A method and device for joint control of urban road network boundary control and path allocation

Abstract

The application discloses a kind of urban road network boundary control and path allocation joint control method, comprising the following steps: (1) obtaining the geometric data of road network in target period, regional division data, the macroscopic basic graph function of each region, the critical value of vehicle number, the saturation flow rate of regional boundary intersection, geometric data and optional phase, macroscopic control step and microcosmic control step;(2) with the road network based on region as the research object, the total number of vehicle in each region, vehicle position and destination are obtained, and the boundary control and path allocation joint optimization model of multi-region network is constructed and solved;(3) with each regional boundary as the research object, the number of queuing vehicles in import lane and export lane of regional boundary intersection is obtained, and the optimal phase of regional boundary intersection is determined;(4) with each region as the research object, the vehicle position in region, destination and optional path set are obtained, and the path allocation model is constructed and solved, to allocate specific path for vehicle in region.

Description

Technical Field

[0001] This invention belongs to the field of traffic safety control, specifically relating to a method and apparatus for joint control of urban road network boundary control and path allocation. Background Technology

[0002] Perimeter control and route assignment are effective measures to control traffic flow and prevent congestion from worsening. Both affect traffic distribution and traffic propagation in the road network. Therefore, it is necessary to analyze the relationship between boundary control and route assignment and perform joint optimization to improve the traffic efficiency of large-scale transportation networks.

[0003] Most existing research addresses the combined optimization problem of perimeter control and route allocation at a macro level, and its solutions cannot be directly applied to reality. Specifically, many strategies allocate traffic flow based on regions rather than assigning a specific route to each vehicle. Furthermore, perimeter control always focuses on the inbound and outbound traffic between adjacent areas, without considering the passage of vehicles from other approach lanes at boundary intersections, and often ignores the capacity constraints of downstream road segments at boundary intersections.

[0004] This patent aims to provide a joint control method for boundary control and route allocation in multi-regional road networks. At the upper level, a joint optimization model for boundary control and route allocation is constructed and solved, and the solution of the model is used as the main control objective for lower-level boundary control and vehicle route allocation. A mixed phase is defined to describe the signal control scheme at boundary intersections, considering the passage of all approach lanes at the intersection and the capacity constraints of downstream road segments. A route selection model is constructed to allocate routes to each vehicle to approximate the solution of the upper-level joint optimization model while ensuring regional homogeneity. Summary of the Invention

[0005] Purpose of the invention: In view of the shortcomings of existing methods, the purpose of this invention is to simultaneously optimize regional boundary control and vehicle route allocation schemes in order to control the traffic distribution of large-scale road networks, avoid congestion accumulation, and improve traffic efficiency.

[0006] Technical Solution: To achieve the above-mentioned objectives, this invention proposes a joint control method for urban road network boundary control and path allocation, which includes the following steps:

[0007] (1) Obtain the geometric data, regional division data, macro basic graph functions of each region and critical values ​​of vehicle number, saturation flow rate of regional boundary intersections, geometric data and optional phases, macro control step size and micro control step size of the road network within the target time period;

[0008] (2) In the macro-control step, taking the regional road network as the research object, the total number of vehicles, vehicle location and destination of each region are obtained, and a joint optimization model of boundary control and path allocation for multi-regional network is constructed and solved. The joint optimization model is a two-step quadratic programming model. In the first step model, the objective is to minimize the difference between the number of vehicles in each region and its critical value. The optimization variable is the product of the boundary control parameter and the path allocation parameter. The constraints of the optimization model include the dynamic balance constraint of regional accumulation and the maximum through flow rate constraint between two adjacent regions. In the second step model, the objective is to minimize the difference between the product of the boundary control parameter and the path allocation parameter and the optimization result of the first step model. The optimization variables are the reciprocal of the boundary control parameter and the path allocation parameter. The constraints of the optimization model include the boundary control parameter constraint and the path allocation parameter constraint.

[0009] (3) In the micro-control step, taking each area boundary as the research object, the number of vehicles queuing at the entrance and exit lanes of the area boundary intersection is obtained. Based on the boundary control parameters and path allocation parameters obtained in step (2), the optimal phase of the intersection of the area boundary is determined, and boundary control is implemented. The boundary control method is as follows: First, based on the solution of the joint optimization model, the expected throughput rate of the macro-control step and the micro-control step is calculated; Second, the estimated throughput rate of each mixed phase is calculated, and the mixed phase that meets the expected throughput rate is selected; Third, the weight of the mixed phase is calculated, the mixed phase with the largest weight is selected, and it is confirmed whether a protection mechanism needs to be implemented.

[0010] (4) In the micro-control step, each region is taken as the research object, and the set of vehicle locations, destinations and optional routes in the region is obtained. Based on the route allocation parameters obtained in step (2), a route allocation model is constructed and solved to allocate specific routes to vehicles in the region. The model uses the probability of vehicles in the region choosing each route as a variable, with the goal of making the proportion of vehicles based on the region's routes consistent with the route allocation parameters obtained in step (2) and minimizing regional heterogeneity.

[0011] In macro-control, the entire time period of the control process is divided into multiple sub-periods, namely macro-control steps. In each macro-control step, step (2) is executed. Each macro-control step is further divided into multiple sub-periods, namely micro-control steps. In each micro-control step, steps (3) and (4) are executed.

[0012] Furthermore, in step (2), the optimization variables of the first step model are ,in, , These are the boundary control parameters, i.e., the macroscopic control steps. From the region Entering the area The proportion of vehicles; Assigning parameters to the path, i.e., the macro-control steps. area China-Israel region Vehicles destined for the region The proportion of vehicles in the next sub-region;

[0013] The optimization objective of the first step model is expressed as follows:

[0014]

[0015] in, A collection of regions within the road network. For macro-control steps Central region The number of vehicles, For the region The critical value for the number of vehicles;

[0016] The dynamic equilibrium constraint of regional accumulation is expressed as follows:

[0017]

[0018]

[0019]

[0020] in, For macro-control steps area China-Israel region The number of vehicles at the destination. For macro-control steps area China-Israel region The number of vehicles at the destination; For macro-control steps area Newly generated regions The number of vehicles at the destination. For macro-control steps area Newly generated regions The number of vehicles at the destination; For macro-control steps area China-Israel region Vehicles destined for the region The proportion of vehicles in the next sub-region. For macro-control steps area China-Israel region Vehicles destined for the region The proportion of vehicles in the next sub-region; For macro-control steps From the region Entering the area The proportion of vehicles; For the region The set of adjacent regions; For macroscopic control of step size; For macro-control steps area The completed flow rate, The macroscopic basic graph function with the number of vehicles as the variable is represented as: ,in, For function parameters;

[0021] The maximum throughflow rate constraint between two adjacent regions is expressed as:

[0022]

[0023] in, These are the macro-control steps. From the region To the area The minimum and maximum through flow rates.

[0024] Furthermore, in step (2), the optimization of the second-step model becomes the boundary control parameters. reciprocal and path allocation parameters ;

[0025] The optimization objective of the second step model is expressed as:

[0026]

[0027] Boundary control parameter constraints are expressed as follows:

[0028]

[0029] The path allocation parameter constraints are expressed as follows:

[0030]

[0031]

[0032] in, They are respectively The minimum and maximum values.

[0033] Furthermore, in step (3), the formula for calculating the expected flow rate of the macro-control step is:

[0034]

[0035] in, For macro-control steps From the region To the area Expected throughput;

[0036] The formula for calculating the expected flow rate of the microcontrolling step is:

[0037]

[0038] in, This is the set of micro-control steps corresponding to macro-control steps. For micro-control steps From the region To the area Expected throughput; For micro-control steps From the region To the area The actual throughflow rate; This represents the number of micro-control steps corresponding to macro-control steps. For micro-control of step size.

[0039] Furthermore, in step (3), the mixed phase is the phase combination at the intersection of the region boundary, and the estimation of each mixed phase in the micro-control step is achieved through the flow rate calculation formula:

[0040]

[0041] in, For micro-control steps Mixed phase From the area under control To the area The estimate is based on the flow rate; For mixed phase The set of phases at each intersection; In phase Released from the area With the region The collection of inlet channels leading to the destination; For import channels The corresponding set of exit lanes; For lane saturation flow rate; To reach the micro-control step k The number of vehicles at the lane stop line or joining the queue is estimated based on the position and speed of vehicles in the current lane; Microscopic control steps Middle lane The number of vehicles queuing; For import channels The corresponding number of exit lanes; For lane Traffic capacity;

[0042] Mixed phase that meets expected flow rate It meets the following conditions:

[0043]

[0044]

[0045] in, For micro-control steps From the area via non-signalized intersection To the area The estimated pass rate For micro-control steps From the area via non-signalized intersection To the area The estimated pass rate; The threshold value is used.

[0046] Furthermore, the formula for calculating the weight of the mixed phase in step (3) is as follows:

[0047]

[0048] in, For micro-control steps Mixed phase The weights; For micro-control steps Mid-phase The weights;

[0049]

[0050] in, For phase Vehicles entering the lanes that are open when the lights are turned on should assemble. For micro-control steps Middle lane The number of vehicles in the queue.

[0051] Furthermore, the protection mechanism described in step (3) is as follows: if the weight of a certain phase at the boundary intersection is greater than four times the average weight of all phases at the intersection, the phase is forcibly activated.

[0052] Furthermore, in step (4), the optimization variables of the described path allocation model are: , indicating micro-control steps area The destination is a region vehicles Select path The probability of is given by the objective function of the path allocation model as:

[0053]

[0054] in, As an indicator of regional heterogeneity, , For parameters; For the region The destination is a region A collection of vehicles; For vehicles The set of optional paths; For vehicles Select path The next area to be reached; For the region A collection of road sections; For micro-control steps End of section The estimated density, ; It is a region Average road segment density, , For micro-control steps area The number of vehicles, , They are The number of lanes and road length of the road section For the region A collection of road sections; Microscopic control steps The estimated route the vehicle will reach at the end;

[0055] The constraints of the path assignment model are expressed as follows:

[0056]

[0057] .

[0058] Based on the same inventive concept, the present invention provides a joint control device for urban road network boundary control and path allocation, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the computer program, when loaded onto the processor, implements any of the aforementioned joint control methods for urban road network boundary control and path allocation.

[0059] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0060] This invention focuses on large-scale urban road networks, simultaneously optimizing regional boundary control and vehicle path allocation schemes to control traffic distribution and evolution within these networks, preventing congestion and improving overall network efficiency. Specifically, this invention optimizes regional boundary control and vehicle path allocation for large-scale urban road networks, contributing to the control of traffic distribution and evolution and preventing congestion. Attached Figure Description

[0061] Figure 1 This is a flowchart of a method according to an embodiment of the present invention.

[0062] Figure 2 This is a schematic diagram of a road network as exemplified in an embodiment of the present invention.

[0063] Figure 3 The numbers of vehicles and traffic volume in each area are examples in this embodiment of the invention. Detailed Implementation

[0064] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solutions of the present invention and provide detailed implementation methods and specific operation processes. However, the scope of protection of the present invention is not limited to the following embodiments.

[0065] like Figure 1 As shown, this invention proposes a joint control method for urban road network boundary control and path allocation, which includes the following steps:

[0066] (1) Obtain the geometric data, regional division data, macro basic graph functions of each region and critical values ​​of vehicle number, saturation flow rate of regional boundary intersections, geometric data and optional phases, macro control step size and micro control step size of the road network within the target time period;

[0067] (2) In the macro-control step, taking the regional road network as the research object, the total number of vehicles, vehicle location and destination of each region are obtained, and a joint optimization model of boundary control and path allocation for multi-regional network is constructed and solved. The joint optimization model is a two-step quadratic programming model. In the first step model, the objective is to minimize the difference between the number of vehicles in each region and its critical value. The optimization variable is the product of the boundary control parameter and the path allocation parameter. The constraints of the optimization model include the dynamic balance constraint of regional accumulation and the maximum through flow rate constraint between two adjacent regions. In the second step model, the objective is to minimize the difference between the product of the boundary control parameter and the path allocation parameter and the optimization result of the first step model. The optimization variables are the reciprocal of the boundary control parameter and the path allocation parameter. The constraints of the optimization model include the boundary control parameter constraint and the path allocation parameter constraint.

[0068] (3) In the micro-control step, taking each area boundary as the research object, the number of vehicles queuing at the entrance and exit lanes of the area boundary intersection is obtained. Based on the boundary control parameters and path allocation parameters obtained in step (2), the optimal phase of the intersection of the area boundary is determined, and boundary control is implemented. The boundary control method is as follows: First, based on the solution of the joint optimization model, the expected throughput rate of the macro-control step and the micro-control step is calculated; Second, the estimated throughput rate of each mixed phase is calculated, and the mixed phase that meets the expected throughput rate is selected; Third, the weight of the mixed phase is calculated, the mixed phase with the largest weight is selected, and it is confirmed whether a protection mechanism needs to be implemented.

[0069] (4) In the micro-control step, each region is taken as the research object, and the set of vehicle locations, destinations and optional routes in the region is obtained. Based on the route allocation parameters obtained in step (2), a route allocation model is constructed and solved to allocate specific routes to vehicles in the region. The model uses the probability of vehicles in the region choosing each route as a variable, with the goal of making the proportion of vehicles based on the region's routes consistent with the route allocation parameters obtained in step (2) and minimizing regional heterogeneity.

[0070] In macro-control, the entire time period of the control process is divided into multiple sub-periods, namely macro-control steps. In each macro-control step, step (2) is executed. Each macro-control step is further divided into multiple sub-periods, namely micro-control steps. In each micro-control step, steps (3) and (4) are executed.

[0071] Furthermore, in step (2), the optimization variables of the first step model are ,in, , These are the boundary control parameters, i.e., the macroscopic control steps. From the region Entering the area The proportion of vehicles; Assigning parameters to the path, i.e., the macro-control steps. area China-Israel region Vehicles destined for the region The proportion of vehicles in the next sub-region;

[0072] The optimization objective of the first step model is expressed as follows:

[0073]

[0074] in, A collection of regions within the road network. For macro-control steps Central region The number of vehicles, For the region The critical value for the number of vehicles;

[0075] The dynamic equilibrium constraint of regional accumulation is expressed as follows:

[0076]

[0077]

[0078]

[0079] in, For macro-control steps area China-Israel region The number of vehicles at the destination. For macro-control steps area China-Israel region The number of vehicles at the destination; For macro-control steps area Newly generated regions The number of vehicles at the destination. For macro-control steps area Newly generated regions The number of vehicles at the destination; For macro-control steps area China-Israel region Vehicles destined for the region The proportion of vehicles in the next sub-region. For macro-control steps area China-Israel region Vehicles destined for the region The proportion of vehicles in the next sub-region; For macro-control steps From the region Entering the area The proportion of vehicles; For the region The set of adjacent regions; For macroscopic control of step size; For macro-control steps area The completed flow rate, The macroscopic basic graph function with the number of vehicles as the variable is represented as: ,in, For function parameters;

[0080] The maximum throughflow rate constraint between two adjacent regions is expressed as:

[0081]

[0082] in, These are the macro-control steps. From the region To the area The minimum and maximum through flow rates.

[0083] Furthermore, in step (2), the optimization of the second-step model becomes the boundary control parameters. reciprocal and path allocation parameters ;

[0084] The optimization objective of the second step model is expressed as:

[0085]

[0086] Boundary control parameter constraints are expressed as follows:

[0087]

[0088] The path allocation parameter constraints are expressed as follows:

[0089]

[0090]

[0091] in, They are respectively The minimum and maximum values.

[0092] Furthermore, in step (3), the formula for calculating the expected flow rate of the macro-control step is:

[0093]

[0094] in, For macro-control steps From the region To the area Expected throughput;

[0095] The formula for calculating the expected flow rate of the microcontrolling step is:

[0096]

[0097] in, This is the set of micro-control steps corresponding to macro-control steps. For micro-control steps From the region To the area Expected throughput; For micro-control steps From the region To the area The actual throughflow rate; This represents the number of micro-control steps corresponding to macro-control steps. For micro-control of step size.

[0098] Furthermore, in step (3), the mixed phase is the phase combination at the intersection of the region boundary, and the estimation of each mixed phase in the micro-control step is achieved through the flow rate calculation formula:

[0099]

[0100] in, For micro-control steps Mixed phase From the area under control To the area The estimate is based on the flow rate; For mixed phase The set of phases at each intersection; In phase Released from the area With the region The collection of inlet channels leading to the destination; For import channels The corresponding set of exit lanes; For lane saturation flow rate; To reach the micro-control step k The number of vehicles at the lane stop line or joining the queue is estimated based on the position and speed of vehicles in the current lane; Microscopic control steps Middle lane The number of vehicles queuing; For import channels The corresponding number of exit lanes; For lane Traffic capacity;

[0101] Mixed phase that meets expected flow rate It meets the following conditions:

[0102]

[0103]

[0104] in, For micro-control steps From the area via non-signalized intersection To the area The estimated pass rate For micro-control steps From the area via non-signalized intersection To the area The estimated pass rate; The threshold value is used.

[0105] Furthermore, the formula for calculating the weight of the mixed phase in step (3) is as follows:

[0106]

[0107] in, For micro-control steps Mixed phase The weights; For micro-control steps Mid-phase The weights;

[0108]

[0109] in, For phase Vehicles entering the lanes that are open when the lights are turned on should assemble. For micro-control steps Middle lane The number of vehicles in the queue.

[0110] Furthermore, the protection mechanism described in step (3) is as follows: if the weight of a certain phase at the boundary intersection is greater than four times the average weight of all phases at the intersection, the phase is forcibly activated.

[0111] Furthermore, in step (4), the optimization variables of the described path allocation model are: , indicating micro-control steps area The destination is a region vehicles Select path The probability of is given by the objective function of the path allocation model as:

[0112]

[0113] in, As an indicator of regional heterogeneity, , For parameters; For the region The destination is a region A collection of vehicles; For vehicles The set of optional paths; For vehicles Select path The next area to be reached; For the region A collection of road sections; For micro-control steps End of section The estimated density, ; It is a region Average road segment density, , For micro-control steps area The number of vehicles, , They are The number of lanes and road length of the road section For the region A collection of road sections; Microscopic control steps The estimated route the vehicle will reach at the end;

[0114] The constraints of the path assignment model are expressed as follows:

[0115]

[0116] .

[0117] Based on the same concept, the present invention provides a joint control device for urban road network boundary control and path allocation, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the computer program, when loaded onto the processor, implements any of the aforementioned joint control methods for urban road network boundary control and path allocation.

[0118] The method of this embodiment of the invention will be further illustrated below with a specific example:

[0119] (1) Overview of the designed road network

[0120] A study was conducted using a portion of the road network in Yangzhou City as an example. The road network geometry and zoning results are as follows: Figure 2 As shown, the road network is divided into 6 zones, with critical vehicle counts of 2765, 2561, 1287, 2420, 2315, and 2307 vehicles in each zone, respectively. SUMO simulation of traffic conditions is used, and the simulation interacts with the proposed method.

[0121] (2) Control Scene

[0122] Consider three different control scenarios: 1) the joint control of boundary control and path allocation proposed in this patent; 2) only boundary control; 3) back pressure control at the intersection of regional boundaries.

[0123] (3) Optimization results

[0124] The vehicle count curves for each area under three different control scenarios are as follows: Figure 3 As shown, Figure 3 In the diagram, a, b, c, d, e, and f represent the vehicle count curves for regions 1, 2, 3, 4, 5, and 6, respectively. Under joint control, the vehicle counts in regions 2, 3, and 4 approach the critical value, effectively improving the regional completion flow rate. For regions 1 and 5, the vehicle counts under joint control decrease rapidly over time, due to their lower traffic demand and the fact that most vehicles have already left these regions in subsequent time. Throughput results further validate the control effect, showing that the throughput of joint control is significantly better than that of boundary control and backpressure control.

[0125] Finally, it should be noted that those skilled in the art will understand that many technical details have been presented in the embodiments of this application to facilitate a better understanding of the present application. However, even without these technical details and various changes and modifications based on the above embodiments, the technical solutions claimed in the claims of this application can be substantially achieved. Therefore, in practical applications, various changes can be made to the above embodiments in form and detail without departing from the spirit and scope of this application.

Claims

1. A joint control method for urban road network boundary control and path allocation, characterized in that, The method includes the following steps: (1) Obtain the geometric data, regional division data, macro basic graph functions of each region and critical values ​​of vehicle number, saturation flow rate of regional boundary intersections, geometric data and optional phases, macro control step size and micro control step size of the road network within the target time period; (2) In the macro-control step, taking the regional road network as the research object, the total number of vehicles, vehicle location and destination of each region are obtained, and a joint optimization model of boundary control and path allocation for multi-regional network is constructed and solved. The joint optimization model is a two-step quadratic programming model. In the first step model, the objective is to minimize the difference between the number of vehicles in each region and its critical value. The optimization variable is the product of the boundary control parameter and the path allocation parameter. The constraints of the optimization model include the dynamic balance constraint of regional accumulation and the maximum through flow rate constraint between two adjacent regions. In the second step model, the objective is to minimize the difference between the product of the boundary control parameter and the path allocation parameter and the optimization result of the first step model. The optimization variables are the reciprocal of the boundary control parameter and the path allocation parameter. The constraints of the optimization model include the boundary control parameter constraint and the path allocation parameter constraint. (3) In the micro-control step, taking each area boundary as the research object, the number of vehicles queuing at the entrance and exit lanes of the area boundary intersection is obtained. Based on the boundary control parameters and path allocation parameters obtained in step (2), the optimal phase of the intersection of the area boundary is determined, and boundary control is implemented. The boundary control method is as follows: First, based on the solution of the joint optimization model, the expected throughput rate of the macro-control step and the micro-control step is calculated; Second, the estimated throughput rate of each mixed phase is calculated, and the mixed phase that meets the expected throughput rate is selected; Third, the weight of the mixed phase is calculated, the mixed phase with the largest weight is selected, and it is confirmed whether a protection mechanism needs to be implemented. (4) In the micro-control step, each region is taken as the research object, and the set of vehicle locations, destinations and optional routes in the region is obtained. Based on the route allocation parameters obtained in step (2), a route allocation model is constructed and solved to allocate specific routes to vehicles in the region. The model uses the probability of vehicles in the region choosing each route as a variable, with the goal of making the proportion of vehicles based on the region's routes consistent with the route allocation parameters obtained in step (2) and minimizing regional heterogeneity. In macro-control, the entire time period of the control process is divided into multiple sub-periods, namely macro-control steps. In each macro-control step, step (2) is executed. Each macro-control step is further divided into multiple sub-periods, namely micro-control steps. In each micro-control step, steps (3) and (4) are executed.

2. The method for joint control of urban road network boundary control and path allocation according to claim 1, characterized in that, In step (2), the optimization variables of the first step model are ,in, , These are the boundary control parameters, i.e., the macroscopic control steps. From the region Entering the area The proportion of vehicles; Assigning parameters to the path, i.e., the macro-control steps. area China-Israel region Vehicles destined for the region The proportion of vehicles in the next sub-region; The optimization objective of the first step model is expressed as follows: ； in, A collection of regions within the road network. For macro-control steps Central region The number of vehicles, For the region The critical value for the number of vehicles; The dynamic equilibrium constraint of regional accumulation is expressed as follows: ； ； ； in, For macro-control steps area China-Israel region The number of vehicles at the destination. For macro-control steps area China-Israel region The number of vehicles at the destination; For macro-control steps area Newly generated regions The number of vehicles at the destination. For macro-control steps area Newly generated regions The number of vehicles at the destination; For macro-control steps area China-Israel region Vehicles destined for the region The proportion of vehicles in the next sub-region. For macro-control steps area China-Israel region Vehicles destined for the region The proportion of vehicles in the next sub-region; For macro-control steps From the region Entering the area The proportion of vehicles; For the region The set of adjacent regions; For macroscopic control of step size; For macro-control steps area The completed flow rate, The macroscopic basic graph function with the number of vehicles as the variable is represented as: ,in, For function parameters; The maximum throughflow rate constraint between two adjacent regions is expressed as: ； in, These are the macro-control steps. From the region To the area The minimum and maximum through flow rates.

3. The method for joint control of urban road network boundary control and path allocation according to claim 2, characterized in that, In step (2), the optimization of the second model becomes the boundary control parameters. reciprocal and path allocation parameters ; The optimization objective of the second step model is expressed as: ； Boundary control parameter constraints are expressed as follows: ； The path allocation parameter constraints are expressed as follows: ； ； in, They are respectively The minimum and maximum values.

4. The method for joint control of urban road network boundary control and path allocation according to claim 3, characterized in that, In step (3), the formula for calculating the expected flow rate of the macro-control step is: ； in, For macro-control steps From the region To the area Expected throughput; The formula for calculating the expected flow rate of the microcontrolling step is: ； in, This is the set of micro-control steps corresponding to macro-control steps. For micro-control steps From the region To the area Expected throughput; For micro-control steps From the region To the area The actual throughflow rate; This represents the number of micro-control steps corresponding to macro-control steps. For micro-control of step size.

5. The method for joint control of urban road network boundary control and path allocation according to claim 4, characterized in that, In step (3), the mixed phase is the phase combination at the intersection of the region boundary. The estimation of each mixed phase in the micro-control step is achieved through the flow rate calculation formula: ； in, For micro-control steps Mixed phase Controlled from the area To the area The estimate is based on the flow rate; For mixed phase The set of phases at each intersection; In phase Released from the area With the region The collection of inlet channels leading to the destination; For import channels The corresponding set of exit lanes; For lane saturation flow rate; To reach the micro-control step k The number of vehicles at the lane stop line or joining the queue is estimated based on the position and speed of vehicles in the current lane; Microscopic control steps Middle lane The number of vehicles queuing; For import channels The corresponding number of exit lanes; For lane Traffic capacity; Mixed phase that meets expected flow rate It meets the following conditions: ； ； in, For micro-control steps From the area via non-signalized intersection To the area The estimated pass rate For micro-control steps From the area via non-signalized intersection To the area The estimated pass rate; The threshold value is used.

6. The method for joint control of urban road network boundary control and path allocation according to claim 5, characterized in that, The formula for calculating the weight of the mixed phase in step (3) is as follows: ； in, For micro-control steps Mixed phase The weights; For micro-control steps Mid-phase The weights; ； in, For phase Vehicles entering the lanes that are open when the lights are turned on should assemble. For micro-control steps Middle lane The number of vehicles in the queue.

7. The method for joint control of urban road network boundary control and path allocation according to claim 1, characterized in that, The protection mechanism described in step (3) is as follows: if the weight of a certain phase at the boundary intersection is greater than four times the average weight of all phases at the intersection, the phase is forcibly activated.

8. The method for joint control of urban road network boundary control and path allocation according to claim 6, characterized in that, In step (4), the optimization variables of the described path allocation model are: , indicating micro-control steps area The destination is a region vehicles Select path The probability of is given by the objective function of the path allocation model as: ； in, As an indicator of regional heterogeneity, , For parameters; For the region The destination is a region A collection of vehicles; For vehicles The set of optional paths; For vehicles Select path The next area to be reached; For the region A collection of road sections; For micro-control steps End of section The estimated density, ; It is a region Average road segment density, , For micro-control steps area The number of vehicles, , They are The number of lanes and road length of the road section For the region A collection of road sections; Microscopic control steps The estimated route the vehicle will reach at the end; The constraints of the path assignment model are expressed as follows: ； 。 9. A joint control device for urban road network boundary control and path allocation, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is loaded into the processor, it implements the joint control method for urban road network boundary control and path allocation according to any one of claims 1-8.

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