A Boundary Signal Control Method Based on Traffic Resilience Theory

Abstract

This invention discloses a boundary signal control method based on traffic elasticity theory, belonging to the fields of network elasticity science and urban traffic management. The method includes: acquiring the urban traffic network topology, traffic flow information, and signal control data; establishing a capacity multigraph of the traffic network based on its topology, defining a finite set of roads, and dividing it into multiple traffic sub-regions; constructing boundary signal phase models for the boundary regions of each traffic sub-region, and constructing a global signal phase matrix based on the signal phases at each node, thus establishing a control signal set; establishing elasticity state curves for each traffic sub-region and calculating the elasticity values ​​of the traffic sub-regions; constructing an incremental model of the road network traffic flow system based on traffic flow information and elasticity values, establishing an output vector prediction expression, establishing an error prediction model, and designing a model predictive controller. This invention can improve the traffic efficiency of the road network and solve the problem of existing signal control schemes being insufficiently timely and effective.

Description

Technical Field

[0001] This invention relates to traffic resilience theory and traffic control strategies and methods, belonging to the fields of network resilience science and urban traffic management, specifically a boundary signal control method based on traffic resilience theory. Background Technology

[0002] With the development of my country's social economy and the increasing number of motor vehicles, urban traffic congestion is becoming more frequent, often leading to traffic gridlock during peak hours. However, existing traffic control strategies, technologies, and methods are not timely or effective in alleviating regional congestion, and outdated traffic signal timing strategies struggle to meet the needs of traffic congestion mitigation. Therefore, under the backdrop of high-intensity travel, effective traffic management and control strategies and methods for severely congested urban areas are currently a key research focus in the field of intelligent transportation.

[0003] Meanwhile, traffic area resilience is being increasingly mentioned. "Resilience" is defined as a system's ability to withstand disturbances while maintaining its structure and function. Specifically, in the context of traffic networks, resilience minimizes the degradation of service levels caused by disturbances and maintains smooth traffic flow. Urban traffic networks are subject to various disturbances during actual operation, such as traffic accidents, large events, and natural disasters. These disturbances can lead to a decline in traffic performance and even severe road blockages. Therefore, changes in the resilience level of traffic sub-regions can measure the quality of road topology and control strategies. Thus, the concept and methods of resilience can provide a unique perspective for network research under extreme environments, enabling the study of the safety and stability of urban traffic systems.

[0004] This invention proposes an effective solution in response to the aforementioned development trends and background. This invention primarily analyzes the elasticity changes of a road network area after being subjected to congestion disturbances. After fully acquiring traffic flow information from congested areas, a signal timing control strategy is implemented for the boundary areas. Following the completion of the control strategy, the elasticity level of regional traffic is further analyzed to achieve congestion dissipation. Summary of the Invention

[0005] The purpose of this invention is to provide a boundary signal control method based on traffic elasticity theory to solve the problem that existing traffic control systems are not timely and effective enough.

[0006] To achieve the above objectives, the technical solution provided by the present invention is as follows:

[0007] A boundary signal control method based on traffic elasticity theory includes the following steps:

[0008] 1) Obtain urban traffic network topology, traffic flow information, and signal control data;

[0009] 2) Construct a capacity multigraph of the traffic network and define a finite set of roads, dividing it into multiple traffic sub-regions;

[0010] 3) Construct a boundary signal phase model for the boundary area of ​​the traffic sub-region, and construct a global signal phase matrix based on the signal phase at each node to establish a set of control signals;

[0011] 4) Introduce traffic elasticity theory, establish elasticity state curves for each traffic sub-region based on the traffic flow information, and calculate the elasticity value of each traffic sub-region;

[0012] 5) Based on the traffic flow information and elasticity value, construct an incremental model of the road network traffic flow system, establish an output vector prediction expression, establish an error prediction model, and design a model prediction controller.

[0013] In step (1), the traffic flow information includes the average vehicle speed and traffic flow; the signal control data includes the signal control method, signal cycle duration and green light duration for each phase.

[0014] In step (2), based on the urban traffic network topology, a capacity multigraph of the traffic network is established, and a finite set of roads is defined and divided into multiple traffic sub-regions, including the following steps:

[0015] 2.1) Describe the traffic network as a capacity multigraph based on its topology. In the form of, Let be a finite set of intersection nodes. Represents the number of intersections; For a finite set of directional roads, Represents the number of roads; This represents the vehicle capacity of each road. The capacity of each road is defined as a set. , It is a diagonal matrix, and all values ​​inside are non-negative.

[0016] 2.2) Define a finite set of roads based on the capacity multigraph. Define a traffic sub-region Traffic sub-zone Internal traffic, For traffic flowing into the traffic sub-zone from other external sub-zones Traffic, For traffic sub-zone The outflow volume, where each volume is less than the sum of the capacities of all roads within the sub-region, i.e. , .

[0017] 2.3) To simulate the propagation of traffic flow in the road network, a routing matrix is ​​introduced. Each element in the list represents a node To the node The proportion of traffic to total traffic, determined by network topology constraints, indicates that 0 represents the absence of a direct road between two nodes. According to the law of conservation of traffic flow, we know that... ,when This indicates the flow rate of the traffic sub-region flowing out of the sub-region. ; and when This indicates that there is no outflow of traffic from the traffic sub-region.

[0018] The construction of the boundary signal phase model in step (3), and the establishment of the global signal phase matrix based on the signal phase at each node, and the establishment of the control signal set, includes the following steps:

[0019] 3.1) Select a traffic sub-zone and construct a boundary signal phase model for its boundary area. The signal light phase state at the intersections at the boundary of the sub-zone determines the flow of traffic entering and leaving the traffic sub-zone. Define all intersections at the boundary as a set. Then define Let k be the set of feasible phases at an intersection. The feasible phases at intersection k can be represented by a phase matrix. , Then, the phase matrices of all nodes on the boundary are stacked to form the global phase matrix. .

[0020] 3.2) Introducing a set of control signals for four-phase intersections Each of them This indicates the phase at intersection k. The proportion of the total signal cycle time to the time of the signal.

[0021] In step (4), the establishment of the elasticity state curves of each traffic sub-zone and the calculation of the elasticity value of the traffic sub-zone include the following steps:

[0022] 4.1) Construct an elastic state curve based on traffic flow information within the traffic sub-zone. The elastic state curve is an elastic level curve of the traffic sub-zone determined based on the average driving speed of the area. When damage occurs, such as major traffic accidents or natural disasters, the curve will show a significant decline, and after receiving control strategies, it will show a significant recovery process.

[0023] 4.2) Introduction The concept is used to characterize the system's resilience value, which simultaneously represents performance degradation and performance recovery. This indicates the amount of loss; the smaller the value, the stronger the ability to resist disturbances. This represents the recovery amount; the larger the value, the stronger the recovery capability. If R equals 1, it indicates that the service level can be restored to the level before the congestion. If it is less than 1, it indicates that the service capability cannot be fully restored to the previous level. If it is greater than 1, it indicates that the resilience after recovery is greater than the initial stable level.

[0024] 4.3) Based on the aforementioned elastic level curve, the specific definition of the elastic value R is expressed by the following formula. ,in, This refers to the time when the elasticity of the traffic sub-region begins to decrease after being disturbed. The time it takes for the elasticity to decrease to a steady state. The time it takes for the elastic body to recover to its initial steady state. It represents the ratio of the cumulative performance recovery during the elastic decline process to the cumulative performance loss during the elastic recovery process in traffic sub-region i. It can more comprehensively and accurately measure the system's recovery capability from both performance recovery and recovery time perspectives.

[0025] In step (5), an incremental model of the road network traffic flow system is constructed, an output vector prediction expression is established, an error prediction model is established, and a model prediction controller is designed, including the following steps:

[0026] 5.1) Based on the aforementioned capacity multigraph, boundary signal phase model, and elasticity theory, construct an incremental model of the road network traffic flow system and list the traffic flow state-space equations for all controlled road segments. ,in Let be the state vector, representing the traffic flow on all roads; This is the control vector, representing the green light duration for each phase; The output vector represents the resilience level of the traffic sub-zone. The state matrix A is an identity matrix; the input matrix B reflects the characteristics of the road network, such as topology, phase, period, saturation flow, and turning rate; the output matrix C is a diagonal matrix that reflects the corresponding calculation relationship between road traffic flow status and the resilience level of the traffic sub-zone.

[0027] 5.2) Based on the relationship between the green light duration increments of intersection signals at two adjacent times. The traffic flow state-space equations are rewritten in incremental form: Among them, , , It can be simplified to the following form .

[0028] 5.3) Based on the incremental model of the road network traffic flow system, construct a prediction expression for the output vector, and set the prediction range to... The prediction expression is ,in, , ;

[0029] Then, based on the difference between two adjacent iterations The prediction expression is organized as follows: ;

[0030] 5.4) Based on the prediction expression of the output vector, establish an error prediction model. The control objective is to restore the traffic sub-region to the most suitable elasticity level. Define the output vector as the elasticity level of the traffic sub-region and introduce the desired elasticity level of the traffic sub-region. elasticity level of actual traffic sub-zone error value Based on the incremental model of the traffic flow system, the error prediction model can be written. The specific expression for the error prediction model is as follows: ;

[0031] 5.5) Based on the aforementioned error prediction expression, design a controller using model predictive control. In actual traffic operations, the specific green light signal duration is subject to many constraints. For example, the signal cycle at an intersection is fixed, and the green light duration for each phase has a maximum and a minimum value. Therefore, this controller has the following constraints. ,in, Let be the set of control signals at intersection p, t be the total loss time of one cycle, and C be the signal cycle time.

[0032] Based on the model predictive controller, the optimal green light duration increment that satisfies the constraints is obtained by solving the problem using MATLAB software. The control signal set is then iteratively updated to optimize the signal control strategy.

[0033] The beneficial effects of this invention are as follows:

[0034] This invention combines the elasticity of traffic sub-regions with a model predictive controller. It utilizes the capacity multigraph of the traffic network to represent the elasticity level of traffic sub-regions and updates the control objective of the model predictive controller. This not only makes full use of historical batch information on the similarity distribution of macroscopic traffic flow in the road network, but also enables online rolling optimization through the predictive model, allowing the traffic signals of the road network to meet control requirements more effectively and quickly, thereby improving the traffic efficiency of the road network. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart of the method described in this invention;

[0037] Figure 2 This is the elastic state curve for the complete process. Detailed Implementation Plan

[0038] To make the technical problem and solution to be solved by the present invention clearer, a detailed description will be provided below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the scope of the invention.

[0039] The purpose of this invention is to solve the problem of declining elasticity levels in traffic sub-regions by designing boundary signal control strategies based on specific traffic flow information, thereby rapidly restoring regional elasticity levels to a stable level.

[0040] This invention discloses a boundary signal control method based on traffic elasticity theory, see [link to invention]. Figure 1 , 2 As shown, the steps are as follows:

[0041] Step 1: Obtain the urban traffic network topology, traffic flow information, and signal control data;

[0042] Step 2: Construct a capacity multigraph of the traffic network and define a finite set of roads, dividing it into multiple traffic sub-regions;

[0043] Step 3: Construct a boundary signal phase model for the boundary region of the traffic sub-region, and construct a global signal phase matrix based on the signal phase at each node to establish a control signal set;

[0044] Step 4: Introduce traffic elasticity theory, establish elasticity state curves for each traffic sub-region based on the traffic flow information, and calculate the elasticity value of each traffic sub-region;

[0045] Step 5: Based on the traffic flow information and elasticity value, construct an incremental model of the road network traffic flow system, establish an output vector prediction expression, establish an error prediction model, and design a model prediction controller.

[0046] According to step 1, the traffic flow information includes the average vehicle speed and traffic volume; the signal control data includes the signal control method, signal cycle duration, and green light duration for each phase.

[0047] According to step 2, "Based on the urban traffic network topology, establish a capacity multigraph of the traffic network and define a finite set of roads, dividing it into multiple traffic sub-regions," the specific steps are as follows:

[0048] Based on the topology, the transportation network is described as a capacity multigraph. In the form of, Let be a finite set of intersection nodes. Represents the number of intersections; For a finite set of directional roads, Represents the number of roads; This represents the vehicle capacity of each road. The capacity of each road is defined as a set. , It is a diagonal matrix, and all values ​​inside are non-negative.

[0049] A finite set of roads is defined based on the capacity multigraph. Define a traffic sub-region Traffic sub-zone Internal traffic, For traffic flowing into the traffic sub-zone from other external sub-zones Traffic, For traffic sub-zone The outflow volume, where each volume is less than the sum of the capacities of all roads within the sub-region, i.e. , .

[0050] To simulate the propagation of traffic flow in the road network, a routing matrix is ​​introduced. Each element in the list represents a node To the node The proportion of traffic to total traffic, determined by network topology constraints, indicates that 0 represents the absence of a direct road between two nodes. According to the law of conservation of traffic flow, we know that... ,when This indicates the flow rate of the traffic sub-region flowing out of the sub-region. ; and when This indicates that there is no outflow of traffic from the traffic sub-region.

[0051] The specific steps for "constructing a boundary signal phase model and building a global signal phase matrix based on the signal phase at each node, and establishing a control signal set" as described in step 3 are as follows:

[0052] A boundary signal phase model is constructed for a selected traffic sub-zone and its boundary area. The signal light phase state at the intersections at the zone boundary determines the flow of traffic entering and leaving the traffic sub-zone. All intersections at the boundary are defined as a set. Then define Let k be the set of feasible phases at an intersection. The feasible phases at intersection k can be represented by a phase matrix. , Then, the phase matrices of all nodes on the boundary are stacked to form the global phase matrix. A set of control signals is introduced for four-phase intersections. Each of them This indicates the phase at intersection k. The proportion of the total signal cycle time to the time of the signal.

[0053] According to step 4, "Establish the elasticity state curves of each traffic sub-region and calculate the elasticity value of the traffic sub-region," the specific steps are as follows:

[0054] An elastic state curve is constructed based on traffic flow information within the traffic sub-zone. The elastic state curve is a traffic sub-zone elastic level curve determined based on the average driving speed in the area. When damage occurs, such as major traffic accidents or natural disasters, the curve will show a significant drop, and after receiving control strategies, a significant recovery process will occur.

[0055] Introduction The concept is used to characterize the system's resilience value, which simultaneously represents performance degradation and performance recovery. This indicates the amount of loss; the smaller the value, the stronger the ability to resist disturbances. This represents the recovery amount; the larger the value, the stronger the recovery capability. If R equals 1, it indicates that the service level can be restored to the level before the congestion. If it is less than 1, it indicates that the service capability cannot be fully restored to the previous level. If it is greater than 1, it indicates that the resilience after recovery is greater than the initial stable level.

[0056] Based on the aforementioned elastic level curve, the specific definition of the elastic value R is expressed by the following formula. ,in, This refers to the time when the elasticity of the traffic sub-region begins to decrease after being disturbed. The time it takes for the elasticity to decrease to a steady state. The time it takes for the elastic body to recover to its initial steady state. It represents the ratio of the cumulative performance recovery during the elastic decline process to the cumulative performance loss during the elastic recovery process in traffic sub-region i. It can more comprehensively and accurately measure the system's recovery capability from both performance recovery and recovery time perspectives.

[0057] According to step 5, "constructing an incremental model of the road network traffic flow system, establishing an output vector prediction expression, establishing an error prediction model, and designing a model prediction controller," the specific steps are as follows:

[0058] Based on the aforementioned capacity multigraph, boundary signal phase model, and elasticity theory, an incremental model of the road network traffic flow system is constructed, and the state-space equations of traffic flow for all controlled road segments are listed. ,in Let be the state vector, representing the traffic flow on all roads; This is the control vector, representing the green light duration for each phase; The output vector represents the resilience level of the traffic sub-zone. The state matrix A is an identity matrix; the input matrix B reflects the characteristics of the road network, such as topology, phase, period, saturation flow, and turning rate; the output matrix C is a diagonal matrix that reflects the corresponding calculation relationship between road traffic flow status and the resilience level of the traffic sub-zone.

[0059] Based on the relationship between the green light duration increments at two adjacent intersection times The traffic flow state-space equations are rewritten in incremental form: Among them, , , It can be simplified to the following form .

[0060] Based on the incremental model of the road network traffic flow system, a prediction expression for the output vector is constructed, and the prediction range is set to... The prediction expression is ,in, , ;

[0061] Then, based on the difference between two adjacent iterations The prediction expression is organized as follows: .

[0062] Based on the prediction expression of the output vector, an error prediction model is established. The control objective is to restore the traffic sub-region to the most suitable elasticity level. The output vector is defined as the elasticity level of the traffic sub-region, and the desired elasticity level of the traffic sub-region is introduced. elasticity level of actual traffic sub-zone error value Based on the incremental model of the traffic flow system, the error prediction model can be written. The specific expression for the error prediction model is as follows: .

[0063] Based on the error prediction expression, a controller is designed using model predictive control. In actual traffic operations, the specific green light signal duration is subject to many constraints. For example, the signal cycle at an intersection is fixed, and the green light duration for each phase has a maximum and a minimum value. Therefore, this controller has the following constraints. ,in, Let be the set of control signals at intersection p, t be the total loss time of one cycle, and C be the signal cycle time.

[0064] Based on the model predictive controller, the optimal green light duration increment that satisfies the constraints is obtained by solving the problem using MATLAB software. The control signal set is then iteratively updated to optimize the signal control strategy.

[0065] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A boundary signal control method based on traffic elasticity theory, characterized by, Includes the following steps: Acquire urban transportation network topology, traffic flow information, and signal control data; Based on the topology of the urban transportation network, a capacity multigraph of the transportation network is established, and a finite set of roads is defined and divided into multiple transportation sub-regions. Based on the traffic sub-region, a boundary signal phase model is constructed for its boundary region, and a global signal phase matrix is ​​constructed based on the signal phase at each node to establish a control signal set; By introducing traffic elasticity theory, elasticity state curves for each traffic sub-region are established based on the traffic flow information, and the elasticity value of each traffic sub-region is calculated. Based on the traffic flow information and elasticity value, an incremental model of the road network traffic flow system is constructed, an output vector prediction expression is established, an error prediction model is established, and a model prediction controller is designed. Specifically, establishing the elasticity state curves of each traffic sub-region and calculating the elasticity value of the traffic sub-region includes: Step 4.1: Elasticity is defined as the ability of a system to maintain or quickly recover to a stable state. It is expressed intuitively by constructing an elasticity state curve based on traffic flow information within the traffic sub-zone. The elasticity state curve is the traffic sub-zone elasticity level curve determined based on the average driving speed of the area. When a major traffic accident or natural disaster occurs, the curve will show a significant drop, and after receiving a control strategy, a significant recovery process will occur. Step 4.2, the elasticity of traffic sub-area is represented by recovery speed, recovery time and recovery amount; for the recovery amount index, the concept of is introduced to represent the system elasticity value, which represents both performance decline and performance recovery, represents the loss amount, the smaller the value, the stronger the resistance to disturbance, represents the recovery amount, the larger the value, the stronger the recovery ability; if R is 1, it means that the service level before congestion can be recovered, if less than 1, it means that the service ability cannot be fully recovered to the previous level, if greater than 1, it means that the elasticity after recovery is greater than the initial stable level; Step 4.3, based on the elastic level curve, the specific definition of the elastic value R is expressed by the following formula. ,in, This refers to the time when the elasticity of the traffic sub-region begins to decrease after being disturbed. The time it takes for the elasticity to decrease to a steady state. The time it takes for the elastic body to recover to its initial steady state. This represents the ratio of the cumulative performance recovery during the elasticity decline process to the cumulative performance loss during the elasticity recovery process in traffic sub-region i. The construction of the incremental model of the road network traffic flow system, the establishment of the output vector prediction expression, the establishment of the error prediction model, and the design of the model prediction controller specifically include: Step 5.1: Based on the capacity multigraph, boundary signal phase model, and elasticity theory, construct an incremental model of the road network traffic flow system and list the traffic flow state-space equations for all controlled road segments. ,in Let be the state vector, representing the traffic flow on all roads; This is the control vector, representing the green light duration for each phase; The output vector represents the resilience level of the traffic sub-zone; the state matrix A is an identity matrix; the input matrix B reflects the topology, phase, period, saturation flow, and turning rate of the road network; the output matrix C is a diagonal matrix that reflects the corresponding calculation relationship between road traffic flow status and the resilience level of the traffic sub-zone. Step 5.2, then based on the relationship between the green light duration increments of the intersection signal at two adjacent times. The traffic flow state-space equations are rewritten in incremental form: Among them, , , It can be simplified to the following form ; Step 5.3: Based on the incremental model of the road network traffic flow system, construct a prediction expression for the output vector, and set the prediction range to... The prediction expression is ,in, , ; Then, based on the difference between two adjacent iterations The prediction expression is organized as follows: ; Step 5.4: Based on the prediction expression of the output vector, establish an error prediction model. The control objective is to restore the traffic sub-region to the most suitable elasticity level. Define the output vector as the elasticity level of the traffic sub-region and introduce the desired elasticity level of the traffic sub-region. elasticity level of actual traffic sub-zone error value Based on the incremental model of the traffic flow system, the error prediction model can be written. The specific expression for the error prediction model is as follows: ; Step 5.5: Based on the error prediction model, design a controller using model predictive control. In actual traffic operations, the specific green light signal duration is subject to many constraints, including a fixed signal cycle at intersections and maximum and minimum green light durations for each phase. Therefore, this controller has the following constraints. ,in, Let be the set of control signals at intersection p, t be the total loss time of one cycle, and C be the signal cycle time; Step 5.6: Based on the model predictive controller, the optimal green light duration increment that satisfies the constraints is obtained by solving the problem using MATLAB software. The control signal set is then iteratively updated to optimize the signal control strategy.

2. The boundary signal control method based on traffic elasticity theory according to claim 1, characterized in that, The traffic flow information includes the average vehicle speed and traffic volume; the signal control data includes the signal control method, signal cycle duration, and green light duration for each phase.

3. The boundary signal control method based on traffic elasticity theory according to claim 2, characterized in that, The process of establishing a capacity multigraph of the urban transportation network based on its topology, and defining a finite set of roads divided into multiple transportation sub-regions, specifically includes: Step 2.1: Describe the urban transportation network as a capacity multigraph based on its topology. In the form of, Let be a finite set of intersection nodes. Represents the number of intersections; For a finite set of directional roads, Represents the number of roads; This represents the vehicle capacity of each road; the capacity of each road is defined as a set. , It is a diagonal matrix, and all values ​​inside it are non-negative. Step 2.2: Define a finite set of roads based on the capacity multigraph. Define a traffic sub-region Traffic sub-zone Internal traffic, For traffic flowing into the traffic sub-zone from other external sub-zones Traffic, For traffic sub-zone The outflow volume, where each volume is less than the sum of the capacities of all roads within the sub-region, i.e. , ; Step 2.3: To simulate the propagation of traffic flow in the road network, a routing matrix is ​​introduced. Each element in the list represents a node To the node The proportion of traffic to the total traffic, as constrained by the network topology, indicates that 0 represents that there is no direct path between the two nodes. Step 2.4, according to the law of conservation of traffic flow, we know... ,when This indicates the flow rate of the traffic sub-region flowing out of the sub-region. ; and when This indicates that there is no outflow of traffic from the traffic sub-region.

4. The boundary signal control method based on traffic elasticity theory according to claim 3, characterized in that, The construction of the boundary signal phase model and the establishment of a global signal phase matrix based on the signal phase at each node, and the creation of a control signal set, specifically include: A boundary signal phase model is constructed for a selected traffic sub-zone and its boundary area. The signal light phase state at the intersections at the zone boundary determines the flow of traffic entering and leaving the traffic sub-zone. All intersections at the boundary are defined as a set. Then define Let k be the set of feasible phases at an intersection; the feasible phases at intersection k can be represented by a phase matrix. , Then, the phase matrices of all nodes on the boundary are stacked to form the global phase matrix. A set of control signals is introduced for four-phase intersections. Each of them This indicates the phase at intersection k. The proportion of the total signal cycle time to the time of the signal.

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