CN114021291A - Simulation evaluation modeling method for urban rail transit network current limiting scheme - Google Patents
Simulation evaluation modeling method for urban rail transit network current limiting scheme Download PDFInfo
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Abstract
A simulation evaluation modeling method for a current limiting scheme of an urban rail transit network comprises the following steps: step 1: according to the current-limiting scheme, designing a mesoscopic simulation scheduling principle; step 2: modeling an abstract agent group; and step 3: passenger arrival event control; and 4, step 4: and establishing a current limiting scheme evaluation index system. According to the simulation evaluation modeling method for the urban rail transit line network current limiting scheme, the efficiency and the cost of modeling and later-stage model transformation are improved, and the operational redundancy of experiments is effectively reduced; the simulation state of the current flow limiting scheme can be displayed through a visual and vivid network passenger flow density map, and station and vehicle data visualization in the simulation process is realized; the generated data result can accurately describe various evaluation index differences under various passenger flow control means, can effectively help the urban rail company to finish the evaluation and management of the current limiting scheme, and solves the problem of the evaluation of the current limiting scheme of the urban rail transit network.
Description
Technical Field
The invention relates to the technical field of rail transit current limiting scheme evaluation.
Background
In recent years, urban rail transit business is actively developed in all big cities in the country, and uneven passenger flow space-time distribution is the main current situation faced by urban rail operation companies in view of the cities with the existing urban rail lines. The implementation of the current limiting scheme is a common means for controlling passenger flow, and the good current limiting scheme can balance the contradictions in the aspects of safety, ride fairness, train capacity and the like. In order to effectively control passenger flow in an urban rail transit operation network, students carry out a series of researches on the design of a current limiting scheme. Due to different angles and targets of passenger flow control means, the related research of current limiting scheme design is roughly divided into two levels of a microscopic station and a macroscopic wire network, but with the continuous deepening of system engineering ideas, the research of the wire network level current limiting scheme is gradually emphasized. From various related researches at present, the line network level current limiting scheme research mainly centers on a passenger flow network controllability judgment method, cooperative control of a train road-crossing driving scheme and each station current limiting scheme, current limiting time period division, reduction of passenger arrival delay time and guarantee of safety of the current limiting scheme. Whether the manufactured current limiting scheme is scientific or not usually needs to be verified and evaluated in a simulation modeling mode, but there are few relevant research and development cases for simulation evaluation of specific wire network current limiting schemes. The difficulty is mainly reflected in that stations and passengers at a network level are often large in quantity and different in characteristics, logic of passenger flow transfer in the network is complex, the calculation amount of a simulation model is extremely large, data tracking of the flow process of each passenger individual in the network is difficult to realize, and even if simulation evaluation of a specific track network current limiting scheme is realized, when the stations, lines and other facilities are reformed or new lines are built in the network, the model can possibly face flow reconstruction with huge workload.
Disclosure of Invention
The invention aims to effectively solve the problems of large quantity of stations, passengers and trains and complex transfer logic in the wire network level simulation, has universality for any urban rail transit network, and can effectively reduce the computational redundancy and model reconstruction cost of passenger data flowing in the complex urban rail network.
The technical scheme adopted by the invention for realizing the purpose is as follows: a simulation evaluation modeling method for a current limiting scheme of an urban rail transit network comprises the following steps:
step 1: according to the current-limiting scheme design mesoscopic simulation scheduling principle, a simulation logic model of passenger flow control is as follows:
aiindicating a certain current limiting periodThe start time of the previous current limit period or the end time of the previous current limit period; biIndicating the arrival time of a certain train; t is tiIndicating the time when a passenger arrives out of the station; t is tmIndicating that the number of passengers in the station has reached the upper limit of the current limit scheme; x is the number of1Is a1To a2The number of current-limiting people at stations corresponding to the time intervals; x is the number of2Is a2To a3The number of current-limiting people at stations corresponding to the time intervals; wherein, tmPassengers arriving later need to wait outside the station, and the passengers arriving at the station can get on the train within the station stop time, if biOccurs at tmBefore, all passengers arriving at the platform of the train get on the train, station aiTo biNo latency is generated; if b isiOccurs at tmThen, all the passengers arriving at the platform of the train get on the train, and the passengers waiting outside the station begin to be released to enter the station until the number of passengers arriving at the station reaches the number of current limits at the station, and the station is from aiTo biGenerating a wait time; if a2Time x2<x1And n is>x2If the next time the passenger is released at the station is defaulted to be at the time of biThe moment and later. At a2The conditions for allowing passengers to enter the station at the moment and later are as follows:
Step 2: modeling an abstract agent group:
step 2-1: establishing a station intelligent body group, taking an abstract station intelligent body as the most basic unit for constructing a physical space model of a rail network, providing a data exchange environment for passengers and train intelligent bodies, realizing passenger data exchange between stations, and finishing the time-space abstract movement of passengers in the rail network;
step 2-2: the method comprises the steps of establishing a passenger intelligent agent group, controlling passenger data to be transferred from an upper station to a lower station by utilizing a train arrival event, releasing data corresponding to passengers getting off the station when a train arrives at a certain station, and continuing to complete subsequent in-station logic, so that the calculated amount of the passenger data in a network model is reduced, and meanwhile, the passenger individual is tracked in a network;
step 2-3: establishing a train intelligent agent group, and simplifying the data exchange process of passengers and trains without considering the transfer and succession of all data of the passenger intelligent agents to the train intelligent agents after the train stops: according to the state data of train stop, the number of passengers getting on or off the train and the like related to the station, the updating of the statistical indexes of the train and the definition of the number of passengers taking the train are completed;
and step 3: passenger arrival event control:
step 3-1: designing a simulation driving event based on the established abstract intelligent agent group;
step 3-2: completing the initial definition of a specific passenger arrival event in the model top-level flow logic;
and 4, step 4: establishing a current limiting scheme evaluation index system:
step 4-1: evaluating the fairness of the rail network, counting the average waiting time of all passengers arriving outside the station by taking the station as a unit, judging the fairness of the current-limiting scheme by comparing the average value and the variance of the average waiting time of each station,
waiting time for waiting passengers outside the ith station of the kth current-limiting station in the model:
Tk(i)＝tk2(i)-tk1(i) (2)
the average waiting time of passengers at the kth current-limiting station in the model is as follows:
average waiting time of current-limiting stations in the network:
current-limiting station latency variance in the wire network:
wherein: m represents the number of current-limiting stations in the model; n iskRepresenting the total number of passengers waiting outside the kth current limiting station in the model; t is tk1(i) The arrival time of waiting passengers outside the ith station of the kth current limiting station is shown; t is tk2(i) The station entering time of waiting passengers outside the ith current limiting station is shown;
step 4-2: the benefit of the passenger transportation system is evaluated, the passenger turnover amount is selected as an evaluation index, the passenger turnover amount of the network is calculated on the basis of OD data of each passenger, the inter-station mileage is used as vector data, and the passenger intelligent agent sequentially accumulates the inter-station mileage from the boarding station to the disembarking station according to the riding direction. For the transfer passengers, the turnover calculation needs to be carried out for a plurality of times according to the multi-section riding road sections of different lines,
turnover mileage of the g-th passenger in the rail network at the top layer of the model:
passenger turnover in the rail network:
wherein: g represents the sequence number of the model top passenger set; h represents the number of passengers in the top passenger set of the model; a isgA first riding section representing a g-th passenger; bgRepresenting the last riding section of the g passenger; ljRepresenting the mileage of the jth riding section of the gth passenger;
step 4-3: evaluating the safety of the riding environment of the train, and expressing the basis for measuring the safety of the train by the actual passenger capacity of the train: actual passenger capacity of the train at the departure time of the kth station:
Nk＝Nk-1+Xk-Yk (8)
the highest passenger capacity of the train in the whole process:
NH＝max{N1,N2,......,Nm} (9)
wherein: xkThe number of passengers getting on the train at the kth station is shown; y iskThe number of the passengers getting off the train at the kth station is shown.
In the step 2-1, according to different attributes of each station in the network, variables such as the number of current passengers, the number of waiting passengers outside the station and the like and passenger action logic flows of different stations are defined based on the uniqueness of individual main codes in the intelligent agent group, so that all stations in the network can be regarded as an intelligent agent group, the process that passengers with complex OD characteristics flow in the network is controlled in an individualized manner, evaluation indexes of huge station groups in the network are counted and summarized, and event scheduling of passenger action logic in the intelligent agent of a single station is realized through train events in the intelligent agent at the top layer.
In step 2-2, the single passenger data processing logic is as follows: the passenger generates and gives a number, a starting point and a riding direction, judges whether the number of people in the station corresponding to the arriving station reaches an upper limit, if not, the number is compiled into a passenger set of a corresponding platform according to the riding direction, judges whether a train arrives, if the train arrives, the passenger data is compiled into the passenger set of the corresponding station according to the station, judges whether the train arrives at a getting-off station for transfer, if so, the passenger data is compiled into the passenger set of the corresponding platform according to the driving direction, and if not, the passenger data is removed by the passenger set of the current platform.
In step 2-3, the data processing logic of the single train agent is as follows: the method comprises the steps that a serial number is generated and given, a train stops at a station, passenger capacity data in the train is updated according to passengers getting on or off the train, whether the current passenger capacity is larger than historical data or not is judged, if the current passenger capacity is larger than the historical data, the maximum passenger capacity of the train is equal to the current passenger capacity, if the current passenger capacity is not larger than the historical data, whether the train reaches a terminal station or not is judged, if the train reaches the terminal station, the train is ended, and if the train does not reach the terminal station, the data of passengers getting on the train at the station are added to a passenger set of each terminal station.
In the step 3-1, the current limiting time period is divided according to the time sections of different passenger arrival rates, corresponding mean values of the obeyed poisson distribution are set in different time periods, and the simulated non-stationary poisson distribution in all the time periods is approximately replaced by stationary poisson distribution with different mean values by a 'straight curve replacing' method.
In the step 3-2, according to the mean value of Poisson distribution obeyed by the arrival of passengers at each station, a single-source object generator is controlled to generate passengers, passenger IDs are sequentially assigned at the generation time of each passenger, the spatial position of the starting station is returned, the data of the terminal station and the transfer station are defined, the whole turnover mileage is automatically calculated, and the initial definition of specific passenger arrival events is completed in the top-level flow logic of the model through the generated unique passenger IDs.
In the step 4-1, the evaluation model counts respective waiting time of all passengers outside the station at the station-entering node in the network, stores the waiting time data of all passengers according to the sequence of the passengers' intelligent bodies in the intelligent bodies of all stations, calculates the average value of the waiting time by taking the station as a unit, and finally calculates the average value and the variance of the waiting time of the network.
According to the simulation evaluation modeling method for the urban rail transit line network current limiting scheme, the efficiency and the cost of modeling and later-stage model transformation are improved, and the operational redundancy of experiments is effectively reduced; the simulation state of the current flow limiting scheme can be displayed through a visual and vivid network passenger flow density map, and station and vehicle data visualization in the simulation process is realized; the generated data result can accurately describe various evaluation index differences under various passenger flow control means, can effectively help the urban rail company to finish the evaluation and management of the current limiting scheme, and solves the problem of the evaluation of the current limiting scheme of the urban rail transit network.
Drawings
FIG. 1 is a schematic view of the current limiting of the unidirectional rail transit line of the present invention.
FIG. 2 is a schematic diagram of the simulation logic for passenger flow control of the present invention.
FIG. 3 is a schematic diagram of an abstract agent group data exchange in accordance with the present invention.
FIG. 4 is a flow diagram of the single passenger data processing logic of the present invention.
Fig. 5 is a flow chart of the present invention single train agent data processing logic.
FIG. 6 is a schematic diagram of the operation state of the current-limiting scheme simulation evaluation model of the large-city continuous rail transit network at a certain time.
Detailed Description
The invention relates to a simulation evaluation modeling method for a current limiting scheme of an urban rail transit network, which has the realization environments of a simulation environment based on AnyLogic software and an operating system of Microsoft Windows 7 and above, wherein a development tool adopts Visual studio 2013, and a development language applies Java language. The modeling method comprises 4 steps: according to the design mesoscopic simulation scheduling principle of the current limiting scheme, an abstract intelligent agent group is modeled, passenger arrival events are classified and controlled, and a current limiting scheme evaluation index system is established, wherein the method specifically comprises the following steps:
step 1: according to the design mesoscopic simulation scheduling principle of the current limiting scheme:
step 1-1: the purpose of analyzing the current limiting scheme is to take a single-direction line in an urban rail transit network as an example, and the description of the current limiting scheme is shown in the attached drawing 1, wherein S1As the originating station, SiTo transfer stations, SnIs a terminal station; the controllable variables comprise the safe capacity of the train and the limited flow of the platform, and the unit can be the number of passengers or the ratio; the decision variable is the optimal inbound passenger flow or the flow limiting rate (the ratio of the flow limiting rate to the inbound demand) of the station i in the flow limiting time period Δ t (generally taking a value of 15min or 30 min). It can be seen that, for a certain running direction, when the passenger flow of getting off each station of the rail transit line is less and the passenger flow of getting on the rail transit line is greater, the condition that the passenger flow is cumulatively propagated along the line by taking the train as a carrier and the safety risk caused by the passenger flow congestion is also propagated along the line is macroscopically presented, and the transportation benefit is changed accordingly. Therefore, the final purpose of the current limiting scheme design is to provide a current limiting scheme for each station on the rail transit line based on riding safety, efficiency and fairness.
Step 1-2: designing an observation simulation scheduling principle in a current limiting scheme. In the implementation process of the current limiting scheme, different station passenger flow states have different modes for system event scheduling of the model. Taking a certain current-limiting time period of a certain station in a network as an example, a simulation logic model of passenger flow control is shown as figure 2,
aiindicating a start time of a certain current limiting period or an end time of a previous current limiting period;
biindicating the arrival time, t, of a trainiIndicating the time when a passenger arrives out of the station;
tmindicating that the number of passengers in the station has reached the upper limit of the current limit scheme;
x1is a1To a2The number of current-limiting people at stations corresponding to the time intervals;
x2is a2To a3The number of current-limiting people at stations corresponding to the time intervals;
wherein, tmPassengers arriving later need to wait outside the station, and the passengers arriving at the station can get on the train within the station stop time, if biOccurs at tmBefore, all passengers arriving at the platform of the train get on the train, station aiTo biNo latency is generated; if b isiOccurs at tmThen, all the passengers arriving at the platform of the train get on the train, and the passengers waiting outside the station begin to be released to enter the station until the number of passengers arriving at the station reaches the number of current limits at the station, and the station is from aiTo biGenerating a wait time; if a2Time x2<x1And n is>x2If the next time the passenger is released at the station is defaulted to be at the time of biThe moment and later. At a2The conditions for allowing passengers to enter the station at the moment and later are as follows:
The passenger flow control logic and the future schedule of the simulation model are composed of two basic events of arrival of passengers and arrival of trains. According to the motion logic type of passengers in the network, the passenger arrival basic events can be subdivided into four types of uplink transfer passenger arrival, uplink non-transfer passenger arrival, downlink transfer passenger arrival and downlink non-transfer passenger arrival. When the simulation clock is pushed to the occurrence moment of a future event, the main program of the simulation system updates the system state according to the event type, exchanges data with each intelligent body related to the event, and pushes the simulation clock to the next future event. E.g. in the presence of train arrival biAt the moment, waiting passengers at the platform can be emptied, and data exchange of passengers is needed between the train and the station and between the station and the station; a is2And the arrival rates of passengers at all stations in the urban rail network can be refreshed at all times, and the current-limiting number of passengers at each station is assigned again.
Step 2: modeling an abstract agent group:
and (3) carrying out interactive modeling on passengers, stations and trains according to the discrete event scheduling principle of the step 1. For the simulation evaluation model of the mesoscopic wire network current limiting scheme, the activity details of passengers in stations and the wire network do not need to be paid much attention, and only the accuracy and the effectiveness of relevant data statistics in the current limiting process need to be paid attention. Therefore, the data exchange of the simulation evaluation model is simplified into a model structure which takes a passenger agent and a train agent as physical flowing objects and takes each station agent in the track network as an abstract data storage and exchange basis. The abstract agent group data exchange logic is shown in fig. 3.
Step 2-1: and establishing a station intelligent agent group. The abstract station intelligent body is used as the most basic unit for constructing a physical space model of the rail network, and provides a data exchange environment for passengers and a train intelligent body, so that the passenger data exchange between the stations is realized, and the space-time abstract movement of the passengers in the rail network is completed. According to different attributes of each station in the network, variables such as the number of current passengers, the number of waiting passengers outside the station and the like and passenger action logic flows of different stations are defined based on the uniqueness of individual main codes in the intelligent agent group, so that all stations in the network can be regarded as an intelligent agent group, the process of flow of passengers with complex OD characteristics in the network is controlled in an individualized manner, and evaluation indexes of huge station groups in the network are counted and summarized. Although the station agent does not belong to the top level agent in the model, event scheduling for passenger action logic within a single station agent can be achieved through train events in the top level agent.
Step 2-2: a passenger agent group is established. The movement of the passenger intelligent agent in the station is simplified into the process of changing the data storage position along with the change of the station state and the arrival condition of the train, namely, the arrival event of the train is utilized to control the data of the passenger to be transferred from the station to the station, and when the train arrives at a certain station, the data corresponding to the passenger getting off the station is released to continuously complete the subsequent logic in the station. In this way, the calculated amount of passenger data in the network model is reduced, and the tracking of the passenger individuals in the network is realized, and a specific single passenger data processing logic flow is shown in fig. 4, and the single passenger data processing logic is as follows: the passenger generates and gives a number, a starting point and a riding direction, judges whether the number of people in the station corresponding to the arriving station reaches an upper limit, if not, the number is compiled into a passenger set of a corresponding platform according to the riding direction, judges whether a train arrives, if the train arrives, the passenger data is compiled into the passenger set of the corresponding station according to the station, judges whether the train arrives at a getting-off station for transfer, if so, the passenger data is compiled into the passenger set of the corresponding platform according to the driving direction, and if not, the passenger data is removed by the passenger set of the current platform.
Step 2-3: and establishing a train agent group. Details of getting on and off of train intelligent agent passengers are ignored, namely, the transfer and inheritance of all data of the train intelligent agent to the train intelligent agent after the train stops are not considered, and the data exchange process of the passengers and the train is simplified as follows: and finishing the updating of the train statistical indexes and the definition of the times of passengers according to the state data of train stop, the number of passengers getting on or off the train and the like related to the station. The data processing logic of the single train agent is shown in the attached figure 5, and the data processing logic of the single train agent is as follows: the method comprises the steps that a serial number is generated and given, a train stops at a station, passenger capacity data in the train is updated according to passengers getting on or off the train, whether the current passenger capacity is larger than historical data or not is judged, if the current passenger capacity is larger than the historical data, the maximum passenger capacity of the train is equal to the current passenger capacity, if the current passenger capacity is not larger than the historical data, whether the train reaches a terminal station or not is judged, if the train reaches the terminal station, the train is ended, and if the train does not reach the terminal station, the data of passengers getting on the train at the station are added to a passenger set of each terminal station.
And step 3: passenger arrival event control method.
Step 3-1: based on the established abstract agent group, simulation driving events, namely the occurrence of passenger arrival events, are designed. Because the current limiting time period is divided according to time sections of different passenger arrival rates (generally obeying non-stationary poisson distribution), the corresponding mean values of the obeyed poisson distribution are set in different time periods, and the simulated non-stationary poisson distribution in all the time periods is approximately replaced by the stationary poisson distribution with different mean values by a 'straight curve-replacing' method.
Step 3-2: and controlling the single-source object generator to generate the passengers according to the mean value of the Poisson distribution obeyed by the arrival of the passengers at each station, wherein the probability of the simultaneous occurrence of the random events in the time dimension is 0. And assigning passenger IDs at the generation time of each passenger in sequence, returning the spatial positions of the initial stations, defining data such as final stations, transfer stations and the like, and automatically calculating the whole turnover mileage. With the unique passenger ID generated, the initial definition of a particular passenger arrival event is done in the model top-level flow logic.
And 4, step 4: and establishing a current limiting scheme evaluation index system.
Step 4-1: and evaluating the fairness of the track network. And counting the average waiting time of all passengers arriving outside the station by taking the station as a unit, and judging the fairness of the current limiting scheme by comparing the average value and the variance of the average waiting time of each station. Therefore, the evaluation model is used for counting the waiting time of all passengers outside the station at the station-entering node in the network, storing the waiting time data of all passengers according to the sequence of the passengers' intelligent bodies gathered in the intelligent bodies of all stations, calculating the average value of the waiting time by taking the station as a unit, and finally calculating the average value and the variance of the waiting time of the network.
Waiting time for waiting passengers outside the ith station of the kth current-limiting station in the model:
Tk(i)＝tk2(i)-tk1(i) (2)
the average waiting time of passengers at the kth current-limiting station in the model is as follows:
average waiting time of current-limiting stations in the network:
current-limiting station latency variance in the wire network:
wherein:
m represents the number of current-limiting stations in the model;
nkrepresenting the total number of passengers waiting outside the kth current limiting station in the model;
tk1(i) the arrival time of waiting passengers outside the ith station of the kth current limiting station is shown;
tk2(i) the station entering time of waiting passengers outside the ith current limiting station is shown;
step 4-2: the profitability of the passenger transportation system is evaluated. The passenger turnover is selected as an evaluation index, and the passenger turnover of the network is calculated on the basis of the OD data of each passenger. The mileage between stations is used as vector data, and the intelligent passenger accumulates the mileage between the upper station and the lower station in sequence according to the riding direction. For the transfer passengers, the turnover calculation needs to be carried out for multiple times according to the multi-section riding road sections of different lines.
Turnover mileage of the g-th passenger in the rail network at the top layer of the model:
passenger turnover in the rail network:
wherein:
g represents the sequence number of the model top passenger set;
h represents the number of passengers in the top passenger set of the model;
aga first riding section representing a g-th passenger;
bgrepresenting the last riding section of the g passenger;
ljindicating the j-th riding section mileage of the g-th passenger.
Step 4-3: and evaluating the riding environment safety of the train. The basis for measuring the safety of the train can be represented by the actual passenger capacity of the train. For example, in the context of a large-scale public health emergency (such as a new crown epidemic situation), the factor endangering the safety of a bus is the safety interval between people in a carriage, namely the number of safe passengers allowed under an epidemic prevention condition, and according to the regional classification prevention and control guide of the new crown pneumonia epidemic situation of passenger stations and transportation vehicles during the spring transport period of 2021 year released by the transportation department in 1 month of 2021, the upper limit of the congestion degree in urban rail transit trains in high-risk and medium-risk areas is 50% and 70% respectively, the congestion degree is converted into the passenger carrying capacity for statistics and calculation in the modeling process. And counting the highest passenger capacity of the train in the whole traffic route, and comparing the highest passenger capacity of all train numbers in each time period to be used as a basis for evaluating the safety index of the current limiting scheme.
Actual passenger capacity of the train at the departure time of the kth station:
Nk＝Nk-1+Xk-Yk (8)
the highest passenger capacity of the train in the whole process:
NH＝max{N1,N2,......,Nm} (9)
wherein:
Xkshowing the number of passengers getting on the train at the kth station
YkShowing the number of the passengers getting off the train at the kth station
The implementation process of the urban rail transit network current limiting scheme is complex, the conditions of all stations in the network are different, the modeling work is complex and tedious, and the calculated amount is large. According to the simulation evaluation model of the large-city continuous rail transit network current limiting scheme developed by the theoretical method, as shown in the attached figure 6, the simulation state of the current limiting scheme can be displayed through a visual and vivid network passenger flow density graph, and station and vehicle data visualization in the simulation process is realized; the generated data result can accurately describe various evaluation index differences under various passenger flow control means, and can effectively help urban rail companies to complete evaluation and management of the current limiting scheme. The method not only solves the problem of evaluating the urban rail transit network current limiting scheme, but also has certain reference significance for the simulation modeling problem of the urban rail transit transportation organization.
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 (7)
1. A simulation evaluation modeling method for a current limiting scheme of an urban rail transit network is characterized by comprising the following steps: the method comprises the following steps:
step 1: according to the current-limiting scheme design mesoscopic simulation scheduling principle, a simulation logic model of passenger flow control is as follows:
aiindicating a start time of a certain current limiting period or an end time of a previous current limiting period; biIndicating the arrival time of a certain train; t is tiIndicating the time when a passenger arrives out of the station; t is tmIndicating that the number of passengers in the station has reached the upper limit of the current limit scheme; x is the number of1Is a1To a2The number of current-limiting people at stations corresponding to the time intervals; x is the number of2Is a2To a3The number of current-limiting people at stations corresponding to the time intervals; wherein, tmPassengers arriving later need to wait outside the station, and the passengers arriving at the station can get on the train within the station stop time, if biOccurs at tmBefore, all passengers arriving at the platform of the train get on the train, station aiTo biNo latency is generated; if b isiOccurs at tmThen, all the passengers arriving at the platform of the train get on the train, and the passengers waiting outside the station begin to be released to enter the station until the number of passengers arriving at the station reaches the number of current limits at the station, and the station is from aiTo biGenerating a wait time; if a2Time x2<x1And n is>x2If the next time the passenger is released at the station is defaulted to be at the time of biThe moment and later. At a2The conditions for allowing passengers to enter the station at the moment and later are as follows:
step 2: modeling an abstract agent group:
step 2-1: establishing a station intelligent body group, taking an abstract station intelligent body as the most basic unit for constructing a physical space model of a rail network, providing a data exchange environment for passengers and train intelligent bodies, realizing passenger data exchange between stations, and finishing the time-space abstract movement of passengers in the rail network;
step 2-2: the method comprises the steps of establishing a passenger intelligent agent group, controlling passenger data to be transferred from an upper station to a lower station by utilizing a train arrival event, releasing data corresponding to passengers getting off the station when a train arrives at a certain station, and continuing to complete subsequent in-station logic, so that the calculated amount of the passenger data in a network model is reduced, and meanwhile, the passenger individual is tracked in a network;
step 2-3: establishing a train intelligent agent group, and simplifying the data exchange process of passengers and trains without considering the transfer and succession of all data of the passenger intelligent agents to the train intelligent agents after the train stops: according to the state data of train stop, the number of passengers getting on or off the train and the like related to the station, the updating of the statistical indexes of the train and the definition of the number of passengers taking the train are completed;
and step 3: passenger arrival event control:
step 3-1: designing a simulation driving event based on the established abstract intelligent agent group;
step 3-2: completing the initial definition of a specific passenger arrival event in the model top-level flow logic;
and 4, step 4: establishing a current limiting scheme evaluation index system:
step 4-1: evaluating the fairness of the rail network, counting the average waiting time of all passengers arriving outside the station by taking the station as a unit, judging the fairness of the current-limiting scheme by comparing the average value and the variance of the average waiting time of each station,
waiting time for waiting passengers outside the ith station of the kth current-limiting station in the model:
Tk(i)＝tk2(i)-tk1(i) (2)
the average waiting time of passengers at the kth current-limiting station in the model is as follows:
average waiting time of current-limiting stations in the network:
current-limiting station latency variance in the wire network:
wherein: m represents the number of current-limiting stations in the model; n iskRepresenting the total number of passengers waiting outside the kth current limiting station in the model; t is tk1(i) The arrival time of waiting passengers outside the ith station of the kth current limiting station is shown; t is tk2(i) The station entering time of waiting passengers outside the ith current limiting station is shown;
step 4-2: the benefit of the passenger transportation system is evaluated, the passenger turnover amount is selected as an evaluation index, the passenger turnover amount of the network is calculated on the basis of OD data of each passenger, the inter-station mileage is used as vector data, and the passenger intelligent agent sequentially accumulates the inter-station mileage from the boarding station to the disembarking station according to the riding direction. For the transfer passengers, the turnover calculation needs to be carried out for a plurality of times according to the multi-section riding road sections of different lines,
turnover mileage of the g-th passenger in the rail network at the top layer of the model:
passenger turnover in the rail network:
wherein: g represents the sequence number of the model top passenger set; h represents the number of passengers in the top passenger set of the model; a isgA first riding section representing a g-th passenger; bgLast riding section representing the g-th passenger；ljRepresenting the mileage of the jth riding section of the gth passenger;
step 4-3: evaluating the safety of the riding environment of the train, and expressing the basis for measuring the safety of the train by the actual passenger capacity of the train:
actual passenger capacity of the train at the departure time of the kth station:
Nk＝Nk-1+Xk-Yk (8)
the highest passenger capacity of the train in the whole process:
NH＝max{N1,N2,......,Nm} (9)
wherein: xkThe number of passengers getting on the train at the kth station is shown; y iskThe number of the passengers getting off the train at the kth station is shown.
2. The method for modeling the current limiting scheme of the urban rail transit network according to claim 1, characterized in that: in the step 2-1, according to different attributes of each station in the network, variables such as the number of current passengers, the number of waiting passengers outside the station and the like and passenger action logic flows of different stations are defined based on the uniqueness of individual main codes in the intelligent agent group, so that all stations in the network can be regarded as an intelligent agent group, the process that passengers with complex OD characteristics flow in the network is controlled in an individualized manner, evaluation indexes of huge station groups in the network are counted and summarized, and event scheduling of passenger action logic in the intelligent agent of a single station is realized through train events in the intelligent agent at the top layer.
3. The method for modeling the current limiting scheme of the urban rail transit network according to claim 1, characterized in that: in step 2-2, the single passenger data processing logic is as follows: the passenger generates and gives a number, a starting point and a riding direction, judges whether the number of people in the station corresponding to the arriving station reaches an upper limit, if not, the number is compiled into a passenger set of a corresponding platform according to the riding direction, judges whether a train arrives, if the train arrives, the passenger data is compiled into the passenger set of the corresponding station according to the station, judges whether the train arrives at a getting-off station for transfer, if so, the passenger data is compiled into the passenger set of the corresponding platform according to the driving direction, and if not, the passenger data is removed by the passenger set of the current platform.
4. The method for modeling the current limiting scheme of the urban rail transit network according to claim 1, characterized in that: in step 2-3, the data processing logic of the single train agent is as follows: the method comprises the steps that a serial number is generated and given, a train stops at a station, passenger capacity data in the train is updated according to passengers getting on or off the train, whether the current passenger capacity is larger than historical data or not is judged, if the current passenger capacity is larger than the historical data, the maximum passenger capacity of the train is equal to the current passenger capacity, if the current passenger capacity is not larger than the historical data, whether the train reaches a terminal station or not is judged, if the train reaches the terminal station, the train is ended, and if the train does not reach the terminal station, the data of passengers getting on the train at the station are added to a passenger set of each terminal station.
5. The method for modeling the current limiting scheme of the urban rail transit network according to claim 1, characterized in that: in the step 3-1, the current limiting time period is divided according to the time sections of different passenger arrival rates, corresponding mean values of the obeyed poisson distribution are set in different time periods, and the simulated non-stationary poisson distribution in all the time periods is approximately replaced by stationary poisson distribution with different mean values by a 'straight curve replacing' method.
6. The method for modeling the current limiting scheme of the urban rail transit network according to claim 1, characterized in that: in the step 3-2, according to the mean value of Poisson distribution obeyed by the arrival of passengers at each station, a single-source object generator is controlled to generate passengers, passenger IDs are sequentially assigned at the generation time of each passenger, the spatial position of the starting station is returned, the data of the terminal station and the transfer station are defined, the whole turnover mileage is automatically calculated, and the initial definition of specific passenger arrival events is completed in the top-level flow logic of the model through the generated unique passenger IDs.
7. The method for modeling the current limiting scheme of the urban rail transit network according to claim 1, characterized in that: in the step 4-1, the evaluation model counts respective waiting time of all passengers outside the station at the station-entering node in the network, stores the waiting time data of all passengers according to the sequence of the passengers' intelligent bodies in the intelligent bodies of all stations, calculates the average value of the waiting time by taking the station as a unit, and finally calculates the average value and the variance of the waiting time of the network.
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| CN117196264B (en) * | 2023-11-07 | 2024-02-27 | 深圳市城市交通规划设计研究中心股份有限公司 | Urban rail passenger flow cooperative control method, electronic equipment and storage medium |
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