CN115440033A - Main line coordination control method, system, device and medium based on macro basic diagram - Google Patents

Main line coordination control method, system, device and medium based on macro basic diagram Download PDF

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CN115440033A
CN115440033A CN202210980172.4A CN202210980172A CN115440033A CN 115440033 A CN115440033 A CN 115440033A CN 202210980172 A CN202210980172 A CN 202210980172A CN 115440033 A CN115440033 A CN 115440033A
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林炫华
林晓辉
谭超健
曹成涛
龙庆文
陈林俊
刘俊
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Guangdong Communications Polytechnic
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Abstract

The invention discloses a trunk line coordination control method, a system, a device and a medium based on a macroscopic basic diagram, wherein the method comprises the following steps: constructing a multi-objective optimization model for trunk line coordination control, wherein the multi-objective optimization model takes the maximization of slope of MFD ascending section, the maximization of road network bearing capacity, the minimization of average delay of vehicles in road network and the minimization of road network queuing coefficient as optimization targets, and takes the trunk line public period, the effective green light duration of phase and the phase difference of intersection as parameters to be optimized; obtaining traffic flow data of a trunk line to be optimized, and optimizing a multi-objective optimization model through a multi-objective particle swarm algorithm and a genetic algorithm according to the traffic flow data to obtain an optimal parameter combination of parameters to be optimized; and performing trunk line coordination control on the trunk line to be optimized according to the optimal parameter combination. The invention ensures that the trunk line coordination control can take macroscopic and microscopic traffic efficiency into account, improves the control effect of the trunk line coordination control and the vehicle passing efficiency, and can be applied to the technical field of road traffic signal control.

Description

Main line coordination control method, system, device and medium based on macro basic diagram
Technical Field
The invention relates to the technical field of road traffic signal control, in particular to a trunk line coordination control method, a system, a device and a medium based on a macroscopic basic diagram.
Background
In the aspect of trunk line coordination control, morgan firstly proposes a bidirectional green wave coordination control model, and the green wave concept is widely applied; LITTLE et al developed a trunk signal design optimization program MAXBAND, i.e., a numerical solution, using a maximum green band phase difference optimization method, which is considered to be the most economical and effective trunk control strategy and is used up to now, but the basic assumption is that all intersections have the same pass bandwidth; zhang Yiyuan and the like insert superposition phase according to the design speed of the green wave band to better match the arrival time of the traffic flow; pan Yuan and the like, speed fluctuation interval constraints are added, and a trunk line coordination control model considering the speed fluctuation interval is established; wang Chenwei and the like construct a double-layer delay model for solving by taking the minimum total delay of a trunk region as a target. At present, most trunk coordination control strategies are modeled from the ideas of delay minimization, green wave band maximization, multi-objective models and the like, and are solved through an intelligent optimization algorithm.
In general, the existing methods focus more on the optimization of several conventional evaluation indexes or consider specific application scenarios. The urban trunk line and the road connected with the urban trunk line are used as local road networks, the global operation state of the network is difficult to represent only from microscopic integrated type indexes such as delay, parking times and the like, and the numerical difference is small and difficult to judge due to the fact that the traffic capacity, the road network output flow, the driving speed and the like are used as evaluation indexes under the saturated traffic flow, and the trunk line control effect and the vehicle traffic efficiency are influenced.
Disclosure of Invention
The present invention aims to solve at least to some extent one of the technical problems existing in the prior art.
To this end, an object of the embodiments of the present invention is to provide a trunk line coordination control method based on a macro basic diagram, which improves the control effect of the trunk line coordination control and the vehicle passing efficiency.
Another object of the embodiments of the present invention is to provide a trunk coordination control system based on a macro basic diagram.
In order to achieve the technical purpose, the technical scheme adopted by the embodiment of the invention comprises the following steps:
in a first aspect, an embodiment of the present invention provides a trunk coordination control method based on a macro basic diagram, including the following steps:
constructing a multi-objective optimization model for trunk line coordination control, wherein the multi-objective optimization model takes the maximization of slope of MFD ascending section, the maximization of road network bearing capacity, the minimization of average delay of vehicles in road network and the minimization of road network queuing coefficient as optimization targets, and takes the trunk line public period, the effective phase green light duration and the intersection phase difference as parameters to be optimized;
obtaining traffic flow data of a trunk line to be optimized, and optimizing the multi-objective optimization model through a multi-objective particle swarm algorithm and a genetic algorithm according to the traffic flow data to obtain an optimal parameter combination of the parameters to be optimized;
carrying out trunk line coordination control on the trunk line to be optimized according to the optimal parameter combination;
in the optimization process of the multi-objective optimization model, the road network vehicle average delay and the road network queuing coefficient corresponding to each particle are determined through simulation, and the MFD ascending section slope and the road network bearing capacity corresponding to each particle are determined through a macroscopic basic graph obtained through cluster fitting.
Further, in one embodiment of the present invention, the objective function of the multi-objective optimization model is:
Figure BDA0003800120370000021
wherein F (C, G, offset) represents an objective function,
Figure BDA0003800120370000022
the average delay of the vehicles in the road network is shown,
Figure BDA0003800120370000023
represents the road network queuing coefficient, A 1 Showing the slope of the MFD up-leg, q max Showing the bearing capacity of the road network;
the constraint conditions of the multi-objective optimization model are as follows:
Figure BDA0003800120370000024
wherein, queue i Indicates the queuing length, L, of the ith lane i Indicates the lane length of the ith lane, C indicates the trunk common period, C min And C max Respectively represent the upper limit and the lower limit of C,
Figure BDA0003800120370000025
the effective green duration of the jth phase at the ith intersection,
Figure BDA0003800120370000026
and
Figure BDA0003800120370000027
respectively represent
Figure BDA0003800120370000028
Upper and lower limits of, offset i Indicating the phase difference between the ith intersection and the first intersection.
Further, in an embodiment of the present invention, the step of optimizing the multi-objective optimization model according to the traffic data by using a multi-objective particle swarm algorithm in combination with a genetic algorithm to obtain an optimal parameter combination of the parameters to be optimized specifically includes:
determining initial values of the position and the speed of each particle in the particle swarm, an initial solution set and a convergence condition of the particle swarm;
simulating according to the traffic flow data and the position and the speed of each particle in the initial particle swarm to obtain a road network vehicle average delay, a road network queuing coefficient and an MFD data point set corresponding to each particle, wherein the abscissa and the ordinate of a data point in the MFD data point set are respectively a road network weighted density and a road network weighted flow;
performing cluster fitting on the MFD data point set to obtain a macroscopic basic graph, and determining the MFD ascending section slope and road network bearing capacity corresponding to each particle according to the macroscopic basic graph;
respectively carrying out non-dominated sorting on each particle to determine the individual optimal position of each particle, and carrying out non-dominated sorting on the particle swarm to determine the group optimal position of the particle swarm;
updating the position and the speed of each particle according to the individual optimal position and the group optimal position;
selecting, crossing and mutating the particle swarm to obtain an optimized particle swarm;
and if the convergence condition is met, taking the parameter value combination of the optimal position of the historical particle swarm as the optimal parameter combination, and if the convergence condition is not met, returning to the step of simulating according to the traffic data and the position and the speed of each particle in the initial particle swarm.
Further, in an embodiment of the present invention, the step of performing cluster fitting on the MFD data point set to obtain a macro basic graph, and determining an MFD ascending segment slope and a road network bearing capacity corresponding to each particle according to the macro basic graph specifically includes:
performing boundary search on the MFD data set by a # -shaped boundary extraction method, and generating an initial MFD map according to the searched boundary points;
clustering the initial MFD graph to obtain two or three categories of data sample sets;
performing piecewise linear fitting on each data sample set to obtain a macroscopic basic graph, wherein the macroscopic basic graph at least comprises an ascending section structure and a stationary section structure;
determining the MFD ascending section slope corresponding to the current particle according to the ratio of the road network weighted flow of the ascending section structure to the road network weighted density;
and determining the road network bearing capacity corresponding to the current particles according to the road network weighted flow of the stable section structure.
Further, in an embodiment of the present invention, the step of clustering the initial MFD map to obtain two or three categories of data sample sets specifically includes:
clustering the initial MFD graph by taking 3 as a clustering number to obtain three categories of undetermined sample sets;
determining the central point position of each undetermined sample set from left to right as a first central point, a second central point and a third central point respectively;
when the road network weighted flow of the first central point and the road network weighted flow of the third central point are both smaller than the road network weighted flow of the second central point, determining that the initial MFD graph is a three-section closed MFD graph, outputting the to-be-determined sample set as a data sample set, otherwise, determining that the initial MFD graph is two-section non-closed MFD graphs, and re-clustering the initial MFD graphs by using 2 as the clustering number to obtain two categories of data sample sets.
Further, in one embodiment of the present invention, in the step of updating the position and velocity of each particle according to the individual optimal position and the population optimal position, the position and velocity of each particle is updated by the following formula:
V 1 ＝V 0 ×w+S×r×(pbest-x 0 )+S×r×(gbest-x 0 )
x 1 ＝x 0 +V 1
wherein, V 0 And V 1 Representing the velocity, x, of the particles before and after the update, respectively 0 And x 1 Representing the particle velocities before and after update, respectively, w represents an inertia factor, S represents a learning factor, r represents [0,1]]Random numbers within the range, pbest, represent the individual optimal location, and gbest, the population optimal location.
Further, in an embodiment of the present invention, the step of selecting, crossing, and mutating the particle group to obtain the optimized particle group specifically includes:
selecting one particle in the particle group by roulette to endow the particle with parameters again;
performing cross processing on two particles which are sequenced most backwards in the particle swarm by a real number cross method to obtain two new particles added into the particle swarm;
traversing the particle swarm, and carrying out variation operation on the particles in the particle swarm according to a preset variation probability to obtain the optimized particle swarm.
In a second aspect, an embodiment of the present invention provides a trunk coordination control system based on a macro basic diagram, including:
the multi-objective optimization model building module is used for building a multi-objective optimization model for trunk line coordination control, the multi-objective optimization model takes the maximization of the slope of the MFD ascending section, the maximization of the bearing capacity of a road network, the minimization of the average delay of vehicles in the road network and the minimization of the queuing coefficient of the road network as optimization targets, and takes the trunk line public period, the effective green light duration of the phase and the phase difference of an intersection as parameters to be optimized;
the model optimization module is used for acquiring traffic flow data of a trunk line to be optimized, and optimizing the multi-objective optimization model through a multi-objective particle swarm algorithm and a genetic algorithm according to the traffic flow data to obtain an optimal parameter combination of the parameters to be optimized;
the coordination control module is used for carrying out trunk coordination control on the trunk line to be optimized according to the optimal parameter combination;
in the optimization process of the multi-objective optimization model, the road network vehicle average delay and the road network queuing coefficient corresponding to each particle are determined through simulation, and the MFD ascending section slope and the road network bearing capacity corresponding to each particle are determined through a macroscopic basic graph obtained through cluster fitting.
In a third aspect, an embodiment of the present invention provides a trunk coordination control apparatus based on a macro basic diagram, including:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one program causes the at least one processor to implement a macro basic diagram-based trunk coordination control method described above.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, in which a processor-executable program is stored, and when the processor-executable program is executed by a processor, the processor-executable program is configured to perform the above-mentioned trunk coordination control method based on the macro basic diagram.
Advantages and benefits of the present invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention:
according to the embodiment of the invention, the macro evaluation index based on the macro basic diagram is considered, the multi-objective optimization model of the trunk coordination control is constructed, the optimization is carried out through the multi-objective particle swarm optimization algorithm and the genetic algorithm to obtain the optimal parameter combination, and further the trunk coordination control can be carried out according to the optimal parameter combination. According to the embodiment of the invention, the macroscopic evaluation index based on the macroscopic basic diagram is brought into the trunk line coordination control optimization, so that the trunk line coordination control can give consideration to both macroscopic and microscopic traffic efficiency, model optimization is carried out through a multi-objective particle swarm optimization algorithm and a genetic algorithm, and the control effect of the trunk line coordination control and the vehicle passing efficiency are improved.
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In order to more clearly illustrate the technical solution in the embodiment of the present invention, the following description is made on the drawings required to be used in the embodiment of the present invention, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solution of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart illustrating steps of a trunk coordination control method based on a macro basic diagram according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an optimization process of a multi-objective optimization model according to an embodiment of the present invention;
FIG. 3 is a morphological schematic of an exemplary macroscopic basic diagram provided by an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a boundary search performed by a # -shaped boundary extraction method according to an embodiment of the present invention;
FIG. 5 (a) is a schematic diagram of a three-segment closed MFD plot obtained by fitting provided by an embodiment of the present invention;
FIG. 5 (b) is a schematic diagram of a two-piece fitted non-closed MFD map provided by an embodiment of the present invention;
fig. 6 (a) is a morphological schematic diagram of a macro basic diagram obtained by a simulation experiment of the trunk coordination control method according to the embodiment of the present invention;
fig. 6 (b) is a morphological schematic diagram of a macroscopic basic diagram obtained by a simulation experiment of a first control experiment group provided in an embodiment of the present invention;
fig. 6 (c) is a morphological schematic diagram of a macroscopic basic diagram obtained by a simulation experiment of a second control experiment group provided in the embodiment of the present invention;
FIG. 6 (d) is a morphological schematic diagram of a macroscopic basic diagram obtained from a simulation experiment of a third control experiment group provided in the embodiment of the present invention;
fig. 7 is a system block diagram of a trunk line coordination control device based on a macro basic diagram according to an embodiment of the present invention;
fig. 8 is a block diagram of a trunk coordination control device based on a macro basic diagram according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
In the description of the present invention, the meaning of a plurality is two or more, if there is a description that the first and the second are only used for distinguishing technical features, but not understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the precedence of the indicated technical features. Furthermore, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.
The Macro Fundamental Diagram (MFD) analyzes the road network at the macro level with road network detection data, reflecting the general relationship between the traffic, density, and total traffic in the road network, and the intrinsic objective laws. Xu Feifei and the like demonstrate that road network traffic control strategies such as public transportation special roads, forbidden measures and the like have great influence on the MFD form through simulation experiments; hui Yanyan and the like analyze the influence of a trunk control strategy on MFD, and find that an optimal common period is existed to maximize the communication efficiency of a trunk network; sun Qiuxia and the like provide a road network key road section identification method by identifying MFD shape change after a road section is missing. In summary, in recent years, the application of MFD has focused on the evaluation index using the MFD form as a control strategy, the study of the MFD influence factors, the road network subdivision and boundary control based on the MFD, and how the MFD is used as one of the optimization targets to guide the optimization of the road network signal control strategy is rarely involved.
Based on MFD characteristics, the invention provides a macroscopic efficiency evaluation index of road network operation efficiency based on MFD by using an MFD modeling method under a simulation platform, combines the traditional evaluation index, establishes an MFD multi-target trunk line control strategy optimization model, performs parameter solution on the model by combining a genetic algorithm and a multi-target particle swarm optimization (GA-MOPSO), and further improves the solving performance. The invention aims to overcome the defect that the existing trunk coordination control method only considers microscopic evaluation indexes, brings macroscopic basic diagram characteristics and indexes into trunk coordination control optimization, constructs a road network macroscopic evaluation index and an extraction method thereof based on the macroscopic basic diagram characteristics, and solves a trunk coordination control strategy by taking the macroscopic index as an optimization target to obtain a better trunk control effect.
Referring to fig. 1, an embodiment of the present invention provides a trunk coordination control method based on a macro basic diagram, which specifically includes the following steps:
s101, constructing a multi-objective optimization model for trunk line coordination control, wherein the multi-objective optimization model takes MFD ascending section slope maximization, road network bearing capacity maximization, road network vehicle average delay minimization and road network queuing coefficient minimization as optimization targets, and takes a trunk line public period, phase valid green light time and intersection phase difference as parameters to be optimized.
Specifically, the slope of an MFD ascending section and the road network bearing capacity are introduced as road network macroscopic efficiency indexes to carry out optimization solution, and the MFD ascending section slope and the road network bearing capacity are obtained through MFD form clustering and fitting solution output by a simulation experiment; and replacing the traditional road network queuing length by using a road network queuing coefficient, wherein the conventional road network queuing length is defined as the sum of all lane queuing lengths and the ratio of the lane lengths. It should be noted that the effective green duration of the phase is the green duration of each phase at each intersection; the intersection phase difference is the phase difference between each intersection and the first intersection.
Further as an optional implementation, the objective function of the multi-objective optimization model is:
Figure BDA0003800120370000071
wherein F (C, G, offset) represents an objective function,
Figure BDA0003800120370000072
the average delay of the vehicles in the road network is shown,
Figure BDA0003800120370000073
represents the road network queuing coefficient, A 1 Showing the slope of the MFD up-leg, q max Showing the bearing capacity of the road network;
the constraint conditions of the multi-objective optimization model are as follows:
Figure BDA0003800120370000074
wherein, queue i Indicates the queuing length, L, of the ith lane i Indicates the lane length of the ith lane, C indicates the trunk common period, C min And C max Respectively represent the upper limit and the lower limit of C,
Figure BDA0003800120370000075
the effective green duration representing the jth phase at the ith intersection,
Figure BDA0003800120370000076
and
Figure BDA0003800120370000077
respectively represent
Figure BDA0003800120370000078
Upper and lower limits of, offset i Indicating the phase difference between the ith intersection and the first intersection. In the embodiment of the invention, the phase difference of the first intersection is set to be 0.
S102, obtaining traffic flow data of a trunk line to be optimized, and optimizing a multi-objective optimization model through a multi-objective particle swarm algorithm and a genetic algorithm according to the traffic flow data to obtain an optimal parameter combination of parameters to be optimized;
in the optimization process of the multi-objective optimization model, the road network vehicle average delay and the road network queuing coefficient corresponding to each particle are determined through simulation, and the MFD ascending section slope and the road network bearing capacity corresponding to each particle are determined through a macroscopic basic graph obtained through cluster fitting.
Specifically, the embodiment of the invention combines a Multi-Objective Particle Swarm Optimization (MOPSO) with a genetic algorithm operation, namely, on the basis of the original Particle Swarm Optimization, the Multi-Objective Optimization algorithm with fast non-dominance of selection, intersection and variation strategies and based on a partial solution is used for finally determining the most dominant solution set in multiple objectives.
Further as an optional implementation mode, the step of optimizing the multi-objective optimization model by combining a multi-objective particle swarm optimization algorithm and a genetic algorithm according to the traffic flow data to obtain an optimal parameter combination of the parameters to be optimized specifically comprises the following steps:
a1, determining initial values of positions and speeds of particles in a particle swarm, an initial solution set of the particle swarm and a convergence condition;
a2, simulating according to the traffic flow data and the position and the speed of each particle in the initial particle swarm to obtain road network vehicle average delay, a road network queuing coefficient and an MFD data point set corresponding to each particle, wherein the abscissa and the ordinate of a data point in the MFD data point set are respectively road network weighted density and road network weighted flow;
a3, carrying out cluster fitting on the MFD data point set to obtain a macroscopic basic graph, and determining the slope of the MFD ascending section and the road network bearing capacity corresponding to each particle according to the macroscopic basic graph;
a4, performing non-dominated sorting on each particle to determine the individual optimal position of each particle, and performing non-dominated sorting on the particle swarm to determine the group optimal position of the particle swarm;
a5, updating the position and the speed of each particle according to the individual optimal position and the group optimal position;
a6, performing selection, crossing and mutation operations on the particle swarm to obtain an optimized particle swarm;
and A7, if the convergence condition is met, taking the parameter value combination of the optimal position of the historical particle swarm as the optimal parameter combination, and if the convergence condition is not met, returning to the step of simulating according to the traffic data and the position and the speed of each particle in the initial particle swarm.
As a further optional implementation, in the step of updating the position and the velocity of each particle according to the individual optimal position and the population optimal position, the position and the velocity of each particle are updated by the following formula:
V 1 ＝V 0 ×w+S×r×(pbest-x 0 )+S×r×(gbest-x 0 )
x 1 ＝x 0 +V 1
wherein, V 0 And V 1 Representing the velocity, x, of the particles before and after the update, respectively 0 And x 1 Representing the particle velocities before and after update, respectively, w represents an inertia factor, S represents a learning factor, r represents [0,1]]Random numbers within the range, pbest, represent the individual optimal location, and gbest, the population optimal location.
As a further optional implementation manner, the step A6 of performing selection, intersection and mutation operations on the particle swarm to obtain an optimized particle swarm specifically includes:
a61, selecting one particle in the particle group by roulette and endowing the particle with parameters again;
a62, carrying out cross processing on two particles which are most ranked at the back in the particle swarm by a real number cross method to obtain two new particles to be added into the particle swarm;
and A63, traversing the particle swarm, and carrying out variation operation on the particles in the particle swarm according to a preset variation probability to obtain an optimized particle swarm.
Fig. 2 is a schematic diagram of an optimization process of the multi-objective optimization model provided in the embodiment of the present invention, which is specifically as follows:
step1: initializing the scale (such as M = 15) of a particle swarm and an initial solution set, namely a trunk public period, effective green light duration of each intersection phase, intersection phase difference and the like, and setting convergence conditions (such as the maximum iteration number);
step2: the particle swarm changes the communication control parameters of each intersection through a com interface, calls Vissim to carry out simulation to obtain the weighting density and the flow of the output road network, and obtains the slope A of the MFD ascending section through clustering and fitting 1 Road network bearing capacity q max Meanwhile, the average delay of vehicles in the road network and the queuing coefficient of the road network are obtained through a detector;
step3: carrying out non-dominated sorting on the historical solution of each particle, wherein the solution set (namely the individual optimal position) of each particle at the top is marked as pbest, and the solution set (namely the group optimal position) of all the particles at the top in the historical sorting is marked as gbest;
step4: the position and velocity of each particle is updated as follows:
V 1 ＝V 0 ×w+S×r×(pbest-x 0 )+S×r×(gbest-x 0 )
x 1 ＝x 0 +V 1
wherein, V 0 And V 1 Representing the velocity, x, of the particles before and after the update, respectively 0 And x 1 Representing the particle velocities before and after update, respectively, w represents an inertia factor, S represents a learning factor, r represents [0,1]]Random numbers in the range, pbest represents the individual optimal position, gbest represents the group optimal position, and the two are updated in each cycle;
step5: a selection operation of selecting one of the particles to be newly given to the parameter in a roulette manner;
step6: and (3) performing a cross operation, namely selecting a real number cross method, wherein the method refers to that the time-matched parameter combination of two chromosomes generates two new individuals through linear combination, for example, the cross operation method of the m-th particle am and the n-th particle an at the j-th phase is as follows:
am j ＝(1-M)am j +Man j
an j ＝(1-M)an j +Mam j
where M is a random number between the intervals [0,1 ]. Crossing two particles ranked most backward in a parent group;
step7: performing mutation operation, traversing the population, and randomly distributing values for the particles again by using the mutation probability Pm;
step8: and repeating the steps 2-7, checking whether the constraint is met in each iteration, finishing the solution when the convergence condition is met, and obtaining the most-front parameter value combination corresponding to the sequence, namely the optimal parameter combination.
As a further optional implementation manner, the step A3 of performing cluster fitting on the MFD data point set to obtain a macroscopic basic graph, and determining an MFD ascending segment slope and a road network bearing capacity corresponding to each particle according to the macroscopic basic graph specifically includes:
a31, performing boundary search on the MFD data point set by a groined-font boundary extraction method, and generating an initial MFD map according to the searched boundary points;
a32, clustering the initial MFD graph to obtain two or three categories of data sample sets;
a33, performing piecewise linear fitting on each data sample set to obtain a macroscopic basic diagram, wherein the macroscopic basic diagram at least comprises an ascending section structure and a stable section structure;
a34, determining the MFD ascending section slope corresponding to the current particle according to the ratio of the road network weighted flow of the ascending section structure to the road network weighted density;
and A35, determining the road network bearing capacity corresponding to the current particles according to the road network weighted flow of the stable section structure.
Specifically, the macro basic graph is used as a basic attribute of a road network, and a corresponding macro basic graph exists in any road network form. Fig. 3 is a schematic diagram of a typical macro basic diagram provided in an embodiment of the present invention, where the typical macro basic diagram includes three-stage structures: the linear function expressions corresponding to the three sections are as follows:
Figure BDA0003800120370000101
clustering collected and drawn MFDs is a common means for dividing the state of a road network, an ascending section, a stable section and a descending section in the MFDs need to be identified and divided through clustering, and Gaussian Mixture clustering (GMM) is used for dividing the state.
The MFD is used for describing the macroscopic efficiency of the road network and is mainly embodied in the maximum flow q of the road network max Slope A of rising section 1 The initial operating efficiency of the road network under free flow and the upper limit of the bearing capacity of the road network under saturated flow are respectively described. Given both characterization of efficiency and spatial "ceiling", the MFD morphology should focus on preserving the upper boundary points of the MFD profile. The problem is how to extract the boundary points close to the upper part in the data set in a two-dimensional plane, and the invention is therefore completed by introducing a well-shaped boundary point extraction method in the image processing technology.
Fig. 4 is a schematic diagram illustrating a boundary search performed by a # -shaped boundary extraction method according to an embodiment of the present invention. The boundary points are searched and extracted in the discrete point set through the 'well-shaped' region in FIG. 4, wherein the circle center i (k) is i ,q i ) Taking R as radius as search range, taking value as average Euclidean distance between each point in data set, and R as 1 、r 2 The values of the horizontal and vertical search distances are respectively the average horizontal and vertical distances between each point, and the search area around the current data point is divided into 8 areas, the embodiment of the invention defines that when 2 continuous blank areas exist in the 8 areas around the point i, the point i is a boundary point, and the embodiment of the invention focuses on searching the boundary point of the trapezoid three-section structure, so the area right below the point i in fig. 4 is not listed in the blank area search. R, r 1 And r 2 Calculated by the following formula:
Figure BDA0003800120370000111
after processing, the MFD morphology can be obtained retaining only the boundary points.
As a further optional implementation manner, the step a32 of clustering the initial MFD graph to obtain two or three categories of data sample sets specifically includes:
a321, clustering the initial MFD graph by taking 3 as a clustering number to obtain three categories of undetermined sample sets;
a322, determining the central point positions of the sample sets to be determined from left to right as a first central point, a second central point and a third central point respectively;
and A333, when the road network weighted flow of the first central point and the road network weighted flow of the third central point are both smaller than the road network weighted flow of the second central point, determining that the initial MFD graph is a three-section closed MFD graph, outputting a sample set to be determined as a data sample set, otherwise, determining that the initial MFD graph is two-section non-closed MFD graphs, and re-clustering the initial MFD graphs by using 2 as the clustering number to obtain two categories of data sample sets.
Specifically, the GMM cluster number may be set to 3, that is, the GMM cluster number corresponds to a three-stage structure in the MFD, and a linear equation set corresponding to the three-stage sample set is fitted by a least square method. However, in practical applications, due to non-uniformity of the road network requirements in space and time or the road network far from reaching an oversaturated state, the plotted MFD shape is often not an ideal three-segment closed trapezoid, but an unclosed approximate parabolic structure (which can be fitted to a two-segment non-closed MFD graph), that is, only an ascending segment and a stationary segment. Considering that the embodiment of the invention mainly studies the effect of the MFD on the improvement of the operation efficiency of the network, and the supersaturation congestion state corresponding to the descending section is not considered, 3 sample sets obtained by clustering are compared, and for the unclosed MFD, the GMM clustering number is changed into 2 for clustering. The MFD acquisition, clustering and fitting steps are as follows:
step1: simulating to obtain the result k w Is the abscissa, q w Generating an MFD data set scatter diagram for the ordinate, traversing all data by using a # -shaped boundary search method, and only reserving boundary points to obtain a primary MFD diagram;
step2: clustering the obtained MFD graph by using GMM, wherein the clustering number is 3, and obtaining a sample set D = { D } corresponding to the clustering 1 ,D 2 ,D 3 }；
Step3: determining center point positions in three category sample sets from left to right { (k) 1 ,q 1 ),(k 2 ,q 2 ),(k 3 ,q 3 ) And determining the MFD form according to the spatial position of the central point, and if q is met 1 <q 2 And q is 3 <q 2 At this time, the MFD form is a typical three-section closed trapezoid; when the above condition is not satisfied, e.g. q 3 >q 2 If the MFD shape is a two-section non-closed structure with only an ascending section and a stable section, the clustering number is changed to 2, and GMM clustering is performed again;
step4: and performing piecewise linear fitting on the finally generated sample set to obtain a final MFD map.
Fig. 5 (a) is a schematic diagram of a three-segment closed MFD graph obtained by fitting according to an embodiment of the present invention, and fig. 5 (b) is a schematic diagram of a two-segment non-closed MFD graph obtained by fitting according to an embodiment of the present invention. According to the MFD characteristics, the following macroscopic efficiency indexes are determined:
(1) MFD ramp slope: namely the ratio of the flow rate of the ascending section to the density is an index for describing the time operation efficiency of the road network.
Slope A 1 The expression is as follows, where k 1 The abscissa of the inflection point of the ascending and stationary segments in FIG. 5 (a) or 5 (b).
Figure BDA0003800120370000121
(2) Road network bearing capacity: in the existing research, the bearing capacity of a road network is defined as the amount of traffic that can be borne when the road network keeps a good running state under certain road conditions and traffic control conditions, and the amount of traffic is an index describing the traffic supply capacity of the road network. q. q.s max The maximum value of the road network flow under the current control scheme is reflected, so that the maximum value is defined as the road network bearing capacity.
And S103, carrying out trunk line coordination control on the trunk line to be optimized according to the optimal parameter combination.
Specifically, trunk coordination control is performed according to the optimized optimal parameter combination, namely the optimal combination of the trunk public period, the phase valid green light duration and the intersection phase difference.
The method flow of the embodiment of the present invention is explained above. The embodiment of the invention provides a method for generating a macroscopic basic graph with three sections (an ascending section, a stationary section and a,Descending segment), two-segment (ascending segment, stationary segment, non-closed segment), and ascending segment slope A 1 Maximum bearing capacity q of road network of stable section max Taking the index as a road network macroscopic operation efficiency index into a trunk line coordination control optimization process; extracting key points of a macroscopic basic graph by using a well-shaped image processing method for data point clustering and linear fitting; and optimally establishing a multi-objective optimization model by taking the common period, the split green ratio of each phase and the phase difference of each intersection in the trunk network as parameters to be solved, maximizing the slope of the MFD ascending section, maximizing the bearing capacity of the road network, minimizing the average delay of vehicles in the road network and minimizing the queuing coefficient of the road network, and solving the model by using a GA-MOPSO algorithm. Compared with the prior art, the embodiment of the invention has the following advantages:
1) The optimization target of the macroscopic basic graph index is considered, so that the trunk line coordination control effect can give consideration to both macroscopic and microscopic traffic efficiency, and the macroscopic basic graph state of the road network has stronger stability and controllability;
2) The method adopts a well-shaped image processing method combined with Gaussian mixed clustering, can quickly and accurately divide the state of a macroscopic basic graph and obtain related indexes, and can be popularized to various types of road networks;
3) And the GA-MOPSO is used for model solution, the genetic algorithm is combined with the multi-target particle swarm algorithm, and the result shows that the solution efficiency and the obtained scheme are better.
The trunk coordination control method according to the embodiment of the present invention is further described below with reference to simulation experiments.
Taking a continuous-rise trunk network in Dongguan city as an example, manually acquiring traffic flow and signal control parameters as experimental inputs, and simultaneously using the other three methods as a comparison experimental group to compare optimization effects with the trunk coordination control method in the embodiment of the invention.
An optimization model used by the trunk line coordination control method is marked as a model F0, and a GA-MOPSO solution model is used; the method comprises the steps that a first contrast experiment group establishes an optimization model of minimizing average delay of vehicles in a road network, minimizing queuing coefficients of the road network and maximizing system traffic capacity, the optimization model is marked as a model F1, and a GA-MOPSO solution model is also used, wherein the system traffic capacity is the sum of output flow in unit time of intersection of the road network; constructing an optimization model F2 for the third control experiment group according to a traditional numerical method and optimizing; the fourth control experiment group also constructs an optimization model F3 with optimization objectives of MFD rise slope maximization, road network bearing capacity maximization, road network vehicle average delay minimization and road network queuing coefficient minimization, but carries out model optimization by using an unmodified MOPSO. The characteristics of each protocol are shown in table 1 below.
Figure BDA0003800120370000131
TABLE 1
The morphological diagrams of the macroscopic basic diagrams of the respective schemes obtained by the simulation experiments are shown in fig. 6 (a) to 6 (d). In order to comprehensively evaluate the advantages and disadvantages of the four models in the optimization effect, delay, queuing length, MFD form parameters and system traffic capacity are respectively used as evaluation indexes, and the comparison results are shown in the following table 2.
Figure BDA0003800120370000132
TABLE 2
From experimental results, the method provided by the embodiment of the invention has the best effect in the indexes such as delay, queuing coefficient and the like, and the MFD bearing capacity of a road network is slightly lower than that of a model F1; the maximum flow contained by the road network under the model F0 control scheme is 1664veh/h, and the slope of the MFD ascending section is maximum, which indicates that the bearing capacity is reached fastest in the three schemes, the road network has higher flow under low density, and the running efficiency of the road network under the free flow is highest. The model F1 is higher than the model F0 in total delay, average delay and queuing coefficient, and the bearing capacity and traffic capacity indexes are slightly higher than those of the model F0 by taking the traffic capacity as an optimization target, so that the model F1 has higher output capacity in a saturated state. The evaluation indexes of the model F2 and the model F3 are not good, the MFD index of the model F3 is good due to the consideration of the MFD parameter, but the model F3 is easy to fall into a local optimal solution due to the limitation of an MOPSO algorithm. The model F2 takes the green wave bandwidth of the main line as priority, so that the green ratio of the main line is too high, the period is too long, and the delay is huge.
From the results of MFD forms and stability, the MFD of the four schemes is an unclosed two-section form, which indicates that the four schemes do not reach an over-saturated or congested state under the existing traffic demand and supply conditions. The corresponding scheme of the model F0 considers the MFD parameters, the slope of the ascending section is maximum, the efficiency is highest under the free flow state, the data points are concentrated from the MFD form, the obvious ascending and stable section states are presented, the RMSE is minimum no matter the non-boundary point is considered or eliminated, the fitting effect is optimal, and the MFD stability is best; although the MFD parameters are also considered in the model F3, the optimization effect is not ideal due to the characteristic that the MOPSO algorithm is easy to fall into local optimum, the data discreteness is large, and the bearing capacity of a stable section is maximum; the corresponding scheme of the model F2 is a traditional numerical solution, the period under the saturated traffic flow is largest, each index is not ideal in performance, the free flow state at the ascending section has higher efficiency, but the steady section under the saturated flow is most discrete and has the phenomenon of 'hysteresis', which shows that the traffic flow is suddenly increased or reduced under the scheme, and the road network is extremely unstable; the corresponding scheme of the model F1 does not consider MFD parameters, but delay and queuing length are also traffic efficiency consideration indexes, so that each evaluation index is closer to the scheme of the embodiment of the invention, but the MFD stability is worse than the scheme of the embodiment of the invention, and the total delay is also different greatly.
The experimental results prove that in a road network and saturated traffic flow scene of a trunk area, the trunk coordination control method provided by the embodiment of the invention can better give consideration to both macroscopic and microscopic traffic efficiency, the MFD form has stronger stability and controllability, the improved GA-MOPSO algorithm effect is far better than that of the MOPSO algorithm, and the effectiveness of the model and the algorithm in the invention is verified.
Referring to fig. 7, an embodiment of the present invention provides a trunk coordination control system based on a macro basic diagram, including:
the multi-objective optimization model building module is used for building a multi-objective optimization model for trunk line coordination control, the multi-objective optimization model takes the maximization of the slope of the MFD ascending section, the maximization of the bearing capacity of the road network, the minimization of the average delay of vehicles in the road network and the minimization of the queuing coefficient of the road network as optimization targets, and takes the trunk line public period, the effective green light duration of the phase and the phase difference of an intersection as parameters to be optimized;
the model optimization module is used for acquiring traffic flow data of a trunk line to be optimized, and optimizing the multi-objective optimization model through a multi-objective particle swarm algorithm and a genetic algorithm according to the traffic flow data to obtain an optimal parameter combination of parameters to be optimized;
the coordination control module is used for carrying out trunk coordination control on the trunk to be optimized according to the optimal parameter combination;
in the optimization process of the multi-objective optimization model, the road network vehicle average delay and the road network queuing coefficient corresponding to each particle are determined through simulation, and the MFD ascending section slope and the road network bearing capacity corresponding to each particle are determined through a macroscopic basic graph obtained through cluster fitting.
The contents in the above method embodiments are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above method embodiment, and the beneficial effects achieved by the present system embodiment are also the same as those achieved by the above method embodiment.
Referring to fig. 8, an embodiment of the present invention provides a trunk coordination control apparatus based on a macro basic diagram, including:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is enabled to implement the above-mentioned trunk coordination control method based on the macro basic diagram.
The contents in the above method embodiments are all applicable to the present apparatus embodiment, the functions specifically implemented by the present apparatus embodiment are the same as those in the above method embodiments, and the advantageous effects achieved by the present apparatus embodiment are also the same as those achieved by the above method embodiments.
An embodiment of the present invention further provides a computer-readable storage medium, in which a program executable by a processor is stored, and the program executable by the processor is used for executing the above-mentioned trunk coordination control method based on the macro basic diagram when being executed by the processor.
The computer-readable storage medium of the embodiment of the invention can execute the trunk coordination control method based on the macro basic diagram provided by the embodiment of the method of the invention, can execute any combination of the implementation steps of the embodiment of the method, and has corresponding functions and beneficial effects of the method.
Embodiments of the present invention also disclose a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and executed by the processor to cause the computer device to perform the method illustrated in fig. 1.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the above-described functions and/or features may be integrated in a single physical device and/or software module, or one or more of the functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer given the nature, function, and interrelationships of the modules. Accordingly, those of ordinary skill in the art will be able to practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is to be determined from the appended claims along with their full scope of equivalents.
The above functions, if implemented in the form of software functional units and sold or used as a separate product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above methods of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Further, the computer readable medium could even be paper or another suitable medium upon which the above described program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the foregoing description of the specification, reference to the description of "one embodiment/example," "another embodiment/example," or "certain embodiments/examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. A trunk line coordination control method based on a macroscopic basic graph is characterized by comprising the following steps:
constructing a multi-objective optimization model for trunk line coordination control, wherein the multi-objective optimization model takes the maximization of slope of MFD ascending section, the maximization of road network bearing capacity, the minimization of average delay of vehicles in road network and the minimization of road network queuing coefficient as optimization targets, and takes the trunk line public period, the effective phase green light duration and the intersection phase difference as parameters to be optimized;
acquiring traffic flow data of a trunk line to be optimized, and optimizing the multi-target optimization model through a multi-target particle swarm optimization combined with a genetic algorithm according to the traffic flow data to obtain an optimal parameter combination of the parameters to be optimized;
carrying out trunk line coordination control on the trunk line to be optimized according to the optimal parameter combination;
in the optimization process of the multi-objective optimization model, the road network vehicle average delay and the road network queuing coefficient corresponding to each particle are determined through simulation, and the MFD ascending section slope and the road network bearing capacity corresponding to each particle are determined through a macroscopic basic graph obtained through cluster fitting.
2. The method for trunk coordination control based on the macroscopic fundamental graph as recited in claim 1, wherein an objective function of the multi-objective optimization model is:
Figure FDA0003800120360000011
wherein F (C, G, offset) represents an objective function,
Figure FDA0003800120360000012
the average delay of the vehicles in the road network is shown,
Figure FDA0003800120360000013
represents the road network queuing coefficient, A 1 Showing the slope of the MFD up-leg, q max Showing the bearing capacity of the road network;
the constraint conditions of the multi-objective optimization model are as follows:
Figure FDA0003800120360000014
wherein, queue i Indicates the queuing length, L, of the ith lane i Indicates the lane length of the ith lane, C indicates the trunk common period, C min And C max Respectively represent the upper limit and the lower limit of C,
Figure FDA0003800120360000015
the effective green duration representing the jth phase at the ith intersection,
Figure FDA0003800120360000016
and
Figure FDA0003800120360000017
respectively represent
Figure FDA0003800120360000018
Upper and lower limits of, offset i Indicating the phase difference between the ith intersection and the first intersection.
3. The trunk coordination control method based on the macroscopic basic graph as recited in claim 1, wherein the step of optimizing the multi-objective optimization model by the multi-objective particle swarm optimization and the genetic algorithm according to the traffic data to obtain the optimal parameter combination of the parameters to be optimized specifically comprises:
determining initial values of the position and the speed of each particle in the particle swarm, an initial solution set of the particle swarm and a convergence condition; simulating according to the traffic flow data and the position and the speed of each particle in the initial particle swarm to obtain a road network vehicle average delay, a road network queuing coefficient and an MFD data point set corresponding to each particle, wherein the abscissa and the ordinate of a data point in the MFD data point set are respectively a road network weighted density and a road network weighted flow;
clustering and fitting the MFD data point set to obtain a macroscopic basic graph, and determining the slope of MFD ascending sections and the road network bearing capacity corresponding to each particle according to the macroscopic basic graph;
respectively carrying out non-dominated sorting on each particle to determine the individual optimal position of each particle, and carrying out non-dominated sorting on the particle swarm to determine the group optimal position of the particle swarm;
updating the position and the speed of each particle according to the individual optimal position and the group optimal position;
selecting, crossing and mutating the particle swarm to obtain an optimized particle swarm;
and if the convergence condition is met, taking the parameter value combination of the optimal position of the historical particle swarm as the optimal parameter combination, and if the convergence condition is not met, returning to the step of simulating according to the traffic data and the position and the speed of each particle in the initial particle swarm.
4. The trunk coordination control method based on the macroscopic fundamental graph as claimed in claim 3, wherein said step of performing cluster fitting on the MFD data point set to obtain the macroscopic fundamental graph, and determining the MFD ascending segment slope and road network bearing capacity corresponding to each particle according to the macroscopic fundamental graph specifically comprises:
performing boundary search on the MFD data set by a # -shaped boundary extraction method, and generating an initial MFD map according to the searched boundary points;
clustering the initial MFD graph to obtain two or three categories of data sample sets;
performing piecewise linear fitting on each data sample set to obtain a macroscopic basic graph, wherein the macroscopic basic graph at least comprises an ascending section structure and a stationary section structure;
determining the MFD ascending section slope corresponding to the current particle according to the ratio of the road network weighted flow of the ascending section structure to the road network weighted density;
and determining the road network bearing capacity corresponding to the current particles according to the road network weighted flow of the stable section structure.
5. The method as claimed in claim 4, wherein the step of clustering the initial MFD graph to obtain two or three categories of data sample sets specifically includes:
clustering the initial MFD graph by taking 3 as a clustering number to obtain three categories of undetermined sample sets;
determining the central point positions of the undetermined sample sets from left to right to be a first central point, a second central point and a third central point respectively;
when the road network weighted flow of the first central point and the road network weighted flow of the third central point are both smaller than the road network weighted flow of the second central point, determining that the initial MFD graph is a three-section closed MFD graph, outputting the to-be-determined sample set as a data sample set, otherwise, determining that the initial MFD graph is two sections of non-closed MFD graphs, and re-clustering the initial MFD graphs by taking 2 as the clustering number to obtain two categories of data sample sets.
6. The method according to claim 3, wherein in the step of updating the position and velocity of each particle according to the individual optimal position and the population optimal position, the position and velocity of each particle are updated according to the following formula:
V 1 ＝V 0 ×w+S×r×(pbest-x 0 )+S×r×(gbest-x 0 )
x 1 ＝x 0 +V 1
wherein, V 0 And V 1 Representing the velocity, x, of the particles before and after the update, respectively 0 And x 1 Representing the particle velocities before and after update, respectively, w represents an inertia factor, S represents a learning factor, r represents [0,1]]Random numbers within the range, pbest, represent the individual optimal location, and gbest, the population optimal location.
7. The method according to claim 3, wherein the step of performing the operations of selecting, crossing, and mutating on the particle swarm to obtain the optimized particle swarm specifically comprises:
selecting one particle in the particle group by roulette to endow the particle with parameters again;
carrying out cross processing on two particles which are sequenced most at the back in the particle swarm by a real number cross method to obtain two new particles added into the particle swarm;
traversing the particle swarm, and carrying out variation operation on the particles in the particle swarm according to a preset variation probability to obtain the optimized particle swarm.
8. A trunk coordination control system based on a macro basic diagram is characterized by comprising:
the multi-objective optimization model building module is used for building a multi-objective optimization model for trunk line coordination control, the multi-objective optimization model takes the maximization of the slope of the MFD ascending section, the maximization of the bearing capacity of a road network, the minimization of the average delay of vehicles in the road network and the minimization of the queuing coefficient of the road network as optimization targets, and takes the trunk line public period, the effective green light duration of the phase and the phase difference of an intersection as parameters to be optimized;
the model optimization module is used for acquiring traffic flow data of a trunk line to be optimized, and optimizing the multi-target optimization model through a multi-target particle swarm algorithm and a genetic algorithm according to the traffic flow data to obtain an optimal parameter combination of the parameters to be optimized;
the coordination control module is used for carrying out trunk coordination control on the trunk line to be optimized according to the optimal parameter combination;
in the optimization process of the multi-objective optimization model, the road network vehicle average delay and the road network queuing coefficient corresponding to each particle are determined through simulation, and the MFD ascending section slope and the road network bearing capacity corresponding to each particle are determined through a macroscopic basic graph obtained through cluster fitting.
9. A trunk line coordination control device based on a macro basic diagram is characterized by comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, the at least one program causes the at least one processor to implement a macro base map-based trunk coordination control method according to any one of claims 1 to 7.
10. A computer readable storage medium in which a processor executable program is stored, wherein the processor executable program, when executed by a processor, is for performing a macro base map based trunk coordination control method as claimed in any one of claims 1 to 7.
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