A ramp coordination control method and device adapted to mixed traffic flow scenarios

Abstract

The present invention discloses the technical field of traffic control, and particularly relates to a ramp coordination control method and device adapted to a new type of mixed traffic flow scenario. It includes: S1. Obtain the macroscopic fundamental diagram of traffic flow; S2. Segment and fit the macroscopic fundamental diagram according to the CAV penetration rate to obtain the MFD segmented fitting function; S3. Identify congested sections based on the average road section density, and determine the control range of the ramp based on the average travel length of vehicles in the congested sections; S4. Construct a discretized process model of the traffic flow according to the MFD segmented fitting function; S5. Based on the discretized process model, adopt the model predictive control method to establish a hierarchical optimization control model and output the coordinated control result of the ramp. This application also includes a device for implementing this method, which transforms the coordinated optimization problem of multiple control variables into the optimization and allocation of a single control variable, reduces the computational complexity of the control model, thereby optimizing traffic signal control and ramp flow allocation, and achieving the effect of alleviating traffic congestion.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic control, and particularly relates to a ramp coordination control method and device adapted to a new type of mixed traffic flow scenario. Background Art

[0002] With the continuous innovation of technologies such as autonomous driving, artificial intelligence, 5G, and big data, it can be foreseen that the mixed operation of manned and autonomous vehicles (AVs) will become an important stage in the full popularization of autonomous driving. Existing research shows that the introduction of AVs can improve the road capacity and driving safety, but at the same time, it also makes the control strategies developed based on traditional traffic flow models no longer applicable to the new type of mixed traffic flow. Since the construction of the new type of mixed traffic flow model needs to introduce more parameters, such as CAV penetration rate, headway, and platoon length, etc., this leads to more uncertain factors between the model and the actual mixed traffic flow dynamics. Therefore, it is necessary to improve the existing management and control methods while studying the characteristics of the expressway mixed traffic flow to prevent and alleviate the congestion of the mixed traffic flow. Summary of the Invention

[0003] The purpose of the present invention is to overcome the problems that the traditional model is difficult to accurately describe the dynamic characteristics of the new type of mixed traffic flow, and the calculation complexity of the control strategy is relatively high, making it difficult to achieve real-time and effective optimization. The present invention provides a ramp coordination control method and device adapted to the new type of mixed traffic flow scenario, which can effectively manage the traffic flow mixed with autonomous driving vehicles and traditional vehicles, and obtain the technical effects of improving the road traffic efficiency and safety. Based on the influence law of the penetration rate of connected and autonomous vehicles (CAVs) on the critical point and congestion inflection point of the macroscopic fundamental diagram (MFD) of the expressway, through the piecewise fitting of the MFD according to the CAV penetration rate, and by constructing an accurate discretized process model, the real-time monitoring and prediction of the traffic flow state are realized. The piecewise fitting function not only has high accuracy in the overall change trend of the MFD, but also ensures the accuracy of the fitting function in tracking the change of the critical state of the expressway. Secondly, based on the constructed piecewise fitting model according to the CAV penetration rate, a hierarchical optimization control method for the expressway is established to be applicable to the expressway congestion scenario under the new type of mixed traffic flow. At the same time, the coordinated optimization problem of multiple control variables is transformed into the optimization and allocation of a single control variable, reducing the calculation complexity of the control model, thereby optimizing the traffic signal control and ramp flow allocation, and achieving the technical effect of alleviating traffic congestion.

[0004] In order to achieve the above invention purpose, the present invention provides the following technical solutions:

[0005] In a first aspect, an embodiment of the present application provides a ramp coordination control method adapted to a new type of mixed traffic flow scenario, including the following steps:

[0006] S1. Obtain the macroscopic fundamental diagram of traffic flow;

[0007] S2. Segment and fit the macroscopic fundamental diagram according to the CAV penetration rate to obtain the MFD segmented fitting function;

[0008] S3. Identify congested sections based on the average density of the sections, and determine the control range of the ramps based on the average travel length of the vehicles in the congested sections, and divide the ramps into bottleneck area ramps and associated area ramps;

[0009] S4. Construct a discretized process model of the traffic flow according to the MFD segmented fitting function;

[0010] S5. Based on the discretized process model, adopt the model predictive control method, with the goal of minimizing the total vehicle travel time, establish a hierarchical optimization control model, and output the coordinated control results of the bottleneck area ramps and the associated area ramps.

[0011] In the above implementation, by obtaining the macroscopic fundamental diagram of traffic flow and performing segmented fitting according to the CAV penetration rate, the present invention can construct an accurate MFD segmented fitting function. This method not only improves the adaptability of the model to the dynamic changes of traffic flow, but also can provide more scientific data support for traffic management departments by accurately simulating the traffic flow states under different penetration rates, thereby realizing accurate prediction and effective control of traffic flow, and significantly improving road use efficiency and safety.

[0012] In some embodiments, the MFD segmented fitting function may include a critical point and a congestion inflection point; the critical point is the demarcation point where the road network changes from a smooth state to a congested state; the congestion inflection point is the starting point where the average density of the section begins to decrease or increase.

[0013] In the above implementation process, by comprehensively considering the MFD segmented fitting function of the critical point and the congestion inflection point, the transition of the traffic flow state from smooth to congested can be more accurately characterized. This method improves the response speed and accuracy of the traffic control system, enabling the traffic management system to adjust the control strategy immediately when the traffic flow begins to change, thereby effectively managing and alleviating congestion.

[0014] In some embodiments, the MFD segmented fitting function includes an MFD smooth section fitting function and an MFD congested section fitting function; establishing the MFD segmented fitting function includes the following steps:

[0015] S21. Respectively establish the MFD segmented fitting functions of the critical point, the congestion inflection point and the CAV penetration rate;

[0016] S22. Establish a cubic fitting function for the unobstructed section of the MFD based on the values and tangent slopes of the MFD piecewise fitting function at the origin and the critical point, and obtain the first coefficient, the second coefficient, and the third coefficient;

[0017] The formula for the cubic fitting function of the unobstructed section of the MFD is:

[0018] , where represents the cubic fitting function; represents the average density of the section, , represents the critical point density value; is the first coefficient, is the second coefficient, is the third coefficient;

[0019] The first coefficient is expressed as: , where is the free flow speed, is the critical point flow;

[0020] The second coefficient is expressed as: ;

[0021] The third coefficient is expressed as: ;

[0022] Establish a cubic fitting function for the congested section of the MFD based on the characteristics of the MFD piecewise fitting function at the critical point and the congestion inflection point, and obtain the fourth coefficient, the fifth coefficient, the sixth coefficient, and the seventh coefficient;

[0023] The formula for the cubic fitting function of the congested section of the MFD is , where < < ， is the congestion inflection point density value;

[0024] The fourth coefficient is expressed as: , where is the congestion inflection point flow, is the slope of the cubic fitting function at the congestion inflection point;

[0025] The fifth coefficient is expressed as:

[0026] ;

[0027] The sixth coefficient is expressed as:

[0028] ;

[0029] The seventh coefficient is expressed as:

[0030] .

[0031] In the above implementation process, by establishing an accurate piecewise fitting function for MFD and defining in detail how to solve seven specific coefficients according to the CAV permeability, each coefficient is defined by a strict mathematical formula, ensuring the high precision and reliability of the model. The accurate calculation of these coefficients not only optimizes the performance of the traffic flow prediction model but also provides a solid mathematical basis for real-time traffic management and control, thus greatly improving the effectiveness of control strategies and the extensiveness of applications.

[0032] In some embodiments, step S4 of establishing the discretization process model of the traffic flow may further include: S41. Based on the inflow and outflow of vehicles on the main road, as well as the total inflow and total outflow of the on-ramp and off-ramp, establish a vehicle accumulation change model for the main road; the formula for the vehicle accumulation change model of the main road is:

[0033] , where represents the vehicle accumulation on the main road within the control area at time interval k, k + 1 represents after k and another time step, represents the inflow of vehicles on the main road at time interval k, represents the outflow of vehicles on the main road at time interval k, represents the total inflow of the on-ramp at time interval k, represents the total outflow of the off-ramp at time interval k, represents the total regulation rate, represents the time interval length;

[0034] S42. Based on the inflow and outflow of vehicles on the on-ramp, establish a vehicle accumulation change model for the on-ramp; the formula for the vehicle accumulation change model of the on-ramp is:

[0035] , where represents the vehicle accumulation on the on-ramp after discretization, represents the inflow of vehicles on the on-ramp after discretization;

[0036] S43. Combine S41, S42 and the piecewise fitting function of MFD to establish the discretization process model of the traffic flow that meets the requirements of model predictive control.

[0037] In the above implementation process, by constructing a discretization process model of the vehicle flow on the main road and the ramp, the present invention allows the traffic management system to precisely adjust the traffic flow on the ramp and the main road on a real-time basis, optimize the vehicle travel time and reduce energy consumption. In addition, the model predictive control method can reduce congestion and accidents while maintaining traffic fluency, improving road safety.

[0038] In some embodiments, establishing the hierarchical optimization control model in step S5 may include the following steps:

[0039] S51. Based on the discretization process model of the traffic flow, with the total inflow traffic of the ramp as the control variable and minimizing the total vehicle travel as the goal, construct an upper-layer model predictive control model; the formula of the upper-layer model predictive control model is: , where is an adjustable parameter, represents the prediction horizon;

[0040] S52. Based on the output result of the upper-layer model predictive control model, combined with the average density of the downstream section of the ramp and the position information of the ramp, establish a two-layer control weight allocation method to allocate the total control weight of the ramp.

[0041] In the above implementation process, the hierarchical optimization control model effectively reduces the computational complexity of the control model by transforming the coordinated optimization problem of multiple control variables into the optimization and allocation of a single control variable. This method can quickly respond to changes in real-time traffic conditions and accurately output the coordinated control results of the ramp and the associated area, thereby significantly reducing traffic congestion while ensuring road traffic efficiency.

[0042] In some embodiments, the two-layer control weight allocation method may include:

[0043] S521. Based on the average density of the downstream section of the entrance ramp, determine the control weight of the ramp in the bottleneck area; the control weight of the ramp in the bottleneck area is , where represents the allocation weight based on the average density, represents the density of the main road downstream of the entrance ramp j;

[0044] S522. Based on the average travel length and the distance from the entrance ramp to the boundary of the bottleneck area, determine the control weight of the ramp in the associated area; the control weight of the ramp in the associated area is , where represents the allocation weight based on the distance, represents the average travel length, represents the distance from the entrance ramp j to the upper boundary of the bottleneck area.

[0045] In the above implementation, by introducing a double-layer control weight allocation method based on the average density of the downstream section of the ramp, this method can dynamically adjust the flow control of the ramp entrance according to the real-time traffic conditions. This strategy not only improves the mobility of the road network, but also optimizes energy consumption and reduces environmental impact.

[0046] In some embodiments, the controlled inflow of the entrance ramp is determined according to the normal inflow of the entrance ramp, the control weight of the ramp in the bottleneck area, the total flow that needs to be restricted from flowing into the main road of the expressway in the bottleneck area ramp, and the maximum inflow limit of the entrance ramp; the maximum inflow limit is determined by the length of the entrance ramp and the vehicle accumulation at the current moment.

[0047] In the above implementation process, by calculating the normal inflow of the entrance ramp and combining the control weight and the maximum inflow limit to accurately control the flow, the continuity and efficiency of the traffic flow are ensured. This method effectively prevents serious congestion caused by excessive inflow and significantly improves the overall operation efficiency of the traffic system.

[0048] In some embodiments, the controlled inflow of the entrance ramp is determined according to the normal inflow of the entrance ramp, the control weight of the ramp in the associated area, the total flow that needs to be restricted from flowing into the main road of the expressway in the associated area ramp, and the maximum inflow limit of the entrance ramp.

[0049] In the above implementation process, by accurately regulating the ramp inflow based on real-time data, the ramp load can be effectively balanced and the traffic pressure can be reduced. This control strategy not only improves the road utilization rate, but also reduces the environmental pollution caused by traffic congestion.

[0050] In some embodiments, based on the control weight of the ramp in the bottleneck area and the control weight of the ramp in the associated area, the ramp coordinated control result is calculated; the ramp coordinated control result is the green light time of the entrance ramp; the formula for calculating the ramp coordinated control result is , where represents the green light time of the entrance ramp in the time interval , represents the controlled inflow of the entrance ramp in the time interval, represents the preset signal cycle, is a fitting parameter.

[0051] In the above implementation process, through the ramp coordination control results based on the calculation of control weights, the green light time can be effectively allocated to optimize traffic signal control. This strategy improves the efficiency and smoothness of traffic flow, reduces vehicle waiting time and total travel time, thereby enhancing the driving experience and road network performance.

[0052] In a second aspect, an embodiment of the present application provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in the above description is implemented.

[0053] In the above implementation manner, by implementing the computer program for implementing this method on the computer device, the present invention improves the automation level of the system and reduces manual operation errors. This automated processing method not only enhances the practicability and reliability of the system, but also improves the efficiency and accuracy of traffic management.

[0054] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0055] First of all, by obtaining the macroscopic fundamental diagram of traffic flow and combining the CAV penetration rate for MFD piecewise fitting, the present invention can more accurately analyze and predict the dynamic changes of mixed traffic flow. This method enables the traffic management system to adjust control strategies according to real-time data, thereby adapting to rapidly changing traffic conditions and improving the traffic capacity and safety of roads.

[0056] Secondly, by identifying congested sections and determining the control range of ramps based on the average vehicle travel length, the present invention further refines the accuracy of traffic control. This not only reduces ineffective and excessive control, but also makes traffic control more targeted and efficient through precise area division (bottleneck ramp and associated ramp), effectively alleviating traffic congestion in specific areas.

[0057] Finally, the traffic flow discretization process model constructed based on the refined MFD piecewise fitting function provides a reliable mathematical basis for the model predictive control method, enabling the entire control system to perform real-time optimization and adjustment with the goal of minimizing the total vehicle travel time. This optimization not only improves the overall operation efficiency of the road network, but also enhances the driving experience of drivers, reduces traffic-related energy consumption and emissions, and provides a more intelligent and sustainable solution for urban traffic management. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flowchart of a ramp coordination control method for adapting to a new mixed traffic flow scenario according to Embodiment 1 of the present invention;

[0059] Figure 2Schematic diagram of the on-ramp control for Embodiment 2 of the present invention;

[0060] Figure 3 Schematic diagram of the hierarchical optimization control process for Embodiment 2 of the present invention. Detailed implementation manners

[0061] The following further elaborates in detail on a ramp coordination control method and device provided by the present invention for adapting to a new type of mixed traffic flow scenario in conjunction with the accompanying drawings and specific embodiments. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following embodiments. Any technology implemented based on the content of the present invention falls within the scope of the present invention. In combination with the following description, the advantages and features of the present invention will be clearer. It should be noted that the accompanying drawings are all in a very simplified form and use non-precise scales, only for the purpose of facilitating and clearly assisting in explaining the objectives of the embodiments of the present invention.

[0062] Embodiment 1

[0063] During the research process, the applicant found that when using traditional traffic flow control methods, if one intends to achieve effective management of a new type of mixed traffic flow (including autonomous and human-driven vehicles), it is necessary to rely on cumbersome steps such as continuously adjusting and reconstructing the traffic model, which can only provide limited improvements. When solving practical engineering problems, in order to achieve the technical objectives of high efficiency and high safety, the prior art cannot meet the accurate prediction and control of dynamic and rapidly changing mixed traffic flows. Therefore, after researching this problem, the applicant proposed a ramp coordination control method based on CAV penetration rate. When addressing the technical problem of freeway traffic congestion management, through the technical solution of establishing a discretized process model based on MFD piecewise fitting, the accurate regulation and optimization management of mixed traffic flows are achieved, thereby achieving the technical effects of improving traffic flow processing efficiency and reducing traffic congestion.

[0064] Please refer to Figure 1 , Figure 1 which is a flowchart of a ramp coordination control method for adapting to a new type of mixed traffic flow scenario according to an embodiment of this application, including the following steps:

[0065] S1. Obtain the macroscopic fundamental diagram of the freeway for the new type of mixed traffic flow; the new type of mixed traffic flow is a scenario where autonomous vehicles and human-driven vehicles are mixed;

[0066] S2. Perform piecewise fitting on the macroscopic fundamental diagram according to the CAV penetration rate to obtain the MFD piecewise fitting function;

[0067] S3. Identify the congested sections of the new type of mixed traffic flow based on the average density of the sections, and determine the control range of the ramps based on the average travel length of the vehicles in the congested sections, and divide the ramps into bottleneck area ramps and associated area ramps;

[0068] S4. Construct a discretization process model for the expressway mixed traffic flow according to the MFD piecewise fitting function;

[0069] S5. Based on the discretization process model, adopt the model predictive control method, with the goal of minimizing the total vehicle travel time, establish a hierarchical optimization control model, and output the ramp coordination control result.

[0070] Specifically, in the embodiment of the present application, the acquisition of the traffic flow macroscopic fundamental diagram is carried out first. This diagram is a representation of the flow state of the entire traffic network. This step provides the basic data for subsequent analysis and the formulation of control strategies. Subsequently, the macroscopic fundamental diagram is piecewise fitted according to different CAV (connected and automated vehicle) penetration rates, so as to obtain the MFD (macroscopic fundamental diagram) piecewise fitting function. This step enables the accurate prediction of traffic flow dynamics in different scenarios according to the different degrees of vehicle automation.

[0071] Next, by analyzing the average travel length of vehicles in the identified congested sections, the control range of the ramp is determined, and the ramp is divided into a bottleneck area ramp and an associated area ramp. This division enables more refined control strategies to be implemented for traffic management in different regions, so as to improve the management efficiency of traffic flow.

[0072] Furthermore, a discretization process model of the traffic flow is constructed according to the obtained MFD piecewise fitting function. This model supports the simulation and prediction of traffic flow changes under various traffic conditions, and provides a basis for making control decisions. Finally, based on this discretization process model, a hierarchical optimization control model is established by using the model predictive control (MPC) method. This model can not only minimize the total vehicle travel time, but also realize the effective coordinated control of the bottleneck area and the associated area ramps, thereby optimizing the traffic flow and significantly improving the operation efficiency and safety of the road network.

[0073] In summary, the ramp coordination control method provided by the embodiment of the present application can be applied to many technical fields, such as intelligent transportation systems, automated road management, and urban traffic planning. In the above implementation, when controlling the expressway traffic flow, the traffic volume at the ramp entrance can be adjusted according to real-time data, and the effective distribution of traffic volume can be realized through the hierarchical optimization control model, so as to achieve the effects of reducing traffic congestion and improving road use efficiency.

[0074] Embodiment 2

[0075] The embodiment of the present application provides a ramp coordination control method adapted to a new mixed traffic flow scenario, and the method includes:

[0076] S1. Characterize the new mixed traffic flow dynamics of the expressway with MFD, and establish an MFD piecewise fitting model according to the CAV penetration rate.

[0077] S2. Identify congested road sections using the average density of road sections, and establish a method for screening control ramps based on the average travel length of vehicles. Specifically, starting from the congested road section and extending upstream, determine the scale of the control ramp based on the average travel length of vehicles. At the same time, divide the ramps that fall into congestion into bottleneck ramps, and the rest are associated area ramps.

[0078] S3. According to the MFD piecewise fitting function obtained in step S1, construct a discretized process model for the expressway under the new type of mixed traffic flow.

[0079] S4. Based on the model predictive control (MPC) method, with the goal of minimizing the total travel time of vehicles, alleviate the congestion of the expressway by controlling the inflow traffic of the on-ramp, and consider the on-ramp queue constraint to establish a hierarchical optimization control model.

[0080] Among them, step S1 includes:

[0081] S11. Define the MFD characteristic points. Among them, the critical point of MFD is defined as the demarcation point where the road network changes from the unobstructed state to the congested state; the congestion inflection point of MFD is the starting point where the average density of the road section begins to decrease and the average density of the road section begins to increase.

[0082] S12. Based on simulation, establish the fitting functions of the abscissa and ordinate values of the MFD critical point and congestion inflection point with the CAV penetration rate. , , , Among them, is the CAV penetration rate, and are respectively the critical point density and congestion inflection point density under the CAV penetration rate , and are respectively the fitting functions of the CAV penetration rate with the critical point flow and density, and are respectively the critical point flow and congestion inflection point flow under the CAV penetration rate , and are respectively the fitting functions of the CAV penetration rate with the congestion inflection point flow and density.

[0083] S13. Use the values and tangent slopes of the MFD piecewise fitting function at the origin and critical point to establish a cubic fitting function for the unobstructed road section of MFD, and calculate the expression of each coefficient of the fitting function by solving a system of linear equations with three variables.

[0084]

[0085] Among them, 、 and are coefficients to be solved. The value range of the average density is: , is the critical point density value.

[0086]

[0087] Among them, is the free flow velocity, the cubic fitting function for the derivative function of the density .

[0088]

[0089]

[0090]

[0091] S14. Utilize the slope characteristics of the MFD piecewise fitting function at the critical point and the congestion inflection point, and since the fitting function passes through the critical point and the congestion inflection point and has a certain slope, establish a cubic fitting function for the MFD congested section. By solving a system of four linear equations, calculate the expressions of the coefficients of the fitting function.

[0092]

[0093]

[0094] Among them, is the slope of the cubic fitting function at the congestion inflection point.

[0095]

[0096]

[0097]

[0098]

[0099] Step S2 includes:

[0100] S21. Use the average density of each main road section to discriminate the congested section.

[0101] S22. Determine the section numbers of the upper and lower boundaries of the bottleneck area.

[0102] S23. Calculate the average travel length of the study section. Starting from the entrance of the upstream section of the bottleneck area, based on the average travel length, determine the upstream boundary of the control area ( ).

[0103] S24. Use the lower boundary of the bottleneck area as the downstream boundary of the control area ( ), and form the control area range under the time interval . , where represents the set of road section numbers in the control area, and respectively represent the road section numbers of the upstream and downstream boundaries of the control area.

[0104] Please refer to Figure 2 , Figure 2 which is the schematic diagram of the on-ramp control in the embodiment of the present application, showing the method of controlling the inflow traffic of the on-ramp to relieve the congestion of the expressway.

[0105] Step S3 includes:

[0106] S31. Establish a discretized equation for the change in the vehicle accumulation on the main road of the expressway.

[0107] (4-3)

[0108] Where represents the vehicle accumulation on the main road of the expressway in the control area under the time interval , and respectively represent the traffic flowing into and out of the control area from the main road of the expressway under the time interval , represents the total inflow traffic of the on-ramp under the time interval , represents the total outflow traffic of the off-ramp under the time interval , represents the total regulation rate, represents the length of the time interval.

[0109] S32. Establish a discretized equation for the change in the vehicle accumulation on the on-ramp.

[0110]

[0111] Where represents the vehicle accumulation on the on-ramp after discretization, represents the inflow traffic of the on-ramp after discretization.

[0112] S33. Introduce the piecewise fitting function of MFD according to CAV permeability obtained in step S1, and establish a discretized process model of the expressway mixed traffic flow that meets the needs of MPC. Specifically, the introduced piecewise fitting function of MFD is expressed as:

[0113]

[0114] Please refer to Figure 3 , Figure 3 which is a schematic diagram of the hierarchical optimization control process of the embodiment of the present application. Specifically, the hierarchical optimization control model described in step S4 includes:

[0115] S41. Utilize the discretized process model established in step S3, take the total ramp inflow as the control variable, minimize the total vehicle travel time as the objective, and introduce a penalty term to prevent the fluctuation of the control quantity to ensure the smoothness of the control, and establish the upper-layer MPC model.

[0116]

[0117] Among them, is an adjustable parameter, represents the prediction horizon.

[0118]

[0119]

[0120]

[0121]

[0122] .

[0123]

[0124]

[0125]

[0126]

[0127]

[0128] Among them, represents the total outflow flow of the control area, represents the traffic volume of the expressway at the time interval , represents the set accumulation threshold of the expressway, is the critical vehicle accumulation threshold of the entrance ramp . is the average travel length of the expressway, is the total length of the main road of the expressway. It should be noted that in step S1, the cubic fitting function represents the continuous weighted average flow rate, while in step S41 after discretization, represents the time interval the weighted average flow rate of the main road of the expressway.

[0129] S42. Establish a two-layer control weight allocation method based on the average density of the downstream section of the ramp and the ramp position information to allocate the total control weight calculated by the upper-layer MPC model. Specifically, the control weight of the ramps in the bottleneck area is allocated according to the average density of the sections downstream of each entrance ramp, and the control weight of the ramps in the associated area is allocated according to the distance of each entrance ramp from the bottleneck area.

[0130]

[0131] The control weight allocation method for the ramps in the bottleneck area is expressed as:

[0132]

[0133]

[0134] Among them, represents the total flow rate that needs to be restricted from flowing into the main road of the expressway solved under the time interval, represents the entrance ramp the vehicle accumulation within 1 km downstream within the time interval, represents the entrance ramp the critical accumulation level within 1 km downstream, represents the allocation weight based on the average density of the section, represents the entrance ramp the density of the main road downstream, represents the entrance ramp at the controlled inflow flow rate under the time interval, represents the entrance ramp at the normal inflow flow rate under the time interval, and the normal inflow flow rate is equal to the traffic flow in the actual traffic scenario; represents the entrance ramp at the maximum inflow flow rate limit that can be tolerated under the time interval, which is determined by the length of the entrance ramp and the current vehicle accumulation level.

[0135]

[0136] is the entrance ramp of the critical vehicle accumulation, is the entrance ramp at the time interval of the vehicle accumulation.

[0137]

[0138] The control weight allocation method for the associated area ramp is expressed as:

[0139]

[0140]

[0141] wherein, represents the total flow rate that needs to be restricted from flowing into the main road of the expressway for the associated area ramp, represents the distance-based allocation weight, represents the average travel length, represents the entrance ramp distance from the upper boundary of the bottleneck area.

[0142] S43. Calculate the green light time of the entrance ramp of the bottleneck area and the entrance ramp of the associated area according to the controlled inflows of the entrance ramp of the bottleneck area and the entrance ramp of the associated area obtained.

[0143]

[0144] wherein, represents the green light time of the entrance ramp at the time interval , represents the preset signal cycle, is the fitting parameter and can be obtained through simulation.

[0145] Embodiment 3

[0146] This embodiment of the present application describes a computer device, which is specifically designed to implement a ramp coordination control method adapted to a new type of mixed traffic flow scenario. This computer device includes a memory, a processor, and a computer program stored in the memory, and the program is encoded with algorithms and instructions for implementing the above control method.

[0147] First, the processor of the device executes a computer program stored in the memory. Through this program, it first obtains the macroscopic fundamental diagram of traffic flow. This fundamental diagram is obtained through real-time traffic data sensors or online traffic services, providing the basic data for subsequent analysis. Subsequently, the program performs piecewise fitting on the macroscopic fundamental diagram according to the input CAV penetration rate to generate MFD piecewise fitting functions. These functions can reflect the traffic flow states under different CAV penetration rates and are used for predicting and managing traffic flow.

[0148] Next, the computer program analyzes the average density of road segments, identifies congested road segments, and automatically calculates and determines the control range of the ramp according to the average travel length of vehicles in these congested road segments. In this process, the ramps are divided into bottleneck area ramps and associated area ramps to optimize the management of traffic flow.

[0149] Finally, the processor constructs a discretization process model of traffic flow using the MFD piecewise fitting functions and adopts the model predictive control (MPC) method. This method is implemented through a computer program with the goal of minimizing the total vehicle travel time, thereby establishing a hierarchical optimization control model. In this way, the computer device can output the coordinated control results of the bottleneck area ramps and associated area ramps, significantly improving the efficiency and effectiveness of traffic management.

[0150] This computer device, by integrating high-efficient data processing capabilities and advanced algorithms, can respond to traffic condition changes in real time, providing a powerful decision-making support tool for traffic management departments to effectively handle complex traffic flow scenarios.

[0151] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects.

[0152] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as within the scope described in this specification.

[0153] The above-described embodiments only represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent should be subject to the appended claims.

Claims

1. A ramp coordination control method adapted to mixed traffic flow scenarios, characterized in that, It includes the following steps: S1. Obtain the macroscopic fundamental diagram of traffic flow; S2. Segment and fit the macroscopic fundamental diagram according to the CAV penetration rate to obtain the MFD segmented fitting function; S3. Identify congested sections based on the average density of road sections, and determine the control range of ramps based on the average travel length of vehicles in the congested sections. Divide the ramps into bottleneck area ramps and associated area ramps; S4. Construct a discretized process model of the traffic flow according to the MFD segmented fitting function; S5. Based on the discretized process model, adopt a model predictive control method, with the goal of minimizing the total vehicle travel time, establish a hierarchical optimization control model, and output the coordinated control results of the bottleneck area ramps and the associated area ramps; Establishing the hierarchical optimization control model includes the following steps: S51. Based on the discretized process model of the traffic flow, with the total inflow of the ramps as the control variable and the goal of minimizing the total vehicle travel, construct an upper-layer model predictive control model; S52. Based on the output results of the upper-layer model predictive control model, combine the average density of the road section downstream of the ramp and the location information of the ramp to establish a two-layer control weight allocation method to allocate the total control weight of the ramp; The two-layer control weight allocation method includes: S521. Determine the control weight of the bottleneck area ramp based on the average density of the road section downstream of the entrance ramp; S522. Determine the control weight of the associated area ramp based on the average travel length and the distance from the entrance ramp to the bottleneck area boundary.

2. The ramp coordination control method for adapting to the mixed traffic flow scenario according to claim 1, characterized in that The MFD segmented fitting function includes a critical point and a congestion inflection point; the critical point is the demarcation point where the road network changes from a smooth state to a congested state; the congestion inflection point is the starting point where the average density of the road section begins to decrease or increase.

3. The ramp coordination control method for adapting to the mixed traffic flow scenario according to claim 2, wherein, The MFD segmented fitting function includes an MFD smooth section fitting function and an MFD congested section fitting function; establishing the MFD segmented fitting function includes the following steps: S21. Respectively establish the MFD segmented fitting functions of the critical point and the congestion inflection point with the CAV penetration rate; S22. According to the values and tangent slopes of the MFD segmented fitting function at the origin and the critical point, establish a cubic fitting function for the MFD smooth section, and obtain the first coefficient, the second coefficient, and the third coefficient; The formula for the cubic fitting function of the unobstructed section of the MFD is as follows: , where represents the cubic fitting function; represents the average density of the section, , represents the critical point density value; is the first coefficient, is the second coefficient, is the third coefficient; The first coefficient is expressed as: , where is the free stream velocity, is the critical point flow rate; The second coefficient is expressed as ; The third coefficient is expressed as: ; According to the characteristics of the MFD segmented fitting function at the critical point and the congestion inflection point, establish a cubic fitting function for the MFD congested section, and obtain the fourth coefficient, the fifth coefficient, the sixth coefficient, and the seventh coefficient; The formula of the cubic fitting function for the MFD congested section is: , where < < , is the density value at the congestion inflection point; The fourth coefficient is expressed as: , where is the traffic flow at the congestion inflection point, is the slope of the cubic fitting function at the congestion inflection point; The fifth coefficient is expressed as: ； The sixth coefficient is expressed as: ； The seventh coefficient is expressed as: 。 4. The ramp coordination control method adapted to the mixed traffic flow scenario according to claim 3, wherein, Step S4 of constructing the discretized process model of the traffic flow includes the following steps: S41. Based on the inflow and outflow of main road vehicles, as well as the total inflow and total outflow of the entrance ramp and the exit ramp, establish a vehicle accumulation change model for the main road; the formula of the vehicle accumulation change model for the main road is: , where represents the vehicle accumulation on the main road within the control area during the time interval . + 1 means after another time step has passed. represents the inflow rate of vehicles on the main road during the time interval . represents the outflow rate of vehicles on the main road during the time interval . represents the total inflow rate of the on-ramp during the time interval . represents the total outflow rate of the off-ramp during the time interval . represents the total regulation rate, represents the length of the time interval; S42. Based on the inflow and outflow of vehicles on the entrance ramp, establish a vehicle accumulation change model for the entrance ramp; the formula of the vehicle accumulation change model for the entrance ramp is: , where represents the vehicle accumulation of the on-ramp after discretization, represents the inflow traffic of the on-ramp after discretization; S43. Combine S41, S42 and the MFD piecewise fitting function to establish a discretized process model of the traffic flow that meets the requirements of model predictive control.

5. The ramp coordination control method for adapting to the mixed traffic flow scenario according to claim 4, characterized in that The formula of the upper-layer model predictive control model described in step S5 is as follows: , where is an adjustable parameter, represents the prediction horizon.

6. The ramp coordination control method for adapting to the mixed traffic flow scenario according to claim 5, characterized in that The control weight of the bottleneck area ramp is , where represents the distribution weight based on the average density of the road section, represents the density of the main road downstream of the entrance ramp ; The control weight of the associated area ramp is , where represents the distribution weight based on the distance, represents the average travel length, represents the distance from the entrance ramp to the upper boundary of the bottleneck area.

7. The ramp coordination control method for adapting to the mixed traffic flow scenario according to claim 6, wherein, Determine the controlled inflow of the on-ramp according to the normal inflow of the on-ramp, the control weight of the ramp in the bottleneck area, the total flow that needs to be restricted from entering the main road of the expressway in the bottleneck area ramp, and the maximum inflow limit of the on-ramp; the maximum inflow limit is determined by the length of the on-ramp and the vehicle accumulation at the current moment.

8. The ramp coordination control method for adapting to the mixed traffic flow scenario according to claim 6, characterized in that, Determine the controlled inflow of the on-ramp according to the normal inflow of the on-ramp, the control weight of the ramp in the associated area, the total flow that needs to be restricted from entering the main road of the expressway in the associated area ramp, and the maximum inflow limit of the on-ramp.

9. A ramp coordination control method adapted to the mixed traffic flow scenario according to claim 6, characterized in that, Calculate the ramp coordination control result based on the control weight of the ramp in the bottleneck area and the control weight of the ramp in the associated area; the ramp coordination control result is the green light time of the on-ramp. The formula for calculating the ramp coordination control result is , where represents the on-ramp the green light time at the time interval , represents the controlled inflow volume of the on-ramp j at the k time interval, represents the preset signal cycle, is the fitting parameter.

10. A computer device, characterized in that, The computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method according to any one of claims 1 to 9.

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