CN117454678B - Pedestrian flow control method for junction station based on pedestrian macroscopic basic diagram - Google Patents
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Abstract
The invention discloses a junction station pedestrian flow control method based on a pedestrian macroscopic basic diagram, which comprises the following steps: dividing pedestrian network subareas in the area between the peripheral area and the waiting area according to the facility arrangement of the passenger junction station and combining the necessary flow and facilities of the passenger arrival; constructing a pedestrian simulation model of the passenger junction station, obtaining pedestrian space-time track data of each subarea through simulation, and calculating to obtain the pedestrian accumulation number and the pedestrian flow of each subarea in different preset length groups; constructing a pedestrian macroscopic basic diagram based on the pedestrian accumulation number and the pedestrian flow, and performing linear function fitting on the pedestrian macroscopic basic diagram; based on the divided pedestrian network subareas, constructing a pedestrian flow optimization model in the junction station and constraint conditions thereof; solving the pedestrian flow optimization model to obtain the opening quantity result of the facilities on the boundaries of each subarea at each moment, and controlling the opening quantity of the facilities on the boundaries of each subarea according to the opening quantity result, thereby achieving the purpose of pedestrian flow control.
Description
Technical Field
The invention relates to the technical field of pedestrian flow control, in particular to a junction station pedestrian flow control method based on a pedestrian macroscopic basic diagram.
Background
In recent years, along with the gradual perfection of a high-speed railway network, the flow of production elements in a traffic circle is increasingly frequent, and the continuous acceleration of the novel urbanization development process is further promoted. In the process, the co-metropolitan effect in the urban area starts to appear, the inter-urban passenger flow scale of the high-speed railway is continuously enlarged, and new characteristics of high density, commute and randomness are generated. Urban transportation junction stations face high-intensity passenger flows, and congestion conditions in the stations are increasingly serious, especially on holidays and passenger flow bursts. The determined facility scale at the initial stage of the construction of the junction station cannot meet the large-scale pedestrian flow, and the pedestrian flow is easy to be jammed and safety accidents occur. Therefore, how to achieve orderly management of pedestrian traffic at a limited facility scale is a difficult problem.
The following two problems are obtained by summarizing and summarizing the existing pedestrian flow control method of the passenger transportation junction station: ① Railway staff usually adjusts the open quantity of facilities according to the current situation of the on-site passenger flow, and then controls the pedestrian flow, and the operation is easy to cause situations of difficult temporary personnel movement, disordered passenger flow control and the like. ② The incoming passenger flow is easy to fall into a crowded condition, and the circulation rate is slow.
Disclosure of Invention
The invention aims to provide a junction station pedestrian flow control method based on a pedestrian macroscopic basic diagram, which is used for effectively relieving the phenomenon of pedestrian flow congestion of a station by controlling the use plan of facilities in the station based on the number of pedestrian flows of the station and shortening the total passing time of pedestrians.
In order to realize the tasks, the invention adopts the following technical scheme:
A junction station pedestrian flow control method based on a pedestrian macroscopic basic diagram comprises the following steps:
dividing pedestrian network subareas in the area between the peripheral area and the waiting area according to the facility arrangement of the passenger junction station and combining the necessary flow and facilities of the passenger arrival;
Constructing a pedestrian simulation model of the passenger junction station, obtaining pedestrian space-time track data of each subarea through simulation, and calculating to obtain the pedestrian accumulation number and the pedestrian flow of each subarea in different preset length groups; constructing a pedestrian macroscopic basic diagram based on the pedestrian accumulation number and the pedestrian flow, and performing linear function fitting on the pedestrian macroscopic basic diagram; the linear function represents the relation between the accumulated number of pedestrians and the flow in the subarea and is used for constructing the subsequent constraint condition;
Constructing a pedestrian flow optimization model in the junction station based on the divided pedestrian network subareas, and taking pedestrian conservation constraint of subareas and peripheral areas, pedestrian transfer flow constraint among different subareas, flow constraint obtained by calculating pedestrian transfer flow among different subareas under the accumulation number of pedestrians and maximum opening quantity constraint of facilities as constraint conditions of the pedestrian flow optimization model;
Solving the pedestrian flow optimization model to obtain the opening quantity result of the facilities on the boundaries of each subarea at each moment, and finally controlling the opening quantity of the facilities on the boundaries of each subarea by the personnel at the junction station according to the opening quantity result, thereby achieving the purpose of controlling the pedestrian flow.
Further, the dividing the area between the passenger junction station and the waiting area from the peripheral area into pedestrian network subareas comprises:
for the region from the peripheral region to the waiting region of the passenger junction station, dividing the closed space between two facilities which are arbitrarily separated into one region to obtain a region set ; TraversingIf the space communication condition exists between the areas, combining the two areas into a subarea; if the area is not communicated with other areas, the area is independently used as a subarea, so that a pedestrian network subarea set is obtained.
Further, the calculating to obtain the accumulated pedestrian number and the accumulated pedestrian flow of each subarea in the groups with different preset lengths includes:
(1) The pedestrian flow density calculating step comprises the following steps:
For statistical time period of Wherein the simulation time statistics interval length isThe second of time is the time required for the device to complete,For any statistical start time,Is a time interval parameter; each of which isTime of dayBased on coordinate values of the pedestrian track points, a Voronoi graph algorithm is adopted to divide pedestrian space and pedestriansAt the position ofThe track point at the moment will obtain the corresponding personal space, which is recorded asThe area is recorded asPedestrian, pedestrianAt the position ofDensity of time of dayThe calculation formula of (2) is as follows:
(1)
Same time sub-zone Density of pedestrian flowThe calculation formula of (2) is as follows, whereinRepresentation ofTime of day the subregionIs the total number of pedestrians:
(2)
statistics time period Sub-regions in a packet of (a)Speed of the flow of peopleThe average value of pedestrian density at each moment in the statistical time period is calculated as follows:
(3)
(2) Statistics time period Middle subregionSpeed of the flow of peopleThe calculation formula is as follows:
(4)
Wherein the method comprises the steps of Is a subregionThe total distance all pedestrians walk in the statistical time period,Is a subregionThe total time that all pedestrians stay in the area in the statistical time period;
(3) Statistics time period Middle subregionFlow of people in the interiorThe calculation formula of (2) is as follows:
(5)
Wherein the method comprises the steps of Is a subregionPedestrian exit width of (2);
(4) Statistics time period Middle subregionAccumulation of people in the interiorThe calculation formula is as follows:
(6)
Wherein the method comprises the steps of Is thatTime subregionThe number of pedestrian accumulation in the interior.
Further, constructing a pedestrian macroscopic basic diagram based on the pedestrian cumulative number and the flow, and performing linear function fitting on the pedestrian macroscopic basic diagram, wherein the method comprises the following steps:
For each sub-region Processing to obtain pedestrian accumulation values and flow in a plurality of statistical time periods, taking the pedestrian accumulation values as abscissa and the flow as ordinate to construct a scatter diagram, wherein the scatter diagram is the subareaIs a macroscopic basic drawing of a pedestrian;
Fitting a linear function to the scatter diagram to obtain a sub-region The correlation formula (7) of the flow and the pedestrian accumulation number is as follows:
(7)
In the middle of Is a subregionThe accumulated number of pedestrians isThe flow value of the flow rate is lower,Is a subregionCorresponding to the maximum flow value，Is a subregionCorresponding to the accumulated number of the congested pedestrians and the value of the congested flow，Is a subregionCan accommodate the cumulative number of pedestrians.
Further, the step of constructing a pedestrian flow optimization model in the junction station based on the divided pedestrian network subareas comprises the following steps:
the pedestrian flow optimization model has the expression:
(8)
In the middle of Is thatTime subregionIs a cumulative number of pedestrians,Is a subregionIs a combination of the optimal number of pedestrian accumulation,Is thatTime zoneIs a cumulative number of pedestrians,AndFor each subareaSum regionWeight coefficient of (2); Representing the number of subregions.
Further, the constraint condition of the pedestrian flow optimization model using the pedestrian conservation constraint of the subarea and the peripheral area, the pedestrian transfer flow constraint between different subareas, the flow constraint obtained by calculating the pedestrian transfer flow between different subareas under the pedestrian accumulation number and the maximum opening quantity constraint of the facilities as the constraint condition of the pedestrian flow optimization model includes:
Subregion The pedestrian conservation equation constraint formula of (2) is expressed as follows:
(9)
Is that Time subregionIs a cumulative number of pedestrians,Is thatTime of day and time of dayThe time length of the interval between the moments, i.e. the length of a time domain; are all the numbers of the sub-areas, Taking real name for verification of facility typeSecurity inspectionOr escalator，Is thatTime of day passing facilitySlave subregionTransfer to subregionIs used for controlling the pedestrian flow of people,Is thatTime of day passing facilitySlave subregionTransfer to subregionIs used for controlling the pedestrian flow of people,Is the sum of sub-areasA collection of connected regions;
Peripheral region The pedestrian conservation equation constraint formula of (2) is expressed as follows:
(10)
Is that Time of day peripheral regionIs a cumulative number of pedestrians,Is thatTime of arrival zoneAnd plan to enter the subregionIs a function of the number of pedestrians,Is thatTime of day passing facilitySlave regionTransfer to subregionIs used for controlling the pedestrian flow of people,Is the same as the peripheral areaA collection of connected regions;
Subregion Sum subregionPedestrian transfer flow betweenThe constraint formula expression of (2) is:
(11)
As a means of Time subregionSum subregionBoundary-mounted facilityIs used for the number of openings of the (a),Is a facilityThe rate of pedestrian traffic for a single aisle,0-1 Variable for distinguishing facilitiesOf the type (C)For real name verificationOr security inspectionIn the time-course of which the first and second contact surfaces,Otherwise;
The pedestrian transfer flow between the subareas must not be greater than that of the subareaSub-zone calculated under pedestrian accumulation numberThe mathematical expression of the inequality is:
(12)
Each subarea The number of accumulated pedestrians in the system is limited, and each subarea is setThe accumulated number of pedestrians is not more than the accumulated number of pedestrians which can be accommodated in the vehicleThe mathematical expression of the inequality is:
(13)
There is a constraint on the maximum open number of the facility, and a control variable is set Must not exceed the maximum number of facilities in the stationThe mathematical expression of the inequality is:
(14)。
Further, the solving the pedestrian flow optimization model includes:
Solving a pedestrian flow optimization model by adopting an MPC method:
The state variable of the system is defined as the accumulated number of pedestrians in each subarea Is thatThe state vector of the time of day system,WhereinRepresentation ofTime zonePedestrian accumulation amounts of (2);
the control variables of the system are defined as the open number of facilities on the boundaries of each subarea Is thatThe control vector of the time of day system,WhereinRepresentation ofTime subregionSum subregionOn-boundary facility real name verificationIs the number of openings of (3);
Disturbance factors of the system include internal disturbance and external disturbance; defining internal disturbances of a system as vectors ,WhereinIs a subregionThe accumulated number of pedestrians isA lower flow value;
defining external disturbances of a system as vectors ，WhereinRepresentation ofTime of arrival zoneAnd plan to enter the subregionPedestrian number of (2);
The state equation of the MPC controller is designed as:
(15)
Equation (15) is used for calculation State vector of time of day system; In the middle ofIs the length of one time domain; to control variables Is used for the coefficient matrix of (a),Is an internal disturbance vectorIs used for the coefficient matrix of (a),As external disturbance variableCoefficient matrix of (a);
The objective function of the MPC control system is as in equation (16):
(16)
for a set number of predicted time-domains, Indicating future firstThe number of time steps is one,，Represent the firstThe time is the same; Is the first Error vectors at each time point represent error values between the system state and the control target,，Is a subregionIs a combination of the optimal number of pedestrian accumulation,Is the transposed vector of the vector,Is the firstA matrix of weight coefficients of the error vector at each instant,Is the firstThe error vector for each moment in time,Is the transposed vector of the vector,Is the firstA weight coefficient matrix of error vectors at each moment; Is the first The control vector for each moment in time,Is the transposed vector of the vector,Is the firstA matrix of weight coefficients for the control vector at each instant.
Further, the specific process of solving the pedestrian flow optimization model by adopting the MPC method is as follows:
① At the position of At that time, the MPC control system predicts future system states, using the state equation of equation (15)At the time, …,The state of the system at the moment of time is predicted,A set predicted time domain number;
② At the position of At time, based on the predicted future system state in ①, a Gurobi solver is used to solve a model that uses equation (16) as an objective function to control the vector, (k=0, …, ) For decision variables, solving the model to obtain control vectors, …,Is the optimum value of (2);
③ At the position of Taking the moment of timeTime control vector solutionApplied to the system to updateSystem state at time；
④ UpdatingSteps ① to ③ are repeated, the objective function of the next moment is solved, the optimization is rolled until the set simulation final moment, the program is stopped, and finally the control vector value of each moment, namely the result of the opening quantity of the facilities on the boundaries of each subarea, is output.
A terminal device comprising a processor, a memory and a computer program stored in the memory; and when the processor executes the computer program, the step of the junction station pedestrian flow control method based on the pedestrian macroscopic basic diagram is realized.
A computer-readable storage medium having a computer program stored therein; and when the computer program is executed by the processor, the steps of the junction station pedestrian flow control method based on the pedestrian macroscopic basic diagram are realized.
Compared with the prior art, the invention has the following technical characteristics:
1. The invention is oriented to the rail transportation junction station, constructs a pedestrian topology network, realizes the prediction of pedestrian flow by combining the traffic flow theory, utilizes the characteristics of a pedestrian macroscopic basic diagram to carry out modeling analysis, solves the open number of facilities in each period in the junction station by using a Model Prediction Control (MPC) method, accurately models a system model by using the pedestrian flow theory, predicts the future system state, realizes more optimized control effect, and finally achieves the aims of improving the fluidity of the pedestrian flow entering the junction station and improving the traffic efficiency.
2. According to the invention, by constructing the inbound pedestrian flow control model, the inbound pedestrian flow quantity is input, the planning result of facilities in the junction station is output, and a reliable passenger flow control scheme is provided for the junction station.
Drawings
FIG. 1 is a schematic diagram of a junction station pedestrian network;
FIG. 2 is a schematic illustration of an individual space of pedestrians divided by a Voronoi diagram;
FIG. 3 is a schematic diagram of a pedestrian macroscopic basic graph fitting function;
FIG. 4 is a schematic diagram of a control framework of the method of the present invention;
FIG. 5 is a graph showing the number of pedestrians in an unfinished course of a system with and without control in accordance with an embodiment of the present invention.
Detailed Description
Aiming at one or more existing problems of the junction station, the invention provides a junction station pedestrian flow control method based on a pedestrian macroscopic basic diagram, which comprises four substeps of dividing a pedestrian network subarea, identifying the subarea pedestrian macroscopic basic diagram, constructing a junction station pedestrian flow optimization model, and designing a control strategy for solving subarea boundary facilities by a controller; the specific steps of the present invention will be described in detail.
1. Dividing pedestrian network subregions
According to the facility arrangement of the passenger junction station, the pedestrian network subarea is divided by combining the necessary flow and facilities of the passenger arrival.
Taking the flow of taking pedestrians as an example, facilities which the pedestrians need to pass through for going to the station in the junction station comprise real-name verification, security inspection and escalator, and the facilities are taken as boundaries to divide areas. The dividing steps are as follows:
① Dividing the closed space between two facilities at random intervals into one region (region where pedestrians can only enter or leave through the facilities) to obtain a region collection ；
② TraversingEach region of (3)If the regions are combinedPresence and region of (a)Areas of communication (space communication, no wall block)Combining two regions into one sub-regionIf not, the area isSeparately as a sub-zoneObtaining a subarea set。
The divided pedestrian network is illustrated in fig. 1, in which the arrows represent the three types of facilities mentioned above, the passenger junction station is analogous to the outermost circle,As a peripheral region of the wafer, there is provided,For 7 sub-areas (areas where pedestrians can only get in and out through the facility) obtained according to the previous dividing step,Is a pedestrian waiting area. Pedestrians who are getting in the train will arrive firstAreas (peripheral areas, e.g. areas where pedestrians reach the terminal), then transfer over the boundaries of the areas by means of facilities, finally reachingArea (waiting area).
2. Constructing a sub-area pedestrian macroscopic basic diagram, wherein the detailed steps comprise:
2.1, constructing a junction station pedestrian simulation model by using the existing pedestrian simulation software AnyLogic, wherein the specific operation is as follows:
① A spatial environment is defined. And building a wall body and a pedestrian space region of the model by taking the hub station drawing as a reference.
② A facility is defined. And adding real-name verification, security inspection and escalator facilities in the model environment.
③ The pedestrian attribute is set. And adding pedestrian agents, and defining attributes such as speed, radius and the like of pedestrians.
④ Pedestrian behavior is defined. A series of behavioral logics are set up for pedestrian generation, going through various facilities, to enter the terminal area.
⑤ And configuring simulation experiment parameters. Setting the change of the input pedestrian flow from low to high along with the time increase, and setting the simulation total duration as。
⑥ And running simulation and outputting data. Running a simulation model, and outputting pedestrian space-time track data of each subarea at intervals of 1 second, wherein fields of the pedestrian space-time track data comprise: model running time, the number of the subarea where the pedestrian is located, the x-axis coordinate value of the pedestrian position and the y-axis coordinate value of the pedestrian position.
2.2 Processing the pedestrian space-time trajectory data output by simulation by using Python to extract each subareaPedestrian flow parameters of (1) including pedestrian flowDensity ofSpeed and velocity ofCumulative number of pedestrians; Setting the length of the simulation time statistical interval asThe second of time is the time required for the device to complete,For arbitrary statistical start time, data is grouped according to the statistical length, and each subarea is calculated in each length groupPedestrian flow parameters of (a).
(1) The pedestrian flow density calculating step comprises the following steps:
For statistical time period of Each of the groups of (1)Time of dayBased on x and y coordinate values of a pedestrian track point, dividing a pedestrian space by adopting a Voronoi graph algorithm, and dividing an effect graph and a pedestrian in FIG. 2At the position ofThe track point at the moment will obtain the corresponding personal space, which is recorded asThe area is recorded asPedestrian, pedestrianAt the position ofDensity of time of dayThe calculation formula of (2) is as follows:
(1)
Same time sub-zone Density of pedestrian flowThe calculation formula of (2) is as follows, whereinRepresentation ofTime of day the subregionIs the total number of pedestrians:
(2)
statistics time period Sub-regions in a packet of (a)Density of the flow of people in the interiorThe average value of pedestrian density at each moment in the statistical time period is calculated as follows:
(3)
(2) Statistics time period Middle subregionSpeed of the flow of peopleThe calculation formula is as follows:
(4)
Wherein the method comprises the steps of Is a subregionThe total distance all pedestrians walk in the statistical time period,Is a subregionThe total time that all pedestrians stay in the area within the statistical time period.
(3) Statistics time periodMiddle subregionFlow of people in the interiorThe calculation formula of (2) is as follows:
(5)
Wherein the method comprises the steps of Is a subregionPedestrian exit width of (2).
(4) Statistics time periodMiddle subregionAccumulation of people in the interiorThe calculation formula is as follows:
(6)
Wherein the method comprises the steps of Is thatTime subregionThe number of pedestrian accumulation in the interior.
2.3 For each sub-zoneProcessing according to the method of 2.2 to obtain pedestrian accumulation values and flow in a plurality of statistical time periods, taking the pedestrian accumulation values as abscissa and the flow as ordinate to construct a scatter diagram, wherein the scatter diagram is the subareaIs a macroscopic basic drawing of a pedestrian.
Fitting a linear function to the scatter diagram to obtain a sub-regionThe form of the function of the correlation formula (7) of the flow and the pedestrian accumulation number is shown in fig. 3, and the fitting formula is as follows:
(7)
In the middle of Is a subregionThe accumulated number of pedestrians isThe flow value of the flow rate is lower,Is a subregionCorresponding to the maximum flow value，Is a subregionCorresponding to the accumulated number of the congested pedestrians and the value of the congested flow，Is a subregionCan accommodate the cumulative number of pedestrians.
The fitting formula consists of three sections of primary functions, wherein the first section is from an origin to coordinatesThe second segment is coordinatesTo coordinatesThe third segment function is coordinatesTo coordinatesIs a connecting line segment.
SubregionThe fitting formula of (2) reveals the relation between the accumulated pedestrian number in the subarea and the flow, and the optimal accumulated pedestrian number in the formulaFitting the regional flow value calculated by the formula to the optimization target in the subsequent pedestrian flow optimization modelThe method is applied to constraint conditions of the follow-up model and state prediction equations in a model prediction control method.
3. The pedestrian flow optimization model in the junction station is constructed, and the detailed steps are as follows:
3.1, establishing a pedestrian flow optimization model based on the pedestrian network divided in the foregoing, wherein the expression is as follows:
(8)
In the middle of Is thatTime subregionIs a cumulative number of pedestrians,Is 2.3 type 7 neutron areaIs a combination of the optimal number of pedestrian accumulation,Is thatTime zoneIs a cumulative number of pedestrians,AndFor each subareaSum regionWeight coefficient of (2); Representing the number of subregions.
The optimization objective consists of two parts: the first part is divided into subareasWith the aim of letting each during the running of the simulationTime subregionThe accumulated number of pedestrians is as close as possible to the control target value, i.e. the optimal accumulated number of pedestriansThe effect of keeping the area flow to be the largest continuously is achieved; another part is a regionWith the objective of optimizing each of the simulation runsTime zoneThe accumulated number of pedestrians is minimum, so that the pedestrians can reach a waiting area through facilities as soon as possible.AndFor adjusting the relative importance of the two optimization objectives, provided herein>To optimize each sub-zone preferentially。
3.2 Building constraint conditions of pedestrian stream optimization model
3.2.1 Pedestrian conservation constraint of the System
SubregionThe pedestrian conservation equation constraint formula of (2) is expressed as follows:
(9)
Is that Time subregionIs a cumulative number of pedestrians,Is thatTime of day and time of dayThe time length of the interval between the moments, i.e. the length of a time domain; are all the numbers of the sub-areas, Taking real name for verification of facility typeSecurity inspectionOr escalator，Is thatTime of day passing facilitySlave subregionTransfer to subregionIs used for controlling the pedestrian flow of people,Is thatTime of day passing facilitySlave subregionTransfer to subregionIs used for controlling the pedestrian flow of people,Is the sum of sub-areasA collection of connected regions.
The meaning of formula (9) is therefore: Time zone Is accumulated by the number of pedestriansTime zoneA cumulative number of pedestrians,Time of day passing facilitySlave regionTransfer to areaThe number of pedestrians,Time of day passing facilitySlave regionTransfer to areaPedestrian traffic determination.
Peripheral regionThe pedestrian conservation equation constraint formula of (2) is expressed as follows:
(10)
Is that Time of day peripheral regionIs a cumulative number of pedestrians,Is thatTime of arrival zoneAnd plan to enter the subregionIs a function of the number of pedestrians,Is thatTime of day passing facilitySlave regionTransfer to subregionThe pedestrian flow of the passenger, the passenger entering the station, and the passenger need to be verified by real name first, so the facility type is thatTaking real name for verification。
Region(s)And sub-areaThe former is the area where pedestrians enter when arriving at the junction station and leave through facilities; the latter is an area where pedestrians can only enter and exit through the facility, so the pedestrian conservation equations of both are expressed independently by the equation (10) and the equation (9).
SubregionSum subregionPedestrian transfer flow betweenThe constraint formula expression of (2) is:
(11)
As a means of Time subregionSum subregionBoundary-mounted facilityIs the decision variable of the optimization model; Is a facility The rate of pedestrian traffic for a single aisle,0-1 Variable for distinguishing facilitiesOf the type (C)For real name verificationOr security inspectionIn the time-course of which the first and second contact surfaces,Otherwise。
Equation (11) represents: when (when)For real name verificationOr security inspectionAt the time, the pedestrian is transferred to the flow from the facilityDetermining the opening quantity of the pedestrian and the single-channel pedestrian passing rate; when (when)Is an escalatorWhen the pedestrian transfer flow is equal to the subarea calculated by the formula (7) in 2.3Is a flow rate of (a).
3.2.2 Inequality constraint
There is a constraint on the magnitude of pedestrian traffic between subareas, the pedestrian traffic between subareas must not be greater than the subarea inThe sub-zone calculated according to the formula (7) under the pedestrian accumulation numberThe mathematical expression of the inequality is:
(12)
Each subarea The number of accumulated pedestrians in the area is limited, and due to the limited space of the area, each subarea is set for safetyThe accumulated number of pedestrians is not more than the accumulated number of pedestrians which can be accommodated in the vehicleThe mathematical expression of the inequality is:
(13)
The maximum open number of facilities has constraint, and the number of facilities for real-name verification and security check in the junction station is limited, so that the control variable is set Must not exceed the maximum number of facilities in the stationThe mathematical expression of the inequality is:
(14)
To this end, the pedestrian flow optimization model is built up by For decision variables, equation (8) is used as an optimization target, and equations (9) to (14) are used as constraints.
4. Control strategy for designing MPC control system solution sub-area boundary facilities
For the pedestrian flow optimization model set forth above, the model is solved using an MPC method that generates control inputs based on predictions of future system states. And converting the pedestrian flow optimization model, regarding the defined pedestrian network as a system, and designing an MPC control system.
4.1 Construction of System model
The state variable of the system is defined as the accumulated number of pedestrians in each subareaIs thatState vectors of the time-of-day system, in which all state variables are stored, and thereforeWhereinRepresentation ofTime zonePedestrian count of (a).
The control variable of the system is the open number of facilities at the boundary of each subarea, namelyDefinition ofIs thatControl vectors of the time-of-day system, in which all control variables are stored, thusWhereinRepresentation ofTime subregionSum subregionOn-boundary facility real name verificationIs a number of openings of the display device.
Disturbance factors of the system include internal disturbance and external disturbance; defining internal disturbances of a system as vectorsRefers to the pedestrian traffic transferred in subintervals by escalator facilities in the system, when the facilities are according to formula (11)Is an escalatorWhen the pedestrian flow transferred in the subinterval is equal to the flow value obtained by each area according to the formula (7)Thus, it isWhereinIs a subregionThe accumulated number of pedestrians isLower flow value.
Defining external disturbances of a system as vectorsRefers to the input passenger flow volume of the system, and the vector stores the information in the formula (10)，WhereinRepresentation ofTime of arrival zoneAnd plan to enter the subareaPedestrian number of (a).
The state equation of the controller is designed as follows:
(15)
Equation (15) is used for calculation State vector of time of day system. In the middle ofConsistent with the foregoing, is the length of one time domain; to control variables Is used for the coefficient matrix of (a),Is an internal disturbance vectorThe matrix of coefficients is used to determine,As external disturbance variableIs the coefficient matrix of constraint (9) - (11) in 3.2, equation (15).
4.2 Construction of objective functions
The objective function of the MPC control system, such as equation (16), is a variation of the objective function (8) of the optimization model in 3.1.
(16)
For a set number of predicted time-domains,Indicating future firstThe number of time steps is one,Thus, it isRepresent the firstThe time is the same; Is the first Error vectors at each time, representing the error values between the system state and the control target, are converted from equation (8) in 3.1,，Is the transposed vector of the vector,Is the firstA matrix of weight coefficients of error vectors at each time instant, which is mentioned by formula (8)A diagonal matrix formed; Is the first The error vectors at the individual moments are as defined above,Is the transposed vector of the vector,Is the firstWeight coefficient matrix of error vector at moment andSimilarly; Is the first The control vector of the moment of time,Is the transposed vector of the vector,First, theA matrix of weight coefficients for the control vector at each instant.
The overall meaning of the objective function of the control system is as follows: at time t, minimizeError cost, control cost and time of dayTime error cost; whileThe three weight coefficient matrices are used to adjust the relative importance of each performance index.
4.3 Constraint
The constraint condition of the control system is obtained by converting inequality constraints (12) - (14) in an optimization model into vector form, wherein the formulas (12), (14) are constraints of control variables, and the formula (13) is constraint of state variables.
4.4 Control System solution
The solution process of the MPC control system is a roll-optimized process. The specific steps of solving are as follows:
① At the position of At that time, the MPC control system predicts future system states, using the state equation of equation (15)At the time, …,The state of the system at the moment of time is predicted,A set prediction time domain number as mentioned in 4.2.
② At the position ofAt time, based on the predicted future system state in ①, a Gurobi solver is used to solve a model that uses equation (16) in 4.2 as an objective function, constraint conditions in 4.3 as constraints, to control the vector, (k=0, …,) Is a decision variable. Solving the model to obtain a control vector,…,Is set to the optimum value of (2).
③ At the position ofTaking the moment of timeTime control vector solutionApplied to the system to updateSystem state at time。
④ UpdatingAnd repeating the steps ① to ③, solving the system model at the next moment, and always performing rolling optimization until the set simulation is finished, stopping the program, and finally outputting a control vector value at each moment, namely the result of the opening number of the facilities on the boundary of each subarea.
5. Pedestrian flow control
For a junction station to be optimized, according to step 1 of the invention, firstly, a pedestrian network is constructed in a pedestrian network subarea; then estimating a sub-area pedestrian macroscopic basic diagram of each sub-area and a fitting formula thereof according to the step 2 of the invention; finally, an optimization model is built according to the steps 3 and 4, and the open number values of facilities are obtained through real name verification and security check at each moment by using an MPC control method, so that a complete facility open time schedule can be built; and finally, the personnel at the junction station controls the opening quantity of the facilities on the boundary of each sub-area according to a plan according to a facility opening time schedule, thereby achieving the purpose of people flow control.
Examples:
Taking Guangzhou south station as an example, based on historical data, pedestrian flow arrival distribution in the five-peak period of the Guangzhou south station is acquired, and the pedestrian flow arrival distribution is input into a system to solve a control strategy. By means of a numerical simulation method, the control strategy provided by the invention and the change of the pedestrian network performance in Guangzhou south station under the condition of no control strategy are compared. Fig. 5 shows the variation in the cumulative number of pedestrians in the system for an unfinished trip, the lower the number, indicating better performance of the strategy. In the early stage of simulation, pedestrian flow is lower, the performance of the uncontrolled strategy is similar to that of the invention, and as the pedestrian flow increases, the cumulative number of pedestrians in the unfinished journey is close to zero when t=51 under the strategy provided by the invention, and the cumulative number of pedestrians is still kept at a quite high level under the uncontrolled strategy. The experimental data show that the pedestrian flow control method provided by the invention can effectively improve the traffic passing efficiency of the junction station.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (8)
1. The junction station pedestrian flow control method based on the pedestrian macroscopic basic diagram is characterized by comprising the following steps of:
dividing pedestrian network subareas in the area between the peripheral area and the waiting area according to the facility arrangement of the passenger junction station and combining the necessary flow and facilities of the passenger arrival;
Constructing a pedestrian simulation model of the passenger junction station, obtaining pedestrian space-time track data of each subarea through simulation, and calculating to obtain the pedestrian accumulation number and the pedestrian flow of each subarea in different preset length groups; constructing a pedestrian macroscopic basic diagram based on the pedestrian accumulation number and the pedestrian flow, and performing linear function fitting on the pedestrian macroscopic basic diagram; the linear function represents the relation between the accumulated number of pedestrians and the flow in the subarea and is used for constructing the subsequent constraint condition;
Constructing a pedestrian flow optimization model in the junction station based on the divided pedestrian network subareas, and taking pedestrian conservation constraint of subareas and peripheral areas, pedestrian transfer flow constraint among different subareas, flow constraint obtained by calculating pedestrian transfer flow among different subareas under the accumulation number of pedestrians and maximum opening quantity constraint of facilities as constraint conditions of the pedestrian flow optimization model;
solving the pedestrian flow optimization model to obtain an opening quantity result of facilities on the boundaries of each subarea at each moment, and finally controlling the opening quantity of the facilities on the boundaries of each subarea by the personnel of the junction station according to the opening quantity result, thereby achieving the purpose of controlling the pedestrian flow;
The step of constructing a pedestrian flow optimization model in the junction station based on the divided pedestrian network subareas comprises the following steps:
the expression of the pedestrian flow optimization model Z is as follows:
Wherein n i (t) is the accumulated number of pedestrians in the sub-region R i at the time t, For the optimal pedestrian accumulation number of the subarea R i, n 0 (t) is the pedestrian accumulation number of the area R 0 at the moment t, and δ i and δ 0 are the weight coefficients of the subarea R i and the area R 0; i represents the number of subregions;
The constraint conditions of the pedestrian flow optimization model are defined by pedestrian conservation constraints of the subareas and the peripheral areas, pedestrian transfer flow constraints among different subareas, flow constraints obtained by calculating pedestrian transfer flows among different subareas under the accumulated number of pedestrians and maximum opening quantity constraints of facilities, and the constraint conditions comprise:
the pedestrian conservation equation constraint formula for the subregion R i is expressed as follows:
n i (t+1) is the accumulated number of pedestrians in the sub-region R i at t+1, and L is the time length of the interval between t and t+1, namely the length of a time domain; i, j, h are all numbers of subareas, m is a facility type, a real name is taken for verification IA, security check SI or escalator EU, Pedestrian traffic, transferred from sub-zone R i to sub-zone R j by facility m at time t,/>Is a collection of regions connected to the sub-region R i;
The pedestrian conservation equation constraint formula for the peripheral region R 0 is expressed as follows:
n 0 (t+1) is the cumulative number of pedestrians in the peripheral region R 0 at time t+1, d 0i (t) is the number of pedestrians reaching the region R 0 at time t and intended to enter the sub-region R i, Pedestrian traffic from region R 0 to sub-region R i through facility m for time t;
Pedestrian transfer flow between subarea R i and subarea R j The constraint formula expression of (2) is:
as the number of openings of facility m at the boundary of sub-region R i and sub-region R j at time t,/> Pedestrian traffic rate for facility m single aisle,/>Is 0-1 variable, is used for distinguishing the type of facility m, and when m is real-name verification IA or security check SI, is used for detecting the type of facility mOtherwise
The pedestrian transfer flow between the subareas is not greater than the flow value of subarea R i calculated by the subarea under the pedestrian accumulation number of n i (t), and the mathematical expression of the inequality is as follows:
The accumulated number of pedestrians in each subarea R i is restrained, and the accumulated number of pedestrians in each subarea R i is set to be not more than the accumulated number of pedestrians which can be accommodated in the subarea R i The mathematical expression of the inequality is:
There is a constraint on the maximum open number of the facility, and a control variable is set Must not exceed the maximum number of facilities in the station/>The mathematical expression of the inequality is:
2. The method for controlling traffic flow at a terminal station based on a macroscopic basic map of pedestrians according to claim 1, wherein said dividing the region between the terminal station and the waiting area from the peripheral region into the pedestrian network subregion comprises:
for the region from the peripheral region to the waiting region of the passenger junction station, dividing the closed space between two facilities which are arbitrarily separated into one region to obtain a region set Traversal/>If the space communication condition exists between the areas, combining the two areas into a subarea; if the area is not communicated with other areas, the area is independently used as a subarea, so that a pedestrian network subarea set is obtained.
3. The method for controlling pedestrian flow at a junction station based on a macroscopic basic diagram of pedestrians according to claim 1, wherein the calculating obtains the cumulative number and the flow of pedestrians in each subarea in different preset length groups, and the method comprises the following steps:
(1) The pedestrian flow density calculating step comprises the following steps:
In the grouping with the statistical time period of (t 0,t0 +delta epsilon), each time t (t epsilon (t 0,t0 +delta epsilon ]), based on the coordinate value of the track point of the pedestrian, the space of the pedestrian is divided by adopting a Voronoi graph algorithm, the track point of the pedestrian p at the time t is marked as A p,t, the area of the track point is marked as |A p,t |, and the calculation formula of the density k p (t) of the pedestrian p at the time t is as follows:
Pedestrian flow density of the same subarea R i at time t Wherein P t represents the total number of pedestrians in the sub-region R i at time t:
Density of pedestrian flow within subregion R i in the grouping for statistical time period (t 0,t0 +Δε) The average value of pedestrian density at each moment in the statistical time period is calculated as follows:
(2) Statistical time period (t 0,t0 +Δε) of pedestrian flow in sub-region R i The calculation formula is as follows:
Where TTD (ε) is the total distance all pedestrians in sub-region R i have traveled during the statistical time period and TTT (ε) is the total time all pedestrians in sub-region R i remain in the region during the statistical time period;
(3) Pedestrian flow in subarea R i in statistical time period (t 0,t0 +delta epsilon) The calculation formula of (2) is as follows:
Wherein b is the pedestrian exit width of subregion R i;
(4) Counting the cumulative number of pedestrians in the subarea R i in a time period (t 0,t0 +delta epsilon) The calculation formula is as follows:
Where n (t) is the cumulative number of pedestrians within the t-moment subregion R i.
4. The method for controlling pedestrian flow at a terminal station based on a macroscopic basic map of pedestrians according to claim 1, wherein constructing and fitting a linear function to the macroscopic basic map of pedestrians based on the accumulated number and flow of pedestrians comprises:
For each subarea R i, processing to obtain the accumulated number and flow of pedestrians in a plurality of statistical time periods, taking the accumulated number of pedestrians as an abscissa and the flow as an ordinate to construct a scatter diagram, wherein the scatter diagram is a macroscopic basic diagram of pedestrians in the subarea R i;
Fitting a linear function to the scatter diagram to obtain a correlation formula (7) of the flow of the subarea R i and the accumulated number of pedestrians, wherein the fitting formula is as follows:
Where g i(ni (t)) is the flow value of the subarea R i at a cumulative number of pedestrians of n i (t), For the optimal pedestrian accumulation number of the subarea R i, corresponding to the maximum flow value/> For the accumulated number of the congested pedestrians in the subarea R i, corresponding to the value of the congested flow/> Is the cumulative number of acceptable persons in the subarea R i.
5. The pedestrian flow control method for the junction station based on the pedestrian macroscopic basic graph according to claim 1, wherein the solving the pedestrian flow optimization model comprises the following steps:
Solving a pedestrian flow optimization model by adopting an MPC method:
The state variable of the system is the accumulated number of pedestrians in each subarea, X (t) is defined as a state vector of the system at the moment t, and X (t) = [ n 0(t),n1(t),…,ni (t) ], wherein n i (t) represents the accumulated number of pedestrians in the region R i at the moment t;
the control variable of the system is the open quantity of facilities on the boundary of each subarea, U (t) is defined as the control variable of the system at the moment t, Wherein/>Representing the open number of the real name verification IA of the facility on the boundary of the subarea R 0 and the subarea R 2 at the time t;
Disturbance factors of the system include internal disturbance and external disturbance; defining the internal disturbance of the system as an internal disturbance variable G (t), G (t) = [ G 1(n1(t)),…,gi(ni (t)) ] wherein G i(ni (t) is the flow value of the sub-region R i at the cumulative number of people in line n i (t);
Defining the external disturbance of the system as an external disturbance variable Q (t), Q (t) = [ d 01(t),…,d0i (t) ], wherein d 0i (t) is the number of pedestrians arriving at the region R 0 at time t and planning to enter the sub-region R i;
The state equation of the MPC controller is designed as:
X(t+1)＝X(t)+L[BU(t)+CG(t)+DQ(t)] (15)
equation (15) is used to calculate the state vector X (t+1) of the system at time t+1; wherein L is the length of one time domain; b is a coefficient matrix of a control variable U (t), C is a coefficient matrix of an internal disturbance variable G (t), and D is a coefficient matrix of an external disturbance variable Q (t);
The objective function J of the MPC control system is as in equation (16):
n p is a set prediction time domain number, k represents a kth time step in the future, k epsilon {0,1, …, N p }, and t+k represents a t+k time; e (t+k) is an error vector at the t+k-th time, and represents an error value between the system state and the control target, E (t+k) T is its transposed vector, M is the weight coefficient matrix of the error vector at the t+k time, E (t+N p) is the error vector at the t+N p time, E (t+N p)T) is its transposed vector, F is the weight coefficient matrix of the error vector at the t+k time, U (t+k) is the control variable at the t+k time, U (t+k) T is its transposed vector, W is the weight coefficient matrix of the control variable at the t+k time.
6. The pedestrian flow control method for the junction station based on the pedestrian macroscopic basic diagram according to claim 5, wherein the specific process of solving the pedestrian flow optimization model by adopting the MPC method is as follows:
① At time t, the MPC control system predicts the state of a future system, predicts the state of the system at time t+1, … and time t+N p in the future by using a state equation of the formula (15), and N p is a set prediction time domain number;
② At time t, based on the predicted future system state in ①, solving a model by using a Gurobi solver, wherein the model takes a formula (16) as an objective function, takes control variables U (t+k), (k=0, …, N p -1) as decision variables, and solves the model to obtain optimal values of the control variables U (t), …, U (t+N p -1);
③ At the time t, taking a control variable solving result U (t) at the time t, applying the control variable solving result U (t) to a system, and updating a system state X (t+1) at the time t+1;
④ Updating t, repeating steps ① to ③, solving an objective function at the next moment, performing rolling optimization all the time, stopping the program until the set simulation final moment, and finally outputting a control variable at each moment, namely the result of the opening quantity of the facilities on the boundaries of each subarea.
7. A terminal device comprising a processor, a memory and a computer program stored in the memory; the method is characterized in that the steps of the junction station pedestrian flow control method based on the pedestrian macroscopic basic graph according to any one of claims 1-6 are realized when the processor executes the computer program.
8. A computer-readable storage medium having a computer program stored therein; the method is characterized in that the steps of the junction station pedestrian flow control method based on the pedestrian macroscopic basic graph according to any one of claims 1-6 are realized when the computer program is executed by a processor.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111429733A (en) * | 2020-03-24 | 2020-07-17 | 浙江工业大学 | Road network traffic signal control method based on macroscopic basic graph |
| CN113537555A (en) * | 2021-06-03 | 2021-10-22 | 太原理工大学 | Traffic sub-region model prediction sliding mode boundary control method considering disturbance |
| CN115186516A (en) * | 2022-09-06 | 2022-10-14 | 深圳市城市交通规划设计研究中心股份有限公司 | Pedestrian simulation model construction method of traffic hub, electronic device and storage medium |
| CN115691138A (en) * | 2022-11-02 | 2023-02-03 | 东南大学 | Road network subregion division and subregion boundary flow control method |
-
2023
- 2023-12-26 CN CN202311804012.5A patent/CN117454678B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111429733A (en) * | 2020-03-24 | 2020-07-17 | 浙江工业大学 | Road network traffic signal control method based on macroscopic basic graph |
| CN113537555A (en) * | 2021-06-03 | 2021-10-22 | 太原理工大学 | Traffic sub-region model prediction sliding mode boundary control method considering disturbance |
| CN115186516A (en) * | 2022-09-06 | 2022-10-14 | 深圳市城市交通规划设计研究中心股份有限公司 | Pedestrian simulation model construction method of traffic hub, electronic device and storage medium |
| CN115691138A (en) * | 2022-11-02 | 2023-02-03 | 东南大学 | Road network subregion division and subregion boundary flow control method |
Non-Patent Citations (5)
| Title |
|---|
| Passenger-oriented traffic management integrating perimeter control and regional bus service frequency setting using 3D-pMFD;Saifei Chen 等;Transportation Research;20220101;第1-28页 * |
| Stabilization of city-scale road traffic networks via macroscopic fundamental diagram-based model predictive perimeter control;Isik Ilber Sirmatel 等;Control Engineering Practice;20210129;第1-14页 * |
| 傅惠 等.面向宏观基本图的多模式交通路网分区算法.工业工程.第23卷(第1期),第1-9页. * |
| 基于MFD的区域双层边界协调控制研究;刘娜;傅惠;;现代计算机(专业版);20170615(17);第12-17页 * |
| 多个MFD子区边界协调控制方法;丁恒;郭放;蒋程镔;张雨;张卫华;;自动化学报;20171231(04);第58-69页 * |
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