Ramp coordination control method and device suitable for novel mixed traffic flow scene

Abstract

The invention discloses the technical field of traffic control, and particularly relates to a ramp coordination control method and device suitable for a novel mixed traffic flow scene. Comprising the following steps: S1, acquiring a traffic flow macroscopic fundamental diagram; s2, performing segmentation fitting on the macroscopic fundamental diagram according to CAV permeability to obtain an MFD segmentation fitting function; s3, recognizing a congested road section according to the road section average density, and determining a ramp control range based on the average travel length of vehicles in the congested road section; s4, according to the MFD piecewise fitting function, constructing a discretization process model of the traffic flow; and S5, based on the discretization process model, establishing a layered optimization control model by adopting a model prediction control method, and outputting a coordination control result of the ramp. The invention further comprises a device for realizing the method, a multi-control-quantity coordinated optimization problem is converted into single-control-quantity optimization and distribution, and the calculation complexity of a control model is reduced, so that traffic signal control and ramp flow distribution are optimized, and the effect of relieving traffic jam is achieved.

Description

Technical Field

[0001] The present invention relates to the field of traffic control technology, and in particular to a ramp coordination control method and device adapted to a new 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 traffic of manned and autonomous vehicles (AVs) will become an important stage in the full popularization of autonomous driving. Existing studies have shown that the introduction of AVs can improve the traffic capacity and driving safety of roads, but at the same time, it also leads to the control strategies developed based on traditional traffic flow models no longer being applicable to new mixed traffic flows. Since the construction of new mixed traffic flow models requires the introduction of more parameters, such as CAV penetration rate, headway time, and fleet length, there are more uncertainties in the dynamics of the model and the actual mixed traffic flow. Therefore, it is necessary to improve the existing control methods while studying the characteristics of mixed traffic flows on expressways to prevent and alleviate congestion in mixed traffic flows. Summary of the invention

[0003] The purpose of the present invention is to overcome the problem that the traditional model is difficult to accurately describe the dynamic characteristics of the new mixed traffic flow, and the control strategy has a high computational complexity and is difficult to achieve real-time and effective optimization. A ramp coordination control method and device adapted to the new mixed traffic flow scenario are provided, which can effectively manage the traffic flow mixed with autonomous driving vehicles and traditional vehicles, and obtain the technical effect of improving road traffic efficiency and safety. The present invention is based on the influence of the penetration rate of connected and autonomous vehicles (CAV) on the critical point and congestion turning point of the macroscopic fundamental diagram (MFD) of the expressway, and according to the MFD segmented fitting of the CAV penetration rate, by constructing an accurate discretization process model, the real-time monitoring and prediction of the traffic flow state is achieved. The segmented fitting function not only has a high accuracy in the overall change trend of the MFD, but also ensures the accuracy of the fitting function in tracking the critical state changes of the expressway. Secondly, a hierarchical optimization control method for expressways is established based on the constructed segmented fitting model according to the CAV penetration rate, which is suitable for expressway congestion scenarios under new mixed traffic flows. At the same time, the coordinated optimization problem of multiple control quantities is transformed into the optimization and allocation of a single control quantity, reducing the computational complexity of the control model, thereby optimizing traffic signal control and ramp flow distribution, and achieving the technical effect of alleviating traffic congestion.

[0004] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present application provides a ramp coordination control method adapted to a new mixed traffic flow scenario, comprising the following steps: S1. Obtaining a macro basic diagram of traffic flow; S2. Performing piecewise fitting on the macro basic map according to the CAV permeability to obtain the MFD piecewise fitting function; S3. Identify the congested section according to the average density of the section, and determine the control range of the ramp based on the average travel length of the vehicle in the congested section, and divide the ramp into a bottleneck area ramp and an associated area ramp; S4. Constructing a discretization process model of the traffic flow according to the MFD piecewise fitting function; S5. Based on the discretized process model, a model predictive control method is adopted to establish a hierarchical optimization control model with the goal of minimizing the total vehicle travel time, and output the coordinated control results of the bottleneck area ramp and the associated area ramp.

[0005] In the above implementation, by obtaining the macro basic 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 provides more scientific data support for traffic management departments by accurately simulating the traffic flow state under different penetration rates, thereby achieving accurate prediction and effective control of traffic flow, and significantly improving road use efficiency and safety.

[0006] In some embodiments, the MFD segmented fitting function may include a critical point and a congestion inflection point; the critical point is the dividing point where the road network enters a congested state from a smooth state; the congestion inflection point is the starting point where the average density of the road section begins to decrease or increase.

[0007] In the above implementation process, by comprehensively considering the MFD piecewise fitting function of the critical point and the congestion inflection point, the transition of traffic flow 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.

[0008] In some embodiments, the MFD piecewise fitting function includes an MFD smooth road section fitting function and an MFD congested road section fitting function; establishing the MFD piecewise fitting function includes the following steps: S21. Establishing the MFD piecewise fitting functions of the critical point, the congestion inflection point and the CAV penetration rate respectively; S22. According to the values ​​and tangent slopes of the MFD segmented fitting function at the origin and the critical point, a cubic fitting function of the MFD unblocked section is established, and a first coefficient, a second coefficient, and a third coefficient are obtained; The formula of the cubic fitting function of the MFD smooth section is: ,in represents the cubic fitting function; represents the average density of the road segment, , represents the critical point density value; is the first coefficient, is the second coefficient, is the third coefficient; The first coefficient It is expressed as: ,in is the free flow speed, is the critical point flow rate; The second coefficient It is expressed as: ; The third coefficient It is expressed as: ; According to the characteristics of the MFD piecewise fitting function at the critical point and the congestion turning point, a cubic fitting function of the MFD congested section is established, and a fourth coefficient, a fifth coefficient, a sixth coefficient, and a seventh coefficient are obtained; The formula of the cubic fitting function of the MFD congested road section is: ,in < < ， is the density value of the congestion inflection point; The fourth coefficient It is expressed as: ,in For congestion inflection point flow, is the slope of the cubic fitting function at the congestion inflection point; The fifth coefficient is expressed as: , where k is the slope of the cubic fitting function at the critical point; The sixth coefficient is expressed as: ; The seventh coefficient It is expressed as: .

[0009] In the above implementation process, by establishing an accurate MFD piecewise fitting function and defining in detail how to solve seven specific coefficients based on CAV penetration, each coefficient is defined by a strict mathematical formula to ensure the high accuracy and reliability of the model. The precise calculation of these coefficients not only optimizes the performance of the traffic flow prediction model, but also provides a solid mathematical foundation for real-time traffic management and control, thereby greatly improving the effectiveness of the control strategy and the breadth of its application.

[0010] In some embodiments, step S4 of establishing the discretization process model of the traffic flow may further include: S41. Based on the inflow flow and outflow flow of vehicles on the main road, and the total inflow flow and total outflow flow of the entrance ramp and the exit ramp, a vehicle accumulation change model of the main road is established; the formula of the vehicle accumulation change model of the main road is: ,in, represents the accumulation of vehicles on the main road in the control area at time interval k, k+1 represents another time step after k, represents the inflow flow of vehicles on the main road at time interval k, represents the outflow flow of vehicles on the main road at time interval k, represents the total inflow flow of the entrance ramp at time interval k, represents the total outflow flow of the exit ramp at time interval k, represents the total regulation rate, Indicates the length of the time interval; S42. Based on the vehicle inflow and outflow of the entrance ramp, a vehicle accumulation change model of the entrance ramp is established; the formula of the vehicle accumulation change model of the entrance ramp is: ,in represents the vehicle accumulation of the entrance ramp after discretization, represents the inflow flow of the entrance 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 model prediction control requirements.

[0011] In the above implementation process, by constructing a discretized process model of vehicle flow on the main road and ramp, the present invention allows the traffic management system to accurately adjust the traffic flow on the ramp and main road on a real-time basis, optimize the vehicle's travel time and reduce energy consumption. In addition, the model predictive control method can reduce congestion and accidents while maintaining traffic smoothness, thereby improving road safety.

[0012] In some embodiments, establishing the hierarchical optimization control model in step S5 may include the following steps: S51. Based on the discretized process model of the traffic flow, taking the total inflow flow of the ramp as the control variable and minimizing the total vehicle travel as the goal, construct an upper-level model predictive control model; the formula of the upper-level model predictive control model is: ,in, is an adjustable parameter, represents the prediction time domain; S52. Based on the output results of the upper-level model predictive control model, combined with the average density of the downstream section of the ramp and the location information of the ramp, a double-layer control weight allocation method is established to allocate the total control weight of the ramp.

[0013] 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 quantities into the optimization and allocation of a single control quantity. This method can quickly respond to changes in real-time traffic conditions and accurately output the coordinated control results of ramps and associated areas, thereby significantly reducing traffic congestion while ensuring road traffic efficiency.

[0014] In some embodiments, the dual-layer control weight allocation method may include: S521. Based on the average density of the road section downstream of the entrance ramp, determine the control weight of the bottleneck ramp; the control weight of the bottleneck ramp is ,in, represents the allocation weight based on the average density of the road segment, represents the density of the main road downstream of the entrance ramp j; S522. Based on the average trip length and the distance between the entrance ramp and the bottleneck area boundary, determine the control weight of the ramp in the associated area; the control weight of the ramp in the associated area is ,in, represents the distance-based allocation weight, represents the average stroke length, Represents the distance between the entrance ramp j and the upper boundary of the bottleneck area.

[0015] 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 fluidity of the road network, but also optimizes energy consumption and reduces environmental impact. In some embodiments, the controlled inflow flow of the entrance ramp is determined based on the normal inflow flow of the entrance ramp, the control weight of the bottleneck ramp, the total flow that needs to be restricted from flowing into the main road of the expressway through the bottleneck ramp, and the maximum inflow flow limit of the entrance ramp; the maximum inflow flow limit is determined by the length of the entrance ramp and the vehicle accumulation at the current moment.

[0016] In the above implementation process, the normal inflow flow of the entrance ramp is calculated, and the flow is accurately controlled by combining the control weight and the maximum inflow flow limit to ensure the continuity and efficiency of traffic flow. This method effectively prevents serious congestion caused by excessive inflow and significantly improves the overall operation efficiency of the transportation system.

[0017] In some embodiments, the controlled inflow flow of the entrance ramp is determined based on the normal inflow flow of the entrance ramp, the control weight of the associated area ramp, the total flow of the associated area ramp that needs to be restricted from flowing into the expressway main road, and the maximum inflow flow limit of the entrance ramp.

[0018] In the above implementation process, by accurately controlling the ramp inflow based on real-time data, the ramp load can be effectively balanced and traffic pressure can be reduced. This control strategy not only improves road utilization, but also reduces environmental pollution caused by traffic congestion.

[0019] 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 coordination control result is calculated; the ramp coordination control result is the green light time of the entrance ramp; the formula for calculating the ramp coordination control result is: ,in Indicates the entrance ramp In time interval The green light time, Indicates the entrance ramp exist Controlled inflow at time intervals, Indicates the preset signal period, is the fitting parameter.

[0020] In the above implementation process, the ramp coordination control results calculated based on the control weight can effectively allocate green light time and optimize traffic signal control. This strategy improves the efficiency and smoothness of traffic flow, reduces vehicle waiting time and total travel time, thereby improving driving experience and road network performance.

[0021] In a second aspect, an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the above description when executing the computer program.

[0022] In the above implementation, by implementing the computer program for implementing the 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 practicality and reliability of the system, but also improves the efficiency and accuracy of traffic management.

[0023] Compared with the prior art, the present invention has the following beneficial effects: First, by obtaining the macro basic diagram of traffic flow and combining it with the CAV penetration rate to perform MFD segment 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 the control strategy according to real-time data, thereby adapting to rapidly changing traffic conditions and improving the traffic capacity and safety of the road.

[0024] Secondly, the present invention further refines the accuracy of traffic control by identifying congested sections and determining the control range of ramps based on the average vehicle travel length. This not only reduces ineffective and excessive control, but also makes traffic control more targeted and efficient through precise regional division (bottlenecks and ramps in associated areas), effectively alleviating traffic congestion in specific areas.

[0025] 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 be optimized and adjusted in real time with the goal of minimizing the total travel time of vehicles. This optimization not only improves the overall operating 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

[0026] Figure 1 This is a flowchart of a ramp coordination control method adapted to a new mixed traffic flow scenario according to Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of entrance ramp control according to Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the hierarchical optimization control process of Example 2 of the present invention. DETAILED DESCRIPTION

[0027] The following is a further detailed description of a ramp coordination control method and device adapted to a new mixed traffic flow scenario provided by the present invention in conjunction with the accompanying drawings and specific embodiments. However, this should not be understood as the scope of the above-mentioned subject matter of the present invention being limited to the following embodiments, and all technologies implemented based on the content of the present invention belong to the scope of the present invention. Combined with the following description, the advantages and features of the present invention will become clearer. It should be noted that the drawings are all in a very simplified form and use non-precise proportions, which are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0028] Example 1 During the research process, the applicant found that when using traditional traffic flow control methods, if one intends to achieve effective management of new mixed traffic flows (including autonomous driving and manually driven vehicles), one needs to rely on cumbersome steps such as continuous adjustment and reconstruction of traffic models, which can only provide limited improvements. When solving practical engineering problems, in order to achieve the technical goals of high efficiency and high safety, the existing technology cannot meet the accurate prediction and control of dynamic and rapidly changing mixed traffic flows. Therefore, after studying the problem, the applicant proposed a ramp coordination control method based on CAV penetration rate. When addressing the technical issues of expressway traffic congestion management, the applicant established a technical solution of a discretized process model based on MFD segmented fitting, which achieved precise regulation and optimization management of mixed traffic flows, thereby achieving the technical effect of improving traffic flow processing efficiency and reducing traffic congestion.

[0029] Please see Figure 1 , Figure 1 This is a flowchart of a ramp coordination control method adapted to a new mixed traffic flow scenario according to an embodiment of the present application, comprising the following steps: S1. Obtaining a macro basic map of an expressway with a new type of mixed traffic flow; the new type of mixed traffic flow is a scene where autonomous driving vehicles and manned vehicles are mixed; S2. Performing piecewise fitting on the macro basic map according to the CAV permeability to obtain the MFD piecewise fitting function; S3. Identify the congested section of the new mixed traffic flow according to the average density of the section, and determine the control range of the ramp based on the average travel length of the vehicles in the congested section, and divide the ramp into a bottleneck area ramp and an associated area ramp; S4. Constructing a discretization process model of mixed traffic flow on the expressway according to the MFD segmented fitting function; S5. Based on the discretized process model, a model predictive control method is adopted to minimize the total vehicle travel time, establish a hierarchical optimization control model, and output the ramp coordination control result.

[0030] Specifically, in the embodiment of the present application, the first step is to obtain a macro basic map of traffic flow, which is a representation of the flow state of the entire traffic network. This step provides basic data for subsequent analysis and formulation of control strategies. Subsequently, the macro basic map is segmentedly fitted according to different CAV (connected and automated vehicles) penetration rates to obtain the MFD (macro basic map) segmented fitting function. This step allows accurate prediction of traffic flow dynamics in different scenarios according to the different degrees of vehicle automation.

[0031] 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 bottleneck area ramps and associated area ramps. This division allows for more sophisticated control strategies to be implemented for traffic management in different areas to improve the management efficiency of traffic flow.

[0032] Furthermore, a discretized process model of traffic flow is constructed based on the obtained MFD segmented fitting function. This model supports the simulation and prediction of traffic flow changes under various traffic conditions, providing a basis for making control decisions. Finally, based on the discretized process model, a hierarchical optimization control model is established using the model predictive control (MPC) method. This model can not only minimize the total travel time of vehicles, but also achieve effective coordinated control of ramps in bottleneck areas and associated areas, thereby optimizing traffic flow and significantly improving the operating efficiency and safety of the road network.

[0033] In summary, the ramp coordination control method provided in 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, etc. In the above implementation, when controlling the traffic flow of the expressway, the flow at the ramp entrance can be adjusted according to real-time data, and the effective distribution of traffic flow can be achieved through the hierarchical optimization control model, thereby achieving the effect of reducing traffic congestion and improving road use efficiency.

[0034] Example 2 The embodiment of the present application provides a ramp coordination control method adapted to a new mixed traffic flow scenario, the method comprising: S1. Use MFD to characterize the dynamics of new mixed traffic flows on expressways, and establish an MFD segmented fitting model based on CAV penetration rate.

[0035] S2. Use the average density of the road section to identify the congested sections and establish a control ramp screening method based on the average vehicle travel length. Specifically, starting from the congested section and extending upstream, the scale of the control ramp is determined based on the average vehicle travel length. At the same time, the ramps that are caught in congestion are divided into bottleneck ramps, and the rest are associated area ramps.

[0036] S3. According to the MFD piecewise fitting function obtained in step S1, a discretization process model of the expressway under the new mixed traffic flow is constructed.

[0037] S4. Based on the model predictive control (MPC) method, with the goal of minimizing the total vehicle travel time, the congestion on the expressway is alleviated by controlling the inflow flow of the entrance ramp, and a hierarchical optimization control model is established considering the ramp queuing constraints.

[0038] Wherein, step S1 comprises: S11. Define MFD feature points, where the critical point of MFD is defined as the dividing point where the road network enters a congested state from a smooth state; the congestion turning 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.

[0039] S12. Based on simulation, a fitting function of the horizontal and vertical coordinate values ​​of the MFD critical point and the congestion inflection point and the CAV penetration rate is established. , , , in, is the CAV penetration rate, and CAV penetration rate The critical point density and congestion inflection point density under and CAV penetration rate Fitting function of critical point flow and density, and CAV penetration rate The critical point flow and congestion inflection point flow under and CAV penetration rate Fitting function of congestion inflection point flow and density. S13. Using the values ​​of the MFD piecewise fitting function at the origin and critical point and the tangent slope, establish the MFD unblocked section cubic fitting function, and calculate the expressions of the coefficients of the fitting function by solving the three-variable linear equation group.

[0040]

[0041] in, , and is the coefficient to be solved, the average density The value range of is: , is the critical point density value.

[0042]

[0043] in, is the free flow speed, Cubic fitting function For density The derivative function of .

[0044]

[0045]

[0046]

[0047] S14. Using the slope characteristics of the MFD segmented fitting function at the critical point and the congestion inflection point, and the fact that the fitting function passes through the critical point and the congestion inflection point with a constant slope, a cubic fitting function for the MFD congested section is established, and the expressions of the coefficients of the fitting function are calculated by solving a set of four-variable linear equations.

[0048]

[0049]

[0050] in, is the slope of the cubic fitting function at the congestion inflection point.

[0051]

[0052]

[0053]

[0054]

[0055] Step S2 includes: S21. Identify congested sections using the average density of sections of each main road.

[0056] S22. Determine the road section numbers of the upper and lower boundaries of the bottleneck area.

[0057] S23. Calculate the average travel length of the study section, take the entrance of the upstream section of the bottleneck area as the starting point, and use the average travel length as the basis for division to determine the upstream boundary of the control area ( ).

[0058] S24, the lower boundary of the bottleneck area is the downstream boundary of the control area ( ), forming a time interval The scope of the control area below. ,in, A set of road segment numbers representing the control area. and The road segment numbers represent the upstream and downstream boundaries of the control area respectively.

[0059] Please see Figure 2 , Figure 2 This is a schematic diagram of entrance ramp control according to an embodiment of the present application, which illustrates a method for alleviating expressway congestion by controlling the inflow flow of the entrance ramp.

[0060] Step S3 includes: S31. Establish a discretized equation for the change in vehicle accumulation on the main road of the expressway.

[0061] (4-3) in, Indicates the time interval of the main expressway in the control area The amount of vehicles accumulated under and Respectively represent the time interval The flow rate from the expressway main road into and out of the control area, Indicates the time interval Total inflow flow at the lower entrance ramp, Indicates the time interval Total outflow from the off-ramp, represents the total regulation rate, Indicates the length of the time interval.

[0062] S32. Establish a discretized equation for the change in the vehicle accumulation amount on the entrance ramp.

[0063]

[0064] in, represents the vehicle accumulation of the entrance ramp after discretization, Represents the inflow flow of the entrance ramp after discretization.

[0065] S33, introducing the MFD segmented fitting function according to the CAV penetration rate obtained in step S1, and establishing a discretized process model of mixed traffic flow on the expressway that meets the MPC requirements. Specifically, the introduced MFD segmented fitting function is expressed as:

[0066] Please see Figure 3 , Figure 3 Schematic diagram of the hierarchical optimization control process of the embodiment of the present application. Specifically, the hierarchical optimization control model in step S4 includes: S41. Using the discretized process model established in step S3, taking the total ramp inflow as the control variable and minimizing the total vehicle travel time as the goal, while introducing a penalty term to prevent fluctuations in the control quantity to ensure smoothness of the control, an upper-level MPC model is established.

[0067]

[0068] in, is an adjustable parameter, Represents the prediction time domain.

[0069]

[0070]

[0071]

[0072]

[0073] .

[0074]

[0075]

[0076]

[0077]

[0078] in, represents the total outflow flow of the control area, Indicates the time interval Traffic volume on the expressway, Indicates the set expressway accumulation threshold. For entrance ramp The critical vehicle accumulation threshold, is the average trip length on the expressway, is the total length of the main road of the expressway. It should be noted that the three-time fitting function in step S1 represents the continuous weighted average flow, and in step S41 After discretization, it represents the time interval The weighted average flow rate of the main road of the lower expressway.

[0079] S42. A two-layer control weight allocation method based on the average density of the downstream section of the ramp and the ramp position information is established to allocate the total control weight calculated by the upper-layer MPC model. Specifically, the control weight allocation of the ramp in the bottleneck area is realized according to the average density of the section downstream of each entrance ramp, and the control weight allocation of the ramp in the associated area is realized according to the distance of each entrance ramp from the bottleneck area.

[0080]

[0081] The control weight allocation method for the bottleneck ramp is expressed as:

[0082]

[0083] in, express The total flow rate that needs to be restricted flowing into the main road of the expressway is solved at the time interval. Indicates the entrance ramp Within 1 km downstream The accumulation of vehicles in a time interval, Indicates the entrance ramp Critical accumulation level within 1 km downstream, represents the allocation weight based on the average density of the road segment, Indicates the entrance ramp The density of downstream main roads, Indicates the entrance ramp exist Controlled inflow at time intervals, Indicates the entrance ramp exist Normal inflow flow at a time interval, which is equal to the vehicle flow in the actual traffic scenario; Indicates the entrance ramp exist The maximum inflow flow limit that can be tolerated during a time interval is determined by the length of the entrance ramp and the vehicle accumulation level at the current moment.

[0084]

[0085] For entrance ramp The critical vehicle accumulation volume, For entrance ramp In time interval The amount of vehicles accumulated under.

[0086]

[0087] The control weight allocation method for the ramp in the association area is expressed as:

[0088]

[0089] in, Indicates that the ramp in the associated area needs to limit the total flow into the main road of the expressway. represents the distance-based allocation weight, represents the average stroke length, Indicates the entrance ramp Distance from the upper boundary of the bottleneck area.

[0090] 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 obtained controlled inflow of the entrance ramp of the bottleneck area and the entrance ramp of the associated area.

[0091]

[0092] in, Indicates the entrance ramp In time interval The green light time, Indicates the preset signal period, is the fitting parameter, which can be obtained through simulation.

[0093] Example 3 The embodiment of the present application describes a computer device, which is specially designed to implement a ramp coordination control method adapted to a new mixed traffic flow scenario. The computer device includes a memory, a processor, and a computer program stored in the memory, wherein the program is encoded with algorithms and instructions for implementing the above control method.

[0094] First, the processor of the device executes a computer program stored in the memory, through which the macro basic map of traffic flow is first obtained. This basic map is obtained through real-time traffic data sensors or online traffic services, providing basic data for subsequent analysis. Subsequently, the program performs piecewise fitting on the macro basic map according to the input CAV penetration rate, and generates MFD piecewise fitting functions, which can reflect the traffic flow status under different CAV penetration rates and are used to predict and manage traffic flow.

[0095] Next, the computer program analyzes the average density of the road section, identifies the congested sections, and automatically calculates and determines the control range of the ramp based on the average travel length of vehicles in these congested sections. In this process, the ramp is divided into bottleneck area ramps and associated area ramps to optimize the management of traffic flow.

[0096] Finally, the processor uses the MFD segmented fitting function to construct a discretized process model of traffic flow and adopts the model predictive control (MPC) method. This method is implemented through a computer program, with the goal of minimizing the total travel time of vehicles, thereby establishing a hierarchical optimization control model. In this way, the computer equipment can output the coordinated control results of the ramps in the bottleneck area and the ramps in the associated area, significantly improving the efficiency and effectiveness of traffic management.

[0097] This computer equipment integrates efficient data processing capabilities and advanced algorithms to respond to changes in traffic conditions in real time, providing traffic management departments with powerful decision-making support tools to effectively deal with complex traffic flow scenarios.

[0098] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0099] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0100] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A ramp coordination control method adapted to a new mixed traffic flow scenario, characterized in that: The following steps are involved: S1. Obtain the macro basic map of traffic flow; S2. Performing piecewise fitting on the macro basic map according to the CAV permeability to obtain the MFD piecewise fitting function; S3. Identify the congested section according to the average density of the section, and determine the control range of the ramp based on the average travel length of the vehicle in the congested section, and divide the ramp into a bottleneck area ramp and an associated area ramp; S4. Constructing a discretization process model of the traffic flow according to the MFD piecewise fitting function; S5. Based on the discretized process model, a model predictive control method is adopted to establish a hierarchical optimization control model with the goal of minimizing the total vehicle travel time, and output the coordinated control results of the bottleneck area ramp and the associated area ramp.

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

3. A ramp coordination control method adapted to a new mixed traffic flow scenario according to claim 2, characterized in that: 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. Establishing the MFD piecewise fitting functions of the critical point, the congestion inflection point and the CAV penetration rate respectively; S22. According to the values ​​and tangent slopes of the MFD segmented fitting function at the origin and the critical point, a cubic fitting function of the MFD unblocked section is established, and a first coefficient, a second coefficient, and a third coefficient are obtained; The formula of the cubic fitting function of the MFD smooth section is: ,in represents the cubic fitting function; represents the average density of road segments, , represents the critical point density value; is the first coefficient, is the second coefficient, is the third coefficient; The first coefficient It is expressed as: ,in is the free flow speed, is the critical point flow rate; The second coefficient It is expressed as: ; The third coefficient It is expressed as: ; According to the characteristics of the MFD piecewise fitting function at the critical point and the congestion turning point, a cubic fitting function of the MFD congested section is established, and a fourth coefficient, a fifth coefficient, a sixth coefficient, and a seventh coefficient are obtained; The formula of the cubic fitting function of the MFD congested road section is: ,in < < , is the density value of the congestion inflection point; The fourth coefficient It is expressed as: ,in For congestion inflection point flow, is the slope of the cubic fitting function at the congestion inflection point; The fifth coefficient It is expressed as: , where k is the slope of the cubic fitting function at the critical point; The sixth coefficient It is expressed as: ； The seventh coefficient It is expressed as: 。 4. A ramp coordination control method adapted to a new mixed traffic flow scenario according to claim 3, characterized in that: Step S4 establishes the discretization process model of the traffic flow, including the following steps: S41. Based on the inflow and outflow of vehicles on the main road, and the total inflow and outflow of the entrance ramp and exit ramp, a vehicle accumulation change model of the main road is established; the formula of the vehicle accumulation change model of the main road is: ,in, Indicates that the main road in the control area is within the time interval The amount of vehicles accumulated under +1 for After another time step, Indicates the time interval The inflow of vehicles from the main road, Indicates the time interval The outflow of vehicles from the main road, Indicates the time interval Total inflow flow at the lower entrance ramp, Indicates the time interval Total outflow from the exit ramp, represents the total regulation rate, Indicates the length of the time interval; S42. Based on the vehicle inflow and outflow of the entrance ramp, a vehicle accumulation change model of the entrance ramp is established; the formula of the vehicle accumulation change model of the entrance ramp is: ,in represents the vehicle accumulation of the entrance ramp after discretization, represents the inflow flow of the entrance 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 model prediction control requirements.

5. A ramp coordination control method adapted to a new mixed traffic flow scenario according to claim 4, characterized in that: The step S5 of establishing the hierarchical optimization control model includes the following steps: S51. Based on the discretized process model of the traffic flow, taking the total inflow flow of the ramp as the control variable and minimizing the total vehicle travel as the goal, construct an upper-level model predictive control model; the formula of the upper-level model predictive control model is: ,in, is an adjustable parameter, represents the prediction time domain; S52. Based on the output results of the upper-level model predictive control model, combined with the average density of the downstream section of the ramp and the location information of the ramp, a double-layer control weight allocation method is established to allocate the total control weight of the ramp.

6. A ramp coordination control method adapted to a new mixed traffic flow scenario according to claim 5, characterized in that: The double-layer control weight allocation method comprises: S521. Based on the average density of the road section downstream of the entrance ramp, determine the control weight of the bottleneck ramp; the control weight of the bottleneck ramp is ,in, represents the allocation weight based on the average density of the road segment, Indicates the entrance ramp density of downstream main roads; S522. Based on the average trip length and the distance between the entrance ramp and the bottleneck area boundary, determine the control weight of the ramp in the associated area; the control weight of the ramp in the associated area is ,in, represents the distance-based allocation weight, represents the average stroke length, Indicates the entrance ramp Distance from the upper boundary of the bottleneck area.

7. A ramp coordination control method adapted to a new mixed traffic flow scenario according to claim 6, characterized in that: The controlled inflow flow of the entrance ramp is determined according to the normal inflow flow of the entrance ramp, the control weight of the bottleneck ramp, the total flow that needs to be restricted from flowing into the expressway main road by the bottleneck ramp, and the maximum inflow flow limit of the entrance ramp; the maximum inflow flow limit is determined by the length of the entrance ramp and the vehicle accumulation at the current moment.

8. The ramp coordination control method adapted to a new mixed traffic flow scenario according to claim 6 is characterized in that: The controlled inflow flow of the entrance ramp is determined according to the normal inflow flow of the entrance ramp, the control weight of the ramp in the associated area, the total flow of the ramp in the associated area that needs to be restricted from flowing into the main road of the expressway, and the maximum inflow flow limit of the entrance ramp.

9. A ramp coordination control method adapted to a new mixed traffic flow scenario according to claim 6, characterized in that: Calculating a 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 entrance ramp; The formula for calculating the ramp coordinated control result is: ,in Indicates the entrance ramp In time interval The green light time, represents the controlled inflow flow of the entrance ramp j at time interval k, Indicates the preset signal period, is the fitting parameter.

10. A computer device, characterized in that: The computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method according to any one of claims 1 to 9 when executing the computer program.

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