CN115239021A - Method for optimizing running scheme of large and small cross-road train of urban rail transit - Google Patents
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Abstract
The invention discloses an optimization method for a large and small traffic route train running scheme of urban rail transit, which comprises the following steps: aiming at the problem of crowded passenger flow in early peak periods of urban rail transit lines, aiming at minimizing the total waiting time cost of passengers and the number of kilometers of train running, establishing an optimization model of a large and small traffic route running scheme of urban rail transit; and solving an urban rail transit large and small traffic road driving scheme optimization model by using a genetic algorithm to obtain an optimal train driving scheme and an objective function value. The method provided by the invention combines theory and practice, is favorable for accelerating train turnover, reducing the number of vehicles in use, balancing passenger flow and improving the imbalance of space load of the trains, and enables operation enterprises to more accurately put in transport capacity and reduce operation cost.
Description
Technical Field
The invention belongs to the field of energy-saving optimization control of rail transit trains, and particularly relates to an optimization method for an urban rail transit large and small traffic route train operation scheme.
Background
Along with the continuous increase of the scale of the urban rail transit network in China, the contradiction between the rapidly-increased passenger flow demand and the transport capacity is increasingly prominent, particularly in the peak period, the passenger flow congestion is very serious, and the traditional urban rail transit operation mode in the form of single marshalling and single traffic causes the transport capacity of partial intervals to be tense or wasted, the distribution is unreasonable, the passenger satisfaction degree is reduced, and the enterprise operation cost is increased. Therefore, a reasonable train operation scheme is necessary to be formulated, the requirement of passengers is met, the service quality is improved, the operation cost of enterprises is reduced, and the waste of transport capacity is reduced. The large and small traffic routes are used as a multi-traffic route operation organization technology capable of balancing the spatial difference of the line passenger flow, are suitable for the situation that the passenger flow of a section is continuously changed and sudden drop occurs on a certain section, and therefore limited vehicle resources are fully utilized, and better service is provided for all passengers in an urban rail transit system. Most of the previous urban rail transit energy consumption researches are only used for unilaterally calculating or optimizing traction energy consumption or station energy consumption, and the road-crossing scheme and the grouping scheme are less researched; with the development of scientific technology and the increasing travel demand of passengers, the comprehensive energy-saving problem of a train combined operation mode based on the people-oriented concept is emphasized, the line condition and the passenger flow space-time distribution characteristics are comprehensively considered, the theory and the practice are combined, the travel quality of the passengers is continuously improved, and the combined operation mode of multi-intersection and multi-marshalling technology is combined, so that the combined operation mode is the difficult point and the focus to be solved in the future in the field of urban rail transit train energy-saving optimization research.
Disclosure of Invention
The invention aims to: aiming at the existing problems, the invention provides the method for optimizing the running scheme of the large and small cross-road train of the urban rail transit, which is favorable for accelerating the train turnover, reducing the number of vehicles in use, balancing passenger flow and improving the space load imbalance of the train, so that an operating enterprise can more accurately put in transport capacity and reduce the operating cost. In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention provides an optimization method for a large and small traffic route train running scheme of urban rail transit, which comprises the following steps: aiming at the problem of congestion of passenger flow in early and high peak periods of an urban rail transit line, establishing an optimization model of a large and small traffic route driving scheme of urban rail transit by taking the minimum total waiting time cost of passengers and the minimum number of kilometers of train running as targets; and solving the optimization model of the urban rail transit large and small traffic road crossing driving scheme by adopting a genetic algorithm to obtain the optimal train driving scheme and an objective function value.
Preferably, the specific process of establishing the optimization model of the urban rail transit large and small traffic route driving scheme by taking the total waiting time cost of passengers and the minimum train running kilometer number as targets comprises the following steps:
step 1: setting a bidirectional urban rail transit train to comprise N stations, wherein S = {1,2, \8230, N } is a station set, and a retracing station of a small traffic section is [ S ] a ，s b ]The total amount of OD traffic in this interval is Q 2 The total amount of OD passenger flow in other intervals is Q 1 The total amount of passenger flow Q 1 、Q 2 Are respectively:
in the formula, q i,j Representing the OD passenger flow from the starting station i to the terminal station j;
step 2: the starting point and the end point are calculated to be both s a ，s b ]Mean waiting time t of passengers in this zone w2 And average waiting time t of passengers in the rest interval w1 ：
And 3, step 3: setting an objective function Z with minimum passenger integral waiting time by taking the minimum number of kilometers of train running as an objective 1 Objective function Z with minimum number of kilometers of vehicle operation 2 And satisfies the following conditions:
min Z 1 ＝Q 1 ·t w1 +Q 2 ·t w2 ；
min Z 2 ＝2f 1 ·n 1 ·L 1 ·T t +2f 2 ·n 2 ·L 2 ·T t ；
in the formula (f) 1 、f 2 Frequency of departure, n, representing major and minor traffic routes 1 、n 2 Number of train groups representing large traffic routes and number of train groups representing small traffic routes, L 1 、L 2 Line length representing large cross-roads and line length of small cross-roads, T t A duration representative of the operating period under study;
and step 3: in order to ensure the driving safety of the train and the running efficiency of the urban rail transit, an urban rail transit big and small traffic route starting scheme optimization model is established according to the constraint conditions of the objective function, and the constraint conditions meet the following conditions:
wherein, the first and the second end of the pipe are connected with each other,
f 1 +f 2 ≤f max for the constraint of the maximum departure frequency of the line,
f 1 ≥f min constraint for minimum departure frequency of the line;
f 1 ,f 2 the epsilon N represents that the train departure frequency of each intersection should be an integer;
n 1 ·[T z，1 ·f 1 ]+n 2 ·[T z,2 ·f 2 ]≤N 0 in order to apply the constraint of the number of the train vehicles;
a is more than or equal to 1 and less than or equal to b and less than or equal to n is the position constraint of the reentry station;
t t,m for the running time of the train in the section m, t t,k For the stop time of the train at station K, t z The time is the return operation time of the train at the return station.
Preferably, the optimization model for solving the urban rail transit big-small traffic road-crossing driving scheme by adopting the genetic algorithm comprises the following steps:
step 2.1: initializing information: frequency f of departure for selecting large and small intersection 1 、f 2 Position S of a small traffic route turning-back station a 、S b Inputting a passenger flow travel matrix OD, a line and station information matrix JL of the subway train and setting genetic algorithm parameters for decision variables: mainly comprises a population scale N, a variation probability Pm, a cross probability Pc and a cycle number Gmax;
step 2.2: initial solution generation: generating an initial solution in a random generation mode, taking the initial solution generated in the genetic algorithm as the departure interval of two adjacent trains at the head station, and converting the initial solution into different forms of one-dimensional binary codes for genetic operation; after each solution of a train departure interval is coded, decoding operation is carried out to obtain a scheduling scheme corresponding to an initial population individual in a genetic algorithm;
step 2.3: and (3) processing an objective function: objective function Z for minimizing passenger overall waiting time by adopting linear weighted sum method 1 Objective function Z with minimum number of kilometers of vehicle operation 2 Converting into a single objective function model minZ;
step 2.4: establishing a fitness function, representing the adaptability of the individual to the existing constraint through the fitness function, and constructing the fitness function by using the minimum passenger waiting time and the minimum vehicle running kilometer number as follows:
step 2.5: selecting proper individuals to enter the next generation of sub-population by using a roulette selection operator, updating the optimal positions of the individuals and the optimal position of the whole situation by using a crossover operator of a genetic algorithm, and generating new particles by using the crossover operator; then calculating the numerical value of the particle in the fitness function, sequentially comparing the numerical value with historical individual optimal positions and historical global optimal positions of a group, and updating the individual optimal positions and the global optimal positions by using a mutation operator of a genetic algorithm; obtaining updated individual optimal positions and global optimal positions after circulation;
step 2.6: optimal train departure frequency f for solving single-target nonlinear optimization model through genetic algorithm 1 、f 2 Calculating a model objective function value and a fitness function value;
step 2.7: recording train operation scheme value S a 、S b 、f 1 、f 2 、n 1 、n 2 And a corresponding Z value;
step 2.8: repeating the steps 2.3 to 2.7 until the maximum iteration times are finished;
step 2.9: and outputting the minimum Z value after the iteration is finished, wherein the output Z value is the optimal train operation scheme.
The specific process of converting the multi-target model into the single-target model by adopting the linear weighted sum method in the step 2.3 comprises the following steps:
step 2.3.1: by usingThe linear weighted sum method converts two targets of the overall waiting time of passengers and the running kilometers of the vehicle into a single target, which is expressed as: min Z = θ 1 ·Z 1 +θ 2 ·Z 2 In the formula, theta 1 And theta 2 Weight coefficients for each objective function;
step 2.3.2: with the background of only driving a single traffic route on an urban rail transit line, assuming that passengers arrive at stations uniformly distributed, the departure intervals of trains are unchanged, and the departure frequency of the trains is f 0 (ii) a Then the average waiting time of the passengers is 1/2 of the departure interval of the train, and then the overall waiting time of the passengers is:
the number of kilometers the vehicle runs is: s km ＝2f 0 ·n 0 ·L 1 ·T t 。
Preferably, in step 2.3.2, the weighting factor θ is calculated 1 And theta 2 The process of (2) is carried out as follows:
preface theta 1 ·t w ＝θ 2 ·S km And take theta 1 =1, then θ 2 ＝θ 1 ·t w /S km 。
Then, normalization processing is carried out on the weight coefficient to obtain theta 1 ＝θ 1 /(θ 1 +θ 2 )，θ 2 ＝θ 2 /(θ 1 +θ 2 )。
In summary, because the invention adopts the above technical scheme, the invention has the following remarkable effects:
the multi-objective optimization model of the urban rail transit train large and small traffic route operation scheme fully considers the constraints of the route passing capacity, the number of trains, the positions of the turning-back stations and the like, comprehensively considers the influence of the whole waiting time of passengers and the number of kilometers of train operation, reduces the whole waiting time of the passengers, reduces the number of kilometers of vehicle operation and sets the reduction as an optimization target.
Drawings
FIG. 1 is a flow chart of an optimization model for solving a train operation scheme by a genetic algorithm
FIG. 2 is a schematic diagram of a large and small cross-road train according to the present invention
FIG. 3 is a graph showing the variation of the cumulative arrival passenger demand at each station of the train according to the present invention
FIG. 4 is a distribution diagram of the total line early peak hour section passenger flow
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings by way of examples of preferred embodiments. It should be noted, however, that the numerous details set forth in the description are merely for the purpose of providing the reader with a thorough understanding of one or more aspects of the present invention, which may be practiced without these specific details.
As shown in fig. 1 to 4, according to the method for optimizing the train operation scheme of the urban rail transit large and small cross roads, according to the present invention, an optimization model of the large and small cross road operation scheme is established, and then a genetic algorithm is adopted to solve, so as to obtain an optimal train operation scheme and an objective function value. The method comprises the following specific steps: initializing information; setting an OD (origin-destination) travel matrix, line length and station information of the subway train, and setting genetic algorithm parameters; initializing a population, and decoding the population to obtain a scheduling scheme corresponding to an initial population individual; converting the multi-target model into a single-target model by adopting a linear weighted sum method; establishing a fitness function, and generating a new population by using a roulette selection operator; carrying out variation and cross operation on the new population, and calculating the individual fitness of the new population; and judging whether the optimization termination condition is met. The method has stronger global optimization capability and stability, and can effectively reduce the travel cost of passengers and the operation cost of trains; the optimization method comprises the following steps: aiming at the problem of crowded passenger flow of the urban rail transit line in the early peak period, under the condition of known passenger flow data and other related operation parameters, considering the constraints of the line passing capacity, the number of trains, the positions of turn-back stations and the like, and establishing an optimization model and constraint conditions of a large-size traffic route running scheme of the urban rail transit by taking the total waiting time cost of passengers and the minimum number of kilometers of train running as targets; and then, solving the optimization model of the urban rail transit large and small traffic road driving scheme by adopting a genetic algorithm to obtain the optimal train driving scheme and an objective function value by taking the linear weighted sum law multiple targets as a single target.
The specific process of establishing the optimization model of the urban rail transit large and small traffic route driving scheme by taking the minimum total waiting time cost of passengers and the minimum train running kilometers as targets comprises the following steps:
step 1: setting a bidirectional urban rail transit train comprising N stations, wherein S = {1,2, \8230:, N } is a station set, wherein a small traffic route interval is [ S ] a ，s b ]The total amount of OD traffic in this interval is Q 2 The total amount of OD passenger flow in other intervals is Q 1 Then Q is 1 ，Q 2 Are respectively:
in the formula, q i,j OD passenger flow representing that the starting station is i and the terminal station is j;
step 2: the starting point and the end point are calculated to be both s a ，s b ]Mean waiting time t of passengers in this zone w2 And average waiting time t of passengers in the rest interval w1 ：
Wherein the passenger is flatMean waiting time t w2 Satisfies the following conditions:
average waiting time t of passengers in other intervals w1 Satisfies the following conditions:
and step 3: setting an objective function Z with minimum passenger overall waiting time by taking the minimum number of kilometers of train running as an objective 1 Objective function Z with minimum number of kilometers of vehicle operation 2 And satisfies the following conditions:
min Z 1 ＝Q 1 ·t w1 +Q 2 ·t w2 ；
min Z 2 ＝2f 1 ·n 1 ·L 1 ·T t +2f 2 ·n 2 ·L 2 ·T t ；
in the formula (f) 1 、f 2 Representing the departure frequency of large traffic roads and small traffic roads;
n 1 、n 2 representing the train grouping number of large cross roads and the train grouping number of small cross roads;
L 1 、L 2 representing the line length of a large cross road and the line length of a small cross road;
T t a duration representative of the operating period under study;
and step 3: in order to ensure the driving safety of the train and the running efficiency of the urban rail transit, an optimization model of the urban rail transit large and small traffic road running scheme is established according to the constraint conditions of the objective function, and the constraint conditions meet the following conditions:
wherein f is 1 ,f 2 The epsilon N represents that the train departure frequency of each intersection should beThe integer is the departure frequency of the large traffic road and the small traffic road;
n 1 、n 2 the train grouping number of large cross roads and the train grouping number of small cross roads are represented;
f 1 +f 2 ≤f max constraint for maximum departure frequency of the line;
f 1 ≥f min is the minimum departure frequency constraint of the line;
n 1 ·[T z，1 ·f 1 ]+n 2 ·[T z,2 ·f 2 ]≤N 0 to apply the train number constraint;
a is more than or equal to 1 and less than or equal to b and less than or equal to n is the position constraint of the reentry station;
t t,m for the running time of the train in the section m,
t t,k for the stop time of the train at the station K,
t z the time is the return operation time of the train at the return station.
In the embodiment of the invention, the actual data of the first line of the subway in Nanning city is adopted to carry out simulation verification, the line is provided with 25 train stations in total, the total length is 32.1km, and the direction from the stone port station to the east station of the train is upward. The following table shows the inter-station distance, running time and stopping time of the train at each station in each section.
TABLE 1 train station spacing, runtime and time to stop
Table 2 below shows the relevant train operating parameters
TABLE 2 train operating parameters
According to the automatic fare collection system for the subway, a change curve (extracted by taking 30min as time granularity) of the cumulative arrival passenger flow demand of each station of the No. 1 subway line in the operation time of a certain working day can be obtained, as shown in FIG. 3. It can be seen that: the traffic volume of the south Ning rail transit No. 1 line of the working day entering and exiting the station in each hour is distributed in a hump shape; the early peak of the incoming and outgoing passenger flow rate is 8-00, the hourly coefficient of the early peak of the incoming passenger flow rate (the proportion of the passenger flow rate of a certain 1h to the total daily passenger flow rate) is 12.59%, the passenger flow rate is the largest in the early peak period of the number 1 line, and the transport capacity is the most intense, so that the early peak one hour (8-00.
According to the early peak hour section passenger flow distribution of each section of the whole line in the figure 4, if a single traffic route is adopted according to the passenger flow data and the line condition, the running number of the trains is 17 pairs/h, and the train marshalling is 6-section B-type trains. And calculating the section unbalance coefficients of the lines in different directions, and judging that the section unbalance coefficients in the uplink direction and the downlink direction are both larger than 1.5, so that the unbalance degree of the section passenger flow distribution is larger. If a single intersection is adopted, the phenomena of too low train full load rate and transportation energy waste can occur between two end parts of the line, or the phenomena of too high train full load rate and very crowded passengers can occur between middle parts of the line. Therefore, the line should be a train operation scheme for large and small traffic routes.
In the embodiment of the invention, the optimization model for solving the urban rail transit big-small traffic route driving scheme by adopting the genetic algorithm comprises the following steps:
step 2.1: initializing information: frequency f of departure for selecting large and small intersection 1 、f 2 Position S of a small traffic route turning-back station a 、S b Setting a passenger flow travel matrix OD, a line length and station information JL of the subway train as a decision variable; and setting genetic algorithm parameters: mainly comprises a population scale N, a variation probability Pm, a cross probability Pc and a cycle number Gmax; the specific setting of genetic algorithm parameters mainly comprises speciesGroup size N =200, probability of variation Pm =0.05, probability of cross Pc =0.9, and cycle number Gmax =1000;
step 2.2: initial solution generation: generating an initial solution in a random generation mode, taking the initial solution generated in the genetic algorithm as the departure interval of two adjacent trains at the head station, and converting the initial solution into different forms of one-dimensional binary codes for genetic operation; after each solution of a train departure interval is coded, decoding operation is carried out to obtain a scheduling scheme corresponding to an initial population individual in a genetic algorithm; the decoding process is to convert binary information into decimal, and after the solution of the optimization problem is coded, each chromosome corresponds to a solution of a specific problem;
step 2.3: and (3) processing an objective function: objective function Z for minimizing passenger overall waiting time by adopting linear weighted sum method 1 Objective function Z with minimum number of kilometers of vehicle operation 2 Converting into a single objective function model minZ; the specific process of converting into the single objective function model comprises the following steps:
step 2.3.1: the method adopts a linear weighted sum method to convert two targets of the whole waiting time of passengers and the running kilometers of the vehicle into a single target, and is represented as follows: min Z = θ 1 ·Z 1 +θ 2 ·Z 2 In the formula, theta 1 And theta 2 Weight coefficients for each objective function; calculating a weight coefficient theta 1 And theta 2 The process of (2) is carried out as follows:
preface theta 1 ·t w ＝θ 2 ·S km And take theta 1 =1, then θ 2 ＝θ 1 ·t w /S km (ii) a Then, normalization processing is carried out on the weight coefficient to obtain theta 1 ＝θ 1 /(θ 1 +θ 2 )，θ 2 ＝θ 2 /(θ 1 +θ 2 )；
Step 2.3.2: with the background of only driving a single traffic route on an urban rail transit line, assuming that passengers arrive at stations uniformly distributed, the departure intervals of trains are unchanged, and the departure frequency of the trains is f 0 (ii) a Then the average waiting time of the passengers is 1/2 of the departure interval of the train, then the passengers are in the whole, and the likeThe waiting time is as follows:
the number of kilometers the vehicle runs is: s km ＝2f 0 ·n 0 ·L 1 ·T t ；
Step 2.4: establishing a fitness function, representing the adaptability of the individual to the existing constraint through the fitness function, and constructing the fitness function by using the minimum passenger waiting time and the minimum vehicle running kilometer number as follows:
step 2.5: selecting proper individuals to enter the next generation of sub-population by using a roulette selection operator, updating the optimal positions of the individuals and the optimal position of the whole situation by using a crossover operator of a genetic algorithm, and generating new particles by using the crossover operator; then calculating the numerical values of the particles in the fitness function, sequentially comparing the numerical values with historical individual optimal positions and historical global optimal positions of the group, and updating the individual optimal positions and the global optimal positions by using a mutation operator of a genetic algorithm; obtaining updated individual optimal positions and global optimal positions after circulation;
step 2.6: optimal train departure frequency f for solving single-target nonlinear optimization model through genetic algorithm 1 、f 2 And calculating the model objective function value min Z = theta 1 ·Z 1 +θ 2 ·Z 2 And a fitness function value, wherein the fitness function value is:
step 2.7: recording train operation scheme variable value S a 、S b 、f 1 、f 2 、n 1 、n 2 And corresponding Z value to obtain an open scheme;
step 2.8: repeating the steps 2.3 to 2.7 until the maximum iteration times are finished; judging whether an optimized termination condition is met or not in the iteration process, namely whether the evolution frequency reaches the set maximum iteration frequency or not, if the termination condition is met, stopping the evolution, outputting the objective function value of the optimal individual and the corresponding decision variable value, and if the termination condition is not met, jumping to the step 2.3 to continue the iteration;
step 2.9: and outputting the minimum Z value after the iteration is finished, wherein the output Z value is the optimal train running scheme. In the present invention, the optimization results are shown in table 3 below:
TABLE 3 comparison of train running indexes for different running schemes
The results show that: when the train marshalling is 6B, the waiting time of passengers at the early peak of the train, the running kilometers of the train and the number of vehicles in use, which adopt the large and small traffic road running scheme, are all reduced by 4.0 percent, 7.0 percent and 5.9 percent respectively. The method shows that the number of vehicles used can be obviously reduced by driving large and small traffic routes, so that the operation cost of enterprises is reduced.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be construed as the protection scope of the present invention.
Claims (5)
1. A method for optimizing a running scheme of a large and small traffic route train of urban rail transit is characterized by comprising the following steps: the optimization method comprises the following steps: aiming at the problem of crowded passenger flow in early peak periods of urban rail transit lines, aiming at minimizing the total waiting time cost of passengers and the number of kilometers of train running, establishing an optimization model of a large and small traffic route running scheme of urban rail transit; and solving an urban rail transit large and small traffic road driving scheme optimization model by using a genetic algorithm to obtain an optimal train driving scheme and an objective function value.
2. The method for optimizing the train driving scheme of the urban rail transit large and small road according to claim 1, wherein the method comprises the following steps: the specific process for establishing the optimization model of the urban rail transit large and small traffic road driving scheme by taking the minimum total waiting time cost of passengers and the minimum number of kilometers of train driving as targets comprises the following steps:
step 1: setting a bidirectional urban rail transit train to comprise N stations, wherein S = {1,2, \8230, N } is a station set, and a retracing station of a small traffic section is [ S ] a ，s b ]The total amount of OD traffic in this interval is Q 2 The total amount of OD passenger flow in other intervals is Q 1 The total amount of passenger flow Q 1 、Q 2 Are respectively:
in the formula, q i,j Representing the OD passenger flow from the starting station i to the terminal station j;
step 2: the starting point and the end point are calculated to be both s a ，s b ]Mean waiting time t of passengers in this zone w2 And average waiting time t of passengers in the rest interval w1 ：
And step 3: setting an objective function Z with minimum passenger integral waiting time by taking the minimum number of kilometers of train running as an objective 1 Objective function Z with minimum number of kilometers of vehicle operation 2 And satisfies the following conditions:
minZ 1 ＝Q 1 ·t w1 +Q 2 ·t w2 ；
minZ 2 ＝2f 1 ·n 1 ·L 1 ·T t +2f 2 ·n 2 ·L 2 ·T t ；
in the formula (f) 1 、f 2 Frequency of departure, n, representing major and minor traffic routes 1 、n 2 Number of train groups representing large traffic routes and number of train groups representing small traffic routes, L 1 、L 2 Line length representing large cross-roads and line length of small cross-roads, T t A duration representative of the operating period under study;
and 3, step 3: in order to ensure the driving safety of the train and the running efficiency of the urban rail transit, an urban rail transit big and small traffic route starting scheme optimization model is established according to the constraint conditions of the objective function, and the constraint conditions meet the following conditions:
min{Z 1 ，Z 2 }
wherein the content of the first and second substances,
f 1 +f 2 ≤f max in order to constrain the maximum departure frequency of the line,
f 1 ≥f min is the minimum departure frequency constraint of the line;
f 1 ,f 2 the epsilon N represents that the train departure frequency of each intersection should be an integer;
n 1 ·[T Z，1 ·f 1 ]+n 2 ·[T Z,2 ·f 2 ]≤N 0 in order to apply the constraint of the number of the train vehicles;
a is more than or equal to 1 and less than or equal to b and less than or equal to n is the position constraint of the reentry station;
t t,m for the running time of the train over the section m, t t,k For the stop time, t, of the train at station K z The time is the return operation time of the train at the return station.
3. The method for optimizing the train driving scheme of the urban rail transit large and small road according to claim 1, wherein the method comprises the following steps: the method for solving the optimization model of the urban rail transit large and small traffic route driving scheme by adopting the genetic algorithm comprises the following steps:
step 2.1: initializing information: frequency f of departure for selecting large and small intersection 1 、f 2 Position S of a small traffic route turning-back station a 、S b Inputting a passenger flow travel matrix OD, a line and station information matrix JL of the subway train and setting genetic algorithm parameters for decision variables: mainly comprises a population scale N, a variation probability Pm, a cross probability Pc and a cycle number Gmax;
step 2.2: initial solution generation: generating an initial solution in a random generation mode, taking the initial solution generated in the genetic algorithm as the departure interval of two adjacent trains at the head station, and converting the initial solution into different forms of one-dimensional binary codes for genetic operation; encoding each solution of a train departure interval, and then performing decoding operation to obtain a scheduling scheme corresponding to an initial population individual in a genetic algorithm;
step 2.3: and (3) processing an objective function: objective function Z for minimizing passenger overall waiting time by adopting linear weighted sum method 1 Objective function Z with minimum number of kilometers of vehicle operation 2 Converting into a single objective function model minZ;
step 2.4: establishing a fitness function, representing the adaptability of the individual to the existing constraint through the fitness function, and constructing the fitness function by using the minimum passenger waiting time and the minimum vehicle running kilometer number as follows:
step 2.5: selecting proper individuals to enter the next generation of sub-population by using a roulette selection operator, updating the optimal positions of the individuals and the optimal position of the whole situation by using a crossover operator of a genetic algorithm, and generating new particles by using the crossover operator; then calculating the numerical value of the particle in the fitness function, sequentially comparing the numerical value with historical individual optimal positions and historical global optimal positions of a group, and updating the individual optimal positions and the global optimal positions by using a mutation operator of a genetic algorithm; obtaining updated individual optimal positions and global optimal positions after circulation;
step 2.6: optimal train departure frequency f for solving single-target nonlinear optimization model through genetic algorithm 1 、f 2 Calculating a model objective function value and a fitness function value;
step 2.7: recording the value S of the train operating scheme a 、S b 、f 1 、f 2 、n 1 、n 2 And a corresponding Z value;
step 2.8: repeating the steps 2.3 to 2.7 until the maximum iteration times are finished;
step 2.9: and outputting the minimum Z value after the iteration is finished, wherein the output Z value is the optimal train operation scheme.
4. The method for optimizing the train driving scheme of the urban rail transit large and small cross roads according to claim 3, wherein the method comprises the following steps: the specific process of converting the multi-target model into the single-target model by adopting the linear weighted sum method in the step 2.3 comprises the following steps:
step 2.3.1: the method adopts a linear weighted sum method to convert two targets of the whole waiting time of passengers and the running kilometers of the vehicle into a single target, and is represented as follows: minZ = θ 1 ·Z 1 +θ 2 ·Z 2 In the formula, theta 1 And theta 2 Weight coefficients for each objective function;
step 2.3.2: with the background of only driving a single traffic route on an urban rail transit line, assuming that passengers arrive at stations uniformly distributed, the departure intervals of trains are unchanged, and the departure frequency of the trains is f 0 (ii) a Then the average waiting time of the passengers is 1/2 of the departure interval of the train, and then the overall waiting time of the passengers is:
the number of kilometers the vehicle runs is: s km ＝2f 0 ·n 0 ·L 1 ·T t 。
5. The method for optimizing the train driving scheme of the urban rail transit large and small cross roads according to claim 4, wherein the method comprises the following steps: in step 2.3.2, the weight coefficient θ is calculated 1 And theta 2 The process of (2) is carried out as follows:
first order theta 1 ·t w ＝θ 2 ·S km And take theta 1 =1, then θ 2 ＝θ 1 ·t w /S km ；
Then, normalization processing is carried out on the weight coefficient to obtain theta 1 ＝θ 1 /(θ 1 +θ 2 )，θ 2 ＝θ 2 /(θ 1 +θ 2 )。
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| CN116882714A (en) * | 2023-09-07 | 2023-10-13 | 中国铁路设计集团有限公司 | Multi-year intersection integrated scheme programming method considering line network construction time sequence |
| CN116957636A (en) * | 2023-09-21 | 2023-10-27 | 深圳市城市交通规划设计研究中心股份有限公司 | Urban rail traffic energy passenger flow matching method, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116882714A (en) * | 2023-09-07 | 2023-10-13 | 中国铁路设计集团有限公司 | Multi-year intersection integrated scheme programming method considering line network construction time sequence |
| CN116882714B (en) * | 2023-09-07 | 2023-11-28 | 中国铁路设计集团有限公司 | Multi-year intersection integrated scheme programming method considering line network construction time sequence |
| CN116957636A (en) * | 2023-09-21 | 2023-10-27 | 深圳市城市交通规划设计研究中心股份有限公司 | Urban rail traffic energy passenger flow matching method, electronic equipment and storage medium |
| CN116957636B (en) * | 2023-09-21 | 2023-12-26 | 深圳市城市交通规划设计研究中心股份有限公司 | Urban rail traffic energy passenger flow matching method, electronic equipment and storage medium |
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