A road lane management method and system for intelligent networked mixed traffic flow

Abstract

The present invention discloses a road lane management method and system for intelligent networked mixed traffic flow, taking into account the different following behaviors of manually driven vehicles and intelligent networked vehicles, constructing a mixed lane capacity model for intelligent networked vehicles and manually driven vehicles that takes into account the intention of different behaviors, and proposing a road section traffic flow model for intelligent networked mixed traffic flow. Finally, a road lane management model for intelligent networked mixed traffic flow is established, and numerical calculations are carried out to solve the optimal lane deployment plan and lane management strategy. The method of the present invention is simple and easy to handle, and provides a strategic decision-making basis for operation managers for the heterogeneous mixed traffic of intelligent networked vehicles and manually driven vehicles, thereby improving the level and efficiency of road traffic management. The use of the method of the present invention has practical engineering application value for lane planning and management of road traffic systems.

Description

Technical Field

[0001] The present invention belongs to the field of traffic engineering and intelligent transportation, and specifically relates to a road lane management method and system for intelligent networked mixed traffic flow. Background Art

[0002] As an emerging product of the cross-integration of information, automobile, transportation and other industries, the Connected and Automated Vehicle (CAV) has emerged in recent years. With the help of network communication and autonomous driving technology, multiple CAVs can form a team to shorten the distance between vehicles, thereby improving traffic efficiency and reducing energy consumption and emissions. However, it will take a long time for the arrival of the full CAV era, and the mixed traffic of CAVs and human-driven vehicles (HDV) will exist for a long time. However, due to the random mixing of CAVs and limited penetration, it is inevitable that CAVs and HDVs will follow each other. For example, if CAVs follow HDVs, the efficiency of continuous teaming between CAVs will be seriously affected. Therefore, the mixed traffic of CAVs and HDVs has become one of the main problems that need to be solved in CAV operation management.

[0003] Existing studies have proposed a CAV dedicated lane strategy to reduce the conflict between HDV and CAV and fully improve the efficiency of CAV teaming. However, existing studies have not considered the impact of heterogeneous following behaviors of HDVs in the process of following CAVs, resulting in limited application of the capacity of the constructed CAV and HDV mixed lanes. At present, most studies consider focusing on CAVs only in dedicated lanes, and CAVs can travel on dedicated lanes and mixed lanes. These two strategies are calculated independently, and then compared and analyzed based on traffic flow results. However, the optimal lane management strategy has not been quantitatively found in these two strategies from the perspective of a unified model. And a single or two-way road lane management model for intelligent networked mixed traffic flow suitable for solving the above decisions has not been constructed. Therefore, the road lane management model provides road operators and managers with strategic decisions for lane management under HDV and CAV mixed traffic, and provides lane planning and management methods, providing scientific support for ensuring efficient operation of road traffic. Summary of the invention

[0004] Purpose of the invention: In view of the deficiencies in existing research on lane capacity for intelligent connected mixed traffic flow, the present invention proposes a road lane management method for intelligent connected mixed traffic flow based on networked communication and autonomous driving technology, accurately estimates the capacity of mixed lanes, optimizes the total traffic flow of mixed lanes in one or two directions, and provides optimal lane deployment plans and lane management strategies, thereby providing effective strategic decisions based on actual traffic input conditions.

[0005] Technical solution: To achieve the above-mentioned purpose, the technical solution of the present invention is:

[0006] A road lane management method for intelligent networked mixed traffic flow includes the following steps:

[0007] Based on the CAV lane traffic model and the CAV and HDV mixed lane traffic capacity model, a one-way or two-way road section traffic flow model for intelligent network mixed traffic flow is constructed; in the mixed lane traffic capacity model, the three behavioral intentions of the HDV following the CAV, namely trust, neutrality, and suspicion, and the different following modes and headway between CAV and HDV are considered when estimating the average headway.

[0008] A one-way or two-way road lane management model is established for intelligent connected mixed traffic flow, with the goal of maximizing the total one-way or two-way traffic flow, and optimizing the number of relevant lanes and management strategies.

[0009] Preferably, in the mixed lane capacity model, when estimating the average headway, different following behaviors of HDV following CAV, different following modes between CAV and HDV and headway are considered, including: the average headway of the traffic flow includes two average headway of CAV and HDV, the average headway of HDV is characterized by the probability of HDV and the expected headway of HDV in different following modes under HDV conditions, and the average headway of CAV is characterized by the probability of CAV and the expected headway of CAV under CAV conditions. It is characterized by the product of the expected headway time under different following modes; among them, the expected headway time under different following modes of HDV is characterized by the probabilities of the three behavioral intentions of HDV trust, neutrality, and suspicion and the corresponding following distances under HDV following CAV, as well as the probability of HDV following HDV and the corresponding following distance; the expected headway time under different following modes of CAV is characterized by the probability of CAV following CAV and the corresponding following distance, as well as the probability of CAV following HDV and the corresponding following distance.

[0010] Preferably, in the mixed lane capacity model, the average headway is estimated by the following formula:

[0011]

[0012] in, is the penetration rate of CAVs on mixed traffic lanes; τ f represents the headway time between CAV and CAV; τ l represents the headway time of CAV following HDV; τ t represents the headway time between the trusting HDV and the CAV; τ hIt represents the headway time between the neutral HDV and the CAV, and also represents the headway time between the HDV and the HDV; τ c represents the headway time between the skeptical HDV and the CAV; w1 and w2 represent the willingness to trust and the willingness to doubt, respectively, and the neutral willingness is 1-w1-w2.

[0013] Preferably, in the mixed lane capacity model, when the trust willingness, the suspicion willingness, and the headway time between CAV and HDV meet one of the following three sufficient conditions, the capacity will increase with the increase of CAV penetration rate;

[0014] Condition 1: w1≤w2≤1&2τ h ≤τ c +τ t

[0015] Condition 2:

[0016] Condition 3:

[0017] As a preferred method, in the one-way section traffic flow model for intelligent network mixed traffic flow, the one-way section traffic flow includes the traffic flow on the CAV dedicated lane and the traffic flow of mixed traffic flows on mixed lanes Where p and d represent the market penetration rate and traffic demand of CAVs on the road, respectively, x represents the number of CAV-dedicated lanes, represents the capacity of the CAV dedicated lane, represents the mixed traffic lane capacity, x n represents the number of mixed traffic lanes; the penetration rate of CAVs on mixed traffic lanes λ=1 means that the CAV is allowed to travel on the mixed traffic lane, otherwise λ=0.

[0018] As a preferred embodiment, the one-way or two-way road lane management model for intelligent network mixed traffic flow is expressed as:

[0019]

[0020] satisfy:

[0021] x≤N

[0022]

[0023] λ∈{0,1}

[0024] Where N is the limit on the number of one-way lanes to be deployed.

[0025] As a preferred option, in the two-way road traffic flow model for intelligent network mixed traffic flow, the traffic flow in direction 1 includes the traffic flow of the CAV dedicated lane CAV traffic flow in the reverse lane and the traffic flow of mixed traffic flows on mixed lanes Traffic flow in direction 2 includes traffic flow in the CAV lane CAV traffic flow in the reverse lane and the traffic flow of mixed traffic flows on mixed lanes Where p represents the market penetration rate of CAVs on the road, d1 and d2 are the traffic demands in directions 1 and 2, respectively. The capacity of the CAV dedicated lane. The mixed lane capacity is and are the number of CAV-only lanes in direction 1 and direction 2, respectively; and are the number of lanes in the opposite direction for CAVs in direction 1 and direction 2, respectively. and are the number of mixed traffic lanes in direction 1 and direction 2 respectively;

[0026] The CAV penetration rate on the mixed traffic lane in direction 1 is:

[0027]

[0028] λ1=1 means that the CAV is allowed to travel in the dedicated lane, the reverse dedicated lane, and the mixed lane in direction 1, and λ1=0 means that the CAV is only allowed to travel in the dedicated lane and the reverse dedicated lane;

[0029] The CAV penetration rate on the mixed lane in direction 2 is:

[0030]

[0031] λ2=1 means that the CAV is allowed to drive in the dedicated lane, the reverse dedicated lane and the mixed lane in direction 2, and λ2=0 means that the CAV is only allowed to drive in the dedicated lane and the reverse dedicated lane. As a preferred embodiment, the two-way road lane management model for intelligent connected mixed traffic flow is expressed as:

[0032]

[0033] satisfy:

[0034]

[0035]

[0036]

[0037]

[0038] λ1,λ2∈{0,1}

[0039] Among them, N1 and N2 are the restrictions on the number of lanes deployed in direction 1 and direction 2 respectively.

[0040] The present invention also provides a computer system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is loaded into the processor, the steps of the lane management method for intelligent connected mixed traffic flow are implemented.

[0041] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the lane management method for intelligent networked mixed traffic flow.

[0042] Beneficial effects: The method of the present invention takes into account the impact of heterogeneous following behaviors in the process of HDV following CAV, and constructs a mixed lane capacity model for HDV and CAV that takes into account behavioral intentions, which can effectively estimate the capacity of mixed lanes under actual traffic conditions. Further, on the basis of accurately constructing a mixed lane capacity model, a one-way or two-way management lane deployment model for intelligent networked mixed traffic flow is proposed. The method is simple and easy, and the model is highly accurate. The model constructed based on the present invention can calculate the theoretically optimal number of dedicated lanes and lane management strategies through input data of traffic conditions, thereby providing a theoretical basis for operators to implement lane management measures for different traffic input conditions, facilitating operators to improve the level of road traffic management, and having practical engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The figure is a flow chart of a method according to an embodiment of the present invention.

[0044] Figure 2 Schematic diagram of the following vehicle distance in an embodiment of the present invention.

[0045] Figure 3 It is a specific schematic diagram of road traffic in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention is further explained below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0047] Example 1

[0048] like Figure 1 As shown, a road lane management method for intelligent networked mixed traffic flow disclosed in an embodiment of the present invention is applicable to one-way multi-lane management, and mainly includes the following steps:

[0049] Step 1: Construct a capacity model for mixed lanes of CAVs and HDVs and a capacity model for dedicated lanes of CAVs that consider different following behavior intentions.

[0050] In this embodiment, when estimating the average headway of traffic on a mixed lane, different following behaviors of HDV following CAV, different following modes between CAV and HDV, and headway are considered, including: the average headway of traffic includes two average headway of CAV and HDV, the average headway of HDV is characterized by the probability of HDV and the expected headway of HDV in different following modes under HDV conditions, and the average headway of CAV is characterized by the product of the probability of CAV and the expected headway of CAV in different following modes under CAV conditions; wherein, the expected headway of HDV in different following modes is characterized by the probabilities of three behavioral intentions of HDV trust, neutrality, and suspicion and the corresponding following distances under HDV following CAV, as well as the probability of HDV following HDV and the corresponding following distances; the expected headway of CAV in different following modes is characterized by the probability of CAV following CAV and the corresponding following distances.

[0051] Consider a multi-lane road segment with N lanes of equal width and no entry and exit ramps. The market penetration of CAVs on the road is p, and the market penetration of HDVs on the road is 1-p. The traffic demand on the road is d.

[0052] Considering the CAV penetration rate on mixed traffic lanes The probability of HDV on the road is p h , its value should be Considering all vehicles randomly distributed on the lanes, based on the Bernoulli distribution, the probability of a CAV following an HDV should be The probability of a CAV following the preceding CAV is

[0053] Consecutive CAVs in the same lane can reduce the headway time between vehicles through V2V / V2I communication. Based on the type of the CAV leading vehicle, i.e., HDV or CAV, there are two types of headway time: (i) τ f represents the headway time between CAV and CAV; (ii) τ l Indicates the headway distance between the CAV and the HDV.

[0054] Consider the impact of three types of HDV following CAV behaviors (i.e., trust, neutrality, and suspicion) on mixed traffic capacity. For different vehicle following behaviors, three types of headway times will be presented for HDV following CAV. Therefore, there are the following types of headway times for HDV: (i) τ t represents the headway time between the trusting HDV and the CAV; (ii) τ h represents the headway time of the neutral HDV following the CAV; (iii) τ c represents the headway time of the skeptical HDV following the CAV; (iv) also uses τ h The headway time of the HDV following the HDV is shown in the figure below: Figure 2 .

[0055] Let p c is the penetration rate of CAVs on mixed traffic lanes, CAV i The probability of following k-1 CAVs can be calculated as follows:

[0056]

[0057] Different from the previous studies that adopt the same following behavior when HDV follows CAV, three types of behavior when HDV follows CAV are considered. Therefore, three types of behavior intention are introduced to characterize different following behaviors when HDV follows CAV. Let w1 and w2 represent trust intention and doubt intention respectively, and neutral intention is 1-w1-w2.

[0058] Therefore, the average headway time of CAV and HDV mixed traffic flow on the mixed lane is estimated as follows:

[0059]

[0060] According to the above average headway formula, the mixed traffic capacity on a lane, that is, relative to p c of It can be calculated as:

[0061]

[0062] When all CAV vehicles are on the CAV lane, the pure CAV vehicle capacity on the CAV lane is calculated based on the average headway of the CAV traffic flow:

[0063]

[0064] The above formula is the most common method for calculating the capacity of CAV dedicated lanes. Other improved calculation methods can also be used, such as considering factors such as the maximum number of platoons, different CAV following modes, and following distances.

[0065] From formula (2), we can see that the partial derivatives of formula (2) for trust willingness w1 and doubt willingness w2 are as follows:

[0066]

[0067]

[0068] It can be seen that is a decreasing function of w1, so the traffic capacity is an increasing function of the trust willingness w1. In addition, τ c ≥τ h , so the capacity is a decreasing function of the willingness to doubt w2.

[0069] Let A = τ f -τ l +w1(τ h -τ t )+w2(τ h -τ c ) and B = 2τ h -τ l +w1(τ h -τ t )+w2(τ h -τ c ). Then formula (2) can be written as:

[0070]

[0071] If A≤0, then is a decreasing function of p, indicating that the traffic capacity increases with the increase of CAV penetration rate; if A>0, let If p e >1, the capacity is a decreasing function of p; if 0 < p e <1, then the traffic capacity is p e There is a maximum value at .

[0072] When the willingness to trust, willingness to doubt, and the headway time between CAVs and HDVs meet the following sufficient conditions, the traffic capacity will increase with the increase of CAV penetration:

[0073] w1≤w2≤1&2τ h ≤τ c +τ t (8)

[0074] It can be seen that when w1≤w2≤1 and 2τ h ≤τc +τ t When the conditions are met, A is always less than 0, and the capacity increases with the increase of CAV penetration rate.

[0075] When the willingness to trust, willingness to doubt, and the headway time between CAVs and HDVs meet the following sufficient conditions, the traffic capacity will increase with the increase of CAV penetration:

[0076]

[0077] If the following conditions are met, A will always be less than 0, thus ensuring that the traffic capacity increases with the increase of CAV penetration rate.

[0078] When the willingness to trust, willingness to doubt, and the headway time between CAVs and HDVs meet the following sufficient conditions, the traffic capacity will increase with the increase of CAV penetration:

[0079]

[0080] Step 2: Based on the above single-lane capacity model, a one-way road section traffic flow model for intelligent connected mixed traffic flow is constructed.

[0081] Based on the pure CAV and mixed traffic capacities, traffic demand, CAV penetration, and total number of lanes, we will calculate the pure CAV traffic throughput on the dedicated lanes and the mixed traffic throughput on the mixed lanes. Let x denote the number of CAV dedicated lanes CDL. Given the mixed traffic demand, the CAV traffic flow on CDL can be calculated by the following formula, with Q d To express:

[0082]

[0083] Let λ be a 0-1 decision variable, λ = 1 means that CAV is allowed to travel on the mixed lane, otherwise λ = 0. By excluding CAVs traveling on CDL, the penetration rate of the remaining CAVs in the mixed traffic flow on the mixed lane can be obtained as follows:

[0084]

[0085] Therefore, we can get:

[0086]

[0087] Use x n represents the number of mixed lanes, which depends on the decision x and is calculated as:

[0088] x n =Nx (14)

[0089] Given The mixed traffic capacity on the mixed lane under the condition of mixed traffic, then the mixed traffic output on the mixed lane is expressed by Q n To express it, the calculation formula is as follows:

[0090]

[0091] Step 3: Establish a one-way road lane management model for intelligent connected mixed traffic flow and optimize the road lane management plan.

[0092] In the lane management LM model proposed in this embodiment, Q is used to represent the total traffic flow of mixed traffic flow on the road segment. The LM problem of mixed traffic flow of HDVs and CAVs on a multi-lane road can be expressed by the following optimization model:

[0093]

[0094] satisfy:

[0095] x≤N (17)

[0096]

[0097] λ∈{0,1} (19)

[0098] The objective function in equation (16) is to maximize the total mixed traffic flow on the road segment, which is the sum of the traffic flow on the dedicated lanes and the mixed lanes. Constraint (21) ensures that the total number of CDLs does not exceed N. Constraints (23) and (24) define the range of decision variables.

[0099] Example 2

[0100] The embodiment of the present invention discloses a road lane management method for intelligent networked mixed traffic flow, which is applicable to bidirectional multi-lane management. The CAV and HDV mixed lane capacity model and the CAV dedicated lane capacity model are consistent with those in Example 1. The bidirectional road section traffic flow model for intelligent networked mixed traffic flow is as follows:

[0101] like Figure 3 As shown in the figure, consider a two-way multi-lane road section, which includes a total of N1+N2 lanes. The number of lanes in each direction is represented by N1 and N2. The width of each lane is equal, and there is no entrance ramp or exit ramp. The numbers "1" and "2" represent the mixed traffic flow in the two opposite directions of the road. The total traffic demand on the road is d, and d1 and d2 are the traffic demands in directions 1 and 2 respectively. The market penetration rate of CAV on the road is p, and the penetration rate of HDV on the road is 1-p.

[0102] and The number of dedicated lanes for CAVs in direction 1 and dedicated lanes for CAVs in the opposite direction in direction 2 are respectively. Given the CAV traffic flow and mixed traffic capacity, traffic demand, CAV market penetration, and the total number of lanes, the traffic flow on the dedicated lanes and mixed lanes, as well as the traffic flow of this section, can be calculated. and The CAV traffic flow in the CAV dedicated lane and the reverse dedicated lane can be calculated as follows:

[0103]

[0104]

[0105] λ1 is a 0-1 dummy variable, λ1=1 means that CAVs are allowed to drive in the dedicated lane, the reverse dedicated lane and the mixed lane in direction 1, and λ1=0 means that CAVs are only allowed to drive in the dedicated lane and the reverse dedicated lane. After excluding CAVs driving in the dedicated lane in direction 1 and the reverse lane in direction 2, the remaining CAV penetration rate in the mixed lane in direction 1 is:

[0106]

[0107] The number of CAV reverse lanes for direction 1 for CAVs traveling in direction 2, is the number of mixed traffic lanes in direction 1, which is exist Under a given mixed traffic capacity, is the traffic flow of mixed traffic flow on mixed lane in direction 1, which can be calculated as follows:

[0108]

[0109] is the number of CAV dedicated lanes in direction 2. λ2 is a 0-1 decision variable, λ2=1 means that CAV is allowed to travel in the dedicated lane, reverse dedicated lane and mixed lane in direction 2, and λ2=0 means that CAV is only allowed to travel in the dedicated lane and reverse dedicated lane. and is the CAV traffic flow in the dedicated lane in direction 2 and the reverse dedicated lane. When the CAVs in the dedicated lane in direction 2 are excluded, the remaining CAV penetration rate in the mixed lane It can be calculated that:

[0110]

[0111]

[0112]

[0113] is the number of mixed lanes in direction 2, and the number of mixed lanes is exist Under a given mixed traffic capacity, is the traffic flow of mixed traffic flow on mixed lane in direction 2, which can be calculated as follows:

[0114]

[0115] Q is the total mixed traffic flow of mixed traffic flow, Q1 and Q2 are the traffic flows of mixed traffic flow in direction 1 and direction 2 respectively. The lane management model of two-way road for mixed traffic flow can be defined as:

[0116]

[0117] Constraints:

[0118]

[0119]

[0120]

[0121]

[0122] λ1,λ2∈{0,1} (33)

[0123] Among them, the above objective function in formula (17) is the total mixed traffic flow of the mixed traffic flow, which is the sum of the traffic flow of multiple lanes. Constraints (18) and (19) ensure that the number of CAV dedicated lanes and reverse dedicated lanes deployed in direction 1 and direction 2 should not exceed N1 and N2. Constraint (20) indicates that CAV reverse dedicated lanes cannot be deployed in both directions at the same time. Constraints (21) and (22) define the domain of decision variables. The proposed LM model is an integer nonlinear optimization model. However, we can see that there are only two decision variables in the model and the number of lanes on the road is limited. Therefore, the proposed LM model can be solved using intelligent enumeration methods and intelligent solution algorithms. For different solutions with the same mixed traffic flow, we will choose the solution with the least CDL.

[0124] Therefore, the heterogeneous following behaviors in the HDV following CAV process can be considered, and a capacity model for mixed lanes of intelligent connected vehicles and manually driven vehicles can be constructed to accurately calculate the capacity of mixed lanes. Different lane management strategies for intelligent connected vehicles can be considered to calculate the traffic flow of one-way or two-way sections for intelligent connected mixed traffic flows. Finally, a lane management model for one-way or two-way roads for intelligent connected mixed traffic flows can be constructed to solve the optimal number of dedicated lanes and reverse dedicated lanes and the optimal lane management strategy, which can help decision makers make better strategic decisions and thus improve the management level of the road traffic system.

[0125] Example 3

[0126] A computer system disclosed in an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, the steps of a road lane management method for intelligent connected mixed traffic flow in embodiment 1 or embodiment 2 are implemented.

[0127] Example 4

[0128] A computer-readable storage medium is disclosed in an embodiment of the present invention, and the computer-readable storage medium stores a computer program. When the computer program is loaded into a processor, the steps of a road lane management method for intelligent connected mixed traffic flow in embodiment 1 or embodiment 2 are implemented.

Claims

1. A road lane management method for intelligent networked mixed traffic flow, characterized in that: The steps include: Based on the intelligent networked vehicle (CAV) lane traffic model and the CAV and HDV mixed lane traffic capacity model, a one-way or two-way road section traffic flow model for intelligent networked mixed traffic flow is constructed; in the mixed lane traffic capacity model, different following behaviors of HDV and CAV, different following modes and headway between CAV and HDV are considered when estimating the average headway. Establish a one-way or two-way road lane management model for intelligent networked mixed traffic flow, with the goal of maximizing the total one-way or two-way traffic flow, and optimize the number of relevant lanes and lane management strategies; In the mixed lane capacity model, when estimating the average headway, different following behaviors of HDV following CAV, different following modes between CAV and HDV, and headway are considered, including: the average headway of the traffic flow includes two average headway of CAV and HDV, the average headway of HDV is characterized by the probability of HDV and the expected headway of HDV in different following modes under HDV conditions, and the average headway of CAV is characterized by the product of the probability of CAV and the expected headway of CAV in different following modes under CAV conditions; wherein, the expected headway of HDV in different following modes is characterized by the probabilities of three behavioral intentions of HDV trust, neutrality, and suspicion and the corresponding following distances under HDV following CAV, and the probability of HDV following HDV and the corresponding following distances; the expected headway of CAV in different following modes is characterized by the probability of CAV following CAV and the corresponding following distances.

2. A road lane management method for intelligent networked mixed traffic flow according to claim 1, characterized in that: In the mixed lane capacity model, the average headway is estimated by the following formula: in, is the penetration rate of CAVs on mixed traffic lanes; τ f represents the headway time of CAV following CAV; τ l represents the headway time of CAV following HDV; τ t represents the headway time between the trusting HDV and the CAV; τ h It represents the headway time between the neutral HDV and the CAV, and also represents the headway time between the HDV and the HDV; τ c represents the headway time between the skeptical HDV and the CAV; w1 and w2 represent the willingness to trust and the willingness to doubt, respectively, and the neutral willingness is 1-w1-w2.

3. A road lane management method for intelligent networked mixed traffic flow according to claim 2, characterized in that: In the mixed lane capacity model, when the willingness to trust, willingness to doubt, and the headway time between CAV and HDV meet one of the following three sufficient conditions, the capacity will increase with the increase of CAV penetration rate; condition1:w1≤w2≤1&2t h ≤τ c +t t Condition 2: Condition 3:

4. The road lane management method for intelligent networked mixed traffic flow according to claim 1, characterized in that: In the one-way traffic flow model for intelligent connected mixed traffic flow, the one-way traffic flow includes the traffic flow on the CAV dedicated lane and the traffic flow of mixed traffic flows on mixed lanes Where p and d represent the market penetration rate and traffic demand of CAVs on the road, respectively, x represents the number of CAV-dedicated lanes, represents the capacity of the CAV dedicated lane, represents the mixed lane capacity, x n represents the number of mixed traffic lanes; the penetration rate of CAVs on mixed traffic lanes λ∈{0,1}, λ=1 means that CAV is allowed to drive on the mixed traffic lane, otherwise λ=0.

5. A road lane management method for intelligent networked mixed traffic flow according to claim 4, characterized in that: The lane management model for one-way or two-way roads for intelligent connected mixed traffic flow is expressed as: satisfy: x≤N λ∈{0,1} Where N is the limit on the number of one-way lanes to be deployed.

6. The road lane management method for intelligent networked mixed traffic flow according to claim 1, characterized in that: In the two-way road traffic flow model for intelligent connected mixed traffic flow, the traffic flow in direction 1 includes the traffic flow in the CAV dedicated lane CAV traffic flow in the reverse lane and the traffic flow of mixed traffic flows on mixed lanes Traffic flow in direction 2 includes traffic flow in the CAV lane CAV traffic flow in the reverse lane and the traffic flow of mixed traffic flows on mixed lanes Where p represents the market penetration rate of CAVs on the road, d1 and d2 are the traffic demands in directions 1 and 2, respectively. The capacity of the CAV dedicated lane. The mixed traffic lane capacity is and are the number of CAV-only lanes in direction 1 and direction 2, respectively; and are the number of lanes in the opposite direction for CAVs in direction 1 and direction 2, respectively. and are the number of mixed traffic lanes in direction 1 and direction 2 respectively; The CAV penetration rate on the mixed traffic lane in direction 1 is: λ1=1 means that the CAV is allowed to travel in the dedicated lane, the reverse dedicated lane, and the mixed lane in direction 1, and λ1=0 means that the CAV is only allowed to travel in the dedicated lane and the reverse dedicated lane; The CAV penetration rate on the mixed lane in direction 2 is: λ2=1 means that the CAV is allowed to travel in the dedicated lane, the reverse dedicated lane and the mixed lane in direction 2, and λ2=0 means that the CAV is only allowed to travel in the dedicated lane and the reverse dedicated lane.

7. A road lane management method for intelligent networked mixed traffic flow according to claim 6, characterized in that: The two-way road lane management model for intelligent connected mixed traffic flow is expressed as: satisfy: λ1,λ2∈{0,1} Among them, N1 and N2 are the restrictions on the number of lanes deployed in direction 1 and direction 2 respectively.

8. A computer system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer program is loaded into the processor, the steps of a road lane management method for intelligent connected mixed traffic flow according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a road lane management method for intelligent connected mixed traffic flow according to any one of claims 1 to 7 are implemented.

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