Multi-traffic sub-area distributed collaborative optimization method and system based on double-layer architecture

Abstract

The present disclosure provides a multi-traffic sub-area distributed collaborative optimization method and system based on a double-layer architecture, which relates to the field of traffic control and automatic control technology, including: using a road network model to construct a weighted directed graph, and dividing the weighted directed graph into a number of traffic sub-areas; constructing an upper-layer multi-sub-area collaborative optimization model based on a macro basic graph of each traffic sub-area, and obtaining the optimal cumulative number of vehicles in each traffic sub-area under collaborative control; using the optimal cumulative number of vehicles in each traffic sub-area as a constraint of a lower-layer single-sub-area optimization control model, optimizing the timing of each intersection within the traffic sub-area, and obtaining the optimal timing plan for the controlled road network; the present invention takes into account the overall traffic efficiency of each sub-area itself and the road network, and the balance of traffic flow distribution between sub-areas. Based on a double-layer cascade architecture, the upper layer obtains the optimal cumulative number of vehicles in each sub-area under collaborative control, and through sub-area boundary control, cascades with the lower layer on the conditions of each intersection within the sub-area and continuously updates parameters to perform multi-sub-area collaborative optimization.

Description

Technical Field

[0001] The present disclosure relates to the technical field of traffic control and automatic control, and in particular to a multi-traffic sub-area distributed collaborative optimization method and system based on a double-layer architecture. Background Art

[0002] With the continuous surge in the number of cars, traffic congestion is becoming more and more serious, and is constantly expanding from single intersections and single routes to regional congestion. Therefore, control over traffic areas is of great practical significance in alleviating urban congestion problems.

[0003] Current regional optimization control is often implemented by regulating signals at the boundaries of one or several smaller regions based on the characteristic curves of their macro-basic graphs when operating independently. However, this control process relies solely on the optimal cumulative vehicle count of each region's macro-basic graph, without considering the mutual influence of traffic flows in coupled regions and the balanced distribution of traffic flows, resulting in limitations.

[0004] In reality, when optimizing traffic flow across multiple regions, the interplay between regions makes it impossible for them to simultaneously achieve the optimal cumulative vehicle count for their macro-basic graph. While ensuring that the cumulative vehicle count for each sub-region is as close as possible to its theoretical optimal cumulative vehicle count, it is also necessary to consider the coordination and differences between sub-regions, as well as the influence of vehicle operations within the sub-region, to achieve an asymmetric balance within each sub-region.

[0005] Therefore, in traditional regional optimization control, there is a lack of consideration of combining the sub-interval collaborative optimization strategy with the signal optimization control strategy within the sub-interval and insufficient consideration of the asymmetric balance problem of the sub-interval, resulting in low control efficiency. Summary of the Invention

[0006] In order to solve the above problems, the present disclosure proposes a distributed collaborative optimization method and system for multiple traffic sub-zones based on a two-layer architecture. Taking into account the traffic efficiency of each sub-zone itself and the overall traffic network, and the balanced traffic distribution among sub-zones, based on a two-layer cascade architecture, the upper layer obtains the optimal cumulative number of vehicles in each sub-zone under collaborative control, and through sub-zone boundary control, cascades with the lower layer on the conditions of each intersection within the sub-zone and continuously updates parameters to perform multi-sub-zone collaborative optimization.

[0007] According to some embodiments, the present disclosure adopts the following technical solutions:

[0008] The distributed collaborative optimization method for multiple traffic sub-areas based on a two-layer architecture includes:

[0009] Based on the topological structure of the controlled road network and the collected traffic flow data, a road network model is established, and the traffic flow data in the road network model is improved through simulation;

[0010] Using the road network model to construct a weighted directed graph, by dividing the region of the weighted directed graph, a number of traffic sub-areas are obtained, and a macro basic map of each traffic sub-area is drawn;

[0011] Based on the macro basic graph of each traffic sub-area, the road network optimization problem is decomposed into multiple sub-area collaborative optimization problems. An upper-level multi-sub-area collaborative optimization model is constructed, and a distributed optimization algorithm is used to solve the global optimal solution. The optimal cumulative number of vehicles in each traffic sub-area under collaborative control is obtained.

[0012] The optimal cumulative number of vehicles in each traffic sub-area is used as the constraint of the lower-level single-sub-area optimization control model. The timing of each intersection within the traffic sub-area is optimized to obtain the optimal timing plan for the controlled road network.

[0013] According to some embodiments, the present disclosure adopts the following technical solutions:

[0014] The multi-traffic sub-area distributed collaborative optimization system based on a two-layer architecture includes:

[0015] The model building module is configured to: build a road network model according to the topological structure of the controlled road network and the collected traffic flow data, and improve the traffic flow data in the road network model through simulation;

[0016] The area division module is configured to: construct a weighted directed graph using a road network model, obtain a number of traffic sub-areas by dividing the weighted directed graph into regions, and draw a macro basic map of each traffic sub-area;

[0017] The collaborative optimization module is configured to: decompose the road network optimization problem into multiple sub-area collaborative optimization problems based on the macro basic graph of each traffic sub-area, construct an upper-level multi-sub-area collaborative optimization model, and use a distributed optimization algorithm to solve the global optimal solution to obtain the optimal cumulative number of vehicles in each traffic sub-area under collaborative control;

[0018] The timing optimization module is configured to use the optimal cumulative number of vehicles in each traffic sub-area as a constraint of the lower-level single-sub-area optimization control model, optimize the timing of each intersection within the traffic sub-area, and obtain the optimal timing plan for the controlled road network.

[0019] According to some embodiments, the present disclosure adopts the following technical solutions:

[0020] A computer program product includes a computer program, which, when executed by a processor, implements the multi-traffic sub-area distributed collaborative optimization method based on a double-layer architecture.

[0021] According to some embodiments, the present disclosure adopts the following technical solutions:

[0022] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the distributed collaborative optimization method of multiple traffic sub-areas based on a two-layer architecture is implemented.

[0023] According to some embodiments, the present disclosure adopts the following technical solutions:

[0024] An electronic device includes: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the multi-traffic sub-area distributed collaborative optimization method based on a two-layer architecture.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] Compared with traditional small-scale regional control, the present invention not only considers the macro-basic map of each region, but also takes into account the coordination between regions and the differences in size between regions. At the same time, it adopts a road network composed of multiple regions on a larger scale. On the basis of using the Louvain algorithm to sub-divide the large-scale road network, the Bregman Alternating Direction Method of Multipliers with Consensus variable (CBADMM) is used to solve the optimization target under collaborative control, which solves the problems in traditional regional optimization control that lack the consideration of combining the sub-interval collaborative optimization strategy with the signal optimization control strategy within the sub-interval and the lack of consideration of the asymmetric balance problem of the sub-interval. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings, which constitute a part of the present disclosure, are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0028] Figure 1 This is a flow chart of the method of Example 1.

[0029] Figure 2 This is an example diagram of the road network model of Example 1.

[0030] Figure 3 This is an example diagram of sub-area division in Example 1. DETAILED DESCRIPTION

[0031] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0032] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure belongs.

[0033] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "comprising" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0034] Example 1

[0035] In one embodiment of the present disclosure, a distributed collaborative optimization method for multiple traffic sub-areas based on a two-layer architecture is provided, including:

[0036] Step S1: Building a road network model based on the topological structure of the controlled road network and the collected traffic flow data, and improving the traffic flow data in the road network model through simulation;

[0037] Step S2: construct a weighted directed graph using the road network model, obtain several traffic sub-areas by dividing the weighted directed graph into regions, and draw a macro basic graph of each traffic sub-area;

[0038] Step S3: Based on the macro basic graph of each traffic sub-area, the road network optimization problem is decomposed into multiple sub-area collaborative optimization problems, an upper-level multi-sub-area collaborative optimization model is constructed, and a distributed optimization algorithm is used to solve the global optimal solution to obtain the optimal cumulative number of vehicles in each traffic sub-area under collaborative control;

[0039] Step S4: Using the optimal cumulative number of vehicles in each traffic sub-area as a constraint of the lower-level single-sub-area optimization control model, the timing of each intersection within the traffic sub-area is optimized to obtain the optimal timing plan for the controlled road network.

[0040] As an embodiment, the disclosed multi-traffic sub-area distributed collaborative optimization method based on a two-layer architecture considers the traffic efficiency of each sub-area itself and the overall traffic network, as well as the balanced traffic distribution between sub-areas. Based on a two-layer cascade architecture, the upper layer obtains the optimal cumulative number of vehicles in each sub-area under collaborative control, and through sub-area boundary control, cascades with the lower layer on the conditions of each intersection within the sub-area and continuously updates parameters to perform multi-sub-area collaborative optimization, such as Figure 1 The specific implementation process is as follows:

[0041] Step 1: Collect the topological information and traffic flow data of the controlled road network and use traffic simulation modeling tools to establish Figure 2The road network model shown in the figure is used, and the traffic flow data in the road network model is improved through simulation.

[0042] The traffic flow data here refers to the unit traffic flow collected through pre-set data collection points and collection cycles; the traffic simulation modeling tool can use existing software, such as Vissim, etc. Of course, those skilled in the art can choose the corresponding traffic simulation modeling tool according to their own situation and needs, and is not limited to the above examples, and is not limited here.

[0043] Furthermore, data collection points are set up 5 meters from the stop line of the intersection, 30 meters from the exit road of the intersection, and in the middle of the road section to make the acquired traffic data more reasonable and accurate; further, in terms of traffic flow data, the number of vehicles collected continuously for 15 minutes is multiplied by 4 as the hourly flow.

[0044] Step 2: Divide the entire road network into multiple traffic sub-areas using the Louvain community detection algorithm for weighted directed graphs based on simulation data.

[0045] Based on the correlation of traffic density, traffic volume, and signal cycle, a correlation model is established. The correlation between adjacent intersections is used as the edge weight to construct a weighted directed graph of the road network model. The Louvain community detection algorithm is used to maximize the modularity increment. Nodes in the weighted directed graph are merged into communities until the community where each node is located no longer changes. This divides the weighted directed graph of the road network model into multiple traffic sub-areas. The specific steps are as follows:

[0046] Step 2.1: Establish association model and modularity model

[0047] The calculation formula for traffic flow correlation is:

[0048]

[0049] in, Indicates the upstream intersection To downstream intersection The traffic flow correlation, represents the traffic dispersion coefficient, Usually take traffic from the intersection To the intersection 0.8 times the travel time, Indicates the upstream intersection Enter the downstream intersection The number of traffic branches, for a standard intersection, =3, For intersections To the intersection The maximum flow rate of the inlet channel, For intersections To the intersection The unit traffic volume of the entrance road.

[0050] Considering that the traffic flow between intersections is bidirectional, the intersection arrive Traffic flow correlation and intersection arrive Traffic flow correlation Different, therefore intersection and The traffic flow correlation between and Average of:

[0051]

[0052] The calculation formula of signal period correlation is:

[0053]

[0054] in, is the maximum possible value of the ratio of signal control periods of adjacent intersections, For intersection The signal period value.

[0055] The calculation formula for the correlation degree of road section traffic density is:

[0056]

[0057]

[0058] in, Indicates that the upstream intersection is in a saturated state. To downstream intersection Vehicle density in the direction, For intersections To the intersection The unit traffic volume of the entrance road, Indicates the number of lanes, For intersection arrive The length of the connecting section.

[0059] In summary, the intersection correlation model is given as:

[0060]

[0061] In the Louvain community detection algorithm, a community is represented by a group consisting of one or more nodes, which are continuously merged during the algorithm process. The modularity is used to measure the quality of the current community division. The modularity formula is given as follows using the above association model as the initial edge weight of the road segment:

[0062]

[0063] in, Indicates intersection The sum of the association degrees of connected road segments, Indicates an intersection intersection Whether they belong to the same community.

[0064] Step 2.2: Use the correlation as the road segment weight to divide the road network into sub-areas

[0065] In the actual division process, Figure 2 The road network model shown in the figure is divided into sub-areas, and the following is obtained: Figure 3 The area division results shown are as follows:

[0066] Step 2.2.1: Modularity optimization stage

[0067] (1) Collect and count the traffic information of intersections and connecting sections in the sub-area (initially the entire road network), substitute it into the established correlation model for calculation, and obtain the correlation between adjacent intersections.

[0068] (2) Initially, each intersection is considered a node, and each node is considered a community. At this time, the number of communities divided by the algorithm is the number of nodes.

[0069] (3) Merge nodes with adjacent communities and calculate the modularity increment:

[0070]

[0071] in, Indicates merging into the community at the intersection Community in the process The sum of the correlations of all internal road segments, Indicates all pointing to the community The sum of the road section correlations at the intersection, Indicates a connecting intersection With the community The sum of the association degrees of all road segments.

[0072] (4) If all modularity increments obtained during the merging of all communities adjacent to the intersection with the node are less than 0, the intersection is returned to the original community. Otherwise, the intersection is merged into the community with the largest modularity increment and the community and corresponding data are updated until the community where any intersection is located no longer changes, and the modularity optimization stage ends.

[0073] Step 2.2.2: Divide the network into cohesive phases

[0074] (1) Each community divided in the modularity optimization phase is merged and compressed into a new node, and the weight is updated according to the correlation of the road segments connected to the new node. The sum of the correlation of all road segments connecting two different communities is used as the weight between the new nodes formed by the compression and merger of the two communities.

[0075] (2) Return to step 2.2.1 and repeatedly optimize the modularity until the modularity of the entire map no longer changes. The community finally merged is a controlled traffic sub-area. The division result is as follows: Figure 3 shown.

[0076] Step 3: In Vissim, gradually increase the traffic volume of each sub-area divided in Step 2 and calibrate them separately, draw the macro basic map of each sub-area, and establish the relationship between the cumulative number of vehicles and the trip completion flow.

[0077] Specifically, step 3.1: using Vissim to input traffic flow into each sub-area based on traffic flow data.

[0078] Step 3.2: Set up data collection points on each road section. In order to obtain the free flow, critical flow, and congested flow of the macro basic diagram, the traffic flow input needs to be gradually increased during the input process.

[0079] Step 3.3: Based on the collected data on the input and output traffic of each sub-area, calculate the cumulative number of vehicles in the sub-area:

[0080]

[0081] in, Indicates the The cumulative number of vehicles in the sub-area within a sampling period is: Indicates the The number of vehicles entering the sub-area during the sampling period, Indicates the The number of vehicles passing through the ion zone during a sampling period, Indicates the sampling period time interval, represents the traffic flow rate of the driving zone, Indicates the total number of lanes that have left the driving zone.

[0082] Step 3.4: Draw a scatter plot with the cumulative number of vehicles as the horizontal axis and the trip completion flow as the vertical axis. Use the least squares method to fit the relationship between the cumulative number of vehicles and the trip completion flow into a cubic function, and draw the characteristic curve of the macro basic graph. The cubic function is expressed as follows:

[0083]

[0084] in, is the cumulative number of vehicles, For the trip completion flow, are the coefficients of the fitted cubic function.

[0085] Step 4: Decompose the road network optimization problem into multiple sub-area collaborative optimization problems, determine the control objectives of the upper-level multi-sub-area collaborative optimization model, and use the distributed optimization algorithm CBADMM to solve the global optimal solution, that is, the optimal cumulative number of vehicles under the collaborative control of each sub-area.

[0086] The collaborative optimization goal needs to consider the interaction and influence of information between sub-areas, which also means that all sub-areas cannot achieve the theoretically optimal cumulative number of vehicles at the same time. We can only find the optimal cumulative number of vehicles in each sub-area under the coordination game. Therefore, the collaborative control aims to maximize the overall traffic efficiency of the road network. At the same time, the asymmetric balance of traffic flow between sub-areas is caused by the differences in traffic costs between each traffic sub-area and the size and capacity of each traffic sub-area. The objective function is non-convex, so the CBADMM algorithm considering non-convex optimization is used to solve the optimal cumulative number of vehicles under collaborative control. CBADMM is an algorithm based on the decomposition and coordination process. It finds the solution to a large global problem by coordinating the solutions of small local sub-problems. Specifically:

[0087] Step 4.1: Construction of multi-subregion collaborative optimization model

[0088] (1) Setting collaborative optimization goals

[0089] When designing the upper-level collaborative optimization objectives, it is necessary to consider the overall traffic status of the road network, the traffic operation efficiency between sub-areas, and the asymmetric balance of vehicle distribution in sub-areas.

[0090] According to the properties of the macro basic graph, when the cumulative number of vehicles in a sub-area reaches the optimal cumulative number of its basic graph, its trip completion flow is the largest, that is, the traffic efficiency is the highest. Therefore, in the coordinated optimization process, it is necessary to ensure that the solution of each sub-area is near the left side of the optimal cumulative number of vehicles. Considering that the macro basic graphs of different traffic sub-areas are different, that is, the degree to which the trip completion flow changes with the cumulative number of vehicles is different, the square of the difference between the trip completion flow and the maximum trip completion flow is used to reflect the quality of the traffic status:

[0091]

[0092] After normalization, the optimization objective is given:

[0093]

[0094] in, represents a subregion set, represents the optimal cumulative number of vehicles, Indicates sub-area Trip completion flow under the optimal cumulative number of vehicles, Indicates the current time, sub-area The cumulative number of vehicles is The trip is completed when the flow.

[0095] When performing sub-area collaborative optimization, a key focus is the efficiency of traffic flow transfer between coupled sub-areas. The efficiency of traffic flow transfer between coupled sub-areas can effectively reflect the effectiveness of sub-area collaborative optimization. The relative road resistance on the connecting road sections of the sub-areas is used as the optimization target. By minimizing the relative road resistance, reasonable traffic flow distribution is achieved to avoid excessive congestion in some sub-areas. Road resistance is often expressed by the travel cost on the road section. Therefore, the optimization target is given as:

[0096]

[0097] in, Indicates the upstream subregion To the downstream sub-area The connecting road section, Passing section during free flow phase The travel time, is the travel time at the saturation moment, For road sections Traffic flow on For road sections The traffic capacity, and is an empirical parameter that affects the road resistance, usually taken as =0.15, =4.

[0098] In order to avoid the problem of unbalanced traffic distribution caused by excessively high traffic loads in some sub-areas and low traffic loads in other sub-areas during the collaborative optimization process, an equilibrium term is introduced. Considering that the balance of sub-intervals is asymmetric, the imbalance of sub-intervals is reduced by minimizing the relative traffic pressure of coupled sub-intervals.

[0099] The sub-area vehicle balance optimization objective is given:

[0100]

[0101] in, For sub-area Current number of vehicles, Indicates sub-area Maximum number of vehicles carried.

[0102] (2) Given constraints

[0103]

[0104] in, For sub-area The current cumulative number of vehicles, Indicates sub-area The portion of the current cumulative number of vehicles whose destinations are within the sub-area, Indicates sub-area The current cumulative number of vehicles in the sub-area part, represents the road network capacity, Indicates sub-area The maximum number of vehicles that can be accommodated, Indicates connection to the upstream sub-region and downstream sub-areas A section of road, Indicates road section traffic capacity.

[0105] Step 4.2: Use the CBADMM algorithm to solve the collaborative optimization objective

[0106] (1) Initialization parameters

[0107] Initialize the timing plan of each intersection through the Webster timing method, and initialize the cumulative number of vehicles in each sub-area based on the current road network information. , Lagrange multipliers , penalty parameters At the same time, in order to realize the information interaction and coordinated control between sub-areas in the distribution optimization process and solve the consistency optimization problem, the co-identification variable is introduced. As an augmentation term of the objective function:

[0108]

[0109] Given the consistency constraints:

[0110]

[0111] in, is the penalty coefficient used to adjust the impact of the equilibrium term to ensure that the traffic efficiency of individual sub-areas is not sacrificed in the process of balancing the vehicle distribution in the sub-areas. represents the optimal cumulative number of vehicles in the macro basic graph of the sub-area, Is a global consensus variable Central and sub-areas The relevant parts, in brief, When is about A linear function of is constructed; a global consensus variable is used to coordinate the local decisions of multiple sub-areas, adjust the traffic flow distribution in each sub-area, and obtain the ideal cumulative number of vehicles in each sub-area under coordination.

[0112] The augmented Lagrangian function form is further given:

[0113]

[0114] (2) Cumulative number of vehicles To update:

[0115]

[0116]

[0117] in, represents the number of iterations, express Divergence, represents the gradient; represents the inner product, since here is a univariate function of the subarea with respect to the cumulative number of vehicles, and its value is The product of .

[0118] (3) Update the consensus variables:

[0119]

[0120] (4) Lagrange multipliers To update:

[0121]

[0122] (5) Determine whether the current solution converges or whether the number of iterations meets the maximum number of iterations. If so, output the upper-level collaborative objective and connect it as a constraint for the lower-level optimization objective. Otherwise, proceed to step (6).

[0123] Raw residuals:

[0124]

[0125] Dual residual:

[0126]

[0127] in represents the original error, represents the dual error.

[0128] (6) Adjust the upstream and downstream intersections of the sub-area connecting road section according to the upper-level collaborative target under the current iteration number, obtain the road section flow through the simulation software and return to step (2) to update the parameters.

[0129] Step 5: The optimal solution of the upper-level multi-sub-area cooperative control model in each iteration is used as the constraint of the lower-level single-sub-area optimization control model, and the feedback controller is used to achieve the upper-level cooperative control goal.

[0130] Solve the lower framework, and use the optimal solution of the upper multi-sub-area coordinated control model as the connection constraint of the signal timing optimization model in the lower sub-area. Use the feedback controller to perform boundary control to maintain the optimal number of vehicles under the coordinated control of each sub-area. Use the genetic-simulated annealing algorithm to optimize the timing of each intersection within the sub-area. Considering the dynamic nature of traffic flow, set the maximum control time of the road network. Once the maximum control time of the road network is exceeded, it is necessary to re-enter the sub-area optimization stage. Specifically:

[0131] Step 5.1 Use feedback controller to implement boundary control on sub-areas

[0132] The boundary control target is the optimal cumulative number of vehicles under the sub-area cooperative control required by the upper model, and the control object is the traffic lights at the boundary intersections of each sub-area.

[0133] The deviation between the optimal cumulative number of vehicles and the actual cumulative number of vehicles in the sub-area in the next sampling period is calculated using the traffic flow entering and leaving the sub-area boundary intersection and the multi-sub-area boundary traffic flow model, and is used as the input of the feedback controller. Given the multi-sub-area boundary traffic flow model:

[0134]

[0135] in, The table is Sub-area within a sampling period The cumulative number of vehicles, For sub-area To sub-area of traffic volume, Indicates the time interval of the sampling period.

[0136] The green-signal ratio of the boundary intersection is allocated according to the optimal cumulative number of vehicles, the deviation of the actual cumulative number of vehicles in the sub-area in the next sampling period, and the entry and exit flow of each intersection at the boundary of the sub-area.

[0137] Step 5.2 Optimize intersections within sub-areas

[0138] Under the constraint of maintaining the optimal sub-area cumulative number of vehicles under boundary control, the signal timing of each intersection is optimized within the framework of a distributed optimization algorithm, with delay time and queue length as optimization objectives.

[0139] Output the timing plan to determine whether the maximum control time of the road network has been reached. If so, return the data to the upper-level framework for sub-area coordination optimization. Otherwise, execute the current plan until the maximum control time of the road network is reached.

[0140] Example 2

[0141] In one embodiment of the present disclosure, a multi-traffic sub-area distributed collaborative optimization system based on a two-layer architecture is provided, comprising:

[0142] The model building module is configured to: build a road network model according to the topological structure of the controlled road network and the collected traffic flow data, and improve the traffic flow data in the road network model through simulation;

[0143] The area division module is configured to: construct a weighted directed graph using a road network model, obtain a number of traffic sub-areas by dividing the weighted directed graph into regions, and draw a macro basic map of each traffic sub-area;

[0144] The collaborative optimization module is configured to: decompose the road network optimization problem into multiple sub-area collaborative optimization problems based on the macro basic graph of each traffic sub-area, construct an upper-level multi-sub-area collaborative optimization model, and use a distributed optimization algorithm to solve the global optimal solution to obtain the optimal cumulative number of vehicles in each traffic sub-area under collaborative control;

[0145] The timing optimization module is configured to use the optimal cumulative number of vehicles in each traffic sub-area as a constraint of the lower-level single-sub-area optimization control model, optimize the timing of each intersection within the traffic sub-area, and obtain the optimal timing plan for the controlled road network.

[0146] Example 3

[0147] In one embodiment of the present disclosure, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the distributed collaborative optimization method for multiple traffic sub-areas based on a two-layer architecture is implemented.

[0148] Example 4

[0149] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, which is used to store computer instructions. When the computer instructions are executed by a processor, the distributed collaborative optimization method of multiple traffic sub-areas based on a two-layer architecture is implemented.

[0150] Example 5

[0151] In one embodiment of the present disclosure, an electronic device is provided, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the multi-traffic sub-area distributed collaborative optimization method based on a two-layer architecture.

[0152] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0153] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0154] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Those skilled in the art should understand that, based on the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A distributed collaborative optimization method for multiple traffic sub-areas based on a two-layer architecture, characterized by: include: Based on the topological structure of the controlled road network and the collected traffic flow data, a road network model is established, and the traffic flow data in the road network model is improved through simulation; Using the road network model to construct a weighted directed graph, by dividing the region of the weighted directed graph, a number of traffic sub-areas are obtained, and a macro basic map of each traffic sub-area is drawn; Based on the macro basic graph of each traffic sub-area, the road network optimization problem is decomposed into multiple sub-area collaborative optimization problems. An upper-level multi-sub-area collaborative optimization model is constructed, and a distributed optimization algorithm is used to solve the global optimal solution. The optimal cumulative number of vehicles in each traffic sub-area under collaborative control is obtained. Collaborative control aims to maximize the overall traffic efficiency of the road network, while also considering the asymmetric balance of traffic flows between sub-areas caused by differences in the traffic costs and sizes and capacities of each sub-area. The square of the difference between the completed trip flow and the maximum completed trip flow reflects the quality of the traffic status. The optimization goal is: ;in, represents a subregion set, represents the optimal cumulative number of vehicles, Indicates sub-area Trip completion flow under the optimal cumulative number of vehicles, Indicates the current time, sub-area The cumulative number of vehicles is The trip is completed when the flow; The relative road resistance on the connecting section of the subinterval is used as the optimization target. The road resistance is represented by the travel cost on the section. The optimization target is: ;in, Indicates the upstream subregion To the downstream sub-area The connecting road section, Passing section during free flow phase The travel time, is the travel time at the saturation moment, For road sections Traffic flow on For road sections The traffic capacity, and It is an empirical parameter that affects the road resistance; By minimizing the relative traffic pressure of the coupled sub-intervals, the imbalance of the sub-intervals is reduced. The sub-interval vehicle balance optimization objective is: ;in, For sub-area Current number of vehicles, Indicates sub-area Maximum number of vehicles carried; Given constraints ; in, For sub-area The current cumulative number of vehicles, Indicates sub-area The portion of the current cumulative number of vehicles whose destinations are within the sub-area, Indicates sub-area The current cumulative number of vehicles in the sub-area part, represents the road network capacity, Indicates sub-area The maximum number of vehicles that can be accommodated, Indicates connection to the upstream sub-region and downstream sub-areas A section of road, Indicates road section traffic capacity; The optimal cumulative number of vehicles in each traffic sub-area is used as a constraint in the lower-level single-sub-area optimization control model to optimize the timing of each intersection within the traffic sub-area and obtain the optimal timing plan for the controlled road network. The single-sub-area optimization control model uses a feedback controller to perform boundary control to maintain the optimal cumulative number of vehicles under the coordinated control of each traffic sub-area; the optimal solution of the upper-level multi-sub-area coordinated control model is used as the connection constraint of the signal timing optimization model in the lower-level sub-area, and the feedback controller is used to perform boundary control to maintain the optimal number of vehicles under the coordinated control of each sub-area, and the maximum control time of the road network is set. Once the maximum control time of the road network is exceeded, it is necessary to re-enter the sub-area optimization stage; the entry and exit flow of the sub-area boundary intersection and the multi-sub-area boundary traffic flow model are used to calculate the deviation between the optimal cumulative number of vehicles and the actual cumulative number of vehicles in the sub-area in the next sampling period, and use it as the input of the feedback controller to allocate the green-signal ratio of the boundary intersection; under the constraint of maintaining the optimal sub-area cumulative number of vehicles under boundary control, the signal timing of each intersection is optimized under the framework of a distributed optimization algorithm with delay time and queue length as optimization objectives.

2. The distributed collaborative optimization method for multiple traffic sub-areas based on a two-layer architecture according to claim 1, characterized in that: The traffic flow data is the unit traffic flow collected through pre-set data collection points and collection cycles; Based on the topological structure of the controlled road network and the collected traffic flow data, a road network model is established using traffic simulation modeling tools.

3. The distributed collaborative optimization method for multiple traffic sub-areas based on a two-layer architecture as claimed in claim 1, characterized in that: The construction of the weighted directed graph is specifically as follows: With intersections as nodes, there are bidirectional edges between two adjacent intersections, and the correlation between the intersections is used as the edge weight; Among them, the intersection correlation is calculated through a correlation model constructed based on the traffic density correlation, traffic volume correlation and signal cycle correlation.

4. The distributed collaborative optimization method for multiple traffic sub-areas based on a two-layer architecture as claimed in claim 1, characterized in that: The above method obtains several traffic sub-areas by dividing the region of the weighted directed graph. The Louvain community detection algorithm is used to merge nodes into communities with the goal of maximizing the modularity increment until the community where each node is located no longer changes. The modularity is calculated based on the intersection correlation.

5. The distributed collaborative optimization method for multiple traffic sub-areas based on a two-layer architecture according to claim 1, characterized in that: The multi-sub-area collaborative optimization model takes maximizing the overall traffic efficiency of the road network as the optimization goal and is solved using the Bregman alternating direction multiplier algorithm considering consensus variables to obtain the optimal cumulative number of vehicles in each traffic sub-area.

6. The distributed collaborative optimization method for multiple traffic sub-areas based on a two-layer architecture according to claim 1, characterized in that: The genetic-simulated annealing algorithm is used to optimize the timing of each intersection within the traffic sub-area.

7. A multi-traffic sub-area distributed collaborative optimization system based on a two-layer architecture is characterized by: include: The model building module is configured to: build a road network model according to the topological structure of the controlled road network and the collected traffic flow data, and improve the traffic flow data in the road network model through simulation; The area division module is configured to: construct a weighted directed graph using a road network model, obtain a number of traffic sub-areas by dividing the weighted directed graph into regions, and draw a macro basic map of each traffic sub-area; The collaborative optimization module is configured to: decompose the road network optimization problem into multiple sub-area collaborative optimization problems based on the macro basic map of each traffic sub-area, construct an upper-level multi-sub-area collaborative optimization model, and use a distributed optimization algorithm to solve the global optimal solution to obtain the optimal cumulative number of vehicles in each traffic sub-area under collaborative control; collaborative control aims to maximize the overall traffic efficiency of the road network, while considering the asymmetric balance of traffic flows between sub-areas caused by the travel costs between each traffic sub-area and the differences in size and capacity of each traffic sub-area; the quality of the traffic status is reflected by the square of the difference between the trip completion flow and the maximum trip completion flow. The optimization objectives are: ;in, represents a subregion set, represents the optimal cumulative number of vehicles, Indicates sub-area Trip completion flow under the optimal cumulative number of vehicles, Indicates the current time, sub-area The cumulative number of vehicles is The trip is completed when the flow; The relative road resistance on the connecting section of the subinterval is used as the optimization target. The road resistance is represented by the travel cost on the section. The optimization target is: ;in, Indicates the upstream subregion To the downstream sub-area The connecting road section, Passing section during free flow phase The travel time, is the travel time at the saturation moment, For road sections Traffic flow on For road sections The traffic capacity, and It is an empirical parameter that affects the road resistance; By minimizing the relative traffic pressure of the coupled sub-intervals, the imbalance of the sub-intervals is reduced. The sub-interval vehicle balance optimization objective is: ;in, For sub-area Current number of vehicles, Indicates sub-area Maximum number of vehicles carried; Given constraints ; in, For sub-area The current cumulative number of vehicles, Indicates sub-area The portion of the current cumulative number of vehicles whose destinations are within the sub-area, Indicates sub-area The current cumulative number of vehicles in the sub-area part, represents the road network capacity, Indicates sub-area The maximum number of vehicles that can be accommodated, Indicates connection to the upstream sub-region and downstream sub-areas A section of road, Indicates road section traffic capacity; The timing optimization module is configured to: use the optimal cumulative number of vehicles in each traffic sub-zone as a constraint of the lower-level single-sub-zone optimization control model, optimize the timing of each intersection within the traffic sub-zone, and obtain the optimal timing plan for the controlled road network; the single-sub-zone optimization control model uses a feedback controller to perform boundary control to maintain the optimal cumulative number of vehicles under the coordinated control of each traffic sub-zone; the optimal solution of the upper-level multi-sub-zone coordinated control model is used as the connection constraint of the signal timing optimization model within the lower-level sub-zone, and the feedback controller is used to perform boundary control to maintain the optimal number of vehicles under the coordinated control of each sub-zone. A maximum control time for the road network is set, and once the maximum control time for the road network is exceeded, the sub-zone optimization stage must be re-entered; the entry and exit flow at the sub-zone boundary intersection and the multi-sub-zone boundary traffic flow model are used to calculate the deviation between the optimal cumulative number of vehicles and the actual cumulative number of vehicles in the sub-zone in the next sampling period, and the deviation is used as the input of the feedback controller to allocate the green signal ratio at the boundary intersection; under the constraint of maintaining the optimal sub-zone cumulative number of vehicles under boundary control, the signal timing of each intersection is optimized within the framework of a distributed optimization algorithm with delay time and queue length as optimization objectives.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the distributed collaborative optimization method for multiple traffic sub-areas based on a double-layer architecture according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the multi-traffic sub-area distributed collaborative optimization method based on a two-layer architecture as described in any one of claims 1 to 6 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the multi-traffic sub-area distributed collaborative optimization method based on a two-layer architecture as described in any one of claims 1 to 6.

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