Multi-level maximum pressure traffic signal control method, application, system and computer readable storage medium

Abstract

The invention relates to the technical field of traffic control, in particular to a multi-level maximum pressure traffic signal control method, application and system and a computer readable storage medium. Traffic pressure regulation and control are carried out according to three levels of areas, paths and intersections, a maximum pressure boundary control strategy considering boundary flow control and traffic flow direction guidance is designed in the area layer, and supply and demand balance and overall stability of area traffic resources are achieved; a dynamic user equilibrium theory is introduced into a path layer, and vehicle path distribution is dynamically optimized based on the maximum pressure change of the path; the intersection layer optimizes the upstream and downstream traffic flow distribution of the intersection through a maximum pressure control method, and avoids the queuing overflow of vehicles at the intersection. The objective of the invention is to solve the problem of how to describe traffic flow dynamics under different spatial scales.

Description

Technical Field

[0001] This application relates to the field of traffic control technology, and in particular to a multi-level maximum pressure traffic signal control method, application, system, and computer-readable storage medium. Background Technology

[0002] The purpose of traffic signal control strategies is to achieve distributed control of regional intersections. However, traditional traffic signal control strategies may be difficult to effectively solve cross-regional traffic flow balance and bottleneck regulation when facing traffic congestion in larger areas.

[0003] Among relevant technical solutions, traffic signal control methods represented by maximum pressure control have attracted much attention due to their ability to optimize both queuing and throughput. This involves prioritizing the flow of traffic in each signal cycle that minimizes the "pressure" on the most congested path in the road network. However, maximum pressure control strategies primarily focus on local intersections, with insufficient research on regional and overall road network traffic operations. On the other hand, to achieve large-scale urban road network regional collaborative management, existing research has proposed using boundary control as a macro-control strategy. This involves regulating traffic flow entering a core area (such as the city center or commercial district) by controlling the boundary intersection signals, thereby protecting the traffic efficiency within the area. However, existing boundary control strategies mostly focus on regional coordination, with insufficient attention to path-level traffic flow optimization, which can easily lead to path-level selection imbalances and exacerbate congestion in key areas.

[0004] In view of this, this application proposes a new traffic signal control method, which aims to overcome the limitations of basic boundary control and maximum pressure control at various levels, and to more comprehensively describe traffic flow dynamics at different spatial scales, thereby achieving more accurate traffic signal control. Summary of the Invention

[0005] The main objective of this application is to provide a multi-level maximum pressure traffic signal control method, which aims to solve the problem of how to describe traffic flow dynamics at different spatial scales.

[0006] To achieve the above objectives, this application provides a multi-level maximum pressure traffic signal control method, the method comprising: S10: Collect the vehicle accumulation, average density, and average speed in the target area at the current time; calculate the area pressure value of the target area at the current time based on the vehicle accumulation, average density, and average speed; determine the correction control ratio based on the area pressure difference between two adjacent target areas; and perform proportional-integral feedback control based on the correction control ratio. S20, calculate the path pressure value of the target path at the current time according to the path type corresponding to the target path in the target area, and correct the steering control ratio according to the path pressure difference between two adjacent target paths, and perform proportional-integral feedback control based on the corrected steering control ratio. S30: Obtain the traffic flow pressure value of each phase at the target intersection, select the target phase with the maximum traffic flow pressure value, and set the position of that phase as a passage marker at the next time step.

[0007] Optionally, in step S10, the expression for calculating the regional pressure value is:

[0008] In the formula, This represents the regional pressure value of region i at the current time t. This represents the optimal vehicle accumulation amount when the total traffic flow in region i reaches its maximum. This indicates the accumulated number of vehicles. Let i be the free-flow velocity of the vehicle in region i. Indicates the average vehicle speed in the area. Indicates the average density of the region. This represents the optimal density corresponding to the maximum total flow rate when the vehicle density in region i reaches its maximum. , , To adjust the parameters, the effects of vehicle accumulation, average vehicle speed, and density on regional pressure were adjusted respectively.

[0009] Optionally, in step S10, the step of determining a correction control ratio based on the regional pressure difference between two adjacent target regions, and performing proportional-integral feedback control based on the correction control ratio, includes: S11, Determine the regional pressure difference between two adjacent target areas. :

[0010] In the formula, the table This shows the regional pressure value of region i at the current time t. This represents the regional pressure value of region j at the current time t. S12, determine the correction control ratio and the boundary pressure correction term based on the regional pressure difference. Correct control ratio for:

[0011] In the formula, This represents the control ratio before correction; K is the proportional gain matrix, representing the degree of influence of boundary pressure on the control ratio. S13, based on the modified control ratio Update the control ratio input formula:

[0012] In the formula, This represents the control ratio input at time step k, where k is the discrete time representation of continuous time. This represents the proportional gain matrix, used to control the proportional response to each state deviation; These are the proportional gain submatrices corresponding to the vehicle accumulation, regional average density, and regional average vehicle speed, respectively. This represents the integral gain matrix, used to control the integral response of each state deviation; , , These represent the average vehicle speed deviation, the regional average density deviation, and the regional average vehicle speed deviation at time step k, respectively. , These represent the average vehicle speed deviation, the regional average density deviation, and the regional average vehicle speed deviation at the previous time step k-1, respectively. , and These represent the area at its optimal balance, i.e., when the inflow and outflow are equal and the instantaneous vehicle accumulation in the area is 0, the area density, and the vehicle speed.

[0013] Optionally, in S20, the path pressure value for:

[0014] in:

[0015] In the formula, This represents the path pressure value of path z at time t. This represents the set of paths whose path type is a boundary path. The length of the queue segment in the path. Indicates the length of path z. To adjust the parameters, This represents the pressure per unit length of path at time t. This represents the optimal average density of vehicles on path z when the maximum total traffic flow is achieved. Indicates the current vehicle speed. This represents the optimal average vehicle speed for path z when the total traffic flow reaches its maximum. Indicates the current travel time. This represents the theoretical travel time required to travel a unit path within target region i at free-flow velocity. , , These are all adjustment parameters, representing the influence of average path density, average path speed, and path travel time, respectively. The adjustment parameters are different for different path types.

[0016] Optionally, in step S20, the step of correcting the steering control ratio based on the path pressure difference between two adjacent target paths, and performing proportional-integral feedback control based on the corrected steering control ratio, includes: S21, Determine the path pressure difference between the two target paths. :

[0017] In the formula, This represents the path pressure value of path z at time t. This represents the path pressure value of the downstream connecting path w adjacent to path z at time t. Represents the set of downstream connection paths; S22, based on the path pressure difference Corrected steering control ratio :

[0018] In the formula, This is the corrected steering control ratio. To adjust the influence coefficient of path pressure on steering control ratio; This is the set of downstream possible paths after the current path z has completed path selection; in:

[0019] S23, based on the modified steering control ratio When a vehicle on the current path z enters a downstream diversion node, it selects the allocation ratio of the downstream available paths.

[0020] Optionally, the expression for calculating the traffic flow pressure value is:

[0021] In the formula, This represents the traffic flow pressure at phase h at intersection m. The sum of This indicates the traffic flow pressure from road segment a to road segment b; in:

[0022]

[0023] In the formula, This represents the pressure weight of the traffic flow at time t. The saturation flow rate of the traffic flow. Let be the queue length from road segment a to road segment b. Let be the turning ratio from road segment b to a downstream road segment l. Let b be the set of all road segments downstream of road segment b. Let be the queue length from road segment b to a downstream road segment l.

[0024] Optionally, the passage identification time of phase h at time t. According to phase pressure Total phase pressure The weights are allocated accordingly:

[0025] In the formula, Indicates the signal period; in:

[0026] In the formula, To fix the total lost time, Total passage time. For phase set; in:

[0027] In the formula, Indicates the minimum pass time. This indicates the maximum passage time.

[0028] In addition, to achieve the above objectives, this application also provides a computer system comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the multi-level maximum pressure traffic signal control method as described in any of the preceding claims.

[0029] In addition, to achieve the above objectives, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the multi-level maximum pressure traffic signal control method as described in any of the preceding claims.

[0030] This application has at least the following beneficial effects: 1. It provides a more comprehensive and accurate description of traffic dynamics within and between regions, improving control and optimization effects while achieving a balance of traffic resources between regions; 2. It makes up for the shortcomings of basic boundary control at the path layer, and rationally guides vehicles to transfer to low-pressure paths based on path pressure, thereby alleviating regional congestion; 3. At the intersection level, maximum pressure control solved the queuing overflow problem of boundary control, while improving throughput. Attached Figure Description

[0031] Figure 1 This is a flowchart illustrating the multi-level maximum pressure traffic signal control method according to an embodiment of this application; Figure 2 For the vehicle accumulation change graph of the Fixed method; Figure 3 A graph showing the accumulated changes in vehicles using the MP method; Figure 4 Accumulated change map of vehicles using the PC method Figure 5 This is a diagram showing the accumulated changes in vehicles according to the method (PRM-PC) of this application; Figure 6 This is a density weight distribution diagram for the Fixed method; Figure 7 The density weight distribution diagram for the MP method; Figure 8 This is a density weight distribution diagram for the PC method. Figure 9 This is a density weight distribution diagram of the method (PRM-PC) in this application; Figure 10 This is a density weight distribution diagram for the Fixed method; Figure 11 A graph showing the total travel time variation using the MP method; Figure 12 A graph showing the total travel time variation using the PC method; Figure 13 This is a graph showing the total travel time variation for the method (PRM-PC) in this application. Figure 14 This is a schematic diagram of the hardware operating environment of the computer system involved in the embodiments of this application.

[0032] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0033] To better understand the above technical solutions, exemplary embodiments of this disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this disclosure to those skilled in the art.

[0034] First Embodiment This embodiment provides a multi-level maximum pressure traffic signal control method. Before executing the steps of this method, a macroscopic fundamental diagram (MFD) model based on vehicle accumulation in multiple regions is first established: Consider a road network divided into N approximately homogeneous subregions, meaning they have similar traffic characteristics. Furthermore, each sub-region has a well-defined MFD. Let the current region be... The set of all regions adjacent to it is The accumulated number of vehicles in region i at time t is... The inflow and outflow traffic volumes are respectively , The demand for traffic flow is The demand for traffic flow includes both internal traffic demand and inflow from outside the region. For any region i, there exists a traffic flow conservation relationship at time t:

[0035] For any region i, there exists an MFD function Describe the amount of vehicles accumulated inside at time t. Total traffic volume Relationship between them:

[0036] Analyzing the flow transfer between two different regions i and j yields the following results:

[0037] In the formula, This represents the traffic flow that completes a journey within region i. Let be the traffic flow transferred from region i to one of its neighboring regions j. Let the control ratio be... This represents the proportion of vehicles entering area j from area i that are restricted from entering due to signal control or other means. For the two areas, there are two control ratios. , The actual transfer flow from region i to region j at time t:

[0038] Combining the actual inflow and outflow traffic flow equations with the traffic flow conservation equation, we can obtain a new conservation equation that incorporates the control ratio parameter:

[0039] Based on the above model architecture and the meaning of the defined parameters, refer to Figure 1 The method includes the following steps: S10: Collect the vehicle accumulation, average density, and average speed in the target area at the current time; calculate the area pressure value of the target area at the current time based on the vehicle accumulation, average density, and average speed; determine the correction control ratio based on the area pressure difference between two adjacent target areas; and perform proportional-integral feedback control based on the correction control ratio. In this embodiment, boundary pressure control is first implemented at the regional level. The regional pressure value is calculated using vehicle accumulation, regional average density, and regional average vehicle speed as indicators.

[0040] Optionally, the formula for calculating the regional pressure value is:

[0041] In the formula, This represents the regional pressure value of region i at the current time t. This represents the optimal vehicle accumulation amount when the total traffic flow in region i reaches its maximum. This indicates the accumulated number of vehicles. Let i be the free-flow velocity of the vehicle in region i. Indicates the average vehicle speed in the area. Indicates the average density of the region. This represents the optimal density corresponding to the maximum total flow rate when the vehicle density in region i reaches its maximum. , , To adjust the parameters, the effects of vehicle accumulation, average vehicle speed, and density on regional pressure were adjusted respectively.

[0042] Furthermore, the regional pressure value reflects the traffic pressure imbalance between regions due to different MFD characteristics and real-time traffic conditions. Similar to prioritizing the activation of higher pressure phases in maximum pressure control, the regional pressure model set in this step prioritizes guiding vehicles to regions with lower regional pressure. Boundary pressure optimizes the boundary control strategy in the direction by adjusting the control ratio; that is, the modified control ratio is determined by the regional pressure difference above the base control ratio. Specifically: S11, Determine the regional pressure difference between two adjacent target areas. :

[0043] In the formula, the table This shows the regional pressure value of region i at the current time t. This represents the regional pressure value of region j at the current time t. S12, determine the correction control ratio and the boundary pressure correction term based on the regional pressure difference. Correct control ratio for:

[0044] In the formula, This represents the control ratio before correction; K is the proportional gain matrix, representing the degree of influence of boundary pressure on the control ratio. S13, based on the modified control ratio Update the control ratio input formula:

[0045] In the formula, This represents the control ratio input at time step k, where k is the discrete time representation of continuous time. This represents the proportional gain matrix, used to control the proportional response to each state deviation; These are the proportional gain submatrices corresponding to the vehicle accumulation, regional average density, and regional average vehicle speed, respectively. This represents the integral gain matrix, used to control the integral response of each state deviation; , , These represent the average vehicle speed deviation, the regional average density deviation, and the regional average vehicle speed deviation at time step k, respectively. , These represent the average vehicle speed deviation, the regional average density deviation, and the regional average vehicle speed deviation at the previous time step k-1, respectively. , and These represent the area at its optimal balance, i.e., when the inflow and outflow are equal and the instantaneous vehicle accumulation in the area is 0, the area density, and the vehicle speed.

[0046] S20, calculate the path pressure value of the target path at the current time according to the path type corresponding to the target path in the target area, and correct the steering control ratio according to the path pressure difference between two adjacent target paths, and perform proportional-integral feedback control based on the corrected steering control ratio. In this embodiment, after regulation is performed at the region level, boundary pressure control is performed at the path level.

[0047] In a multi-regional road network, there are multiple paths connecting different regions, each with different characteristics and functions. Therefore, when modeling the traffic characteristics of internal routes within a region, classification is the first step.

[0048] In some alternative implementations, let R be the set of all paths in a certain region i within a multi-regional road network N. This set includes three path types: internal paths, boundary paths, and detour paths. Internal paths describe the circular traffic within the region, boundary paths connect different regions and handle cross-regional traffic demand, and detour paths do not cross region boundaries and are used to alleviate traffic pressure within a region. Assume that the density distribution of internal and detour paths is uniform, while boundary paths include queuing segments and free-flow segments. Queuing segments represent the portion of the path where vehicles are waiting in line at the path entrance due to congestion or inter-regional inflow restrictions.

[0049] Different path types have different weight values ​​(also known as adjustment parameters), so different types of paths will have different path pressure values ​​calculated under the same conditions.

[0050] Optionally, path pressure value The calculation expression is:

[0051] in:

[0052] In the formula, This represents the path pressure value of path z at time t. This represents the set of paths whose path type is a boundary path. The length of the queue segment in the path. Indicates the length of path z. To adjust the parameters, This represents the pressure per unit length of path at time t. This represents the optimal average density of vehicles on path z when the maximum total traffic flow is achieved. Indicates the current vehicle speed. This represents the optimal average vehicle speed for path z when the total traffic flow reaches its maximum. Indicates the current travel time. This represents the theoretical travel time required to travel a unit path within target region i at free-flow velocity. , , These are all adjustment parameters, representing the influence of average path density, average path speed, and path travel time, respectively. The adjustment parameters are different for different path types.

[0053] Furthermore, in S20, a path pressure model is also established at the path layer based on the maximum pressure control concept to describe the path traffic operation status. For any path within any region... The sets of its upstream and downstream connection paths are respectively , At time t, the non-boundary path z has density. Total path traffic have:

[0054] In the formula, Let z be the average vehicle speed corresponding to the density of path z at time t. Define the steering control ratio between path z and the downstream path set at time t. :

[0055] After introducing the steering control ratio, path z at time t has a nonlinear traffic flow conservation equation:

[0056] For the boundary path z, we have the traffic flow conservation equation:

[0057] In the formula , These represent the actual inflow and outflow traffic volumes at time t along the lower boundary path z.

[0058] A comprehensive pressure function based on factors such as path density, average vehicle speed, and travel time is used, with different weights assigned to accommodate the demand characteristics of three types of paths. Since the density distribution within the boundary paths is not uniform, with queuing sections and free-flow sections, the path pressure per unit length at time t is first defined, i.e., the road segment pressure. :

[0059] In the formula This is the travel time required to travel a unit of road segment at free-flow speed. , , These are the adjustment parameters, which determine the influence of the density, average speed, and travel time terms in the road segment pressure function. The path pressure z at time t is defined. :

[0060] In the formula This represents the length of the queue segment in the path. To adjust the parameters, greater weight is given to queuing segment pressure to represent the greater pressure in queuing segments within the boundary path. Turning pressure is defined as the pressure difference between paths. :

[0061] From the traveler's perspective, based on the deterministic user equilibrium principle (in an equilibrium state, no traveler can reduce their travel costs by unilaterally changing their route), it is determined which routes will actually be used. Only on this basis will the level of route pressure affect route selection, allowing system administrators to control high-pressure routes. DUE assumes that travelers choose based on a comparison of travel times for different routes until the travel times of all available routes are equal.

[0062]

[0063] In the formula The delay caused by vehicles entering the queuing section from the free-flow section. To travel through time zones, This refers to the travel time for the detour route. Let be the travel time per unit length required for a vehicle traveling in region i at free-flow speed. The path pressure model will further optimize path traffic flow allocation. Let be... The set of downstream possible paths after the deterministic user equilibrium principle is applied to the current path z. Steering pressure. Further path selection optimization is achieved by adding a correction term to the steering control ratio. The corrected steering control ratio is:

[0064] In the formula The degree of influence of adjusting path pressure on steering control ratio.

[0065] The results were: S21, Determine the path pressure difference between the two target paths. :

[0066] In the formula, This represents the path pressure value of path z at time t. This represents the path pressure value of the downstream connecting path w adjacent to path z at time t. Represents the set of downstream connection paths; S22, based on the path pressure difference Corrected steering control ratio :

[0067] In the formula, This is the corrected steering control ratio. To adjust the influence coefficient of path pressure on steering control ratio; This is the set of downstream possible paths after the current path z has completed path selection; in:

[0068] S23, based on the modified steering control ratio When a vehicle on the current path z enters a downstream diversion node, it selects the allocation ratio of the downstream available paths.

[0069] In this step, to ensure that the path selection can respond promptly to changes in path pressure, the steering control ratio is dynamically adjusted to implement proportional-integral feedback control. Based on the adjusted steering control ratio... This determines the allocation ratio of downstream available paths for vehicles on the current path z when they enter downstream diversion nodes.

[0070] S30: Obtain the traffic flow pressure value of each phase at the target intersection, select the target phase with the maximum traffic flow pressure value, and set the position of that phase as a passage marker at the next time step.

[0071] In this embodiment, after completing the boundary pressure control at the path level, the maximum pressure control method is applied at the intersection in this step to make it the current release phase, allowing the traffic flow in the corresponding direction to pass through the intersection in order to alleviate the high traffic pressure in that direction, release queues, and avoid upstream overflow.

[0072] Optionally, the traffic sign can be a green light or other sign indicating that vehicles are allowed to pass.

[0073] Optionally, a road network may be established. a signal intersection Its entrance road sections are set as follows The exit road sections are grouped as follows The phase set is Signal period The total time of fixed loss is The total passage time is At time t, the passage signal time at intersection m in phase h satisfies:

[0074] At the same time, any phase h satisfies the passage identifier time constraint:

[0075] Let there be a traffic flow in phase h. The upstream segment is a, and the downstream segment is b. The maximum pressure controller defines the traffic flow pressure weight at time t based on the difference in queue length between the upstream and downstream segments of the intersection.

[0076] In the formula, Let b be the set of all road segments downstream of road segment b. Let be the queue length from road segment a to road segment b. Let be the turning ratio from road segment b to a downstream road segment l. Let be the queue length from road segment b to a downstream road segment l. The traffic pressure of this flow is defined as the product of its pressure weight and saturation flow rate.

[0077] In the formula, This represents the saturation flow rate of that traffic stream. The pressure at intersection phase m (h) is the sum of the pressures of all its traffic streams.

[0078] The results were: The formula for calculating traffic flow pressure is:

[0079] In the formula, This represents the traffic flow pressure at phase h at intersection m. The sum of This indicates the traffic flow pressure from road segment a to road segment b; in:

[0080]

[0081] In the formula, This represents the pressure weight of the traffic flow at time t. The saturation flow rate of the traffic flow. Let be the queue length from road segment a to road segment b. Let be the turning ratio from road segment b to a downstream road segment l. Let b be the set of all road segments downstream of road segment b. Let be the queue length from road segment b to a downstream road segment l.

[0082] In the technical solution provided in this embodiment, traffic pressure is regulated at three levels: region, path, and intersection. At the region level, a maximum pressure boundary control strategy is designed that takes into account both boundary flow control and traffic flow direction guidance to achieve a balance between supply and demand of regional traffic resources and overall stability. At the path level, dynamic user equilibrium theory is introduced to dynamically optimize vehicle path allocation based on changes in maximum path pressure. At the intersection level, the maximum pressure control method is used to optimize the upstream and downstream traffic flow allocation at the intersection to avoid vehicle queue overflow at the intersection.

[0083] Second Embodiment Based on the first embodiment, in this embodiment, after step S12 and before step S13, the multi-level maximum pressure traffic signal control method further includes the following: Let the nonlinear traffic flow conservation equation for region i satisfy:

[0084] In the formula, This represents the instantaneous vehicle accumulation in region i at time t.

[0085] By linearizing using several sets of specific values, each set of specific values ​​can make the vehicle accumulation within region i equal to 0, thus achieving the optimal steady state. The linear steady-state flow conservation equation for region i is:

[0086] The solution to this equation provides multiple sets of control ratios that balance flow between regions. , To solve this equation, we define the variables and the steady-state deviation. It includes three types of system variable bias, vehicle cumulative bias. Average vehicle speed deviation Density deviation Transform the continuous-time state into a discrete-time state and write it in matrix form:

[0087] In the formula, Let be the control ratio deviation vector at time step k.

[0088] This is the state deviation vector. State matrix. This also reflects the interaction between vehicle accumulation, density, and average vehicle speed, and the control matrix. These reflect the impact of the control ratio on the three factors, respectively.

[0089] Based on the regional pressure model, a proportional-integral (PI) controller is designed to achieve boundary control. To reduce the system error generated in steady state, an integral term is introduced into the state vector to ensure that the controller accumulates the error for each state variable.

[0090] In the formula, The integral state vector contains the integral errors of vehicle accumulation, average density, and average speed. The integral state vector records the current cumulative deviation of the system, helping the system to further adjust its control behavior and gradually eliminate the deviation when faced with persistent errors. This is a diagonal integral matrix used to select the states to be integrated. LQI optimal control is achieved by optimizing the objective function.

[0091] In the formula This is the state deviation weight matrix. Let be the integral error weight matrix. The above formula minimizes the system performance deviation from the optimal state by optimizing the control ratio input. The optimal control performance is achieved by adjusting the corresponding weight matrices for each state variable, control input, and integral term in the system, typically using a trial-and-error method. First, the relative importance of each system state is analyzed, a suitable initial weight matrix is ​​set, and the controller performance is evaluated through simulation or application in a real system. Based on the evaluation results, it is determined whether the weights need to be adjusted.

[0092] Finally, based on the above optimization process, step S23 is executed.

[0093] Third Embodiment Based on any of the above embodiments, in this embodiment, the passage identification time of phase h at time t is... According to phase pressure Total phase pressure The weights are allocated accordingly:

[0094] In the formula, Indicates the signal period; in:

[0095] In the formula, To fix the total lost time, Total passage time. For phase set; in:

[0096] In the formula, Indicates the minimum pass time. This indicates the maximum passage time.

[0097] In the technical solution provided in this embodiment, the traffic flow pressure of different phases often varies significantly. If the phase order is selected solely based on the maximum pressure without allocating the passage sign time according to the pressure weight ratio, uneven use of passage sign resources and a decrease in overall throughput may occur. Weighting based on phase pressure allows for the proportional allocation of the total passage sign time according to the real-time traffic demand of different phases, enabling high-pressure phases to receive longer passage times. This alleviates queuing in severely congested directions, prevents upstream overflow, improves the utilization efficiency of passage sign resources, and ensures stable operation of the intersection under high-load conditions.

[0098] Verification Implementation Examples In this embodiment, a certain area was selected as a simulated road network to verify the signal control method of this patent. This area was modeled in SUMO, containing approximately 262 signalized intersections and 1040 road segments of varying lengths and lane numbers, with a free-flow speed of 50 km / h. The intersection traffic lights operated according to a fixed timing schedule with a constant cycle length. The road network was divided into four sub-regions using a "snake" algorithm. The results are shown in Tables 1 to 3 below: Table 1 Average vehicle delay time in each region

[0099] Table 2 Average total travel time for each region

[0100] Table 3 Average total queue length in each region

[0101] As can be seen from the results in Tables 1, 2, and 3, compared with the Fixed control method, the Maximum Pressure Control method (MP), and the Basic Boundary Control method (PC), the method of this application (PRMPC) reduces the average vehicle delay time by 4.3%-41.0%, 3.4%-47.2%, and 0.9%-25.1%, respectively; the average total travel time is reduced by 40.3%-65.1%, 7.1%-55.9%, and 24.8%-64.7%, respectively; and the average queue length is reduced by 33.9%-72.8%, 0.8%-66.6%, and 22.6%-72.2%, respectively. At the same time, the density weight changes are smaller, and it shows stable optimization effects at all levels.

[0102] In addition, the changes in vehicle accumulation, density weight distribution, and total travel time in each region under the four schemes of Fixed, MP, PC, and PRMPC are as follows: Figure 2 — Figure 5 , Figure 6 — Figure 8 ,as well as Figure 9 — Figure 13As shown.

[0103] from Figures 2 to 5 As can be seen, after about 3 hours of simulation, all regions exhibited a certain degree of distribution imbalance. In particular, region 3, with its high traffic volume, occupied a large density distribution weight under the Fixed, MP, and PC schemes, while region 2, with its low demand, consistently occupied a low density distribution weight. This indicates that the overall heterogeneity of the road network was continuously amplified. A large influx of vehicles into region 3 caused its traffic demand and pressure to increase dramatically, resulting in a severe imbalance in congestion compared to other regions, while region 2 had not yet reached saturation. The PRMPC scheme significantly alleviated the density distribution imbalance in region 3 by guiding vehicles to region 2, which was not yet congested, thus increasing the density distribution weight of region 2. This maintained a relatively uniform density distribution across regions while reducing the accumulation of vehicles in each region, meaning that PRMPC effectively improved the overall throughput of the road network. from Figures 6 to 8 As can be seen, the normalized density weights of different regions at different time periods represent the relative density between regions under different schemes. The different colored blocks intuitively reflect the pressure distribution and traffic operation status of different regions in the road network. Under the Fixed scheme, the severe congestion of the road network is manifested by the rapid increase in the density weights of each region. After 2 hours, regions 1 and 3 both reach the red peak, and the vehicles have not completely dissipated even near the end of the simulation. The MP scheme performs better than the Fixed scheme during the high-demand periods in regions 1 and 3. However, although MP can improve the queuing overflow problem in high-traffic areas, the inter-regional control ratio remains constant, making the density weights of regions 2 and 4 even higher than those of the Fixed scheme during high-demand periods. The PC scheme significantly improves the high-density state of regions 2 and 4 through inter-regional regulation, but it is not sensitive enough to direction and heterogeneity. The density weight of region 1 increases faster than that of the MP scheme. The PRMPC scheme, by introducing pressure correction in PC, better considers the differences in traffic status among multiple regions, achieving a decrease in density weights throughout the simulation period. The road network reaches a stable state as early as 14000 s, and the density distribution of each region is more balanced. from Figures 9 to 13As can be seen, the Fixed scheme caused severe congestion due to static control, with the total travel time in each area maintaining a rapid increase throughout the simulation period. Area 3 even showed a near-linear steep increase during high-demand periods. The MP scheme, through phase initiation sequence adjustment at the intersection level, effectively reduced the growth rate and peak value of the total travel time in areas 1 and 3. However, the optimization effect in areas 2 and 4 was not significant. The PC scheme, lacking path-level guidance mechanisms and micro-control at the intersection level, was significantly weaker than MP in reducing the total travel time in areas 1 and 3, both in terms of peak level and growth rate. The peak value of the total travel time in area 1 even exceeded that of the Fixed scheme. However, the optimization effect in areas 2 and 4 was stronger than that of MP, which also reflects the limitations of the MP scheme at the area level. The PRMPC scheme effectively solved the problems of PC through its pressure guidance strategy at the path level and deepened the macro-level traffic flow control at the area level based on MP. It significantly reduced the total travel time in each area, especially area 1, and effectively alleviated the growth rate of the total travel time in area 3, which had more severe congestion. At the same time, it reached the steady state earlier than other schemes. Furthermore, as an implementation scheme, this embodiment also provides a multi-level maximum pressure traffic signal control method as described in any of the preceding claims, and its application in traffic signal control.

[0104] Furthermore, as an implementation scheme, Figure 14 This is a schematic diagram of the hardware operating environment of the computer system involved in the embodiments of this application.

[0105] like Figure 14 As shown, the computer system may include: a processor 1001, such as a CPU; a memory 1005; a user interface 1003; a network interface 1004; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0106] Those skilled in the art will understand that Figure 14 The computer system architecture shown does not constitute a limitation on the computer system and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0107] like Figure 14 As shown, the memory 1005, as a storage medium, may include an operating system, a network communication module, a user interface module, and computer programs. The operating system is a program that manages and controls the hardware and software resources of the computer system, as well as the operation of the computer programs and other software or programs.

[0108] exist Figure 14 In the computer system shown, the user interface 1003 is mainly used to connect to the terminal and communicate with the terminal; the network interface 1004 is mainly used to communicate with the backend server; and the processor 1001 can be used to call the computer program stored in the memory 1005.

[0109] In this embodiment, the computer system includes: a memory 1005, a processor 1001, and a computer program stored in the memory and executable on the processor, wherein: When processor 1001 calls a computer program stored in memory 1005, it performs the following operations: S10: Collect the vehicle accumulation, average density, and average speed in the target area at the current time; calculate the area pressure value of the target area at the current time based on the vehicle accumulation, average density, and average speed; determine the correction control ratio based on the area pressure difference between two adjacent target areas; and perform proportional-integral feedback control based on the correction control ratio. S20, calculate the path pressure value of the target path at the current time according to the path type corresponding to the target path in the target area, and correct the steering control ratio according to the path pressure difference between two adjacent target paths, and perform proportional-integral feedback control based on the corrected steering control ratio. S30: Obtain the traffic flow pressure value of each phase at the target intersection, select the target phase with the maximum traffic flow pressure value, and set the position of that phase as a passage marker at the next time step.

[0110] Furthermore, those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in a computer system to implement the process steps of the embodiments of the above methods.

[0111] Therefore, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the various steps of the multi-level maximum pressure traffic signal control method as described in the above embodiments.

[0112] The computer-readable storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0113] It should be noted that, since the storage medium provided in the embodiments of this application is the storage medium used to implement the methods of the embodiments of this application, those skilled in the art can understand the specific structure and variations of the storage medium based on the methods described in the embodiments of this application, and therefore will not be repeated here. All storage media used in the methods of the embodiments of this application fall within the scope of protection of this application.

[0114] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0116] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0117] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0118] It should be noted that any reference signs placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. This application can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0119] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0120] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A multi-level maximum pressure traffic signal control method, characterized in that, The method includes the following steps: S10: Collect the vehicle accumulation, average density, and average speed in the target area at the current time; calculate the area pressure value of the target area at the current time based on the vehicle accumulation, average density, and average speed; determine the correction control ratio based on the area pressure difference between two adjacent target areas; and perform proportional-integral feedback control based on the correction control ratio. S20, calculate the path pressure value of the target path at the current time according to the path type corresponding to the target path in the target area, and correct the steering control ratio according to the path pressure difference between two adjacent target paths, and perform proportional-integral feedback control based on the corrected steering control ratio. S30, obtain the traffic flow pressure value of each phase at the target intersection, select the target phase with the maximum traffic flow pressure value, and set the target phase position as a passage marker at the next time step.

2. The method as described in claim 1, characterized in that, In step S10, the expression for calculating the regional pressure value is as follows: ； In the formula, This represents the regional pressure value of region i at the current time t. This represents the optimal vehicle accumulation amount when the total traffic flow in region i reaches its maximum. This indicates the accumulated number of vehicles. Let i be the free-flow velocity of the vehicle in region i. Indicates the average vehicle speed in the area. Indicates the average density of the region. This represents the optimal density corresponding to the maximum total flow rate when the vehicle density in region i reaches its maximum. , , To adjust the parameters, the effects of vehicle accumulation, average vehicle speed, and density on regional pressure were adjusted respectively.

3. The method as described in claim 1 or 2, characterized in that, In step S10, the step of determining a correction control ratio based on the regional pressure difference between two adjacent target regions and performing proportional-integral feedback control based on the correction control ratio includes: S11, Determine the regional pressure difference between two adjacent target areas. : ； In the formula, the table This shows the regional pressure value of region i at the current time t. This represents the regional pressure value of region j at the current time t. S12, Based on the regional pressure difference, determine the boundary pressure correction term. The corrected control ratio is obtained. : ； In the formula, This represents the control ratio before correction; K is the proportional gain matrix, representing the degree of influence of boundary pressure on the control ratio. S13, based on the modified control ratio Update the control ratio input formula: ； In the formula, This represents the control ratio input at time step k, where k is the discrete time representation of continuous time. This represents the proportional gain matrix, used to control the proportional response to each state deviation; These are the proportional gain submatrices corresponding to the vehicle accumulation, regional average density, and regional average vehicle speed, respectively. This represents the integral gain matrix, used to control the integral response of each state deviation; , , These represent the average vehicle speed deviation, the regional average density deviation, and the regional average vehicle speed deviation at time step k, respectively. , These represent the average vehicle speed deviation, the regional average density deviation, and the regional average vehicle speed deviation at the previous time step k-1, respectively. , and These represent the area at its optimal balance, i.e., when the inflow and outflow are equal and the instantaneous vehicle accumulation in the area is 0, the area density, and the vehicle speed.

4. The method as described in claim 1, characterized in that, In S20, the path pressure value for: ； in: ； In the formula, This represents the path pressure value of path z at time t. This represents the set of paths whose path type is a boundary path. The length of the queue segment in the path. Indicates the length of path z. To adjust the parameters, This represents the pressure per unit length of path at time t. This represents the optimal average density of vehicles on path z when the maximum total traffic flow is achieved. Indicates the current vehicle speed. This represents the optimal average vehicle speed for path z when the total traffic flow reaches its maximum. Indicates the current travel time. This represents the theoretical travel time required to travel a unit path within target region i at free-flow velocity. , , These are all adjustment parameters, representing the influence of average path density, average path speed, and path travel time, respectively. The adjustment parameters are different for different path types.

5. The method as described in claim 1 or 4, characterized in that, In step S20, the step of correcting the steering control ratio based on the path pressure difference between two adjacent target paths and performing proportional-integral feedback control based on the corrected steering control ratio includes: S21, Determine the path pressure difference between the two target paths. : ； In the formula, This represents the path pressure value of path z at time t. This represents the path pressure value of the downstream connecting path w adjacent to path z at time t. Represents the set of downstream connection paths; S22, based on the path pressure difference Corrected steering control ratio : ； In the formula, This is the corrected steering control ratio. To adjust the influence coefficient of path pressure on steering control ratio; This is the set of downstream possible paths after the current path z has completed path selection; in: ； S23, based on the modified steering control ratio When a vehicle on the current path z enters a downstream diversion node, it selects the allocation ratio of the downstream available paths.

6. The method as described in claim 1, characterized in that, The formula for calculating the traffic flow pressure value is: ； In the formula, This represents the traffic flow pressure at phase h at intersection m. The sum of This indicates the traffic flow pressure from road segment a to road segment b; in: ； ； In the formula, This represents the pressure weight of the traffic flow at time t. The saturation flow rate of the traffic flow. Let be the queue length from road segment a to road segment b. Let be the turning ratio from road segment b to a downstream road segment l. Let b be the set of all road segments downstream of road segment b. Let be the queue length from road segment b to a downstream road segment l.

7. The method as described in claim 6, characterized in that, The passage identification time of phase h at time t According to phase pressure Total phase pressure The weights are allocated accordingly: ； In the formula, Indicates the signal period; in: ; In the formula, To fix the total lost time, Total passage time. For phase set; in: ; In the formula, Indicates the minimum pass sign time. Indicates the maximum passage time.

8. The application of a multi-level maximum pressure traffic signal control method as described in any one of claims 1 to 7 in traffic signal control.

9. A computer system, characterized in that, The computer system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the multi-level maximum pressure traffic signal control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the multi-level maximum pressure traffic signal control method as described in any one of claims 1 to 7.

Images