CN112465211A - Rail transit train full load rate control method and application - Google Patents

Rail transit train full load rate control method and application Download PDF

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CN112465211A
CN112465211A CN202011327712.6A CN202011327712A CN112465211A CN 112465211 A CN112465211 A CN 112465211A CN 202011327712 A CN202011327712 A CN 202011327712A CN 112465211 A CN112465211 A CN 112465211A
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唐金金
赵晴晴
侯凯文
李利君
贾文哲
李超
唐水雄
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Abstract

The application belongs to the technical field of urban rail transit, and particularly relates to a rail transit train full load rate control method and application. Control of the train occupancy results in a number of problems for a given train operating scenario. If the travel requirements of most passengers are difficult to meet by the driving scheme, the discontent mood of the passengers can be caused; if the number of columns is increased, the resources may be wasted. The application provides a method for controlling the full load rate of a rail transit train, which comprises the following steps of 1) determining a train operation plan by taking the maximum number of passengers as a target; 2) determining the number of passengers which can be accommodated according to the train operation plan and the requirement of the full load rate of the relevant interval; 3) screening through the passenger reservation information to determine passengers capable of boarding; 4) and adjusting the train operation plan according to the determined boarding passenger information to realize the coordination and optimization of the operation organization.

Description

Rail transit train full load rate control method and application
Technical Field
The application belongs to the technical field of urban rail transit, and particularly relates to a rail transit train full load rate control method and application.
Background
And (3) scoring the importance of each travel demand by using an analytic hierarchy process, evaluating the risk value of each person and the maximum risk value allowed by the operation interval, and then constructing a model. The analytic hierarchy process decomposes the problem into different composition factors according to the nature of the problem and the total target to be achieved, and combines the factors according to the mutual correlation influence and membership relation among the factors in different levels to form a multi-level analytic structure model, thereby finally leading the problem to be summarized into the determination of the relative important weight of the lowest level (scheme, measure and the like for decision making) relative to the highest level (total target) or the scheduling of the relative order of superiority and inferiority.
In order to better solve the model, the objective function is logarithmized, and the model is solved by using a dynamic programming principle. Dynamic programming algorithms are typically used to solve problems with some optimal nature. If the answers of the solved subproblems can be stored and the obtained answers can be found out when needed, a large amount of repeated calculation can be avoided, and time is saved. A table may be used to record the answers to all the solved sub-questions. Regardless of whether the sub-problem is used later, as long as it is computed, its results are filled into the table. This is the basic idea of dynamic programming. For passenger i, if x existsijIf 0, the passenger cannot take the bus; otherwise, a second round of screening is performed.
Control of the train occupancy results in a number of problems for a given train operating scenario. If the travel requirements of most passengers are difficult to meet by the driving scheme, the discontent mood of the passengers can be caused; if the number of columns is increased, the resources may be wasted.
Disclosure of Invention
1. Technical problem to be solved
There are a number of problems with the control based on the full load rate of the train that results in a given train operating scheme. If the travel requirements of most passengers are difficult to meet by the driving scheme, the discontent mood of the passengers can be caused; if the number of the running columns is increased singly, the problem of wasting of transportation capacity resources is possibly caused, and the application provides a rail transit train full load rate control method and application.
2. Technical scheme
In order to achieve the above object, the present application provides a method for controlling a full load rate of a rail transit train, the method comprising the steps of:
1) determining a train operation plan by taking the maximum passenger carrying number as a target;
2) determining the number of passengers which can be accommodated according to the train operation plan and the requirement of the full load rate of the relevant interval;
3) screening through the passenger reservation information to determine passengers capable of boarding;
4) and adjusting the train operation plan according to the determined boarding passenger information to realize the coordination and optimization of the operation organization.
Another embodiment provided by the present application is: in the step 1), in order to meet the requirement of passengers to the maximum, a model is constructed by taking the maximum number of passengers as an objective function and taking the train interval and the total train number as constraints, and a train operation plan is determined.
Another embodiment provided by the present application is: determining a passenger travel path according to the K short circuit algorithm and the classified evaluation non-ensemble model in the step 3); scoring the importance of each travel demand, scoring the risk value of each person and the maximum risk value allowed by the operation interval, and then constructing a passenger reservation model; and solving the passenger reservation model by utilizing dynamic programming according to the passenger reservation model to preliminarily determine reserved passengers.
Another embodiment provided by the present application is: the travel path determination comprises: calculating the shortest path from the starting point to the end point; if the shortest circuit exists, deleting one edge of the shortest circuit, and calculating to obtain a temporary shortest circuit; repeating until all edges are deleted to obtain a feasible path set.
Another embodiment provided by the present application is: the shortest path is calculated by adopting a Dijkstra algorithm, and the temporary shortest path is calculated by adopting the Dijkstra algorithm.
Another embodiment provided by the present application is: the passenger reservation model is constructed after scoring the risk value of the passenger, the trip purpose and the risk value of each interval through an analytic hierarchy process.
Another embodiment provided by the present application is: the objective function of the passenger reservation module is the highest score of the passenger trip purpose capable of meeting the trip requirement.
Another embodiment provided by the present application is: 8. the rail transit train full load rate control method according to any one of claims 1 to 7, characterized in that: and 4) if all the vehicles reach the bottom line of the full load rate, operating according to the original train operation plan, otherwise, canceling the train, screening passengers capable of riding in the second round, and informing the passengers capable of riding in the train to stop riding in the second round.
Another embodiment provided by the present application is: the passenger reservation information comprises a departure place, a destination and available train numbers.
The application also provides an application of the rail transit train full load rate control method, and the rail transit train full load rate control method is applied to coordination and optimization of urban rail passenger flow management and control and train operation plan during epidemic situations.
3. Advantageous effects
Compared with the prior art, the rail transit train full load rate control method and the application have the advantages that:
the method for controlling the full load rate of the rail transit train realizes coordination and optimization of the passenger flow and the urban rail transit operation scheme.
The application of the rail transit train full load rate control method is a coordination optimization method for active passenger flow management and control and train operation plan of urban rail transit under the epidemic situation, and is mainly characterized in that passenger flow paths are distributed, passengers capable of boarding are determined on the basis of the full load rate on the premise of considering the epidemic situation, compilation of the train operation plan is completed, and the operation plan and the passenger flow are coordinated.
The application of the rail transit train full load rate control method provided by the application is a coordination optimization method for active passenger flow management and control and train operation plan of urban rail transit under epidemic situations.
The application of the control method for the full load rate of the rail transit train provides a feasible scheme for controlling passenger flow and organizing traveling in an epidemic situation period of an city.
The application of the rail transit train full load rate control method provided by the application is designed for an urban rail transit network, and the urban rail transit active passenger flow management and train operation plan coordination optimization method under the epidemic situation condition meets the requirement of an urban rail transit operation management department on passenger flow management and control during the epidemic situation. Meanwhile, coordination and optimization of train operation plan and passenger flow are realized.
Drawings
FIG. 1 is a schematic diagram of a method for controlling the full load rate of a rail transit train according to the present application;
FIG. 2 is a schematic diagram of a passenger reservation algorithm framework of the present application;
fig. 3 is a schematic diagram of a train operation plan adjustment implementation framework of the present application.
Detailed Description
Hereinafter, specific embodiments of the present application will be described in detail with reference to the accompanying drawings, and it will be apparent to those skilled in the art from this detailed description that the present application can be practiced. Features from different embodiments may be combined to yield new embodiments, or certain features may be substituted for certain embodiments to yield yet further preferred embodiments, without departing from the principles of the present application.
The outbreak of new crown pneumonia brings great difficulty to the operation of urban rail transit, and the control of full load rate and current limiting become the first major tasks of operation managers. The traditional current limiting scheme only aims at current limiting in a certain time period of morning and evening peaks, generally aims at improving the passenger service level, reducing the crowding degree and the like, and does not consider the problems of personnel gathering, travel necessity and the like. However, during an epidemic situation, an operation manager needs to consider the epidemic situation of the area covered by the wire network, the necessity of the travel purpose of the passengers and other factors, reduce unnecessary travel and personnel gathering, manage and control the passenger flow all day long, control the full load rate, and reduce the risk of infection when the passengers take the bus.
The Logit model (a Logit model is also translated into an evaluation model and a classification evaluation model and also a Logistic regression model) is one of the discrete selection method models, and the Logit model is the earliest discrete selection model and is also the most widely applied model at present.
The Dijkstra algorithm is characterized in that: the Dikstra algorithm uses breadth-first search to solve the problem of the single-source shortest path of an empowered directed graph or an undirected graph, and finally obtains a shortest path tree. This algorithm is often used in routing algorithms or as a sub-module of other graph algorithms. The Dijkstra algorithm employs a greedy strategy that states an array dis to store the shortest distance from the source point to each vertex and a set of vertices for which the shortest path has been found: initially, the path weight of the origin s is given 0(dis [ s ] ═ 0). If there is a directly reachable edge (s, m) for vertex s, dis [ m ] is set to w (s, m), and the path lengths of all other vertices (where s cannot be directly reached) are set to infinity. Initially, the set T has only vertices s. Then, the minimum value is selected from the dis array, then this value is the shortest path from the source point s to the vertex to which this value corresponds, and this point is added to T, at which point a vertex is completed, then it is necessary to see if the newly added vertex can reach other vertices and to see if the path length through this vertex to other points is shorter than the direct arrival of the source point, and if so, the values of these vertices in dis are replaced. Then, the minimum value is found again from dis, and the above-described actions are repeated until T includes all the vertices of the graph.
Referring to fig. 1 to 3, the present application provides a method for controlling a full load rate of a rail transit train, including the following steps:
1) determining a train operation plan by taking the maximum passenger carrying number as a target;
2) determining the number of passengers which can be accommodated according to the train operation plan and the requirement of the full load rate of the relevant interval;
3) screening through the passenger reservation information to determine passengers capable of boarding;
4) and adjusting the train operation plan according to the determined boarding passenger information to realize the coordination and optimization of the operation organization.
Firstly, preliminarily making a train operation plan; then, according to the passenger flow reservation condition, the K short circuit algorithm path search algorithm can list all the effective paths of passenger travel, then the local non-ensemble model is used for matching the passenger flow to the road network, and each passenger is marked in the passing section. And then scoring the risk value and the trip purpose of the passenger, and judging the risk value of each section. Then, constructing a model by taking the highest score of the passenger trip purpose as a target function and the interval risk value as a constraint, and screening the passengers capable of traveling in the first round; and finally, screening passengers available for riding in the second round according to the full load rate bottom line.
Further, in step 1), in order to maximally meet the passenger requirements, a model is constructed by taking the maximum number of passengers as an objective function and taking the train interval and the total train number as constraints, and a train operation plan is determined.
In order to meet the requirement of passengers maximally, a maximum number of passengers is taken as an objective function, a running interval and the total number of trains are taken as constraint construction models, and preparation is made for determining the maximum number of passengers and getting-on time. And then, determining the number of passengers which can be accommodated according to the train operation plan and the requirement of the full load rate of the relevant interval, realizing passenger reservation by utilizing a designed passenger reservation platform, and performing primary screening of passengers which can get on the train in the first round. And finally, determining the train operation plan again according to the actual reservation condition of the passenger, thereby reducing unnecessary waste of train resources.
As shown in fig. 1: and constructing a model by taking the maximum number of the carrying passengers as an objective function and taking the train running interval and the number of the usable trains as the target as constraints.
Figure BDA0002794833530000041
If the kth vehicle is sent at the time t, the kth vehicle is 1, otherwise, the kth vehicle is 0;
Figure BDA0002794833530000042
is at tkWhether the departure vehicle reaches the end point in delta or not, if the departure vehicle reaches the end point, the ending point is 0, and if not, the ending point is 1;
tkthe kth departure time is the kth departure time;
t(k,l)is the departure time of the kth vehicle at the l station;
c is a train operator;
n is the number of available trains;
h is the minimum running interval of the train.
The objective function is shown in equation (1).
Figure BDA0002794833530000051
The running interval of two adjacent trains must be larger than the minimum running interval
tk+1-tk≥h (2)
The number of on-line trains is less than the number of available trains
Figure BDA0002794833530000052
Figure BDA0002794833530000056
Is at tmWhether the departure vehicle reaches the terminal, if the terminal is 0, otherwise the terminal is 1
Figure BDA0002794833530000053
Determining departure time t of the train of each station according to the model calculation result, the interval running time and the station stop duration(k,l)。
Further, in the step 3), a passenger travel path is determined according to a K short circuit algorithm and a classification evaluation non-ensemble model; scoring the importance of each travel demand, scoring the risk value of each person and the maximum risk value allowed by the operation interval, and then constructing a passenger reservation model; and solving the passenger reservation model by utilizing dynamic programming according to the passenger reservation model to preliminarily determine reserved passengers.
And determining the travel path of the passenger by using a K short-circuit algorithm path search algorithm according to information such as the departure place, the destination and the like filled in by the passenger during reservation.
Step 1: a network is given, and the shortest path from a starting point O to a terminal point D is calculated by using a Dijkstra algorithm;
step 2: if the shortest circuit exists, deleting one edge of the shortest circuit, and obtaining a temporary shortest circuit by utilizing a Dijkstra algorithm;
step 3: and repeating the second step until all edges are deleted to obtain a feasible path set.
The Logit non-centralized model is used for distributing the passenger flow to the feasible paths, and the n-th OD interval is assumed to share omega effective transfer paths,
Figure BDA0002794833530000054
the generalized cost of the passenger selecting the transfer path omega in the nth OD interval is shown, the passenger often selects the path with the minimum cost for the passenger, and the travel time and the travel distance are mainly considered in the cost in the scheme.
Figure BDA0002794833530000055
Is a random variable, as shown in equation (5).
Figure BDA0002794833530000061
Figure BDA0002794833530000062
A fixed cost term for the passenger to transfer the path omega in the nth OD interval;
Figure BDA0002794833530000063
a random cost item for the passenger to transfer the path omega in the nth OD interval;
Figure BDA0002794833530000064
subject to the extreme value distribution, the expected value is 0, so only consideration needs to be given
Figure BDA0002794833530000065
To pair
Figure BDA0002794833530000066
The influence of (2) is as follows.
Assuming that the fixed cost term has i variables, then
Figure BDA0002794833530000067
May be represented by formula (6).
Figure BDA0002794833530000068
Wherein,
Figure BDA0002794833530000069
is a parameter to be determined, representing a variable
Figure BDA00027948335300000610
The weight of the passenger is based on the trip condition of the passenger under the epidemic situation, the full load rate of the train is lower, the trip comfort level of the passenger is higher, therefore, the crowdedness and the like are not considered in the fixed cost,
Figure BDA00027948335300000611
mainly points out the travel time and the travel distance.
The probability of the path ω being selected is shown in equation (7).
Figure BDA00027948335300000612
Theta represents
Figure BDA00027948335300000613
The function of which is to translate the path cost impedance into utility.
And (3) scoring the importance of each travel demand by using an analytic hierarchy process, evaluating the risk value of each person and the maximum risk value allowed by the operation interval, and then constructing a model.
xijWhether the requirement of the jth arc of the ith person can be met by taking the kth vehicle at the moment t or not is represented by a variable of 0-1;
θia score representing the trip purpose of the ith individual;
βia risk value score representing an ith individual;
δjrepresents the maximum risk value allowed by the jth arc;
ζiindicating a time deviation tolerable to the passenger;
γ represents a loading rate;
the objective function of the model is the highest score of the passenger trip purpose capable of meeting the trip demand
Figure BDA00027948335300000614
The constraint (9) is that the sum of all passenger risks in the operating interval must be less than the maximum risk value of the arc
Figure BDA0002794833530000071
The constraint (10) is a full load rate constraint, and the full load rate of the train must meet the requirements of an operation department
Figure BDA0002794833530000072
The constraint (10) indicates that the difference between the reservation time and departure time of the passenger is within the tolerance range of the passenger
|t(k,l)-ti|≤ζi (11)
If present
Figure BDA0002794833530000074
It indicates that the ith individual cannot take the car.
And solving the model. The model can be simplified as follows:
Figure BDA0002794833530000073
to prevent infinitesimal phenomena, if xijX is equal to 0ij0.0001. And (5) solving the model by utilizing dynamic programming, and screening passengers available for riding in the first round.
Further, the travel path determination includes: calculating the shortest path from the starting point to the end point; if the shortest circuit exists, deleting one edge of the shortest circuit, and calculating to obtain a temporary shortest circuit; repeating until all edges are deleted to obtain a feasible path set.
Further, the shortest path is obtained by dijkstra algorithm, and the temporary shortest path is obtained by dijkstra algorithm.
Further, the passenger reservation model is constructed after scoring the risk values of the passengers, the trip purposes and the risk values of all the intervals through an analytic hierarchy process.
Further, the objective function of the passenger booking module is the highest score of the passenger travel purpose capable of meeting the travel requirement.
Further, in the step 4), if all the trains reach the bottom line of the full load rate, the trains are operated according to the original train operation plan, otherwise, the trains are cancelled, passengers available in the second round are screened, and the passengers who take the trains cannot take the trains.
Further, the passenger reservation information includes a departure place, a destination, and a number of available vehicles. By utilizing an appointment mechanism, the travel OD, the travel purpose and the travel time of the passenger are collected, and tolerable time deviation is obtained.
And according to the travel OD of the passenger, the passenger flow path distribution is realized. According to the passenger flow reservation condition, the K short circuit algorithm path search algorithm can list all effective paths of passenger travel, then a logic non-centralized model is utilized to match the passenger flow to a road network, and each passenger is marked in a passing section.
Based on information such as trip purposes filled by passengers in the reservation, the importance of each trip demand is scored by using an analytic hierarchy process, and the risk value of each person and the maximum risk value allowed by the operation interval are scored.
And constructing a model by taking the highest score of the trip purpose of the passenger as a target function and taking the interval risk value, the full load rate and the like as constraints. The model is solved using dynamic programming. For passenger i, if x existsijIf 0, the passenger cannot take the bus; otherwise, entering a second round of screening.
And judging whether the train can be driven according to the lowest value of the full load rate, if so, informing the passengers taking the train that the reservation is successful, otherwise, informing the passengers that the reservation is failed, and canceling the train.
The application also provides an application of the rail transit train full load rate control method, and the rail transit train full load rate control method is applied to coordination and optimization of urban rail passenger flow management and control and train operation plan during epidemic situations.
The method meets the necessary travel requirements of passengers as much as possible on the premise of effectively controlling passenger flow, reduces the spreading situation of the epidemic situation in the vehicle, provides executable current-limiting suggestions for urban rail transit operation managers, and defines passengers who can take the vehicle. Meanwhile, a train operation plan scheme can be provided for an urban rail transit operation department, the waste of transport capacity resources is reduced, the matching of the transport capacity resources and the requirements of passengers is completed to the maximum extent, and the coordination and optimization of the transport capacity resources and the requirements of the passengers are realized.
Examples
The method is mainly used for solving the problems of urban rail transit flow limiting and train operation plan design under the epidemic situation, and therefore the method for coordinating and optimizing the urban rail transit active passenger flow management and control and the train operation plan under the epidemic situation is designed, and a feasible scheme is provided for controlling passenger flow and designing train operation in the urban area during the epidemic situation.
During an epidemic situation, an operation manager needs to consider the epidemic situation of an area covered by a wire network, the necessity of a passenger for a trip purpose and other factors, reduce unnecessary trips and personnel gathering, manage and control passenger flow all day long, control the full load rate and reduce the risk of infection when passengers take a bus; meanwhile, the matching of the train operation plan and the passenger requirements is realized as much as possible, and the maximum utilization of train resources is realized.
According to the technical scheme, firstly, an optimization theory is utilized to solve a correlation model which is constructed by taking the maximum number of carrying passengers as an objective function, taking train running intervals and the number of usable trains as a target as constraints, and the arrival and departure time of each station is calculated according to the running time and the stop time of the trains in the interval
And determining the travel path of the passenger by using information such as a starting place, a destination and the like filled in by the passenger during reservation and a path search algorithm of a K short-circuit algorithm, and distributing the passenger flow to a feasible path by using a Logit non-ensemble model. Based on information such as trip purposes filled by passengers in the reservation, the importance of each trip demand is graded by using an analytic hierarchy process, the risk value of each person and the maximum risk value allowed by the operation interval are evaluated, then a model is constructed, and the primary screening of passengers capable of getting on the bus is carried out in the first round.
And finally, if all the vehicles reach the bottom line of the full load rate, operating according to the original train operation plan, otherwise, canceling the train, screening passengers capable of taking the second round, and informing the passengers capable of taking the train to stop taking the train.
According to the process, basic data of the algorithm are passenger OD demand, trip purpose, tolerable time deviation, basic road network, available train condition, train running interval, train fixed number and full load rate setting. The method for coordinating and optimizing the active passenger flow management and control of urban rail transit and the train operation plan under the epidemic situation mainly comprises five steps. The first step is to preliminarily determine a train operation plan according to the constructed model; the second step is to assign paths according to the K-short algorithm and the logit non-lumped model. And thirdly, scoring the risk value of the passenger, the trip purpose and the risk value of each interval by an analytic hierarchy process. And fourthly, according to the model, solving the model by utilizing dynamic programming to preliminarily determine the reserved passenger. And fifthly, finally, performing second-round screening according to the train operation plan to determine the final passable passengers and the train operation plan. Further extension of the algorithm framework can lead to the following detailed algorithm:
inputting data: passenger OD demand, trip purpose, tolerable time deviation, basic road network, available train condition, train operation interval and full load rate.
Outputting data: passengers can get on the train and the train operation plan is carried out.
Step 1: according to the available train condition, train running interval, solving model and preliminarily determining train running plan
Step 2: and allocating the passengers to the paths by utilizing a K short circuit algorithm and a logic model according to the passenger OD demand.
Step 3: scoring the risk value of the passenger, the trip purpose and each interval risk value by using an analytic hierarchy process;
step 4: and solving a passenger reservation model by utilizing dynamic programming according to the travel purpose of the passenger, the tolerable time deviation and the full load rate setting.
Step 5: if all the vehicles reach the bottom line of the full load rate, the vehicles are operated according to the original train operation plan, otherwise, the train is cancelled, passengers which can take the vehicle in the second round are screened, and the passengers which can take the vehicle in the train are informed that the passengers which can take the vehicle cannot take the vehicle.
And designing a train operation plan with the aim of maximizing the number of passengers. Then, a passenger reservation platform is designed, passenger flow path distribution is achieved according to passenger input information and the actual urban epidemic situation, first-round screening is carried out based on full load rate setting, and passengers capable of boarding are preliminarily determined. And finally, performing second-round screening according to the train operation plan, finally determining passengers capable of boarding, determining the final train operation plan, and realizing the coordination and optimization of the train operation organization.
Although the present application has been described above with reference to specific embodiments, those skilled in the art will recognize that many changes may be made in the configuration and details of the present application within the principles and scope of the present application. The scope of protection of the application is determined by the appended claims, and all changes that come within the meaning and range of equivalency of the technical features are intended to be embraced therein.

Claims (10)

1. A rail transit train full load rate control method is characterized by comprising the following steps: the method comprises the following steps:
1) determining a train operation plan by taking the maximum passenger carrying number as a target;
2) determining the number of passengers which can be accommodated according to the train operation plan and the requirement of the full load rate of the relevant interval;
3) screening through the passenger reservation information to determine passengers capable of boarding;
4) and adjusting the train operation plan according to the determined boarding passenger information to realize the coordination and optimization of the operation organization.
2. The rail transit train full load rate control method as claimed in claim 1, characterized in that: in the step 1), in order to meet the requirement of passengers to the maximum, a model is constructed by taking the maximum number of passengers as an objective function and taking the train interval and the total train number as constraints, and a train operation plan is determined.
3. The rail transit train full load rate control method as claimed in claim 1, characterized in that: determining a passenger travel path according to the K short circuit algorithm and the classified evaluation non-ensemble model in the step 3); scoring the importance of each travel demand, scoring the risk value of each person and the maximum risk value allowed by the operation interval, and then constructing a passenger reservation model; and solving the passenger reservation model by utilizing dynamic programming according to the passenger reservation model to preliminarily determine reserved passengers.
4. The rail transit train full load rate control method according to claim 3, characterized in that: the travel path determination comprises: calculating the shortest path from the starting point to the end point; if the shortest circuit exists, deleting one edge of the shortest circuit, and calculating to obtain a temporary shortest circuit; repeating until all edges are deleted to obtain a feasible path set.
5. The rail transit train full load rate control method according to claim 4, characterized in that: the shortest path is calculated by adopting a Dijkstra algorithm, and the temporary shortest path is calculated by adopting the Dijkstra algorithm.
6. The rail transit train full load rate control method according to claim 3, characterized in that: the passenger reservation model is constructed after scoring the risk value of the passenger, the trip purpose and the risk value of each interval through an analytic hierarchy process.
7. The rail transit train full load rate control method according to claim 6, characterized in that: the objective function of the passenger reservation module is the highest score of the passenger trip purpose capable of meeting the trip requirement.
8. The rail transit train full load rate control method according to any one of claims 1 to 7, characterized in that: and 4) if all the vehicles reach the bottom line of the full load rate, operating according to the original train operation plan, otherwise, canceling the train, screening passengers capable of riding in the second round, and informing the passengers capable of riding in the train to stop riding in the second round.
9. The rail transit train full load rate control method according to claim 8, characterized in that: the passenger reservation information comprises a departure place, a destination and available train numbers.
10. The application of the rail transit train full load rate control method is characterized in that: the method for controlling the full load rate of the rail transit train according to any one of claims 1 to 9 is applied to coordination and optimization of urban rail passenger flow management and train operation plan during epidemic situations.
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