A mixed traffic management plan formulation method and system

Abstract

The present invention provides a method and system for formulating a traffic management plan for mixed driving, which relates to the technical field of traffic management plan formulation, including obtaining operating data information of manually driven vehicles and intelligent networked vehicles, intersection turning ratio data and road section parameter information, and constructing a basic graph model of mixed traffic flow for calculating the average speed of mixed traffic flow. Based on the model, combined with the average speed, turning ratio and road section parameters of the traffic flow, a feedback queuing model of the vehicle is further constructed. The feedback queuing model is used to calculate the system status data of the transportation system where the vehicle is operating, the fuel consumption data of fuel vehicles, the energy consumption data of electric vehicles and the vehicle delay time information. Finally, based on the various types of information obtained, a complete traffic management plan is formulated. The present invention can improve the efficiency of traffic flow management, and effectively reduce traffic congestion, energy consumption and delay time.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic management plan formulation, and in particular to a method and system for formulating a traffic management plan for mixed driving. Background Art

[0002] With the continuous acceleration of urbanization, traffic congestion is becoming increasingly serious, especially in densely populated urban areas. Traditional traffic management methods mainly rely on manual control and fixed traffic signal timing, which are usually not flexible enough to adjust according to dynamic information such as real-time traffic flow and vehicle speed. Most existing technologies use a single traffic flow analysis model based on manually driven vehicles, ignoring the impact of intelligent connected vehicles in mixed traffic flows. Although this method can provide a basic traffic management solution, it fails to effectively integrate the operating data of intelligent connected vehicles, resulting in slow traffic management response and low efficiency, and cannot adapt to the complex and changing road traffic environment. In addition, the traditional traffic management system fails to fully consider environmental factors such as energy consumption and emissions, and lacks accurate prediction of details such as intersection queues and vehicle delays, resulting in waste of resources and increased delay time.

[0003] Therefore, there is an urgent need for a mixed traffic management plan formulation method and system to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for formulating a mixed traffic management plan to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0005] In a first aspect, the present application provides a method for formulating a mixed driving traffic management plan, comprising:

[0006] Acquire first information, second information and third information, wherein the first information includes operation data information of a manually driven vehicle and operation data information of an intelligent network-connected vehicle, the second information includes steering ratio data of the vehicle at an intersection, and the third information includes road section parameter information;

[0007] Constructing a mixed traffic flow basic graph model based on the operating data information of the manually driven vehicles and the operating data information of the intelligent networked vehicles, wherein the mixed traffic flow basic graph model is a model for calculating the average speed of the mixed traffic flow of the manually driven vehicles and the intelligent networked vehicles;

[0008] Calculating an average speed of a mixed traffic flow of manually driven vehicles and intelligent networked vehicles based on the mixed traffic flow basic graph model, and constructing a feedback queuing model of vehicles based on the average speed of the mixed traffic flow, the second information, and the third information;

[0009] The fourth information is calculated based on the feedback queuing model of the vehicle, and the fourth information includes system status data of the transportation system where the vehicle is running, fuel consumption data of the fuel vehicle, and energy consumption data of the electric vehicle;

[0010] A traffic management plan for the transportation system where the vehicle is operating is formulated based on the fourth information to obtain a formulated traffic management plan.

[0011] In a second aspect, the present application also provides a mixed driving traffic management plan formulation system, including:

[0012] an acquisition unit, configured to acquire first information, second information, and third information, wherein the first information includes operation data information of a manually driven vehicle and operation data information of an intelligent network-connected vehicle, the second information includes steering ratio data of the vehicle at an intersection, and the third information includes road section parameter information;

[0013] A first calculation unit is used to construct a mixed traffic flow basic graph model based on the operation data information of the manually driven vehicle and the operation data information of the intelligent networked vehicle, wherein the mixed traffic flow basic graph model is a model for calculating the average speed of the mixed traffic flow of the manually driven vehicle and the intelligent networked vehicle;

[0014] a second calculation unit, configured to calculate an average speed of a mixed traffic flow of manually driven vehicles and intelligent networked vehicles based on the mixed traffic flow basic graph model, and construct a feedback queuing model of the vehicles based on the average speed of the mixed traffic flow, the second information, and the third information;

[0015] A third calculation unit, configured to calculate fourth information based on the feedback queuing model of the vehicle, wherein the fourth information includes system status data of the transportation system in which the vehicle is operating, fuel consumption data of the fuel vehicle, and energy consumption data of the electric vehicle;

[0016] A processing unit is used to formulate a traffic management plan for the transportation system where the vehicle is operating based on the fourth information to obtain a formulated traffic management plan.

[0017] The beneficial effects of the present invention are:

[0018] The present invention constructs a dynamic and accurate mixed traffic flow model by integrating the operating data of manually driven vehicles and intelligent networked vehicles, combined with real-time traffic flow, intersection turning ratio, road section parameters and other information. Based on this model, the present invention can calculate the average speed, headway, traffic density and other key indicators of mixed traffic flow in real time, and optimize the traffic management plan accordingly, significantly improving the efficiency of traffic flow control. In this plan, by performing correlation analysis with historical traffic management plans, the optimization plan that best suits the current traffic conditions is selected, further improving the adaptability and accuracy of the plan. Compared with the prior art, the present invention can more accurately respond to the dynamic changes of traffic flow, effectively reduce traffic congestion, reduce energy consumption and emissions, and shorten vehicle delay time, thereby realizing the intelligence and efficiency of the transportation system.

[0019] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A flow chart of a method for formulating a traffic management scheme for mixed driving according to an embodiment of the present invention;

[0022] Figure 2 A schematic diagram of the system structure is provided for formulating the mixed driving traffic management scheme described in an embodiment of the present invention.

[0023] In the figure: 701, acquisition unit; 702, first calculation unit; 703, second calculation unit; 704, third calculation unit; 705, processing unit. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0025] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0026] Embodiment 1:

[0027] This embodiment provides a method for formulating a traffic management plan for mixed driving.

[0028] See also Figure 1 , the figure shows that the method includes step S1, step S2, step S3, step S4 and step S5.

[0029] Step S1, obtaining first information, second information and third information, wherein the first information includes operating data information of a manually driven vehicle and operating data information of an intelligent networked vehicle, the second information includes steering ratio data of the vehicle at an intersection, and the third information includes road section parameter information;

[0030] It is understandable that the first information in this step includes the operating data information of manually driven vehicles and intelligent networked vehicles, which are usually derived from the vehicle's sensors, on-board equipment and vehicle networking systems, such as GPS positioning, vehicle speed, acceleration, braking status, etc. This information helps analyze the performance of different types of vehicles in mixed traffic flows, especially the differences between manually driven vehicles and intelligent networked vehicles in driving modes, reaction speeds and road occupancy. The second information is the turning ratio data of the intersection, which reflects the proportion of vehicle flow and turning behavior in different directions. This is particularly important for the optimization of traffic flow, because traffic management at intersections directly affects the road's capacity and traffic efficiency. The third information is the parameter information of the road section, including the length, width, traffic signs, signal light cycle, traffic flow, etc. of the road, which constitute external factors that affect the dynamic changes of traffic flow.

[0031] Step S2: constructing a mixed traffic flow basic graph model based on the operating data information of the manually driven vehicles and the operating data information of the intelligent networked vehicles, wherein the mixed traffic flow basic graph model is a model for calculating the average speed of the mixed traffic flow of the manually driven vehicles and the intelligent networked vehicles;

[0032] It can be understood that the model of this step can provide accurate traffic flow speed prediction, which is helpful for analyzing the relative speed and speed distribution of different types of vehicles in a mixed driving environment, thereby revealing how to optimize the coordination between the two, reduce traffic congestion and improve traffic efficiency. In this step, step S2 includes step S21, step S22 and step S23.

[0033] Step S21, constructing a car-following model of a manually driven vehicle based on a preset calculation formula of the expected acceleration of the manually driven vehicle;

[0034] It is understood that the calculation formula for the expected acceleration of a manually driven vehicle is as follows:

[0035] ;

[0036] in, is the expected speed; is the expected acceleration; is the free flow speed; is the speed difference between the front and rear workshops; is the length of the vehicle; It is the safe headway time; For safe headway; is the maximum acceleration; Deceleration for comfort; Estimate parameters for the model; Headroom.

[0037] Step S22, constructing a car-following model of an intelligent networked vehicle based on a preset car-following speed calculation formula and a car-following error calculation formula of the intelligent networked vehicle;

[0038] It is understandable that the intelligent connected vehicle following model can accurately describe and predict the driving behavior of intelligent connected vehicles in complex traffic environments. The model helps to achieve dynamic coordination between intelligent connected vehicles and other vehicles (including manually driven vehicles), reduce traffic congestion and improve road capacity. The preset intelligent connected vehicle following speed calculation formula and following error calculation formula are as follows:

[0039] ;

[0040] in, The speed of the smart vehicle following the vehicle at the current moment; is the speed of the following vehicle at the previous control moment; is the error between the actual headway and the expected headway, and its derivative form is ; is the expected headway; For safe headway; is the length of the vehicle; is the distance between the front and rear vehicles; , is the control coefficient.

[0041] Step S23: Substitute the calculation formulas in the manually driven vehicle following model and the intelligent connected vehicle following model into the preset mixed traffic flow average speed calculation formula to obtain a model of the mixed traffic flow average speed of the intelligent connected vehicle.

[0042] It can be understood that this step provides an important tool for accurately evaluating the driving efficiency of vehicles in mixed traffic flow. By considering the behavioral characteristics of different vehicle types, the model can more accurately predict the speed changes in traffic flow. Among them, the headway of manually driven vehicles and intelligent connected vehicles in the steady state is calculated separately. In the steady state, the vehicle follows the front vehicle at a constant speed, and the vehicle acceleration is 0. Therefore, let =0; then the headway distances of manually driven vehicles and intelligent connected vehicles in a stable state can be obtained as follows:

[0043] ;

[0044] in, The headway of a manually driven vehicle under stable conditions; The headway of intelligent connected vehicles under stable conditions; is the safe time interval of intelligent connected vehicles. Assume that the total number of vehicles in the mixed traffic flow is , then the average headway distance when the mixed traffic flow is stable is:

[0045] ;

[0046] After simplification, we can get:

[0047] ;

[0048] in, is the penetration rate of intelligent connected vehicles, that is, the ratio of the number of intelligent connected vehicles to the total number of vehicles in mixed traffic flow.

[0049] Step S3, calculating the average speed of the mixed traffic flow of manually driven vehicles and intelligent networked vehicles based on the mixed traffic flow basic graph model, and constructing a feedback queuing model of the vehicles based on the average speed of the mixed traffic flow, the second information and the third information;

[0050] It can be understood that the mixed traffic flow basic graph model can more accurately describe the characteristics of the transportation system. In this step, step S3 includes step S31, step S32, step S33 and step S34.

[0051] Step S31, substituting the calculation formulas in the manually driven vehicle following model and the intelligent networked vehicle following model into the preset headway distance calculation formula to obtain the headway distance of the manually driven vehicle and the headway distance of the intelligent networked vehicle;

[0052] Step S32, based on the headway of manually driven vehicles and the headway of intelligent networked vehicles, a calculation formula for the average headway of mixed traffic flow is used to calculate to obtain the average headway of mixed traffic flow;

[0053] Step S33, calculating the mixed traffic flow density based on the average headway of the mixed traffic flow;

[0054] It can be understood that by bringing into the preset headway calculation formula in step S2, the headway of manually driven vehicles, the headway of intelligent connected vehicles, the average headway of mixed traffic flow and the mixed traffic flow density can be directly obtained.

[0055] Step S34: Calculate the average speed of the mixed traffic flow based on the mixed traffic flow density and a preset average speed calculation formula of the mixed traffic flow.

[0056] It can be understood that the mixed traffic flow density and the preset average speed calculation formula of the mixed traffic flow are as follows:

[0057] Among them, the mixed traffic flow density can be expressed as:

[0058] ;

[0059] in, is the density of mixed traffic flow. When the traffic flow is stable, the average speeds of intelligent vehicles and manually driven vehicles tend to be the same, so .in represents the average speed of mixed traffic flow, and then the relationship between traffic flow density and average vehicle speed under stable conditions can be obtained:

[0060] ;

[0061] Similarly, the relationship between traffic flow and average speed is:

[0062] ;

[0063] Among them, the characteristic point parameters of the exponential velocity model are (0, ),( , )as well as( , ). First, the characteristic point parameters need to be calibrated before the exponential speed model can be applied to model the mixed traffic flow system.

[0064] The exponential velocity model is:

[0065] ;

[0066] ;

[0067] ;

[0068] in, is the number of mixed vehicles on the road section; The number of vehicles on the road section is The average speed of mixed traffic flow at ; is the capacity of the road section; , is the calibration parameter of the exponential velocity model; , Respectively represent the number of vehicles at the feature points; , Respectively represent the number of vehicles , The corresponding mixed traffic flow vehicle speed.

[0069] It should be noted that the feature point parameters , and road section capacity The length of the node queue system and width The characteristic point parameters are obtained through the optimization model. The optimization model can be implemented using the fmincon function provided by Maltab.

[0070] It can be understood that in this step, step S3 also includes step S35, step S36 and step S37.

[0071] Step S35, comprehensively analyzing the average speed of the mixed traffic flow, the second information and the third information, and constructing an intersection queuing network based on the analysis results;

[0072] It is understood that the intersection is composed of sections with different functions. According to their classification, they are generally divided into: entrance section (using the set denoted by), exit section (with set ) and turn segments (represented by the set If represents the set of road segments that make up the intersection, then ,by Describes the inherent transition requirements between paths, as a 0-1 variable Representation Node to Whether it is connected (i.e. whether the current phase is green), then the node to The actual transition probability can be expressed as:

[0073] ;

[0074] in, express Time section to inherent potential transfer needs; express Time section to The connectivity status; express Time section to The transition probability.

[0075] Then, for the actual arrival rate of each node in the intersection queuing network, there is the following rule:

[0076] First, for nodes that arrive at the system from outside the system (i.e., import sections), their arrival rate can be obtained through video monitoring and other methods, which can be described as:

[0077] ;

[0078] in, express Time section The vehicle arrival rate; express Arrival time from outside the system The vehicle arrival rate.

[0079] Secondly, for other sections except the import section:

[0080] ;

[0081] in, express Upstream node at time To research node The vehicle output rate is related to the road section service rate. express Time section to The transition probability. The expression is as follows:

[0082] ;

[0083] in, express In the real-time feedback queue system Time section service rate; In the feedback queue system Time section System status; In the feedback queue system Time section The idle probability of express Upstream node at time To research node vehicle output rate.

[0084] Step S36, determining the congestion probability and outflow rate of all types of vehicles on the corresponding road section based on the intersection queuing network, and constructing a loss queuing model based on the congestion probability and outflow rate to obtain a loss queuing model for vehicles on the road section;

[0085] It is understandable that the vehicles in the road system will present a mixed driving state of different types of vehicles. Assuming that different types of vehicles arrive at the road section All obey the parameters If the Poisson process is It can be expressed as:

[0086] ;

[0087] Where T represents different types of vehicle numbers. For example, when T=F, it represents a fuel vehicle; when T=C, it represents a charging vehicle; and when T=BS, it represents a battery-swap vehicle. Indicates the intersection section number; express Time section The arrival rate of mixed traffic flow; express Time section Arrival rates of different types of traffic.

[0088] Then at this time, Number of vehicles on the road section at any given time Can be represented as different types The sum of the number of vehicles, that is:

[0089] ;

[0090] in, express Time section The number of mixed vehicles; express Time section The number of different types of vehicles.

[0091] In addition, according to the law of flow conservation, the rate of change of the number of vehicles on a road section should be the sum of the rates of change of different types of vehicles:

[0092] ;

[0093] in, Representing a loss queuing system Time section The rate of change of the number of vehicles in the upper mixed traffic flow; Representing a loss queuing system Time section The rate of change of the number of vehicles in different types of traffic; Represents a lossy queuing system.

[0094] In the road section queuing system, for any type of vehicle, the number of vehicles can be described as:

[0095] ;

[0096] in, Indicates different types of vehicle numbers; express The type of the time segment is The inflow rate of vehicles; express The type of the time segment is The outflow rate of vehicles; Representing a loss queuing system Time section The above type is The number of vehicles; Representing a loss queuing system Time section The above type is The number of vehicles.

[0097] In the above formula, Taking the derivative, we get:

[0098] ;

[0099] in, Representing a loss queuing system Time section The above type is The rate of change of the number of vehicles. That is, the rate of change of the number of any type of vehicles is equal to the net inflow rate of such vehicles.

[0100] In the above formula, Not only the arrival rate of vehicles It is also related to the road section The probability of congestion Related, can be described as:

[0101] ;

[0102] in, In the loss queue system, the road segment The number of vehicles in mixed traffic flow at the time.

[0103] The above formula shows that when the vehicle is not congested, the road section can reach the vehicle normally, otherwise the vehicle cannot enter the road section, and the blocking probability Existing models can be used for calibration.

[0104] Not only with Overall service capability of the time section Related to Idle probability of the road segment at a given time , and the proportion of vehicles of this type:

[0105] ;

[0106] in Represents loss in a queueing system Time section In the mixed vehicle number The corresponding service rate when

[0107] The above formula shows that when a road section is idle, there are no vehicles that cannot flow out of the road section. is the service rate of the road section. When a vehicle enters the road section, it means the service starts, and when a vehicle leaves the road section, it means the service ends. The service rate It can be expressed as:

[0108] ;

[0109] in The number of mixed traffic flows in the loss queuing system is Time section The average vehicle speed is given by the exponential speed model in the previous section. Indicates road segment Length.

[0110] Based on the above analysis, the loss queuing model can be obtained as follows:

[0111] ;

[0112] In the above formula, represents the actual traffic output rate of the road section in the loss queuing system, and express:

[0113] ;

[0114] in, Represents the loss of the line segment in the queue system exist System output rate at time; Represents the loss of the line segment in the queue system service rate.

[0115] Step S37: constructing a feedback queuing model for vehicles based on the loss queuing model of the road section vehicles and the preset artificial virtual node parameters.

[0116] It is understandable that if congestion occurs on the road section, the vehicles arriving at the road section will directly leave the system (i.e., the loss system), which is different from the actual situation where vehicles queue up and wait on the road section. Therefore, an artificial virtual node needs to be virtualized to store the vehicles waiting in line.

[0117] At this point, it can be described as a feedback queuing model as follows:

[0118] ;

[0119] in, Represents a feedback queuing system; Represents the road segment in the feedback queuing system The above type is The number of vehicles; Represents the road segment in the feedback queuing system The above type is The rate of change of the number of vehicles; for Artificial virtual nodes at all times The service rate after feedback elimination, for The utilization rate of artificial virtual nodes at each moment.

[0120] ;

[0121] In the formula, To consider the virtual space, the system The blocking probability at time. is the service rate of the virtual space.

[0122] ;

[0123] In the formula, Indicates the service rate when the system is blocked, is the square of the coefficient of variation. When the service time of a road section follows a negative exponential distribution, , further .

[0124] Utilization of artificial virtual nodes It is expressed as:

[0125] ;

[0126] express Artificial virtual nodes at all times The system state, i.e. the number of overflowed vehicles, can be described by the following difference equation:

[0127] ;

[0128] The vehicle output rate of the feedback queuing system It can be described as:

[0129] ;

[0130] in Indicates the service rate of the road section taking feedback into account:

[0131] ;

[0132] in, Indicates that considering feedback in the queuing system Time section service rate; Represents the loss of the line segment in the queue system service rate; Represents loss in a queueing system Time section The number of mixed vehicles.

[0133] Step S4, calculating and obtaining fourth information based on the feedback queuing model of the vehicle, the fourth information including system status data of the transportation system where the vehicle is running, fuel consumption data of the fuel vehicle and energy consumption data of the electric vehicle;

[0134] It can be understood that this step calculates the fourth information through a preset calculation formula to prepare for the subsequent traffic management plan. In this step, step S4 includes step S41, step S42 and step S43.

[0135] Step S41, calculating the system state data information corresponding to each vehicle based on the system state calculation formula in the feedback queuing model of the vehicle;

[0136] It can be understood that the system state calculation formula in the feedback queuing model of the vehicle is the calculation formula of the loss queuing model, and the system state data information corresponding to each vehicle can be directly obtained.

[0137] Step S42, calculating the fuel consumption data information of the fuel vehicle based on a preset fuel consumption calculation formula of the fuel vehicle and the system status data information corresponding to the fuel vehicle;

[0138] It is understandable that the fuel consumption calculation formula for fuel vehicles is as follows:

[0139] ;

[0140] in, express Time section The above type is average pollutant emissions or fuel consumption rate; Indicates pollutant type or fuel consumption; represents carbon monoxide; It means hydrocarbon; represents nitrogen oxides; Indicates fuel consumption; express The moment type is The number of vehicles; Indicates road segment The above type is pollutant emissions or fuel consumption. Indicates the time period to be studied.

[0141] Step S43: Calculate the energy consumption data information of the electric vehicle based on the preset energy consumption calculation formula of the electric vehicle and the system status data information corresponding to the electric vehicle.

[0142] It is understandable that the energy consumption calculation formula for electric vehicles is as follows:

[0143] ;

[0144] in, express Time section The type of The electric energy consumption rate of electric vehicles; express Time section The type of Number of electric vehicles; Indicates road segment The type of The power consumption of electric vehicles; Indicates the time period to be studied.

[0145] Step S5: formulate a traffic management plan for the transportation system where the vehicle is operating based on the fourth information to obtain a formulated traffic management plan.

[0146] It can be understood that the traffic management plan formulated in this step based on the fourth information can effectively integrate real-time data, improve traffic fluidity, reduce delays, improve energy efficiency, and provide strong support for the development of intelligent transportation systems. In this step, step S5 includes step S51, step S52 and step S53.

[0147] Step S51, preprocessing and cleaning the fourth information to obtain preprocessed fourth information;

[0148] It is understandable that the preprocessed data can accurately reflect the operating status of the traffic system and avoid unnecessary decisions caused by data errors. By eliminating abnormal data and removing redundant information, the stability of the data processing system can be improved, and the interference of inconsistent or redundant data on traffic management decisions can be avoided.

[0149] Step S52: Perform traffic flow analysis and energy consumption analysis on the pre-processed fourth information to generate a comprehensive data report;

[0150] It is understandable that by analyzing traffic distribution, traffic density and speed analysis, and traffic fluency and delay analysis, we can understand the load status of the current traffic system, identify traffic bottlenecks, and provide a basis for traffic flow optimization. By analyzing the impact of implementing different traffic management measures (such as optimizing signal light timing, improving road capacity, adjusting lane allocation, etc.) on fuel consumption and power consumption, we can evaluate the energy-saving effect brought about by traffic flow optimization measures. The evaluation of energy-saving effects provides a quantitative basis for subsequent decision-making. The comprehensive report visualizes traffic flow and energy consumption data, helping decision makers quickly understand the current status and optimization potential of the traffic system, and providing data support for the optimization of traffic management solutions.

[0151] Step S53: Perform correlation analysis based on the comprehensive data report and the preset historical traffic management plans, and select the historical traffic management plan with the greatest correlation as the corresponding traffic management plan.

[0152] It is understandable that through the high-precision matching of historical solutions with current traffic conditions, the applicability and execution effect of the selected solution in the current environment can be ensured. This precise matching not only saves time, but also avoids unnecessary trial and error processes.

[0153] Embodiment 2:

[0154] like Figure 2 As shown, this embodiment provides a mixed traffic management plan formulation system, see Figure 2 The system includes an acquisition unit 701 , a first calculation unit 702 , a second calculation unit 703 , a third calculation unit 704 and a processing unit 705 .

[0155] An acquisition unit 701 is used to acquire first information, second information and third information, wherein the first information includes operation data information of a manually driven vehicle and operation data information of an intelligent networked vehicle, the second information includes steering ratio data of the vehicle at an intersection, and the third information includes road section parameter information;

[0156] A first calculation unit 702 is used to construct a mixed traffic flow basic graph model based on the operation data information of the manually driven vehicle and the operation data information of the intelligent networked vehicle, wherein the mixed traffic flow basic graph model is a model for calculating the average speed of the mixed traffic flow of the manually driven vehicle and the intelligent networked vehicle;

[0157] The second calculation unit 703 is used to calculate the average speed of the mixed traffic flow of manually driven vehicles and intelligent networked vehicles based on the mixed traffic flow basic graph model, and construct a feedback queuing model of the vehicles based on the average speed of the mixed traffic flow, the second information and the third information;

[0158] A third calculation unit 704 is used to calculate and obtain fourth information based on the feedback queuing model of the vehicle, wherein the fourth information includes system state data of the transportation system where the vehicle is running, fuel consumption data of fuel vehicles, and energy consumption data of electric vehicles;

[0159] The processing unit 705 is used to formulate a traffic management plan for the transportation system where the vehicle is operating based on the fourth information, and obtain a formulated traffic management plan.

[0160] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0161] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0162] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for formulating a mixed traffic management plan, characterized in that: include: Acquire first information, second information and third information, wherein the first information includes operation data information of a manually driven vehicle and operation data information of an intelligent network-connected vehicle, the second information includes steering ratio data of the vehicle at an intersection, and the third information includes road section parameter information; Constructing a mixed traffic flow basic graph model based on the operating data information of the manually driven vehicles and the operating data information of the intelligent networked vehicles, wherein the mixed traffic flow basic graph model is a model for calculating the average speed of the mixed traffic flow of the manually driven vehicles and the intelligent networked vehicles; Calculating an average speed of a mixed traffic flow of manually driven vehicles and intelligent networked vehicles based on the mixed traffic flow basic graph model, and constructing a feedback queuing model of vehicles based on the average speed of the mixed traffic flow, the second information, and the third information; The fourth information is calculated based on the feedback queuing model of the vehicle, and the fourth information includes system status data of the transportation system where the vehicle is running, fuel consumption data of the fuel vehicle, and energy consumption data of the electric vehicle; Formulate a traffic management plan for the transportation system where the vehicle is operating based on the fourth information to obtain a formulated traffic management plan; Among them, the feedback queuing model is as follows: Where F represents the feedback queue system; λ Ti (t) represents the arrival rate of different types of traffic on road section i at time t; represents the rate of change of the number of vehicles of type T on road section i in the feedback queuing system; represents the congestion probability of road section i; In the loss queuing system, the number of mixed vehicles on section i at time t is x. i L The corresponding service rate when represents the idle probability of the road section at time t; σ' i (t) is the artificial virtual node h' at time t i The service rate after feedback elimination, ρ(t) is the utilization rate of the artificial virtual node at time t; represents the number of vehicles in mixed traffic flow on section i at time t in the loss queuing system, Represents the number of vehicles of type T on road segment i at time t in the loss queuing system.

2. The method for formulating a mixed traffic management plan according to claim 1, characterized in that , calculating the average speed of the mixed traffic flow of manually driven vehicles and intelligent networked vehicles based on the mixed traffic flow basic graph model, including: Substitute the calculation formulas in the following model of manually driven vehicles and the following model of intelligent networked vehicles into the preset headway distance calculation formula to obtain the headway distance of manually driven vehicles and the headway distance of intelligent networked vehicles; The average headway of the mixed traffic flow is calculated by applying the headway of manually driven vehicles and the headway of intelligent networked vehicles to the calculation formula of the average headway of the mixed traffic flow to obtain the average headway of the mixed traffic flow; The density of mixed traffic flow is calculated based on the average headway of mixed traffic flow; Based on the mixed traffic flow density and a preset average speed calculation formula for the mixed traffic flow, the average speed of the mixed traffic flow is calculated.

3. The method for formulating a mixed traffic management plan according to claim 1, characterized in that: A feedback queuing model of vehicles is constructed based on the average speed of the mixed traffic flow, the second information and the third information, including: Comprehensively analyzing the average speed of the mixed traffic flow, the second information, and the third information, and constructing an intersection queuing network based on the analysis results; Determine the congestion probability and outflow rate of all types of vehicles on the corresponding road section based on the intersection queuing network, and construct a loss queuing model based on the congestion probability and outflow rate to obtain a loss queuing model for vehicles on the road section; The vehicle feedback queuing model is constructed based on the loss queuing model of road section vehicles and preset artificial virtual node parameters.

4. The method for formulating a mixed traffic management plan according to claim 1, characterized in that , the fourth information is calculated based on the feedback queuing model of the vehicle, including: Calculating the system state data information corresponding to each vehicle based on the system state calculation formula in the feedback queuing model of the vehicle; The fuel consumption data information of the fuel vehicle is calculated based on a preset fuel consumption calculation formula of the fuel vehicle and the system status data information corresponding to the fuel vehicle; The energy consumption data information of the electric vehicle is calculated based on a preset energy consumption calculation formula of the electric vehicle and the system status data information corresponding to the electric vehicle.

5. A mixed traffic management plan formulation system, characterized in that: include: an acquisition unit, configured to acquire first information, second information, and third information, wherein the first information includes operation data information of a manually driven vehicle and operation data information of an intelligent network-connected vehicle, the second information includes steering ratio data of the vehicle at an intersection, and the third information includes road section parameter information; A first calculation unit is used to construct a mixed traffic flow basic graph model based on the operation data information of the manually driven vehicle and the operation data information of the intelligent networked vehicle, wherein the mixed traffic flow basic graph model is a model for calculating the average speed of the mixed traffic flow of the manually driven vehicle and the intelligent networked vehicle; a second calculation unit, configured to calculate an average speed of a mixed traffic flow of manually driven vehicles and intelligent networked vehicles based on the mixed traffic flow basic graph model, and construct a feedback queuing model of the vehicles based on the average speed of the mixed traffic flow, the second information, and the third information; A third calculation unit, configured to calculate fourth information based on the feedback queuing model of the vehicle, wherein the fourth information includes system status data of the transportation system in which the vehicle is operating, fuel consumption data of the fuel vehicle, and energy consumption data of the electric vehicle; A processing unit, configured to formulate a traffic management plan for the transportation system where the vehicle is operating based on the fourth information, and obtain a formulated traffic management plan; Among them, the feedback queuing model is as follows: Where F represents the feedback queue system; λ Ti (t) represents the arrival rate of different types of traffic on road section i at time t; represents the rate of change of the number of vehicles of type T on road section i in the feedback queuing system; represents the congestion probability of road section i; In the loss queuing system, the number of mixed vehicles on section i at time t is The corresponding service rate when represents the idle probability of the road section at time t; σ' i (t) is the artificial virtual node h' at time t i The service rate after feedback elimination, ρ(t) is the utilization rate of the artificial virtual node at time t; represents the number of vehicles in mixed traffic flow on section i at time t in the loss queuing system, Represents the number of vehicles of type T on road segment i at time t in the loss queuing system.

6. The traffic management plan formulation system for mixed driving according to claim 5, characterized in that: The second computing unit comprises: A fourth calculation subunit is used to bring the calculation formulas in the manually driven vehicle following model and the intelligent networked vehicle following model into the preset headway distance calculation formula to calculate and obtain the headway distance of the manually driven vehicle and the headway distance of the intelligent networked vehicle; The average headway of the mixed traffic flow is calculated by applying the headway of manually driven vehicles and the headway of intelligent networked vehicles to the calculation formula of the average headway of the mixed traffic flow to obtain the average headway of the mixed traffic flow; A fifth calculation subunit, configured to calculate the mixed traffic flow density based on the average headway of the mixed traffic flow; The sixth calculation subunit is used to calculate the average speed of the mixed traffic flow based on the mixed traffic flow density and a preset average speed calculation formula of the mixed traffic flow.

7. The mixed traffic management plan formulation system according to claim 5, characterized in that: The second computing unit further includes: a first processing subunit, configured to comprehensively analyze the average speed of the mixed traffic flow, the second information, and the third information, and construct an intersection queuing network based on the analysis result; A second processing subunit is used to determine the congestion probability and outflow rate of all types of vehicles on the corresponding road section based on the intersection queuing network, and to construct a loss queuing model based on the congestion probability and outflow rate to obtain a loss queuing model for vehicles on the road section; The third processing subunit is used to construct a feedback queuing model of vehicles based on the loss queuing model of vehicles on the road section and preset artificial virtual node parameters.

8. The mixed traffic management plan formulation system according to claim 5, characterized in that: The third computing unit comprises: A seventh calculation subunit, configured to calculate system state data information corresponding to each vehicle based on a system state calculation formula in the feedback queuing model of the vehicle; an eighth calculation subunit, configured to calculate the fuel consumption data information of the fuel vehicle based on a preset fuel consumption calculation formula of the fuel vehicle and the system status data information corresponding to the fuel vehicle; The ninth calculation subunit is used to calculate the energy consumption data information of the electric vehicle based on a preset energy consumption calculation formula of the electric vehicle and the system status data information corresponding to the electric vehicle.

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