CN113240908B - Traffic network congestion control method and system based on ant colony copulation strategy - Google Patents

Traffic network congestion control method and system based on ant colony copulation strategy Download PDF

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CN113240908B
CN113240908B CN202110666755.5A CN202110666755A CN113240908B CN 113240908 B CN113240908 B CN 113240908B CN 202110666755 A CN202110666755 A CN 202110666755A CN 113240908 B CN113240908 B CN 113240908B
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CN113240908A (en
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李大庆
赵稀
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Beihang University
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing

Abstract

The invention provides a traffic network congestion control method and system based on an ant colony coordination strategy, relates to the technical field of intersection of traffic science and network science, and takes various complex characteristics of heterogeneity, currency, nonlinearity, multiple coupling and the like of a huge system of an urban traffic network into consideration, the invention builds a complex network traffic flow propagation simulation framework suitable for the congestion propagation rule of the urban traffic network by applying a classical LTM traffic flow model to simulate the urban traffic network based on a complex network, then, on a complex network traffic flow propagation simulation framework, a traffic jam intelligent regulation and control model based on ant colony feeding-feeding behavior inspiration is constructed, so that an extremely complex macro road network overall regulation and control problem is decomposed into thousands of simple local regulation and control problems of small node control units, and the urban traffic road network jam problem is effectively relieved from the top strategy design level.

Description

Traffic network congestion control method and system based on ant colony copulation strategy
Technical Field
The invention relates to the technical field of intersection of traffic science and network science, in particular to a traffic network congestion control method and system based on an ant colony coordination strategy.
Background
In the process of rapid development of construction processes of various careers, urban traffic is changed unprecedentedly. Although the new planning of urban traffic is followed by the departure, the new problems in actual urban traffic are still endless, and the new problems are always a process of continuous conflict and coordination between early planning and later treatment. The problems in urban traffic are very serious at present, and once the problems cannot be solved or managed reasonably and effectively, the economic robustness and sustainable development can be threatened extremely. Among them, traffic congestion has risen to be a very serious problem in urban traffic problems due to the reasons of serious shortage of urban road capacity, excessively fast increase of urban automobile holding capacity, and the like.
Due to the continuous progress of science and technology, the growth of intelligent traffic becomes stronger, so that the intelligent traffic is also trended to slow down traffic jam. At the present stage, traffic intellectualization has been gradually improved, the acquisition quality and the acquisition precision of traffic information are also continuously improved, and accurate and real-time acquisition of traffic data from a road traffic network has become possible, so that support of massive data information is provided for real-time analysis and prediction research of traffic conditions.
The determination and prediction of urban road congestion is the focus of managing traffic congestion. Therefore, how to construct a simulation model for traffic flow data acquired from a traffic network by using a scientific, reasonable and effective method to effectively evaluate and predict urban road congestion and finally adopt an effective regulation and control method to reasonably evacuate urban road congestion has important research significance and is one of key technologies for delaying urgent treatment of traffic congestion. Currently, the related art is relatively few.
Disclosure of Invention
In view of this, the invention provides an ant colony coordination strategy-based traffic network congestion control method and system.
In order to achieve the purpose, the invention provides the following scheme:
a traffic network congestion control method based on an ant colony copulation strategy comprises the following steps:
constructing a road network complex network model corresponding to a research city according to the topological parameters of the traffic road network structure of the research city; the road network complex network model comprises network nodes and network connecting edges;
determining road section relevant information corresponding to the research city, and building a traffic network congestion propagation simulation frame corresponding to the research city based on the road section relevant information, the LTM traffic flow propagation model and the road network complex network model; the road section related information comprises road section related parameters and road network characteristic information; the road section related parameters comprise a bidirectional traffic flow transmission road section, real-time load traffic flow in the road section, road section upstream traffic demand and road section downstream traffic flow; the road network characteristic information comprises research time period information; the traffic road network congestion propagation simulation framework is a simulation framework which can update real-time load traffic flow in a road section according to the corresponding relation between the upstream traffic demand of the road section and the residual capacity of the road section;
determining a regulation and control area of the research city, and constructing an ant colony hybridization strategy-based traffic jam intelligent regulation and control model corresponding to the regulation and control area according to the regulation and control area and the traffic network jam propagation simulation framework; the traffic jam intelligent control model comprises an ant control unit and neighboring control units around the ant control unit; the ant regulation and control unit is determined based on the network nodes of the traffic network congestion propagation simulation framework;
and operating the traffic network congestion propagation simulation framework, and then regulating and controlling the traffic network traffic congestion state of the regulation and control area according to the ant colony nursing strategy-based traffic congestion intelligent regulation and control model.
A traffic network congestion control system based on an ant colony coordination strategy comprises:
the road network complex network model building module is used for building a road network complex network model corresponding to a research city according to the topological parameters of the traffic road network structure of the research city; the road network complex network model comprises network nodes and network connecting edges;
the traffic network congestion propagation simulation framework building module is used for determining road section related information corresponding to the research city and building a traffic network congestion propagation simulation framework corresponding to the research city based on the road section related information, the LTM traffic flow propagation model and the road network complex network model; the road section related information comprises road section related parameters and road network characteristic information; the road section related parameters comprise a bidirectional traffic flow transmission road section, real-time load traffic flow in the road section, road section upstream traffic demand and road section downstream traffic flow; the road network characteristic information comprises research time period information; the traffic road network congestion propagation simulation framework is a simulation framework which can update real-time load traffic flow in a road section according to the corresponding relation between the upstream traffic demand of the road section and the residual capacity of the road section;
the traffic jam intelligent regulation and control model building module is used for determining a regulation and control area of the research city and building an ant colony copulation strategy-based traffic jam intelligent regulation and control model corresponding to the regulation and control area according to the regulation and control area and the traffic network jam propagation simulation frame; the traffic jam intelligent control model comprises an ant control unit and neighboring control units around the ant control unit; the ant regulation and control unit is determined based on the network nodes of the traffic network congestion propagation simulation framework;
and the regulation and control module is used for operating the traffic network congestion propagation simulation framework and then regulating and controlling the traffic network traffic flow congestion state of the regulation and control area according to the ant colony-feeding strategy-based traffic congestion intelligent regulation and control model.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
aiming at the defects in the prior art, the invention provides a traffic network congestion control method and system based on an ant colony coordination strategy, and considering various complex characteristics of a huge system of an urban traffic network, such as heterogeneity, eruption, nonlinearity, multiple coupling and the like, the invention builds a complex network traffic flow propagation simulation framework suitable for the urban traffic network congestion propagation rule by applying a classical LTM traffic flow model to simulate the urban traffic network based on a complex network, then, on a complex network traffic flow propagation simulation framework, a traffic jam intelligent regulation and control model based on ant colony feeding behavior inspiration is constructed, so that the extremely complex overall regulation and control problem of the macroscopic road network is decomposed into thousands of simple local regulation and control problems of small node control units, and the problem of urban traffic road network jam is effectively relieved from the top strategy design level.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a traffic network congestion control method based on an ant colony coordination strategy according to the present invention;
FIG. 2 is a schematic structural diagram of a traffic network congestion control system based on an ant colony coordination strategy according to the present invention;
FIG. 3 is an overall flowchart of a traffic network congestion control method based on an ant colony coordination strategy according to the present invention;
FIG. 4 is a schematic flow chart of step E3 according to the present invention;
FIG. 5 is a schematic flow chart of step E4 according to the present invention;
FIG. 6 is a flowchart illustrating step E5 according to the present invention.
In fig. 4: beta: the shunt scaling factor is generated by a random number. Satisfies beta1+β2+β3＝1。Qinput: the primary traffic demand component accumulated on the upstream of the road is output outwards (relative to the central node in the graph), and the downstream output flow of the inflow road of all 4 rounds (relative to the central node) needs to be traversed to completely calculate the upstream traffic demand accumulated on the current round of the road. q. q.soutput+: the flow rate of the current wheel, which is calculated according to FD, to the road is the sum of the current value and the flow rate of the current wheel, which is to be output outwards.
In fig. 5: qmiddle: total upstream traffic demand at the central intersection. Qnorth: northbound to the upstream traffic demand of the neighbor node. Qsouth: the upstream traffic demand of the southbound neighbor node. Qwest: upstream traffic demand of the west neighbor node. Qeast: upstream traffic demand of the east-facing neighbor node. tropicity: the amount of food delivery. Delta Qm-n: a difference between the upstream traffic demand of the northbound neighbor node and the upstream traffic demand of the central node. Delta Qm-s: a difference between an upstream traffic demand of the southbound neighbor node and an upstream traffic demand of the central node. Delta Qm-w: the difference between the upstream traffic demand of the west neighbor node and the upstream traffic demand of the central node. Delta Qm-e: the difference between the upstream traffic demand of the east neighbor node and the upstream traffic demand of the central node. ratio (R)m-n: north intersection coefficient of the central node. ratio (R)m-s: the south contribution factor of the central node. ratio (R)m-w: the west feeding coefficient of the central node. ratio (R)m-e: east-handed coefficient of central node. Qn-input+: and the central node outputs the regulated and controlled upstream traffic demand of the road in the north direction. Qs-input-: and the central node outputs the regulated and controlled upstream traffic demand of the road in the south direction. Qw-input+: and the upstream traffic demand of the west output road of the central node is regulated. Qe-input+: and the central node outputs the regulated upstream traffic demand of the road to the east.
In fig. 6: capacity: maximum traffic capacity of the road section. Qinput: road segment upstream traffic demand. Cleft: link remaining capacity. Qinput1-: and updating the upstream traffic demand before the west road section is updated. Cleft1-: and residual capacity before western road section updating. Qinput1- new: and updating the upstream traffic demand of the west road section. Cleft1- new: and (4) updating the western-direction road section to obtain the residual capacity. Qinput3+: the eastern road segment updates the prior upstream traffic demand. Cleft3+: the remaining capacity before east road segment update. Qinput3+ new: and updating the upstream traffic demand of the east road section. Cleft3+ new: and the east road section is updated to the residual capacity.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Due to the serious shortage of urban road capacity, the excessively fast increase of urban automobile holding capacity and the like, traffic congestion has risen to a very serious problem in urban traffic. For example, in 2016, dutch navigation technologies released the most congested cities in the world, the top 4 cities in china, chongqing, chengdu, tainan and beijing, respectively. In this report, the congestion index in Beijing is 42%, which means that it takes on average 42% more time per vehicle than in normal running during the congestion occurrence period, and it takes on average 2 times more time per vehicle during the peak period. Moreover, the congestion condition of the Beijing expressway is more serious than that of the ordinary expressway, and the congestion rate of the expressway is ranked sixth globally. According to the '2016 second quarter China main city traffic analysis report' published on the map, it can be known that Beijing is located in the most congested cities in China. The data shows that the congestion delay index of Beijing at the peak is as high as 2.16, the congestion delay of the whole day is 1.79, namely the time spent by the Beijing residents on driving to get on or off duty is twice as long as the time spent in the unobstructed period, and the Beijing residents have the highest congestion time cost in the country.
The complex network is used as an important component of a complex system theory, and provides a new visual angle for researching urban traffic systems and urban traffic jam propagation dynamic behavior characteristics. The basic characteristics of the network system mainly come from the topological structure of the network, and many complex network macrosystems exist in the real world, such as a neural network, an Internet network, a social network and the like. In order to study the network structure characteristics of these complex network macros as a whole, deep analysis and research can be performed by introducing complex network principles. Specifically, a complex network macro system can be decomposed into a plurality of subsystems (elements) with association relations, when the decomposed subsystems (elements) are abstracted into nodes and the association relations between the subsystems (elements) are abstracted into edges, the complex network macro system can be abstracted into a network formed by the nodes and the edges, and then correlation principles and correlation techniques of the complex network are introduced to simulate and solve a series of phenomena and problems of the complex network macro system.
For a complex urban traffic system, because the complex urban traffic system is a typical complex network huge system, a traffic jam propagation simulation model suitable for an urban traffic jam propagation rule can be constructed for the urban traffic system abstracted as a complex network based on a complex network principle and by combining a classical traffic flow propagation model, influence factors and action rules in the urban traffic jam propagation process are analyzed and researched, and then an intelligent traffic control strategy of an urban traffic network is tried to be designed, so that the current situation of urban road traffic jam is prevented and effectively relieved to a certain extent in time.
The social behaviors ubiquitous in nature, such as fish, bird, bee, ant, etc., all contain the group intelligence that a large number of simple and low-intelligence individuals gather and produce interaction and then "emerge". "emerging" refers to the phenomenon that when individuals in the system obey simple rules but form a whole through local interactions, some new characteristics or rules suddenly appear at the system level. Thus, the emerging attribute or rule does not break the individual rule, however it cannot be simply interpreted by the low level individuals, so the emerging can be simply understood as "the whole of the system is greater than the sum of the parts".
Taking ant colony as an example, each ant is a set of combination of a sensing machine and a reactor which hardly has individual thinking, only has simple behavior rules, reacts and releases pheromone according to environmental events, and determines own behavior according to environment and received pheromone types and concentrations, namely a block chain system formed by thousands of distributed nodes without centers, each node participates in, but each node follows the whole operation result of the block chain system, individuals in the ant colony do not have 'intelligence', the real colony intelligence is excited and conducted by the pheromone network of the ant colony to form a 'brain' which controls the behavior of the whole ant colony, appears to be visible everywhere and surpasses the individuals, and under the situation, each ant seems to be a cell of the 'super individual' of the ant colony, and ten million ants jointly form a five-organ and six-organ of the ant colony, The limbs and the bones form a complete body of the whole ant colony, the body has no muscle and ligament shape and is as flexible as water, the body has no attacking organs and can hunt and kill prey which is much larger than any ant, and the body has no developed brain and can build underground nests with complexity which is comparable to the human city. Just like the equation of Ling, a simple rule description can burst a complex and engaging pattern.
When a prey is discovered, patrolling workers will release a large amount of pheromone to attract other workers to come until the prey is completely subdued, and then all workers who move about will gradually lose the prey. In addition, the ant colony has a 'copulation' phenomenon, ants really in direct contact with food can only account for 10% of the whole ant colony, while most of ants rely on the copulation of other workers, namely, ants with more food in the abdomen transmit the semi-digested liquid food to ants with less food in the abdomen through a channel formed after the ants are butted by an inter-ant mouth device through an inter-ant feeler. Under the phenomenon of 'copulation' which widely exists, only part of ants are required to perform hunting-copulation behaviors, and the residual labor force of the ant colony can be used for tasks with higher importance for the population continuation, such as guarding nests, feeding larvae and the like, so that the operation efficiency, the survival capability and the adaptability of the 'super-individual' of the whole ant colony are greatly improved.
Based on the problems and the phenomenon inspiration, the invention provides an effective solution for simulating an urban traffic network and carrying out intelligent regulation, namely a traffic network congestion regulation and control method and system based on an ant colony coordination strategy. Firstly, applying a classic LTM (Link Transmission Model) traffic flow propagation Model to a complex network, and building a complex network traffic flow propagation simulation frame suitable for the urban traffic network congestion propagation rule, namely an LTM traffic network simulation frame; then, on an LTM traffic network simulation framework, an ant colony feeding strategy-based traffic jam intelligent regulation and control model is constructed, so that the extremely complex overall regulation and control problem of the macroscopic road network is decomposed into thousands of simple local regulation and control problems of small node control units. The traffic network congestion control method and system based on the ant colony feeding strategy provided by the invention greatly reduce the solving complexity of the overall control problem of the macroscopic complex road network, and have the advantages of simple and convenient calculation, practical scheme, reliable result and good engineering application prospect.
Example one
The urban traffic network is extremely high in complexity per se, is a complex network huge system with heterogeneity, occurrence and nonlinearity, couples various urban road types such as cross plane intersection, annular plane intersection, Y-shaped plane intersection, staggered plane intersection and the like, and solves the problem of urban traffic network congestion control extremely difficultly due to the extremely large number of urban roads. For the regulation and control of the congestion of the urban traffic network, the existing research mainly focuses on a novel indicator light, a flow monitoring device and a novel rationalization timing scheme for the regulation and control of the traffic, or provides a certain congestion identification method, and the top-level intelligent strategy design related to the regulation and control of the congestion of the urban traffic network is rare. Therefore, the embodiment provides a method for intelligently and efficiently regulating and controlling urban traffic network congestion evacuation, that is, a method for regulating and controlling traffic network congestion based on an ant colony coordination strategy, please refer to fig. 1, which includes the following steps:
step 100: constructing a road network complex network model corresponding to a research city according to the topological parameters of the traffic road network structure of the research city; the road network complex network model comprises network nodes and network connecting edges.
The specific method of the step 100 is as follows: according to the method, accurate mapping from a traffic network in a real physical space to a complex network model in a virtual time-space domain is established according to the topological parameters of the traffic network structure of a research city, including corresponding nodes, edges (namely the cross-linking relation between the nodes), position coordinates, capacity, weight and the like, so as to serve as a reasonable basis for building a complex network traffic flow propagation simulation framework (namely the traffic network congestion propagation simulation framework) suitable for the urban traffic network congestion propagation rule by using an LTM traffic flow propagation model.
In this embodiment, step 100 includes the following steps:
step A1: the distribution conditions of nodes such as intersections, buffer areas and the like of a traffic network of a study city are analyzed and extracted to determine a point set V of a complex network model of the road network and topological structure parameters corresponding to the nodes.
The specific method comprises the following steps: and traversing and counting nodes such as intersections, buffer areas and the like of the researched urban traffic network and topological parameters thereof, and determining the nodes as network nodes. On the basis, all network nodes are stored in a list to construct a point set V, and the topological structure parameters corresponding to all the network nodes are directly stored in the list.
Step A2: analyzing and extracting the distribution condition of the cross-linking relation among all nodes of the urban traffic network to determine an edge set E of the complex network model of the network and topological structure parameters corresponding to all the cross-linking relations.
The specific method comprises the following steps: traversing and counting the communication paths (namely the cross-linking relation among nodes) and the topology parameters of each road of the researched urban traffic network, taking the communication paths as network connecting edges, storing the network connecting edges into a list to construct an edge set E, and directly storing the topology structure parameters corresponding to the network connecting edges into the list.
Step A3: and constructing a road network complex network model G.
The specific method comprises the following steps: and accurately constructing a road network complex network model G through the point set V, the edge set E, the topological structure parameters corresponding to the network nodes and the topological structure parameters corresponding to the network connecting edges.
The "road network complex network model" in step 100 is abstracted from a specific urban traffic road network, and is a road network complex network model G composed of a point set V and an edge set E, that is: g ═ V, E; the road network complex network model can clearly show the interactive link relation formed by road sections among the intersections, and is easy to carry out congestion propagation simulation analysis on an urban traffic system. Due to the bidirectional reciprocating of the urban road sections, the constructed road network complex network models are undirected network models.
Step 101: and determining road section related information corresponding to the research city, and building a traffic network congestion propagation simulation frame corresponding to the research city based on the road section related information, the LTM traffic flow propagation model and the road network complex network model.
The specific meaning of step 101 is: on a road network complex network model, road section related parameters such as turnover intersection nodes, bidirectional traffic flow transmission road sections and real-time load traffic flows in the road sections are established, downstream traffic flow of the road sections can be output according to the rule of an LTM traffic flow propagation model, upstream traffic demand of the road sections is generated, and finally a simulation framework capable of updating the real-time load traffic flows in the road sections is established according to the corresponding relation between the upstream traffic demand of the road sections and the residual capacity of the road sections.
In this embodiment, step 101 includes the following steps:
step B1: and determining the road section related information corresponding to the research city. The road section related information comprises road section related parameters and road network characteristic information; the road section related parameters comprise turnover intersection nodes, bidirectional traffic flow transmission road sections, real-time load traffic flows in the road sections, road section upstream traffic demand and road section downstream traffic flow; the road network characteristic information includes research time period information.
Step B2: and adding road section related parameters on the road network complex network model.
The specific method comprises the following steps: road section related parameters such as turnover intersection nodes, bidirectional traffic flow transmission road sections, real-time load traffic flow in road sections, road section upstream traffic demands, road section downstream traffic flows and the like are added on network connection sides of the road network complex network model, and reasonable initial values are given according to actual common parameter values of the urban traffic network.
Step B3: and determining a triangular FD parameter according to the road network characteristic information.
The specific method comprises the following steps: the general triangular FD parameter is formed by the maximum output flow q of the downstream road sectionmaxCritical density kcritDensity of congestion kjamDetermined, so that a given triangle FD can be equivalent to providing the maximum output flow q downstream of the road sectionmaxCritical density kcritDensity of congestion kjamThree determined parameters, namely, the triangular FD parameter needs to be matched with the road network characteristic information to be analyzed, otherwise, it is difficult to analyze the correct result.
Wherein "FD" means: the basic Diagram, Fundamental Diagram, on the flow density plane, the steady state of the road network traffic flow corresponds to a flow density curve passing through the origin and having at least one maximum, which curve is referred to as the basic Diagram. For triangular FD, it is actually composed of (0,0), (k)crit,qmax)、(kjam0) a continuous broken line defined by three Cartesian two-dimensional coordinate points, which forms a three-dimensional broken line with the X-axisThe angle shape is called as a triangle basic picture.
Step B4: and updating the road network traffic flow state in real time according to the rule defined by the LTM traffic flow propagation model based on the road network complex network model added with the road section relevant parameters, the triangular FD parameters and the time domain driving engine, and establishing a traffic network congestion propagation simulation frame corresponding to the research city when the updating is finished.
The specific method comprises the following steps: on the basis of the road network complex network model and the triangular FD parameters after road section related parameters are added, updating the road network traffic flow state by the rule defined by the LTM traffic flow propagation model according to a time step-by-step iteration mode until the road network state iteration of the whole time domain is completed, and further building a traffic network congestion propagation simulation frame corresponding to the research city, wherein the method comprises the following steps:
step B41: the nth iteration is started.
Step B42: traversing each road section, and calculating the downstream traffic flow q of the road section corresponding to the current road sectionoutputAnd is distributed in a random mode and converted into the road section upstream traffic demand Q corresponding to the downstream road sectioninput. The downstream road section is a downstream road section associated with the current road section.
Step B43: traversing each road section and according to the upstream traffic demand Q of the road section corresponding to each road sectioninputAnd its corresponding current remaining capacity CleftAnd updating the road section traffic flow state.
Step B44: and finishing the nth iteration and preparing to carry out the (N + 1) th iteration until the iteration is completely finished.
Wherein, in the step B42, the step "traverse each road segment, and calculate the downstream traffic flow q of the road segment corresponding to the current road segmentoutputAnd is distributed in a random mode and converted into the road section upstream traffic demand Q corresponding to the downstream road sectioninput", the specific way is as follows: when traversing a certain road section, firstly, according to the existing traffic flow C of the current road sectionaldAnd length of road section L, by formula
Figure BDA0003117728260000101
Calculating the current road section traffic flow density k, and then calculating the road section downstream traffic flow q according to the current road section traffic flow density k and the triangle FDoutputAnd then n in total is 1downA random number (n)downThe number of the sections adjacent to the section downstream) as the distribution proportion, and the traffic flow q of the section downstream is used as the distribution proportionoutputDistributing to downstream road sections and converting to road section upstream traffic demand Q corresponding to corresponding downstream road sectionsinput。
Wherein, the step B43 is executed for traversing each road section according to the upstream traffic demand Q of the road section corresponding to each road sectioninputAnd its corresponding current remaining capacity CleftUpdating the traffic flow state of the road section, which comprises the following specific steps: when traversing a certain road section, reading the accumulated total traffic demand Q at the upstream of the road sectioninputAnd the current remaining capacity C of the road sectionleftIf Q isinput< 0, hold QinputAnd CleftUnchanged (in fact Q)input< 0, which is typically only the case after ant colony feeding strategy regulation). If 0 < Qinput≤CleftThen Q will beinput< 0 to 0, updated CleftSatisfies Cleft new＝Cleft-Qinput. If Qinput＞CleftThen C will beleftSet to 0, updated QinputSatisfy Qinput new＝Qinput-Cleft。
Step 102: determining a regulation and control area of the research city, and constructing an ant colony hybridization strategy-based traffic jam intelligent regulation and control model corresponding to the regulation and control area according to the regulation and control area and the traffic network jam propagation simulation framework; the traffic jam intelligent control model comprises an ant control unit and neighboring control units around the ant control unit; the ant regulation and control unit is determined based on the network nodes of the traffic network congestion propagation simulation framework.
The specific method comprises the following steps: each network node of the urban traffic road network congestion propagation simulation framework is regarded as an ant regulation and control unit, namely a node regulation and control unit; and constructing a corresponding traffic jam intelligent regulation and control model based on the ant colony coordination strategy based on the network nodes corresponding to the regulation and control area.
The traffic jam intelligent regulation and control model based on the ant colony feeding strategy specifically comprises the following steps: each ant regulating and controlling unit senses the congestion condition of the adjacent regulating and controlling units around under each time step, and then compares the congestion condition with the congestion condition of the ant regulating and controlling unit to obtain the upstream traffic demand Q of the road section corresponding to the downstream road section generated in the step B42inputAnd reasonable redistribution is carried out, so that the traffic flow is restrained from further rushing to a point with a serious congestion degree, the traffic flow is guided to an area with a light congestion degree, and the traffic congestion of the urban traffic network is relieved.
The traffic jam intelligent regulation and control model based on the ant colony feeding strategy comprises the following execution steps:
step C1: and traversing the ant regulating and controlling units of the regulating and controlling area based on the network nodes in the regulating and controlling area.
Step C2: for each ant regulation and control unit, reading the total upstream traffic demand accumulated by the ant regulation and control unit
Figure BDA0003117728260000121
And according to the management and control compliance coefficient ratioobeyAnd total upstream traffic demand
Figure BDA0003117728260000122
Calculating the feeding total amount Trophacity.
Step C3: for each ant regulation and control unit, reading the respective accumulated upstream traffic demand Q of the adjacent regulation and control unitsneighbour。
Step C4: according to the delivery rule, calculating the upstream traffic demand Q redistributed and obtained by each downstream road sectionnewAnd handed over for distribution.
Wherein, the regulation and control unit for each ant described in the step C2 reads the accumulated total upstream traffic demand
Figure BDA0003117728260000123
The specific method comprises the following steps: falseSetting a central control unit (an ant control unit) as a crossroad node, and connecting N control units to the central control unit road4 downstream road sections for which the upstream traffic demand Q isN old、QS old、QE old、QW oldSequentially accumulating to obtain the total upstream traffic demand Q correspondingly accumulated by the central control unittotal。
Wherein the regulation compliance coefficient ratio described in step C2obey", the specific meaning thereof is: under a real city traffic road network, 100% of vehicles cannot be subjected to management and control completely, and the management and control can only be a means for relieving congestion in a regulating manner, but cannot be completely stopped. Thus setting ratioobeyThe parameter means the traffic flow proportion subject to control, and in the simulation operation of the road network, only the traffic flow with the proportion is completely subject to the control of the road network control means, and the rest traffic flows keep the original operation.
Wherein, the "feeding total amount tropcity" in the step C2 has the specific meaning: due to the regulatory compliance coefficient ratioobeyPresence of only Qtotal*ratioobeyThe traffic flow of the size participates in the traffic regulation, so that the part of the traffic flow is defined as the total traffic regulation flow to be performed, namely the total traffic capacity.
Wherein, for each ant regulation and control unit in the step C3, the upstream traffic demand Q accumulated by the adjacent regulation and control units respectively is readneighbour", the specific method is as follows: assuming that the central control unit is a crossroad node, QneighbourDivided into 4, i.e. north neighbor nodes NodeNSouth direction adjacent NodeSEast adjacent NodeEWestern-direction neighbor NodeWThe required reading is the accumulated northbound adjacent node upstream traffic demand of the adjacent regulation unit
Figure BDA0003117728260000124
Southbound neighbor node upstream demand
Figure BDA0003117728260000125
Upstream traffic demand of east neighbor node
Figure BDA0003117728260000131
Upstream traffic demand of west-direction neighbor node
Figure BDA0003117728260000132
Wherein, the step C4 describes that the upstream traffic demand Q redistributed and responded by each downstream road section is calculated according to the rules of deliverynewAnd performing blending distribution ", the specific method comprises the following steps: assuming that the central control unit is a crossroad node, respectively calculating the total upstream traffic demand Q accumulated by the central control unittotalAnd the traffic demand of the north neighbor node
Figure BDA0003117728260000133
Southbound neighbor node upstream demand
Figure BDA0003117728260000134
Upstream traffic demand of east neighbor node
Figure BDA0003117728260000135
Upstream traffic demand of west-direction neighbor node
Figure BDA0003117728260000136
Difference value Δ Q ofN、ΔQS、ΔQE、ΔQEThen the sum of the differences DeltaQ is determined0＝ΔQN+ΔQS+ΔQE+ΔQWThen, the difference value DeltaQ is calculatedN、ΔQS、ΔQE、ΔQEAt the sum of the differences DeltaQ0Respectively account for ratio ofN、ratioS、ratioE、ratioW。
Taking the north direction as an example, according to QN new＝(1-ratioobey)*QN old+ratioobey*ratioN*QN oldAnd calculating the regulated traffic demand on the upstream of the northbound output road section.
Step 103: and operating the traffic network congestion propagation simulation framework, and then regulating and controlling the traffic network traffic congestion state of the regulation and control area according to the ant colony nursing strategy-based traffic congestion intelligent regulation and control model.
In this embodiment, step 103 includes the following steps:
step D1: and setting disturbance parameters of the simulation model.
The method comprises the following specific steps: and selecting K network nodes in the complex network model of the road network in a random mode, and uniformly increasing the upstream traffic demand with the set Load size for each downstream road section of the K network nodes, namely, finishing the setting of the disturbance parameters. The disturbance can be regarded as a flow suddenly appearing at a certain position of a traffic network, and during simulation operation, the traffic network congestion propagation simulation framework can automatically inject disturbance into a complex network model of the traffic network according to disturbance parameters.
Step D2: and updating the traffic network congestion propagation simulation framework according to the simulation model disturbance parameters.
Step D3: and starting to operate the updated traffic network congestion propagation simulation framework.
Step D2: the nth iteration is started.
Step D3: traversing each road section, and calculating the downstream traffic flow q of the road section corresponding to the current road sectionoutputAnd is distributed in a random mode and converted into the road section upstream traffic demand Q corresponding to the downstream road sectioninput. The downstream road section is a downstream road section associated with the current road section.
Step D4: according to the traffic jam intelligent regulation and control model based on the ant colony coordination strategy, the road section upstream traffic demand Q of the regulation and control areainputThe reallocation is performed.
Step D5: after the redistribution is finished, each road section is traversed, and the upstream traffic demand Q of the road section corresponding to the current road section is obtainedinputCurrent remaining capacity C corresponding to the current road sectionleftAnd updating the traffic flow congestion state of the road network.
Step D6: the nth iteration is ended.
Step D7: and finishing the operation.
The current road section is one of all road sections, and each road section is processed according to the current road section.
Through the steps, the invention provides the traffic network congestion control method based on the ant colony feeding strategy, so that the extremely complex overall control problem of the macroscopic road network is decomposed into thousands of simple local control problems of small node control units, the urban traffic network congestion problem is effectively relieved from the design level of the top-level strategy, and the method is convenient, efficient, easy to calculate and good in practical application value and prospect.
Example two
To achieve the above object, the present embodiment further provides a traffic network congestion control system based on an ant colony coordination strategy, please refer to fig. 2, which includes:
the road network complex network model building module 200 is used for building a road network complex network model corresponding to a research city according to the topological parameters of the traffic road network structure of the research city; the road network complex network model comprises network nodes and network connecting edges.
A traffic network congestion propagation simulation frame building module 201, configured to determine road segment related information corresponding to the research city, and build a traffic network congestion propagation simulation frame corresponding to the research city based on the road segment related information, the LTM traffic flow propagation model, and the road network complex network model; the road section related information comprises road section related parameters and road network characteristic information; the road section related parameters comprise a bidirectional traffic flow transmission road section, real-time load traffic flow in the road section, road section upstream traffic demand and road section downstream traffic flow; the road network characteristic information comprises research time period information; the traffic road network congestion propagation simulation framework is a simulation framework which can update real-time load traffic flow in a road section according to the corresponding relation between the road section upstream traffic demand and the road section residual capacity.
The traffic jam intelligent control model building module 202 is used for determining a control area of the research city, and building an ant colony hybridization strategy-based traffic jam intelligent control model corresponding to the control area according to the control area and the traffic network jam propagation simulation framework; the traffic jam intelligent control model comprises an ant control unit and neighboring control units around the ant control unit; the ant regulation and control unit is determined based on the network nodes of the traffic network congestion propagation simulation framework.
And the regulation and control module 203 is used for operating the traffic network congestion propagation simulation framework and then regulating and controlling the traffic network traffic flow congestion state of the regulation and control area according to the ant colony-feeding strategy-based traffic congestion intelligent regulation and control model.
The road network complex network model building module 200 specifically includes:
and the node information determining unit is used for analyzing and extracting the nodes of the traffic network of the research city and the topological structure parameters corresponding to the nodes.
And the cross-linking relation determining unit is used for analyzing and extracting the cross-linking relation among all nodes of the traffic network of the research city and the topological structure parameters corresponding to the cross-linking relation.
And the point set constructing unit is used for determining the node as a network node and constructing a point set according to the network node.
And the edge set building unit is used for determining the cross-linking relation as a network connecting edge and building an edge set according to the network connecting edge.
And the road network complex network model building unit is used for building a road network complex network model corresponding to the research city based on the point set, the edge set, the topological structure parameters corresponding to the nodes and the topological structure parameters corresponding to the cross-linking relationship.
The traffic road network congestion propagation simulation framework building module 201 specifically comprises:
the road section related information determining unit is used for determining road section related information corresponding to the research city; the road section related information includes road section related parameters and road network characteristic information.
And the adding unit is used for adding the road section related parameters on the road network complex network model and endowing the initial values of the road section related parameters.
The triangle basic graph parameter determining unit is used for determining triangle basic graph parameters according to the road network characteristic information; the triangular basic graph parameters comprise the maximum output flow, the critical density and the congestion density of the downstream of the road section.
And the traffic network congestion propagation simulation framework building unit is used for updating the traffic flow state of the road network in real time according to the rules defined by the LTM traffic flow propagation model based on the road network complex network model added with the road section related parameters, the triangular FD parameters and the time domain driving engine, and building a traffic network congestion propagation simulation framework corresponding to the research city when the updating is finished.
The regulation and control module 203 specifically comprises:
the operation unit is used for operating the updated traffic network congestion propagation simulation framework; the updated traffic network congestion propagation simulation framework is obtained by updating the traffic network congestion propagation simulation framework according to the disturbance parameters of the simulation model; and the disturbance parameters of the simulation model are uniformly increased road section upstream traffic demand of each downstream road section corresponding to the K network nodes.
The first traversal unit is used for traversing each road section and determining the road section upstream traffic demand corresponding to each road section; the method specifically comprises the following steps: calculating the downstream traffic flow of the road section corresponding to the current road section, distributing the downstream traffic flow in a random mode, and converting the downstream traffic flow into the upstream traffic demand of the road section corresponding to the downstream road section; the downstream road section is a downstream road section associated with the current road section.
And the redistribution unit is used for redistributing the upstream traffic demand of the road section of the regulation area according to the intelligent regulation and control model of traffic congestion based on the ant colony nursing strategy.
The second traversal unit is used for traversing each road section after the redistribution is finished and updating the traffic flow congestion state of the road network; the method specifically comprises the following steps: and updating the traffic flow congestion state of the road network according to the road section upstream traffic demand corresponding to the current road section and the current residual capacity corresponding to the current road section.
Wherein, the redistribution unit specifically includes:
the first reading subunit is used for reading the total upstream traffic demand accumulated by the ant control unit for each ant control unit; the calculating subunit is used for calculating the traffic total amount of each ant regulation and control unit according to the management and control compliance coefficient and the total upstream traffic demand accumulated by the ant regulation and control unit; the second reading subunit is used for reading the upstream traffic demand accumulated by the adjacent regulation and control units corresponding to each ant regulation and control unit for each ant regulation and control unit; and the redistribution subunit is used for calculating the upstream traffic demand obtained after redistribution of each downstream road section according to the blending rule based on the blending total amount of each ant regulation and control unit and the upstream traffic demand of each adjacent regulation and control unit.
EXAMPLE III
In this embodiment, a lattice virtual city complex road network composed of square grids with a certain topology is taken as an example to illustrate the technical solution method provided by the present invention. Specifically, the urban complex road network comprises 10000 nodes, each node is connected with 4 road segments except edge nodes, and due to the complexity and the emergence of the road network structure and the uncertainty of traffic demands in the road network, congestion which occurs already needs to be evacuated, and new congestion nodes which may occur need to be prevented as much as possible, so that congestion is reduced, the operation efficiency of the urban road network is improved, and the stable and healthy operation of an urban traffic system is maintained.
The traffic network congestion control method based on the ant colony coordination strategy provided by the embodiment comprises the following steps:
step A: and constructing a road network complex network model according to the topological parameters of the urban traffic road network.
The step establishes the bottom layer topology of the whole simulation framework, the network topology greatly influences the bottom layer propagation characteristic of the urban road network traffic flow, and the complex network model of the road network is accurately established, so that the method is the premise of effectively simulating the road network congestion propagation rule and the regulation and control effect. The method comprises the steps of analyzing the spatial distribution of each node and each road section of the urban road network, constructing a point set V and an edge set E of the road network complex network model, and further constructing the road network complex network model under a virtual time-space domain.
And B: and building a congestion propagation simulation framework of the urban traffic network based on the LTM traffic flow propagation model.
Firstly, establishing road section related parameters on a road network complex network model. Then, a simulation framework with the following functions is built according to the LTM road section transmission model which is a classical model of traffic flow propagation: calculating downstream output flow q of each path section according to the triangular FD graphoutputAnd is distributed in a random mode and converted into the upstream traffic demand Q of the downstream road sectioninputThen, each road section is traversed according to Q accumulated at the upstream of the road sectioninputAnd its current remaining capacity CleftAnd updating the road section state parameters.
And C: and constructing an ant colony feeding strategy-based traffic jam intelligent regulation and control model.
The step traverses the ant regulating units in the area to be regulated, and reads the accumulated total upstream traffic demand of each ant regulating unit
Figure BDA0003117728260000171
And according to the management and control compliance coefficient ratioobeyCalculating the total amount of transportation, for each ant regulating and controlling unit, reading the upstream traffic demand Q accumulated by the adjacent regulating and controlling units respectivelyneighbourCalculating the upstream traffic demand Q redistributed and obtained by each downstream road section according to the delivery rulenewAnd performs a nursing dispense.
Step D: setting disturbance parameters of a simulation model;
in the step, K nodes in a road network complex network model are selected in a random mode, and upstream traffic demand with Load of 150 to be increased is set for each downstream road section of the K nodes in a unified mode, namely the set of disturbance parameters is finished. The disturbance can be regarded as a sudden flow at a certain position of the road network, and during simulation operation, the simulation framework automatically injects the disturbance into the road network model according to disturbance parameters.
Step E: and regulating and controlling the road network traffic flow congestion by using the ant colony coordination strategy according to the disturbance parameters and the urban road network congestion propagation operation simulation embedded with the ant colony coordination strategy.
The step of running the simulation framework to iterate, traversing each road section and calculating the downstream output flow q of each road sectionoutputAnd is distributed in a random mode and converted into the upstream traffic demand Q of the downstream road sectioninput(ii) a Upstream traffic demand Q of area to be regulated and controlled based on ant colony copulation strategyinputCarrying out reasonable redistribution; traversing each segment according to the accumulated Q upstream thereofinputAnd its current remaining capacity CleftAnd updating the road section state parameters.
The step A specifically comprises the following steps: according to the topological parameters of the specific urban road network structure to be analyzed, including corresponding nodes, connecting edges and cross-linking relations thereof, position coordinates, capacity, weight and the like, establishing accurate mapping from a road network in a real physical space to a road network complex network model in a virtual time-space domain, and taking the mapping as a reasonable basis for LTM traffic flow propagation construction; the step A comprises the following steps:
step A1: and analyzing and extracting the distribution conditions of nodes such as intersections, buffer areas and the like of the urban road network, and constructing a point set V of the road network complex network model. The specific method comprises the following steps: and traversing and counting nodes of each intersection, buffer area and the like of the urban traffic network to be analyzed and topological parameters of the nodes, and analyzing basic statistical characteristics of the network nodes. On the basis, all the nodes and the parameters thereof are stored into a list construction point set V.
Step A2: analyzing and extracting the cross-linking relation among the nodes, and constructing an edge set E of the road network complex network model; the specific method comprises the following steps: and C, performing communication path traversal statistics on each road of the urban traffic network to be analyzed, simultaneously performing network connection edge basic statistical characteristic analysis, and storing each connection edge into a list building edge set E on the basis of the analysis and the step A1.
Step A3: constructing a road network complex network model G; the specific method comprises the following steps: and (3) accurately constructing a road network complex network model G through the point set V, the edge set E, the network characteristic parameters and the topology structure parameters which are obtained through analysis in the step A1 and the step A2.
The step B specifically comprises the following steps: on the urban traffic network complex network model constructed in the step A, parameters such as turnover intersection nodes, bidirectional traffic flow transmission road sections, real-time load traffic flows in road sections and the like are established, the downstream traffic flow of the road sections can be output according to the LTM road section transmission model rule, the upstream traffic demand of the road sections is generated, and a simulation framework for updating the real-time load traffic flows of the road sections according to the relation between the upstream traffic demand and the residual capacity of the road sections is established; the step B comprises the following steps:
step B1: establishing road section related parameters on a road network complex network model; the specific method comprises the following steps: adding parameters such as bidirectional traffic flow transmission road sections, real-time load traffic flow in road sections, road section upstream traffic demand, road section downstream traffic flow and the like at the connecting edges of the road network complex network model, and giving reasonable initial values according to actual common parameter values of the urban traffic network.
Step B2: a triangle FD (base map) is given according to the road network characteristics to be analyzed; the specific method comprises the following steps: the general triangular FD parameter is formed by the maximum output flow q of the downstream road sectionmaxCritical density kcritDensity of congestion kjamDetermined, so that a given triangle FD can be equivalent to providing the maximum output flow q downstream of the road sectionmaxCritical density kcritDensity of congestion kjamThree determined parameters, namely, the triangular FD parameter needs to be matched with the road network characteristic information to be analyzed, otherwise, it is difficult to analyze the correct result.
Taking a lattice virtual city complex road network formed by a square grid with a certain topological structure as an example, taking the maximum output flow q of the downstreammax1600 pieces/h, critical density kcrit40/km, congestion density k jam100/km.
Step B3: establishing a time domain driving engine to update the road network parameters in real time according to the LTM road section transmission model rule; the specific method comprises the following steps: on the basis of the road section related parameters established in the step B1 and the triangle FD parameters given in the step B2, updating the road network traffic flow state by using the rules defined by the LTM model in a time step-by-step iteration mode until the road network state iteration of the whole time domain is completed, and the method comprises the following steps of:
step B31: the nth iteration is started.
Step B32: traversing each road section and calculating the downstream output flow q thereofoutputAnd is distributed in a random mode and converted into the upstream traffic demand Q of the downstream road sectioninput。
Step B33: traversing each segment according to the accumulated Q upstream thereofinputAnd its current remaining capacity CleftAnd updating the road section state parameters.
Step B34: and finishing the nth iteration and preparing to carry out the (N + 1) th iteration until the iteration is completely finished.
Wherein, the step B32 comprises the following specific steps: taking a certain road section of a lattice virtual city complex road network formed by a certain topological structure as a square grid as an example, when traversing a certain road section, firstly, assuming C according to the existing traffic flow of the current road sectionald90 and link length L3 km pass formula
Figure BDA0003117728260000201
Calculating the current road section traffic flow density k to be 30, and then calculating q according to k and the triangle FDoutput1200 pieces/h, and then a total of n and 1downA random number (n)downNumber of adjacent sections at the downstream of the section) as an allocation proportion, and q is used as the allocation proportionoutputDistributing to downstream road sections and converting to road section upstream traffic demand Q corresponding to corresponding downstream road sectionsinput。
The step B33 specifically includes: taking a certain road section of a lattice virtual city complex road network composed of square grids with a certain topological structure as an example, if Q isinput< 0, hold QinputAnd CleftUnchanged (in fact Q)input< 0 typically only occurs after ant colony feeding strategy regulation). If QinputAnd C left30 and 50 respectively, then 0 < Qinput≤CleftIs mixing Q withinputSet to 0, updated CleftSatisfies Cleft new＝Cleft-Q input20. If QinputAnd C left60 and 30, respectively, then Qinput＞CleftMixing C withleftSet to 0, updated QinputSatisfy Qinput new＝Qinput-Cleft＝30。
The step C is specifically as follows: regarding each node of the urban traffic road network congestion propagation simulation framework as an ant regulating and controlling unit, sensing the congestion condition of the surrounding neighbor regulating and controlling units by each ant regulating and controlling unit under each time step, and comparing the congestion condition with the congestion condition of the ant regulating and controlling unit to obtain the upstream traffic demand Q of the downstream road section generated in the step B32inputReasonable redistribution is carried out, further traffic is restrained from further rushing to a point with a serious congestion degree, the point is guided to an area with a light congestion degree, and traffic congestion of an urban road network is relieved, and the method comprises the following steps:
step C1: and traversing the ant regulating and controlling unit of the area to be regulated and controlled.
Step C2: for each ant regulation and control unit, reading the total upstream traffic demand accumulated by the ant regulation and control unit
Figure BDA0003117728260000202
And according to the management and control compliance coefficient ratioobeyCalculating the feeding total amount Trophacity.
Step C3: for each ant regulation and control unit, reading the respective accumulated upstream traffic demand Q of the adjacent regulation and control unitsneighbour。
Step C4: according to the delivery rule, calculating the upstream traffic demand Q redistributed and obtained by each downstream road sectionnewAnd handed over for distribution.
Assuming that the central control unit is a crossroad node, N for controlling node connection road4 downstream road sections for which the upstream traffic demand Q isN old、QS old、QE old、QW oldIf 10, 30, 15 and 40 are respectively assumed to be sequentially accumulated, the total upstream traffic demand Q correspondingly accumulated by the regulation and control unit is obtainedtotal95 pieces.
Let ratio be assumedobey＝0.6、Q total100, then we get:
Trophacity＝Qtotal*ratioobey100 x 0.6 x 60.
Assuming that the central control unit is a crossroad node, QneighbourDivided into 4, i.e. north neighbor nodes NodeNSouth direction adjacent NodeSEast adjacent NodeEWestern-direction neighbor NodeWThe required reading is the accumulated northbound adjacent node upstream traffic demand of the adjacent regulation unit
Figure BDA0003117728260000211
Southbound neighbor node upstream demand
Figure BDA0003117728260000212
Upstream traffic demand of east neighbor node
Figure BDA0003117728260000213
Upstream traffic demand of west-direction neighbor node
Figure BDA0003117728260000214
Assuming that the central control unit is a crossroad node, respectively calculating the total upstream traffic demand Q accumulated by the central control unit total200 pieces of traffic demand with the upstream of the northbound neighbor node
Figure BDA0003117728260000215
Southbound neighbor node upstream demand
Figure BDA0003117728260000216
Upstream traffic demand of east neighbor node
Figure BDA0003117728260000217
Upstream traffic demand of west-direction neighbor node
Figure BDA0003117728260000218
Difference value Δ Q ofN150, Δ Q S100, Δ Q E100 pieces of ═ Δ Q E50 pieces of the total weight of the two pieces of
ΔQ0＝ΔQN+ΔQS+ΔQE+ΔQWThe difference Δ Q is calculated again when the number of the vehicles is 200N、ΔQS、ΔQE、ΔQEAt the sum of the differences DeltaQ0Respectively account for ratio ofN＝0.75、ratioS＝0.5、ratioE＝-0.5、ratioW＝0.25ΔQS＝100
Taking the north direction as an example, according to QN new＝(1-ratioobey)*QN old+ratioobey*ratioN*QN oldThe regulated traffic demand upstream of the northbound output link is calculated as 42.5.
The step D is specifically as follows: selecting 720 nodes in a complex network model of the road network in a random mode, and uniformly setting the upstream traffic demand to be increased with Load of 150 for each downstream road section of the K nodes, namely, finishing the setting of the disturbance parameters. The disturbance can be regarded as the flow which suddenly appears at a certain position of the road network, and during the simulation operation, the simulation framework can automatically inject the disturbance into the road network model according to the disturbance parameters;
the step E specifically comprises the following steps: sequentially operating simulation submodules according to the urban traffic road network congestion propagation simulation framework built in the step A, B, C, D, the traffic congestion control model based on the ant colony hybridization strategy and the disturbance parameters, wherein the simulation submodules comprise the following steps:
step E1: the simulation framework operation begins.
Step E2: the nth iteration is started.
Please refer to fig. 4, step E3: traversing each road section and calculating the downstream output flow q thereofoutputAnd is distributed in a random mode and converted into the upstream traffic demand Q of the downstream road sectioninput。
Please refer to fig. 5, step E4: upstream traffic demand of area to be regulated and controlled based on ant colony copulation strategyQuantity QinputThe reallocation is performed.
Please refer to fig. 6, step E5: traversing each segment according to the accumulated Q upstream thereofinputAnd its current remaining capacity CleftAnd updating the road section state parameters.
Step E6: the nth iteration is ended.
Step E7: and ending the operation of the simulation framework.
The invention has the following innovation points:
the calculation is concise: the urban traffic road network congestion propagation simulation framework based on the LTM traffic flow propagation model has a definite quantitative calculation formula in each step, the model is scientific, simple and easy to calculate, numerical values are easy to obtain and monitor, road network parameter statistical analysis is easy to realize by using various traversal search strategies, the requirement threshold for a hardware and software system is low, and engineering practice is easy to carry out;
the strategy is advanced: the ant colony cooperation congestion control strategy provided by the invention is created for the first time, and based on the inspiration of the hunting-cooperation behavior of the natural ant colony tribe, the extremely complex overall control problem of the macroscopic road network is decomposed into thousands of simple local control problems of tiny node control units, and the congestion problem of the urban traffic road network is tried to be effectively relieved from the design level of a top-level strategy;
the universality is strong: the traffic network congestion control method based on the ant colony coordination strategy is suitable for congestion control of most traffic networks, when congestion control of road networks with different topological structures is carried out, only an initial road network complex network model is required to be changed, other changes are not required, and the traffic network congestion control method is high in mobility, good in transportability and high in universality;
the accuracy is good: the traffic network congestion control method based on the ant colony feeding strategy is adopted to simulate the urban complex road network congestion microscopic control, so that the emerging performance, nonlinearity and self-organization adaptivity of the complex traffic network can be better simulated, and the evolution process of the complex traffic network under control can be truly and accurately reflected;
in conclusion, the traffic network congestion control method based on the ant colony coordination strategy provides a good solution for traffic network micro congestion control in engineering application.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (2)

1. A traffic network congestion control method based on an ant colony coordination strategy is characterized by comprising the following steps:
constructing a road network complex network model corresponding to a research city according to the topological parameters of the traffic road network structure of the research city; the road network complex network model comprises network nodes and network connecting edges;
determining road section relevant information corresponding to the research city, and building a traffic network congestion propagation simulation frame corresponding to the research city based on the road section relevant information, a road section transmission model (LTM) traffic flow propagation model and the road network complex network model; the road section related information comprises road section related parameters and road network characteristic information; the road section related parameters comprise a bidirectional traffic flow transmission road section, real-time load traffic flow in the road section, road section upstream traffic demand and road section downstream traffic flow; the road network characteristic information comprises research time period information; the traffic road network congestion propagation simulation framework is a simulation framework which can update real-time load traffic flow in a road section according to the corresponding relation between the upstream traffic demand of the road section and the residual capacity of the road section;
determining a regulation and control area of the research city, and constructing an ant colony hybridization strategy-based traffic jam intelligent regulation and control model corresponding to the regulation and control area according to the regulation and control area and the traffic network jam propagation simulation framework; the traffic jam intelligent control model comprises an ant control unit and neighboring control units around the ant control unit; the ant regulation and control unit is determined based on the network nodes of the traffic network congestion propagation simulation framework;
operating the traffic network congestion propagation simulation framework, and then regulating and controlling the traffic network traffic congestion state of the regulation and control area according to the ant colony coordination strategy-based traffic congestion intelligent regulation and control model;
the method for constructing the road network complex network model corresponding to the research city according to the traffic road network structure topological parameters of the research city specifically comprises the following steps:
analyzing and extracting nodes of a traffic network of the research city and topological structure parameters corresponding to the nodes; analyzing and extracting a cross-linking relation among all nodes of a traffic network of the research city and topological structure parameters corresponding to the cross-linking relation; determining the nodes as network nodes, and constructing point sets according to the network nodes; determining the cross-linking relation as a network connection edge, and constructing an edge set according to the network connection edge; constructing a road network complex network model corresponding to the research city based on the point set, the edge set, topological structure parameters corresponding to the nodes and topological structure parameters corresponding to the cross-linking relationship;
the determining of the road section relevant information corresponding to the research city, and building a traffic network congestion propagation simulation frame corresponding to the research city based on the road section relevant information, a road section transmission model (LTM) traffic flow propagation model and the road network complex network model specifically include:
determining road section related information corresponding to the research city; the road section related information comprises road section related parameters and road network characteristic information; adding the road section related parameters on the road network complex network model, and giving initial values of the road section related parameters; determining triangle basic graph parameters, namely triangle FD parameters, according to the road network characteristic information; the triangular basic graph parameters comprise the maximum output flow, the critical density and the congestion density of the downstream of the road section; on the basis of the road network complex network model added with the road section relevant parameters, the triangular FD parameters and the time domain driving engine, updating the road network traffic flow state in real time according to rules defined by a road section transmission model LTM traffic flow propagation model, and establishing a traffic network congestion propagation simulation frame corresponding to the research city when the updating is finished;
the operating the traffic network congestion propagation simulation framework and then regulating and controlling the traffic network traffic congestion state of the regulation and control area according to the ant colony nursing strategy-based traffic congestion intelligent regulation and control model specifically comprise:
running the updated traffic network congestion propagation simulation framework; the updated traffic network congestion propagation simulation framework is obtained by updating the traffic network congestion propagation simulation framework according to the disturbance parameters of the simulation model; the simulation model disturbance parameters are uniformly increased road section upstream traffic demand on each downstream road section corresponding to the K network nodes; traversing each road section, and determining the road section upstream traffic demand corresponding to each road section; the method specifically comprises the following steps: calculating the downstream traffic flow of the road section corresponding to the current road section, distributing the downstream traffic flow in a random mode, and converting the downstream traffic flow into the upstream traffic demand of the road section corresponding to the downstream road section; the downstream road section is a downstream road section associated with the current road section; redistributing the road section upstream traffic demand of the regulation area according to the intelligent traffic jam regulation and control model based on the ant colony nursing strategy; after the redistribution is finished, traversing each road section, and updating the traffic flow congestion state of the road network; the method specifically comprises the following steps: updating a road network traffic flow congestion state according to the road section upstream traffic demand corresponding to the current road section and the current residual capacity corresponding to the current road section;
the redistribution of the road section upstream traffic demand of the regulation and control area according to the ant colony coordination strategy-based traffic jam intelligent regulation and control model specifically comprises the following steps:
for each ant regulation and control unit, reading the total upstream traffic demand accumulated by the ant regulation and control unit; calculating the traffic total amount of each ant regulation and control unit according to the control compliance coefficient and the total upstream traffic demand accumulated by the ant regulation and control unit; for each ant regulation and control unit, reading the respective accumulated upstream traffic demand of the adjacent regulation and control units corresponding to the ant regulation and control unit; and calculating the upstream traffic demand obtained after redistribution of each downstream road section according to the blending rule based on the blending total amount of each ant regulation unit and the upstream traffic demand of each adjacent regulation unit.
2. A traffic network congestion control system based on an ant colony coordination strategy is characterized by comprising:
the road network complex network model building module is used for building a road network complex network model corresponding to a research city according to the topological parameters of the traffic road network structure of the research city; the road network complex network model comprises network nodes and network connecting edges;
the traffic network congestion propagation simulation framework building module is used for determining road section related information corresponding to the research city and building a traffic network congestion propagation simulation framework corresponding to the research city based on the road section related information, a road section transmission model (LTM) traffic flow propagation model and the road network complex network model; the road section related information comprises road section related parameters and road network characteristic information; the road section related parameters comprise a bidirectional traffic flow transmission road section, real-time load traffic flow in the road section, road section upstream traffic demand and road section downstream traffic flow; the road network characteristic information comprises research time period information; the traffic road network congestion propagation simulation framework is a simulation framework which can update real-time load traffic flow in a road section according to the corresponding relation between the upstream traffic demand of the road section and the residual capacity of the road section;
the traffic jam intelligent regulation and control model building module is used for determining a regulation and control area of the research city and building an ant colony copulation strategy-based traffic jam intelligent regulation and control model corresponding to the regulation and control area according to the regulation and control area and the traffic network jam propagation simulation frame; the traffic jam intelligent control model comprises an ant control unit and neighboring control units around the ant control unit; the ant regulation and control unit is determined based on the network nodes of the traffic network congestion propagation simulation framework;
the regulation and control module is used for operating the traffic network congestion propagation simulation framework and then regulating and controlling the traffic network traffic flow congestion state of the regulation and control area according to the ant colony-feeding strategy-based traffic congestion intelligent regulation and control model;
the road network complex network model building module specifically comprises:
the node information determining unit is used for analyzing and extracting nodes of a traffic network of the research city and topological structure parameters corresponding to the nodes;
the cross-linking relation determining unit is used for analyzing and extracting cross-linking relations among all nodes of a traffic network of the research city and topological structure parameters corresponding to the cross-linking relations;
the point set constructing unit is used for determining the nodes as network nodes and constructing point sets according to the network nodes;
the edge set building unit is used for determining the cross-linking relation as a network connecting edge and building an edge set according to the network connecting edge;
a road network complex network model constructing unit, configured to construct a road network complex network model corresponding to the research city based on the point set, the edge set, the topological structure parameters corresponding to the nodes, and the topological structure parameters corresponding to the cross-linking relationship;
the traffic road network congestion propagation simulation framework building module specifically comprises:
the road section related information determining unit is used for determining road section related information corresponding to the research city; the road section related information comprises road section related parameters and road network characteristic information;
the adding unit is used for adding the road section related parameters on the road network complex network model and endowing the road section related parameters with initial values;
a triangle basic graph parameter determining unit, configured to determine a triangle basic graph parameter, that is, a triangle FD parameter, according to the road network characteristic information; the triangular basic graph parameters comprise the maximum output flow, the critical density and the congestion density of the downstream of the road section;
the traffic network congestion propagation simulation framework building unit is used for updating the traffic flow state of the traffic network in real time according to rules defined by a road section transmission model LTM traffic flow propagation model based on a road network complex network model added with road section related parameters, the triangular FD parameters and a time domain driving engine, and building a traffic network congestion propagation simulation framework corresponding to the research city when the updating is finished;
the regulation and control module specifically comprises:
the operation unit is used for operating the updated traffic network congestion propagation simulation framework; the updated traffic network congestion propagation simulation framework is obtained by updating the traffic network congestion propagation simulation framework according to the disturbance parameters of the simulation model; the simulation model disturbance parameters are uniformly increased road section upstream traffic demand on each downstream road section corresponding to the K network nodes;
the first traversal unit is used for traversing each road section and determining the road section upstream traffic demand corresponding to each road section; the method specifically comprises the following steps: calculating the downstream traffic flow of the road section corresponding to the current road section, distributing the downstream traffic flow in a random mode, and converting the downstream traffic flow into the upstream traffic demand of the road section corresponding to the downstream road section; the downstream road section is a downstream road section associated with the current road section;
the redistribution unit is used for redistributing the road section upstream traffic demand of the regulation area according to the ant colony coordination strategy-based traffic jam intelligent regulation and control model;
the second traversal unit is used for traversing each road section after the redistribution is finished and updating the traffic flow congestion state of the road network; the method specifically comprises the following steps: updating a road network traffic flow congestion state according to the road section upstream traffic demand corresponding to the current road section and the current residual capacity corresponding to the current road section;
the redistribution unit specifically includes:
the first reading subunit is used for reading the total upstream traffic demand accumulated by the ant control unit for each ant control unit;
the calculating subunit is used for calculating the traffic total amount of each ant regulation and control unit according to the management and control compliance coefficient and the total upstream traffic demand accumulated by the ant regulation and control unit;
the second reading subunit is used for reading the upstream traffic demand accumulated by the adjacent regulation and control units corresponding to each ant regulation and control unit for each ant regulation and control unit;
and the redistribution subunit is used for calculating the upstream traffic demand obtained after redistribution of each downstream road section according to the blending rule based on the blending total amount of each ant regulation and control unit and the upstream traffic demand of each adjacent regulation and control unit.
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