of passengers in the unit time period can be regarded as obeying Poisson distribution, and the influence of passengers leaving the station is ignored in the model; b. supposing that when the rail transit is congested and the passenger flow demand cannot be met, the passengers cannot transfer to other traffic modes in a short time, namely the total passenger flow demand remains unchanged in a certain time; c. assuming that trains in the research period normally and stably run according to the running chart and arrive on time without emergency; d. assuming that the number of passengers allowed to enter the station in each station of the line does not exceed the designed safety capacity of the station, the passengers can enter the station for queuing and waiting within the specified arrival interval time of the train; (ii) before the model is constructed, proposing the constraint conditions of the passenger flow control model:
a. the number of passengers carried by the train is safely restricted, and the number of people in the train and the platform can meet the following requirements:
p≤α·P (1)
in the formula: p-represents the number of people in the train after the train departs from the station;
alpha-the train full load rate in the medium and high risk areas;
p represents the train design capacity;
b. and (3) constraint of the maximum passenger flow control rate, setting a maximum passenger flow control rate:
β＝50％ (2)
c. the passenger boarding fairness constraint takes the minimum variance of the passenger average queuing waiting time of each station as the passenger boarding fairness constraint:
Figure BDA0003269059920000021
in the formula:
Figure BDA0003269059920000022
-means representing the average queuing wait time of passengers at each station;
tj-represents the average queuing waiting time of station j passengers;
S2-variance representing the average queuing wait time of passengers at each station;
(iii) decision makingThe variable is set as the optimal number of the passengers at the station in the corresponding control time period, and the optimal number of the passengers xij: in each control time interval i, when the train departing from the station j arrives at the station in the corresponding time interval to carry out passenger flow control, the flow rate omega is controlled in the time intervalijIn this case, the maximum traffic control rate β is set to 50%, so that the optimal number of passengers entering the station can be represented as:
Figure BDA0003269059920000023
in the formula: x is the number ofij-the optimal number of inbound people for station j within control period i;
Dij-the actual number of passengers arriving at station j in control period i;
ωij-controlling the passenger flow control rate for station j within time period i;
beta-the maximum flow control rate set for ensuring the service quality of the station, wherein beta is 50%;
(iv) an auxiliary variable, wherein the number of people getting off the vehicle and the number of people on the vehicle are introduced as auxiliary variables for convenient modeling, all the variables are not negative, and the number of people on the vehicle zij: the number of people in the train after the train departs from the station j in the control time period i is as follows:
Figure BDA0003269059920000031
in the formula: z is a radical ofijThe number of people in the train after the train departs from the station j in the control time interval i;
xij-the optimal number of inbound people for station j within control period i;
yij-the number of people getting off at station j in the control period i.
② the number of getting-off people yij: the number of alighting persons can be expressed as the product of the number of persons in the vehicle and the rate of getting off at the destination station:
Figure BDA0003269059920000032
in the formula: y isij-the number of people getting off at station j in control period i;
zij-the optimal number of inbound people for station j within control period i;
δij-controlling the getting-off rate of passengers at station j in time interval i;
(v) selecting the total time delayed by passengers and the passenger turnover number as target functions, and obtaining the optimal number x of passengers getting into the station according to the formula (4)ijThe total delay time is related to the number of passengers delayed and the average queuing waiting time; the number z of passengers in the car can be obtained by the formula (5)ijThe passenger turnover is the product of the number of passengers in the vehicle and the station distance; the number of people getting off at each station can be obtained by the formula (6)ijThe number of passengers getting off at each station can influence the number of passengers in the station and the number of passengers getting in the station, and in conclusion, the objective function is as follows:
Figure BDA0003269059920000033
in the formula: f. of1(x) -representing the total delay time of passengers affected by passenger flow control;
Dij-the actual passenger flow demand for station j within control interval i;
xij-the optimal number of inbound people for station j within control period i;
Δtij-average queuing waiting time for passengers at station j within control period i;
f2(x) -representing passenger turnover;
zijthe number of people in the train after the train departs from the station j in the control time interval i;
dj-is the length of the section from station j to station j + 1;
the simultaneous formulas (1) to (7) form a passenger flow control model of a railway station;
(3) bringing the data into a model, and solving by using an improved genetic algorithm, wherein the method comprises the following steps:
encoding a solution of a multi-objective optimization problem;
(II) generating an initial population;
(III) determining the constraint type of the multi-objective optimization model;
(IV) determining decision variables as solutions to the genetic algorithm;
(V) determining a target function as a fitness function;
the fitness function is formulated according to the objective function of the passenger flow control model, the advantages and disadvantages of parent individual solutions are determined according to the calculation result of the fitness function value, individuals with the fitness function value meeting the requirement of the objective function of the passenger flow control model are reserved to form a new population, and the total passenger delay time and the turnover number are used as the fitness function in the solving process of the model.
(4) And after the model and the algorithm are verified to be reasonable and effective, generating a passenger flow control scheme by utilizing a Matlab software writing program.
In the step (1), the urban rail transit passenger flow data and the relevant operation parameters comprise passenger flow demands and arrival volumes at station peak periods of the urban rail transit lines, train departure interval time, arrival interval time of each station, train design passenger capacity, a train schedule, station platform design capacity, line design passing capacity and unit time transport capacity.
And (3) adopting real number coding in the step (I).
In the step (iii) of the step (3), in the calculation process, the gacommon function is called to determine the constraint type of the passenger flow control model.
In the step (IV) of the step (3), a function gamtobjsolve is called to solve the multi-objective optimization problem, the solution x obtained by calculation of a genetic algorithm represents the optimal number of station entering people in a certain passenger flow control period, in the function gamtobjsolve, the function gamtobjMakeState is called first to generate an initial population, then whether the algorithm can be quitted is judged, and if the algorithm can be quitted, a Pareto optimal solution is obtained; if the calculation result does not meet the model constraint condition, calling a function stepgammulibj to enable the population to evolve for one generation, then calling a function gaddsplot to draw, and calling a function gmultibjoverged to judge the termination condition.
In the step (3), (v) the fitness function is formulated according to the objective function of the passenger flow control model, and according to the calculation result of the fitness function value, the advantages and disadvantages of parent individual solutions are determined, individuals with fitness function values meeting the requirements of the objective function of the passenger flow control model are reserved to form a new population, and the total passenger delay time and the turnover number are used as the fitness function.
The method for compiling the passenger flow control scheme of the rail transit line station can solve the problem that the passenger flow control effect cannot be ensured when the existing urban rail transit system carries out the passenger flow control in the peak period by depending on manual experience. The method can also improve the compiling efficiency of the passenger flow control scheme, further strengthen the passenger flow control effect of the scheme, and provide a feasible solution for the problem of passenger flow control in peak hours.
Drawings
Fig. 1 is a flow chart of the construction of the urban rail transit passenger flow control scheme of the invention.
FIG. 2 is a flow chart of the modeling of the passenger flow control model of the present invention.
FIG. 3 is a flow chart of the present invention for solving a passenger flow control model using an improved genetic algorithm.
FIG. 4 is a schematic diagram of a parameter input interface in a passenger flow control scheme generating program written by Matlab software according to the present invention.
Fig. 5 is a schematic diagram of a main interface in a passenger flow control scheme generation program written by Matlab software according to the present invention.
Detailed Description
The invention discloses a passenger flow control scheme compiling method of a rail transit line station based on an improved genetic algorithm, which is shown as the attached figure 1 and comprises the following steps:
(1) and collecting and arranging the OD data of the urban rail transit passenger flow and related operation parameters, including the OD data of the passenger flow in the peak period, station and train design parameters, rail transit operation data, train operation diagrams and the like, so that the following model construction and scheme are facilitated.
Passenger flow demand and arrival volume of urban rail transit line stations in peak periods (passenger flow distribution of each station along the line can be obtained through arrangement and statistics of AFC card swiping data, and the shortest recording time can be 15 or 30 minutes);
urban rail transit operation parameters (departure interval time of a train, arrival interval time of each station, train design passenger capacity, train schedule and the like);
line and station design capacity (station platform design capacity, line design passing capacity, transport capacity per unit time, etc.). (2) And establishing a passenger flow control model by using the processed data, as shown in fig. 2, because many factors need to be considered in the actual operation of the rail transit, before establishing the passenger flow control model, assumptions need to be made on the condition which can not be solved temporarily in the modeling process, secondary contradictions are ignored, the main problems are caught, the modeling process is simplified, and the efficiency is improved. Selecting a target function in the model, wherein the target function can select the most concerned problems of an operation enterprise and passengers in the passenger flow control process, such as passenger turnover and queuing delay time; the constraint condition is the regulation and requirement which must be met in the process of model construction and design, such as passenger transport capacity design, epidemic prevention regulation and the like; the decision variables are selected as closely as possible to the independent variables which are most closely related to the objective function, and not only need to be representative, but also have certain practical and mathematical meanings.
Before the model is built, assumptions need to be made about influencing factors of the passenger flow control model:
it is assumed that the OD of the traffic in the peak control period is stable and no sudden large traffic will occur. For example, the main body of the passenger flow in the morning and evening peak periods is commuting passenger flow, the traveling condition is relatively stable, and the relatively complete passenger flow condition can be obtained by counting historical AFC passenger flow data of rail transit and analyzing and predicting the historical AFC passenger flow data;
assuming that the passenger flow conditions within the study period are stable, the number of passengers arriving per unit time period may be considered to be subject to a poisson distribution. The outbound passengers can rapidly exit, and the influence on passengers queued and waiting in the platform and the station hall is small, so the influence of the outbound passengers is ignored in the model;
supposing that when the rail transit is congested and the passenger flow demand cannot be met, the passengers cannot transfer to other traffic modes in a short time, namely the total passenger flow demand remains unchanged in a certain time;
assuming that trains in the research period normally and stably run according to the running chart and arrive on time without emergency;
assuming that the number of passengers allowed to enter the station in each station of the line does not exceed the designed safety capacity of the station, the passengers can enter the station for queuing and waiting within the specified arrival interval time of the train;
before the model is constructed, the constraint conditions of the passenger flow control model need to be proposed:
a. safety restraint of train passenger carrying quantity:
under the background of normalized epidemic situation prevention and control, the requirements of a passenger station and a transportation means new coronary pneumonia epidemic situation zoning grading prevention and control guide (fourth edition) and a passenger station and a transportation means new coronary pneumonia epidemic situation zoning grading prevention and control guide during 2021 spring are printed by a transportation department, the full load rate of a train and a platform in a high risk area is less than or equal to 50 percent in the transportation process, the full load rate of the train and the platform in an intermediate risk area is less than or equal to 70 percent, so the number of people in the train and the platform is satisfied:
p≤α·P (1)
in the formula: p-represents the number of people in the train after the train leaves the station;
alpha-represents epidemic prevention standard (train full load rate in medium risk area, high risk area);
p-represents train design capacity.
b. And (3) constraint of maximum passenger flow control rate:
in order to ensure the service quality of the urban rail transit system, a maximum passenger flow control rate needs to be set:
β＝50％ (2)
this means that at least half of passengers can enter the station to wait in line each time a train arrives, so as to ensure the transportation efficiency of the urban rail transit system and meet the basic service requirements of the line station.
c. The passenger boarding fairness constraint:
one important factor affecting the quality of service level and passenger satisfaction of rail transit is the fairness of boarding opportunities for passengers at various stations. If passenger flow control is not carried out, passengers at a front station can occupy a large amount of transport capacity of a train without getting on the train without limit, the train can be fully loaded quickly, passengers at a rear station can not get on the train, and more queuing waiting time than the passengers at the front station can be spent. Aiming at the phenomenon, the minimum variance of the average queuing waiting time of passengers at each station is taken as the fairness constraint of the passengers getting on the bus.
Figure BDA0003269059920000071
In the formula:
Figure BDA0003269059920000072
-means representing the average queuing wait time of passengers at each station;
tj-represents the average queuing waiting time of station j passengers;
S2-variance representing the average queuing wait time of passengers at each station.
And setting the decision variable as the optimal number of the station-entering people in the corresponding control period. In order to facilitate modeling, the number of people getting off a train, the number of people on the train and the like are introduced as auxiliary variables, and all the variables are not negative.
Decision variables: optimum number of inbound people xij: in each control time interval i, when the train departing from the station j arrives at the station in the corresponding time interval to carry out passenger flow control, the flow rate omega is controlled in the time intervalijThe process is carried out. To ensure that passenger flow control does not significantly reduce the service level of the station, a maximum flow control rate β is set to 50%, so the optimal number of passengers entering the station can be expressed as:
Figure BDA0003269059920000073
in the formula: x is the number ofij-the optimal number of inbound people for station j within control period i;
Dij-the actual number of passengers arriving at station j in control period i;
ωij-controlling the passenger flow control rate for station j within time period i;
β — the maximum flow rate set for ensuring the quality of service in the station, β is 50%.
Auxiliary variables:
vehicle carried number zij: the number of people in the train after the train departs from the station j in the control time period i is as follows:
Figure BDA0003269059920000074
in the formula: z is a radical ofijThe number of people in the train after the train departs from the station j in the control time interval i;
xij-the optimal number of inbound people for station j within control period i;
yij-the number of people getting off at station j in the control period i.
② the number of getting-off people yij: the number of alighting persons can be expressed as the product of the number of persons in the vehicle and the rate of getting off at the destination station:
Figure BDA0003269059920000081
in the formula: y isij-the number of people getting off at station j in control period i;
zij-the optimal number of inbound people for station j within control period i;
δij-controlling the getting-off rate of passengers at station j in time interval i.
Aiming at the passenger flow control problem of the rail transit line station in the peak period, the total delay time and the passenger turnover number of passengers are selected as target functions, and the optimal number x of passengers entering the station can be obtained by a formula (4)ijThe total delay time is related to the number of passengers delayed and the average queuing waiting time; the number z of passengers in the car can be obtained by the formula (5)ijThe passenger turnover is the number of passengers in the vehicleThe product of the distance between the stations; the number of people getting off at each station can be obtained by the formula (6)ijThe number of passengers getting off at each station can influence the number of passengers in the bus and the number of passengers getting in at the station. Therefore, the objective function can be obtained as follows:
Figure BDA0003269059920000082
in the formula: f. of1(x) -representing the total delay time of passengers affected by passenger flow control;
Dij-the actual passenger flow demand for station j within control interval i;
xij-the optimal number of inbound people for station j within control period i;
Δtijthe average queuing waiting time of passengers at the station j in the control time interval i is in a value range of 0-10min (related to the average arrival interval time of the train and the number of passengers waiting outside the station);
f2(x) -representing passenger turnover;
zijthe number of people in the train after the train departs from the station j in the control time interval i;
djthe length of the interval from the station j to the station j + 1.
And (4) the simultaneous formulas (1) to (7) form a railway traffic line station passenger flow control model.
3. The data is brought into the model and solved using an improved genetic algorithm as shown in the figure:
the method is characterized in that the construction process of the passenger flow control model is combined, and the improved genetic algorithm is utilized to solve the problems and comprises the following steps:
(1) and coding the solution of the multi-objective optimization problem, wherein the length of the chromosome is the number of variables, and taking the solving algorithm as an example, one chromosome represents the optimal number of the station entrances of all stations in a certain period. Real number coding is adopted, population initialization of the real number coding is relatively simple, individuals of the population are the solution of the problem, and transformation is not needed to be carried out by using a transform function like binary coding.
(2) Generating an initial population, randomly generating initial individuals according with variable numbers, forming a population by all the individuals, and carrying out a series of genetic evolutions by a genetic algorithm by taking the initial population as a starting point.
(3) And determining the constraint type of the multi-objective optimization model, and calling a function gacommon to determine the constraint type of the passenger flow control model in the calculation process. Besides safety constraints on the number of passengers, the model also has linear equality and inequality constraints, and the value range of x is solved, namely the optimal number of passengers entering the station at different stations is constrained, so that the passenger flow control effect is ensured.
(4) And determining a decision variable as a solution of a genetic algorithm, and calling a function gamtobjsolve to solve the multi-objective optimization problem. Each individual represents a solution, and different individuals constitute a population. The solution x calculated by the genetic algorithm represents the optimal number of the station passengers in a certain passenger flow control period. In the function galutobjolve, firstly calling the function galutobjmakeState to generate an initial population, then judging whether the algorithm can be exited, and if the algorithm can be exited, obtaining a Pareto optimal solution; if the calculation result does not meet the model constraint condition, calling a function stepgammulibj to enable the population to evolve for one generation, then calling a function gaddsplot to draw, and calling a function gmultibjoverged to judge the termination condition.
(5) Determining an objective function as a fitness function, wherein the fitness function is formulated according to the objective function of the passenger flow control model, determining the quality of a parent individual solution according to the calculation result of the fitness function value, reserving individuals of which the fitness function value meets the requirement of the objective function of the passenger flow control model, forming a new population, and taking the total passenger delay time and the turnover number as the fitness function in the solving process of the model.
And in the algorithm parameter setting part, the optimal front-end individual coefficient is a special concept in the multi-objective optimization algorithm, the value range of the optimal front-end individual coefficient is 0-1, and the optimal front-end individual coefficient represents the proportion of individuals with the sequence value of 1 in the population. The optimal front-end individual coefficient can be set to be 0.7-0.9.
Randomly generating initial individuals according with variable numbers, forming a population by all the individuals, and starting a series of genetic evolutions by using the initial population as a starting point by a genetic algorithm. The number of the initial population can be set to be 100-200.
The intersection is the most important genetic operation, and the new individuals obtained through the intersection combine the characteristics of the parents of the new individuals, thereby embodying the idea of information exchange. The algorithm adopts intermediate crossing (crossovers), which means that two new individuals are generated by linear combination of two individuals, an operation object is generally an individual represented by floating-point number coding and is defined as combination of two vectors (chromosomes), and the crossing probability is a main genetic mode of most individuals in a population and can be set to be about 0.9.
Mutation needs to randomly select an individual from a group, and the value of certain data in the individual string structure is randomly changed with a certain probability, so that the mutation probability is very low, and the value is usually very small. Because the multi-objective problem to be optimized has a plurality of constraint conditions, a constraint adaptive variation (multivariate adaptive variation) function is adopted, namely random individual generation is carried out in the range of individual values and linear constraint conditions according to the variation result of the previous generation, so that the variation is generated. The mutation probability may be set to 0.05.
And calling a function gamtobjsolve to solve the multi-objective optimization problem. Each individual represents a solution, and different individuals constitute a population. The solution calculated by the genetic algorithm represents the optimal number of the station passengers in a certain passenger flow control period. In the function galutobjolve, firstly calling the function galutobjmakeState to generate an initial population, then judging whether the algorithm can be exited, and if the algorithm can be exited, obtaining a Pareto optimal solution; if the calculation result does not meet the model constraint condition, calling a function stepgammulibj to enable the population to evolve for one generation, then calling a function gaddsplot to draw, and calling a function gmultibjoverged to judge the termination condition. The maximum evolution algebra can be set to be 100-500, the less the evolution algebra is, the faster the calculation speed is, but the calculation result may not be accurate enough; when a certain evolutionary algebra is reached, the calculation result gradually tends to a stable value, and the 300-out value in the model is more suitable.
The fitness function value deviation may be set to 1 e-100.
The operation data and parameter setting comprises total line length, total station number, station distance of each station, departure interval time of the train, arrival interval time of the train at the station, design capacity of the train and the platform, classification prevention and control standards and the like. These data can help model building and solving
4. After the model and the algorithm are verified to be reasonable and effective, generating a passenger flow control scheme by utilizing a Matlab software writing program:
(1) and an objective function, wherein for the established passenger flow control model, a function name f (x) can be set, and the objective function code is as follows:
function y＝f(x)
% objective function of certain current limiting period
y(1)＝(P-(x(1)+x(2)+x(3)+...+x(n)))*T；
% y (1) represents the total waiting time → min for the restricted passenger;
y(2)＝-x(1)*d(1)-(x(1)*(1-z(2))+x(2))*d(2)-((x(1)+x(2))*(1-z(3))+x(3))*d(3)
-...
-((x(1)+x(2)+x(3)+...+x(i-1))*(1-z(i))+x(i))*d(i)-70％*R*(d(i+1)+d(i+2)+...+d(j))；
% y (2) represents the passenger turnover on the line → max, (Matlab finds the minimum value by default and adds a minus sign);
(2) initializing and setting, calling a multi-objective optimization function in a command line mode for calculation, wherein the initial code is as follows:
fitnessfcn＝@f；
nvars＝n；
% number of variables n
% lb < ═ X < ═ ub, upper limit ub and lower limit lb of variable X constrain
lb＝[]；
ub＝[]；
% A X < ═ b, linear inequality constraint
A＝[]；
b＝[]；
% Aeq X beq, constrained by linear equations
Aeq＝[]；
beq＝[]；
options＝gaoptimset('paretoFraction',,'populationsize',,'generations',,'stallGenLimit',,'TolFun',1e-
10,'PlotFcns',@gaplotpareto)；
% optimal front-end individual coefficient pareto fraction
% population size outpationsize
% maximum evolution generations
% stopping algebra stallGenLimit
% fitness function deviation TolFun
[x,fval]＝gamultiobj(fitnessfcn,nvars,A,b,Aeq,beq,lb,ub,options)
(3) Solving function
And calling a function gamtobjsolve to solve the multi-objective optimization problem, wherein the code is as follows:
function[x,fval,exitFlag,output,population,scores]＝gamultiobjsolve(FitnessFcn,GenomeLength,...
Aineq,bineq,Aeq,beq,lb,ub,ConstrFcn,options,output)
% algorithm initialization: population, fitness function, data
Figure BDA0003269059920000111
Figure BDA0003269059920000121
Taking the passenger flow control scheme programming system of the number 1 line of the large-scale connected subway as an example, the parameter interfaces of the algorithm and the model are designed by utilizing the GUI design function of Matlab software, as shown in FIG. 4.
In the passenger flow control scheme generation program interface, the left side is a passenger flow data input area of each station of the rail transit line; the middle part provides the calculation result of the optimal number of the passengers entering the station and the calculation result of the model objective function; the right side gives a graphical representation of the optimal solution, as shown in fig. 5.
While the invention has been described with reference to exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.

Claims (6)

1. A passenger flow control scheme compilation method for rail traffic line station is characterized by comprising the following steps: the method comprises the following steps:
(1) collecting and arranging urban rail transit passenger flow data and related operation parameters;
(2) the data is used to establish a passenger flow control model,
before model construction, making assumptions about influencing factors of the passenger flow control model: a. assuming that the passenger flow state in the research period is stable, the arrival number of passengers in the unit time period can be regarded as obeying Poisson distribution, and the influence of passengers leaving the station is ignored in the model; b. supposing that when the rail transit is congested and the passenger flow demand cannot be met, the passengers cannot transfer to other traffic modes in a short time, namely the total passenger flow demand remains unchanged in a certain time; c. assuming that trains in the research period normally and stably run according to the running chart and arrive on time without emergency; d. assuming that the number of passengers allowed to enter the station in each station of the line does not exceed the designed safety capacity of the station, the passengers can enter the station for queuing and waiting within the specified arrival interval time of the train;
(ii) before the model is constructed, proposing the constraint conditions of the passenger flow control model:
a. the number of passengers carried by the train is safely restricted, and the number of people in the train and the platform can meet the following requirements:
p≤α·P (1)
in the formula: p-represents the number of people in the train after the train departs from the station;
alpha-the train full load rate in the medium and high risk areas;
p represents the train design capacity;
b. and (3) constraint of the maximum passenger flow control rate, setting a maximum passenger flow control rate:
β＝50％ (2)
c. the passenger boarding fairness constraint takes the minimum variance of the passenger average queuing waiting time of each station as the passenger boarding fairness constraint:
Figure FDA0003269059910000011
in the formula:
Figure FDA0003269059910000012
-means representing the average queuing wait time of passengers at each station;
tj-represents the average queuing waiting time of station j passengers;
S2-variance representing the average queuing wait time of passengers at each station;
(iii) setting the decision variable as the optimal number of the station-entering people in the corresponding control time period, and setting the optimal number of the station-entering people as xij: in each control time interval i, when the train departing from the station j arrives at the station in the corresponding time interval to carry out passenger flow control, the flow rate omega is controlled in the time intervalijIn this case, the maximum traffic control rate β is set to 50%, so that the optimal number of passengers entering the station can be represented as:
Figure FDA0003269059910000021
in the formula: x is the number ofij-the optimal number of inbound people for station j within control period i;
Dij-the actual number of passengers arriving at station j in control period i;
ωij-controlling the passenger flow control rate for station j within time period i;
beta-the maximum flow control rate set for ensuring the service quality of the station, wherein beta is 50%;
(iv) auxiliary variables, wherein the number of people getting off the vehicle and the number of people on the vehicle are introduced as auxiliary variables for convenient modeling, all the variables are not negative,
vehicle carried number zij: the number of people in the train after the train departs from the station j in the control time period i is as follows:
Figure FDA0003269059910000022
in the formula: z is a radical ofijThe number of people in the train after the train departs from the station j in the control time interval i;
xij-the optimal number of inbound people for station j within control period i;
yij-the number of people getting off at station j in the control period i.
② the number of getting-off people yij: the number of alighting persons can be expressed as the product of the number of persons in the vehicle and the rate of getting off at the destination station:
Figure FDA0003269059910000023
in the formula: y isij-the number of people getting off at station j in control period i;
zij-the optimal number of inbound people for station j within control period i;
δij-controlling the getting-off rate of passengers at station j in time interval i;
(v) selecting the total time delayed by passengers and the passenger turnover number as target functions, and obtaining the optimal number x of passengers getting into the station according to the formula (4)ijThe total delay time is related to the number of passengers delayed and the average queuing waiting time; the number z of passengers in the car can be obtained by the formula (5)ijThe passenger turnover is the product of the number of passengers in the vehicle and the station distance; the number of people getting off at each station can be obtained by the formula (6)ijThe number of passengers getting off at each station can influence the number of passengers in the station and the number of passengers getting in the station, and in conclusion, the objective function is as follows:
Figure FDA0003269059910000031
in the formula: f. of1(x) -representing the total delay time of passengers affected by passenger flow control;
Dij-the actual passenger flow demand for station j within control interval i;
xij-the optimal number of inbound people for station j within control period i;
Δtij-average queuing waiting time for passengers at station j within control period i;
f2(x) -representing passenger turnover;
zijthe number of people in the train after the train departs from the station j in the control time interval i;
dj-is the length of the section from station j to station j + 1;
the simultaneous formulas (1) to (7) form a passenger flow control model of a railway station;
(3) bringing the data into a model, and solving by using an improved genetic algorithm, wherein the method comprises the following steps:
encoding a solution of a multi-objective optimization problem;
(II) generating an initial population;
(III) determining the constraint type of the multi-objective optimization model;
(IV) determining decision variables as solutions to the genetic algorithm;
(V) determining a target function as a fitness function;
the fitness function is formulated according to the objective function of the passenger flow control model, the advantages and disadvantages of parent individual solutions are determined according to the calculation result of the fitness function value, individuals with the fitness function value meeting the requirement of the objective function of the passenger flow control model are reserved to form a new population, and the total passenger delay time and the turnover number are used as the fitness function in the solving process of the model.
(4) And after the model and the algorithm are verified to be reasonable and effective, generating a passenger flow control scheme by utilizing a Matlab software writing program.
2. The method for programming the passenger flow control scheme of the rail transit station according to claim 1, wherein the method comprises the following steps: in the step (1), the urban rail transit passenger flow data and the relevant operation parameters comprise passenger flow demands and arrival volumes at station peak periods of the urban rail transit lines, train departure interval time, arrival interval time of each station, train design passenger capacity, a train schedule, station platform design capacity, line design passing capacity and unit time transport capacity.
3. The method for programming the passenger flow control scheme of the rail transit station according to claim 1, wherein the method comprises the following steps: and (3) adopting real number coding in the step (I).
4. The method for programming the passenger flow control scheme of the rail transit station according to claim 1, wherein the method comprises the following steps: in the step (iii) of the step (3), in the calculation process, the gacommon function is called to determine the constraint type of the passenger flow control model.
5. The method for programming the passenger flow control scheme of the rail transit station according to claim 1, wherein the method comprises the following steps: in the step (IV) of the step (3), a function gamtobjsolve is called to solve the multi-objective optimization problem, the solution x obtained by calculation of a genetic algorithm represents the optimal number of station entering people in a certain passenger flow control period, in the function gamtobjsolve, the function gamtobjMakeState is called first to generate an initial population, then whether the algorithm can be quitted is judged, and if the algorithm can be quitted, a Pareto optimal solution is obtained; if the calculation result does not meet the model constraint condition, calling a function stepgammulibj to enable the population to evolve for one generation, then calling a function gaddsplot to draw, and calling a function gmultibjoverged to judge the termination condition.
6. The method for programming the passenger flow control scheme of the rail transit station according to claim 1, wherein the method comprises the following steps: in the step (3), (v) the fitness function is formulated according to the objective function of the passenger flow control model, and according to the calculation result of the fitness function value, the advantages and disadvantages of parent individual solutions are determined, individuals with fitness function values meeting the requirements of the objective function of the passenger flow control model are reserved to form a new population, and the total passenger delay time and the turnover number are used as the fitness function.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115688971A (en) * 2022-09-23 2023-02-03 北京交通大学 Wire network passenger flow control and train adjustment collaborative optimization method under train delay

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115688971A (en) * 2022-09-23 2023-02-03 北京交通大学 Wire network passenger flow control and train adjustment collaborative optimization method under train delay

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