CAV special lane deployment strategy double-layer optimization design method

Abstract

The invention discloses a CAV dedicated lane deployment strategy double-layer optimization design method, and relates to the technical field of traffic network computers in an intelligent network connection environment, and the method comprises the steps: constructing an intelligent network connection expansion network; constructing a road section impedance function considering a CAV proportion and a time headway parameter, and constructing a travel cost function of CAV users and HV users; constructing a double-layer optimization model of the traffic bearing capacity of the intelligent network connection expansion network; forming a double-layer optimization model of a CAV special lane deployment strategy; converting the nonlinear user equalization UE traffic distribution model into a linearized user equalization UE traffic distribution model; performing linearization processing on the nonlinear road section impedance function to obtain a CAV special lane deployment strategy double-layer optimization model in a mixed integer linear programming form; and solving the CAV special lane deployment strategy double-layer optimization model in the mixed integer linear programming form to obtain a design result of the urban road network CAV special lane deployment strategy. According to the method, the optimal deployment scheme can be found.

Description

Technical Field

[0001] The present invention relates to the field of traffic network computer technology in an intelligent network environment, and in particular to a double-layer optimization design method for a CAV dedicated lane deployment strategy. Background Art

[0002] With the emergence of intelligent connected autonomous vehicles (CAVs), the future transportation system is expected to undergo major changes. Compared with human driving vehicles (HVs), CAVs can reduce the distance between vehicles because they can drive in collaboration with each other through advanced sensors and communication systems. In addition, CAVs can also reduce some errors and delays associated with human driving through autonomous driving technology. Therefore, CAVs are expected to bring many benefits to transportation network performance, such as improving road capacity, reducing fuel consumption, and reducing traffic congestion.

[0003] Considering the multiple benefits of intelligent connected technology, many studies have optimized CAV driving trajectories through simulation-based experiments. With the advancement of emerging technologies and policy support, CAVs may be increasingly used by people. But in fact, before vehicles are fully connected and autonomous, there will still be a long transition period of coexistence of HVs and CAVs in the road traffic system, and optimistic predictions suggest that it will take at least thirty years. It is worth noting that many of the above-mentioned CAV-related advantages depend on a 100% or particularly high market share of CAVs. Therefore, in a mixed traffic environment of CAVs and HVs, how to develop the potential advantages of CAVs in improving network capacity and optimizing network efficiency is a huge challenge. In order to cope with the problems of the transition period, it is of practical significance to study the characteristics of mixed traffic flows under different CAV market shares and form corresponding traffic network management strategies. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a two-layer optimization design method for CAV dedicated lane deployment strategy. This method constructs a two-layer optimization model to evaluate the network performance in a mixed traffic flow environment, and integrates the deployment decision variables of intelligent connected dedicated lanes to find the optimal deployment plan.

[0005] The present invention adopts the following technical solutions to solve the above technical problems:

[0006] A double-layer optimization design method for CAV dedicated lane deployment strategy proposed in the present invention includes:

[0007] Construct an intelligent connected expansion network including conventional lanes and alternative arc sections of CAV-dedicated lanes;

[0008] Construct a road section impedance function that takes into account the CAV ratio and headway parameters. The headway parameters include four situations: HV-HV, HV-CAV, CAV-CAV, and CAV-HV. HV-HV means that both the front and rear vehicles are HVs, HV-CAV means that the rear vehicle is an HV and the front vehicle is a CAV, CAV-CAV means that both the front and rear vehicles are CAVs, and CAV-HV means that the rear vehicle is a CAV and the front vehicle is an HV.

[0009] Considering the time value cost and fuel cost, the travel cost function of CAV users and HV users is constructed;

[0010] Construct a two-layer optimization model for the traffic carrying capacity of the intelligent connected network. The two-layer optimization model includes an upper optimization model and a lower optimization model. The upper optimization model aims to maximize the travel demand carried by the road network, and the lower optimization model is a user equilibrium UE traffic allocation model that aims to minimize the cumulative value of the integral of the travel cost function.

[0011] In the constructed two-layer optimization model of traffic network carrying capacity, CAV dedicated lane deployment constraints are introduced. The CAV dedicated lane deployment constraints include CAV market share, potential unfair factors for HV users, and CAV dedicated lane construction costs, forming a two-layer optimization model for CAV dedicated lane deployment strategy.

[0012] By introducing sufficiently large values ​​and binary 0-1 variables, the nonlinear user equilibrium UE traffic assignment model is transformed into a linear user equilibrium UE traffic assignment model;

[0013] The nonlinear road impedance function is linearized to obtain a two-level optimization model for CAV lane deployment strategy in the form of mixed integer linear programming.

[0014] The standard mathematical optimization solver is used to solve the bi-level optimization model of CAV dedicated lane deployment strategy in the form of mixed integer linear programming, and the design results of the CAV dedicated lane deployment strategy in the urban road network are obtained.

[0015] As a further optimization scheme of the double-layer optimization design method for CAV dedicated lane deployment strategy described in the present invention, an intelligent connected extension network including conventional lanes and CAV dedicated lane alternative arcs is constructed; specifically as follows:

[0016] Step S1.1: Define the arc element a in the basic road network 0 ∈A 0 , A 0 is a set of arc elements, and the number of lanes in each arc is The basic traffic capacity is The free flow travel time is Mixed traffic includes two travel modes, CAV and HV. Mixed traffic is expressed as M = {CAV, HV}, m∈M, where M is the set of travel modes and m is the element contained in M;

[0017] Step S1.2: Arc element a 0 ∈A 0 Expand a CAV dedicated lane alternative arc Forming a set of candidate arc segments

[0018] Step S1.3: For each a 0 ∈A 0 Introducing 0-1 variables like The alternative arc section of the CAV dedicated lane Set as a CAV dedicated lane, otherwise the arc Not set up as a CAV-only lane;

[0019] Step S1.4: A smart connected extended network is formed through steps S1.1 to 1.3. All arcs belong to the smart connected extended network arc set A, which is expressed as:

[0020]

[0021] Among them, a represents all arcs in the intelligent connected extended network.

[0022] As a further optimization scheme of the double-layer optimization design method for CAV dedicated lane deployment strategy described in the present invention, a road section impedance function considering CAV ratio and headway parameters is constructed, and the specific steps are as follows:

[0023] Step S2.1: Introduce parameter ρ a represents the CAV ratio of the traffic flow in arc segment a∈A, ρ a Expressed as:

[0024]

[0025] in, is the CAV traffic flow of arc segment a∈A, is the traffic flow of arc segment a∈A for travel mode m;

[0026] Step S2.2, introduce the correction coefficient ε of the road section impedance function, expressed as:

[0027]

[0028] Among them, γ, γ1, and γ2 are the headway coefficients of CAV-CAV, CAV-HV, and HV-CAV driving modes relative to HV-HV driving mode, respectively, and k2 is the size of the CAV fleet;

[0029] Step S2.3: Combined with the correction coefficient of step S2.2, the impedance function of the mixed traffic flow section in the form of the BPR function of the Federal Highway Administration is:

[0030]

[0031] in, for a 0 The free circulation time, For arc segment a 0 The basic traffic capacity, and Arc segment a 0 of CAV and HV traffic flows, and For arc segment of CAV and HV traffic flows, For arc segment a 0 The number of lanes, α and β are the parameters of the BPR function, and Arc segment a 0 and arc segments The impedance function.

[0032] As a further optimization scheme of the double-layer optimization design method for CAV dedicated lane deployment strategy described in the present invention, the travel cost function of CAV users and HV users is constructed by considering the time value cost and fuel cost; the details are as follows:

[0033] Step S3.1, the travel cost function includes travel time cost and fuel cost;

[0034]

[0035] in, represents the travel cost of travel mode m through arc segment a, VOT m is the travel time value of residents, t a is the travel time of arc segment a, χ m is the fuel cost per unit distance of travel mode m, L a is the length of arc segment a;

[0036] Step S3.2, constructing a travel cost function for each path according to the travel cost function of each arc segment;

[0037]

[0038] in, K is the cost required for travel mode m to travel through the path k of the traffic origin and destination OD pair (r, s), rs is the path set of (r,s), It is a binary 0-1 variable representing the relationship between arc segments and paths. Indicates that path k passes through arc segment a, It means that path k does not pass through arc a, r is the starting traffic area, R is the starting traffic area set, s is the starting traffic area, and S is the end traffic area set.

[0039] As a further optimization scheme of the double-layer optimization design method for CAV dedicated lane deployment strategy described in the present invention, a double-layer optimization model for the traffic carrying capacity of the intelligent connected extended network is constructed. The double-layer optimization model includes an upper optimization model and a lower optimization model. The upper optimization model aims to maximize the travel demand carried by the road network, and the lower optimization model is a user equilibrium UE traffic allocation model with the goal of minimizing the cumulative value of the integral of the travel cost function; the details are as follows:

[0040] Step S4.1, constructing an upper optimization model of the two-layer optimization model of the traffic network carrying capacity, with the goal of maximizing the travel demand carried by the road network under the constraint that the flow rate of each road section does not exceed the traffic capacity;

[0041]

[0042] Limited by

[0043]

[0044] in, represents the maximum traffic volume that the OD pair (r,s) can accommodate, Indicates all The vector matrix composed of Represents a vector matrix The elements and for The traffic flow of arc segment a under the scale, C a is the capacity of arc segment a;

[0045] Step S4.2: Based on the user equilibrium UE traffic allocation model, a lower optimization model of the double-layer optimization model of the traffic network carrying capacity is constructed, and its objective function is to minimize the cumulative value of the integral of the travel cost function;

[0046]

[0047] Limited by

[0048]

[0049]

[0050] in, is the traffic flow of travel mode m through path k on the OD pair (r, s), is the traffic volume of travel mode m in the OD pair (r, s), is the traffic flow of mode m in arc segment a, v a is the traffic flow of arc segment a, Z is the objective function value, is the travel cost of travel mode m through arc segment a, ω is the traffic flow variable of the travel cost function of arc segment a;

[0051] Step S4.3: According to the UE conditions, when the urban traffic system reaches a stable state, the path flows of HVs and CAVs meet the following constraints:

[0052]

[0053] in, represents the traffic flow of travel mode m on path k between OD pair (r, s), represents the travel cost of travel mode m on path k between OD pair (r, s), u rs,m It represents the minimum travel cost of travel mode m between the OD pair (r, s).

[0054] As a further optimization scheme of the double-layer optimization design method for CAV dedicated lane deployment strategy described in the present invention, CAV dedicated lane deployment constraints are introduced into the constructed double-layer optimization model of traffic network carrying capacity. The CAV dedicated lane deployment constraints include CAV market share, potential unfair factors for HV users, and CAV dedicated lane construction costs, forming a double-layer optimization model for CAV dedicated lane deployment strategy; specifically as follows:

[0055] Step S5.1: Construct the constraints of CAV market share:

[0056]

[0057] in, and They represent the CAV and HV traffic volumes of OD pair (r, s), ρ represents the market share of CAV;

[0058] Step S5.2: Construct constraints for potential unfairness factors for HV users:

[0059]

[0060] in, represents the travel cost of HV users passing through the OD pair (r, s) after the deployment of CAV dedicated lanes, represents the travel cost of HV users passing through the OD pair (r, s) when CAV dedicated lanes are not deployed, and λ represents the degree of unfair impact of deploying CAV dedicated lanes on HV users;

[0061] Step S5.3: Constructing the constraints of the construction cost of CAV dedicated lanes

[0062]

[0063] Among them, y a is a binary 0-1 variable, indicating the deployment of CAV dedicated lanes in arc a, y a =1 means that arc a is equipped with a CAV dedicated lane, otherwise y a =0,L a represents the length of arc segment a, d represents the unit mileage construction cost of the CAV dedicated lane, represents the total budget cost of CAV dedicated lane deployment.

[0064] As a further optimization scheme of the double-layer optimization design method for CAV dedicated lane deployment strategy described in the present invention, the nonlinear user equilibrium UE traffic allocation model is transformed into a linear user equilibrium UE traffic allocation model by introducing sufficiently large values ​​and binary 0-1 variables; specifically as follows:

[0065]

[0066] Among them, U is a sufficiently large value, is a binary 0-1 variable representing the relationship between the path and the travel mode. Indicates that the traffic flow of travel mode m on path k between OD pair (r, s) is non-zero, otherwise

[0067] As a further optimization scheme of the double-layer optimization design method for CAV dedicated lane deployment strategy described in the present invention, the nonlinear road section impedance function is linearized to obtain a double-layer optimization model for CAV dedicated lane deployment strategy in the form of mixed integer linear programming; specifically, it includes:

[0068] Step S7.1: Introduce auxiliary variable w a and l a :

[0069]

[0070] Among them, w a The lower bound of the range w a and upper bound They are wa =0 and is the basic capacity of arc segment a∈A, is the HV traffic flow of arc segment a∈A;

[0071] Step S7.2: Using auxiliary variables and Convert the link impedance function into a univariate polynomial:

[0072]

[0073] Step S7.3, in the auxiliary variable w a The value range of Set N+1 breakpoints in the

[0074] The pth breakpoint is p∈P={1,2,...,N,N+1},

[0075] p is an integer, P is a set of integers, and N is any positive integer;

[0076] Step S7.4: Introduce variables Indicates breakpoint The weight coefficient of The variable w in a Expressed as is the p+1th breakpoint, For breakpoint The weight coefficient of , according to the partition linear approximation method, obtains the following linear constraints:

[0077]

[0078] in, Indicates l a The piecewise linear approximation of ;

[0079] Step S7.5: According to the special order set second type SOS2 constraint, there are at most two adjacent is an integer, and its constraints are expressed as:

[0080]

[0081] in, is a binary 0-1 variable, express otherwise All are parameters;

[0082] Step S7.6: Introduce auxiliary variable ω a = l a ya , its value range is:

[0083]

[0084] Linearize the product formula of continuous variables and binary variables to obtain the linear expression formula of the road section impedance function:

[0085]

[0086] At this point, the two-level optimization model of the CAV dedicated lane deployment strategy is reconstructed into a mixed integer linear programming.

[0087] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0088] (1) The method of the present invention can provide a mathematical optimization model for the deployment strategy of CAV dedicated lanes, thereby serving all stages of the development of intelligent connected vehicles and avoiding excessive deployment of CAV dedicated lanes;

[0089] (2) This method constructs a two-layer optimization model to evaluate the network performance in a mixed traffic flow environment and integrates the deployment decision variables of intelligent connected dedicated lanes to find the optimal deployment solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 It is a flow chart of the overall implementation of the present invention;

[0091] Figure 2 A road network diagram according to an embodiment of the present invention;

[0092] Figure 3 Schematic diagram of headway time under different driving modes;

[0093] Figure 4 It is a schematic diagram of the partition linear approximation method;

[0094] Figure 5 This is a diagram showing the design results of a dedicated lane deployment strategy according to an embodiment of the present invention. DETAILED DESCRIPTION

[0095] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0096] In order to minimize the negative impact caused by CAVs and HVs running together and maximize the advantages of CAV fleets, the deployment of CAV dedicated lanes has been proven to be one of the most effective solutions in the future. Its purpose is to separate CAV and HV traffic flows, thereby constructing a local ideal area with 100% CAV traffic flow. However, the deployment of CAV dedicated lanes may reduce the feasible routes for HV users and increase the travel time of HV users, thereby generating social injustice. Therefore, the deployment strategy of CAV dedicated lanes should be based on a full consideration of the various influencing factors on CAV users and HV users, and a mathematical design method should be proposed.

[0097] This paper proposes a two-layer optimization design method for CAV dedicated lane deployment strategy based on mixed traffic road network carrying capacity evaluation. The Sioux-Falls urban road network is used as an example to design the CAV dedicated lane deployment strategy. Figure 1 As shown, the following steps are included:

[0098] S1: Build an intelligent connected extended network including conventional lanes and alternative arcs of CAV dedicated lanes;

[0099] Step S1.1: Define the arc element a in the basic road network 0 ∈A 0 , A 0 is a set of arc elements, and the number of lanes in each arc is The basic traffic capacity is The free flow travel time is Mixed traffic includes two travel modes, CAV and HV. Mixed traffic is expressed as M = {CAV, HV}, m∈M, where M is the set of travel modes and m is the element contained in M;

[0100] S1.2: Each arc element a 0 ∈A 0 Expand a CAV dedicated lane alternative arc Forming a collection

[0101] S1.3: For each a 0 ∈A 0 Introducing 0-1 variables like The alternative arc section of the CAV dedicated lane Set as a CAV dedicated lane, otherwise the arc Not set up as a CAV-only lane;

[0102] S1.4: Through steps S1.1 to 1.3, an intelligent connected extended network is formed, such as Figure 2As shown in the figure, all arcs belong to the intelligent connected extended network arc set A, which can be expressed as:

[0103]

[0104] Among them, a represents all arcs in the intelligent connected extended network.

[0105] S2: Construct a road section impedance function that takes into account the CAV ratio and headway parameters, where the headway parameters include four cases: HV-HV, HV-CAV, CAV-CAV, and CAV-HV.

[0106] S2.1: Introducing parameter ρ a As the CAV ratio of the traffic flow of arc segment a∈A, it is expressed as:

[0107]

[0108] in, is the CAV traffic flow of arc segment a∈A, is the traffic flow of arc segment a∈A for travel mode m;

[0109] S2.2: Introduce ε as the correction coefficient of the section impedance function, expressed as:

[0110]

[0111] Among them, γ, γ1, and γ2 are the headway coefficients of CAV-CAV, CAV-HV, and HV-CAV driving modes relative to HV-HV driving mode, respectively, and k2 is the size of the CAV fleet, such as Figure 3 As shown, in this example, γ=0.6, γ1=0.9, γ2=1.4, k2=4;

[0112] S2.3: Combined with the correction coefficient in step S2.2, the impedance function of the mixed traffic flow section in the form of BPR function can be expressed as:

[0113]

[0114] in, For arc segment a 0 The free circulation time, For arc segment a 0 The basic traffic capacity, and Arc segment a 0 of CAV and HV traffic flows, and For arc segment of CAV and HV traffic flows, For arc segment a 0The number of lanes, α and β are the parameters of the BPR function, and Arc segment a 0 and arc segments The impedance function is set to α = 0.15, β = 4;

[0115] S3: Construct travel cost functions for CAV users and HV users, taking into account time value cost and fuel cost;

[0116] S3.1: The travel cost function includes travel time cost and fuel cost;

[0117]

[0118] in, It represents the travel cost of mode m through arc a (unit: yuan), VOT m is the travel time value of residents (unit: yuan / hour), t a is the travel time of arc segment a (unit: hour), m is the fuel cost per unit distance of travel mode m (unit: yuan / km), L a is the length of arc segment a (unit: km), in this example it is set to x HV =0.40 (yuan / km), χ CAV =0.35 (yuan / km);

[0119] S3.2: Based on the travel cost function of each arc segment, the travel cost function of each path can be constructed;

[0120]

[0121] in, K is the cost required for travel mode m to travel through the path k of the traffic origin and destination OD pair (r, s), rs is the path set of (r,s), It is a binary 0-1 variable representing the relationship between arc segments and paths. Indicates that path k passes through arc segment a, It means that path k does not pass through arc a, r is the starting traffic area, R is the starting traffic area set, s is the starting traffic area, and S is the end traffic area set.

[0122] S4: Construct a two-layer optimization model for the traffic carrying capacity of the intelligent connected extended network. The two-layer optimization model includes an upper optimization model and a lower optimization model. The upper optimization model aims to maximize the travel demand carried by the road network, and the lower optimization model is a user equilibrium UE traffic allocation model that aims to minimize the cumulative value of the integral of the travel cost function. The details are as follows:

[0123] S4.1: Construct the upper optimization model of the two-layer optimization model of the road network carrying capacity, with the goal of maximizing the travel demand that the road network can carry, under the constraint that the flow rate of each road section does not exceed the traffic capacity;

[0124]

[0125] Limited by

[0126]

[0127] in, represents the maximum traffic volume that the OD pair (r,s) can accommodate, Indicates all The vector matrix composed of Represents a vector matrix The elements and for The traffic flow of arc segment a under the scale, C a is the capacity of arc segment a;

[0128] S4.2: Based on the User Equilibrium (UE) traffic assignment model, the lower optimization model of the two-layer optimization model of the traffic network carrying capacity is constructed, and its objective function is to minimize the cumulative value of the integral of the travel cost function;

[0129]

[0130] Limited by

[0131]

[0132] in, is the traffic flow of travel mode m through path k on the OD pair (r, s), is the traffic volume of travel mode m in the OD pair (r, s), is the traffic flow of mode m in arc segment a, v a is the traffic flow of arc segment a, Z is the objective function value, is the travel cost of travel mode m through arc segment a, ω is the traffic flow variable of the travel cost function of arc segment a;

[0133] S4.3: According to the UE conditions, when the urban traffic system reaches a stable state, the path flow of HVs and CAVs should meet the following constraints:

[0134]

[0135]

[0136] in, represents the traffic flow of travel mode m on path k between OD pair (r, s), represents the travel cost of travel mode m on path k between OD pair (r, s), u rs,m It represents the minimum travel cost of travel mode m between OD pairs (r, s);

[0137] S5: Introduce CAV dedicated lane deployment constraints into the constructed two-layer optimization model of traffic network carrying capacity. The CAV dedicated lane deployment constraints include CAV market share, potential unfair factors for HV users, and CAV dedicated lane construction costs, forming a two-layer optimization model for CAV dedicated lane deployment strategy. The specific contents are as follows:

[0138] S5.1: Constructing the constraints of CAV market share:

[0139]

[0140] in, and They represent the CAV and HV traffic volumes of OD pair (r, s), ρ represents the market share of CAV;

[0141] S5.2: Constructing constraints on potential unfairness for HV users

[0142]

[0143] in, represents the travel cost of HV users passing through the OD pair (r, s) after the deployment of CAV dedicated lanes, represents the travel cost of HV users passing the OD pair (r, s) when CAV dedicated lanes are not deployed, λ represents the unfair impact of deploying CAV dedicated lanes on HV users, and in this example, λ is set to 1.6;

[0144] S5.3: Constructing the cost constraints of CAV lanes

[0145]

[0146] Among them, y a is a binary 0-1 variable indicating the deployment of CAV dedicated lanes in arc a, y a =1 means that arc a is equipped with a CAV dedicated lane, otherwise y a =0,L a represents the length of arc segment a (unit: km), d represents the unit mileage construction cost of CAV dedicated lanes (unit: yuan / km), represents the total budget cost of CAV dedicated lane deployment (unit: yuan), which is set to d = 8.5 × 10 5 (Yuan / km), (Yuan),;

[0147] S6: By introducing sufficiently large values ​​and binary 0-1 variables, the nonlinear user equilibrium UE traffic assignment model is transformed into a linear user equilibrium UE traffic assignment model:

[0148]

[0149]

[0150] Among them, U is a sufficiently large value, is a binary 0-1 variable representing the relationship between the path and the travel mode. Indicates that the traffic flow of travel mode m on path k between OD pair (r, s) is non-zero, otherwise

[0151] S7: Linearize the nonlinear road impedance function to obtain a two-level optimization model for CAV dedicated lane deployment strategy in the form of mixed integer linear programming; specifically, it includes:

[0152] S7.1: Introducing the auxiliary variable w a and l a :

[0153]

[0154] Among them, w a The lower and upper bounds of the range are w a =0 and is the basic capacity of arc segment a∈A, is the HV traffic flow of arc segment a∈A;

[0155] S7.2: Using auxiliary variables and Convert the link impedance function into a univariate polynomial:

[0156]

[0157] S7.3: In the auxiliary variable w a The value range of Set N+1 breakpoints in

[0158] Among them, the pth breakpoint is p∈P={1,2,...,N,N+1},

[0159] p is an integer, P is a set of integers, and N is any positive integer;

[0160] S7.4: Introducing variables Indicates breakpoint The weight coefficient of The variable w in a Expressed as is the p+1th breakpoint, For breakpoint The weight coefficient of Figure 4 As shown, according to the partition linear approximation method, the following linear constraints can be obtained:

[0161]

[0162]

[0163] in, Indicates l a The piecewise linear approximation of ;

[0164] S7.5: According to the SOS2 constraint (Special Order Set Type 2), there are at most two adjacent is an integer, and its constraints are expressed as:

[0165]

[0166] in, is a binary 0-1 variable, express otherwise

[0167] S7.6: Introducing the auxiliary variable ω a = l a y a , whose value range is

[0168]

[0169] Linearize the product formula of continuous variables and binary variables to obtain the linear expression formula of the road section impedance function:

[0170]

[0171] At this point, the two-level optimization model of the CAV dedicated lane deployment strategy is reconstructed into a mixed integer linear programming.

[0172] S8: The standard mathematical optimization solver CPLEX is used to solve the two-level optimization model of CAV dedicated lane deployment strategy in the form of mixed integer linear programming, and the design results of the CAV dedicated lane deployment strategy in the urban road network are obtained, such as Figure 5 shown.

[0173] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A two-layer optimization design method for CAV dedicated lane deployment strategy, characterized in that: include: Construct an intelligent connected expansion network including conventional lanes and alternative arc sections of CAV-dedicated lanes; Construct a road section impedance function that takes into account the CAV ratio and headway parameters. The headway parameters include four situations: HV-HV, HV-CAV, CAV-CAV, and CAV-HV. HV-HV means that both the front and rear vehicles are HVs, HV-CAV means that the rear vehicle is an HV and the front vehicle is a CAV, CAV-CAV means that both the front and rear vehicles are CAVs, and CAV-HV means that the rear vehicle is a CAV and the front vehicle is an HV. Considering the time value cost and fuel cost, the travel cost function of CAV users and HV users is constructed; Construct a two-layer optimization model for the traffic carrying capacity of the intelligent connected network. The two-layer optimization model includes an upper optimization model and a lower optimization model. The upper optimization model aims to maximize the travel demand carried by the road network, and the lower optimization model is a user equilibrium UE traffic allocation model that aims to minimize the cumulative value of the integral of the travel cost function. In the constructed two-layer optimization model of traffic network carrying capacity, CAV dedicated lane deployment constraints are introduced. The CAV dedicated lane deployment constraints include CAV market share, potential unfair factors for HV users, and CAV dedicated lane construction costs, forming a two-layer optimization model for CAV dedicated lane deployment strategy. By introducing sufficiently large values ​​and binary 0-1 variables, the nonlinear user equilibrium UE traffic assignment model is transformed into a linear user equilibrium UE traffic assignment model; The nonlinear road impedance function is linearized to obtain a two-level optimization model for CAV lane deployment strategy in the form of mixed integer linear programming. The standard mathematical optimization solver is used to solve the bi-level optimization model of CAV dedicated lane deployment strategy in the form of mixed integer linear programming, and the design results of the CAV dedicated lane deployment strategy in the urban road network are obtained.

2. A double-layer optimization design method for CAV dedicated lane deployment strategy according to claim 1, characterized in that: Construct an intelligent connected extended network including conventional lanes and alternative arc sections of CAV dedicated lanes; the details are as follows: Step S1.1: Define the arc element a in the basic road network 0 ∈A 0 , A 0 is a set of arc elements, and the number of lanes in each arc is The basic traffic capacity is The free flow travel time is Mixed traffic includes two travel modes, CAV and HV. Mixed traffic is expressed as M = {CAV, HV}, m∈M, where M is the set of travel modes and m is the element contained in M; Step S1.2: Arc element a 0 ∈A 0 Expand a CAV dedicated lane alternative arc Forming a set of candidate arc segments Step S1.3: For each a 0 ∈A 0 Introducing 0-1 variables like The alternative arc section of the CAV dedicated lane Set as a CAV dedicated lane, otherwise the arc Not set up as a CAV-only lane; Step S1.4: A smart connected extended network is formed through steps S1.1 to S1.

3. All arcs belong to the smart connected extended network arc set A, which is expressed as: Among them, a represents all arcs in the intelligent connected extended network.

3. A double-layer optimization design method for CAV dedicated lane deployment strategy according to claim 2, characterized in that: Construct a road section impedance function that takes into account the CAV ratio and headway parameters. The specific steps are as follows: Step S2.1: Introduce parameter ρ a represents the CAV ratio of the traffic flow in arc segment a∈A, ρ a Expressed as: in, is the CAV traffic flow of arc segment a∈A, is the traffic flow of arc segment a∈A for travel mode m; Step S2.2, introduce the correction coefficient ε of the road section impedance function, expressed as: Among them, γ, γ1, and γ2 are the headway coefficients of CAV-CAV, CAV-HV, and HV-CAV driving modes relative to HV-HV driving mode, respectively, and k2 is the size of the CAV fleet; Step S2.3: Combined with the correction coefficient of step S2.2, the impedance function of the mixed traffic flow section in the form of the BPR function of the Federal Highway Administration is: in, for a 0 The free circulation time, For arc segment a 0 The basic traffic capacity, and Arc segment a 0 of CAV and HV traffic flows, and For arc segment of CAV and HV traffic flows, For arc segment a 0 The number of lanes, α and β are the parameters of the BPR function, and Arc segment a 0 and arc segments The impedance function.

4. A double-layer optimization design method for CAV dedicated lane deployment strategy according to claim 3, characterized in that: Considering the time value cost and fuel cost, the travel cost function of CAV users and HV users is constructed; the details are as follows: Step S3.1, the travel cost function includes travel time cost and fuel cost; in, represents the travel cost of travel mode m through arc segment a, VOT m is the travel time value of residents, t a is the travel time of arc segment a, χ m is the fuel cost per unit distance of travel mode m, L a is the length of arc segment a; Step S3.2, constructing a travel cost function for each path according to the travel cost function of each arc segment; in, K is the cost required for travel mode m to travel through the path k of the traffic origin and destination OD pair (r, s), rs is the path set of (r,s), It is a binary 0-1 variable representing the relationship between arc segments and paths. Indicates that path k passes through arc segment a, It means that path k does not pass through arc a, r is the starting traffic area, R is the starting traffic area set, s is the starting traffic area, and S is the end traffic area set.

5. A double-layer optimization design method for CAV dedicated lane deployment strategy according to claim 4, characterized in that: Construct a two-layer optimization model for the traffic carrying capacity of the intelligent connected extended network. The two-layer optimization model includes an upper optimization model and a lower optimization model. The upper optimization model aims to maximize the travel demand carried by the road network, and the lower optimization model is a user equilibrium UE traffic allocation model that aims to minimize the cumulative value of the integral of the travel cost function. The details are as follows: Step S4.1, constructing an upper optimization model of the two-layer optimization model of the traffic network carrying capacity, with the goal of maximizing the travel demand carried by the road network under the constraint that the flow rate of each road section does not exceed the traffic capacity; Limited by in, represents the maximum traffic volume that the OD pair (r,s) can accommodate, Indicates all The vector matrix composed of Represents a vector matrix The elements and for The traffic flow of arc segment a under the scale, C a is the traffic capacity of arc segment a; Step S4.2: Based on the user equilibrium UE traffic allocation model, a lower optimization model of the double-layer optimization model of the traffic network carrying capacity is constructed, and its objective function is to minimize the cumulative value of the integral of the travel cost function; Limited by in, is the traffic flow of travel mode m through path k on the OD pair (r, s), is the traffic volume of travel mode m in the OD pair (r, s), is the traffic flow of mode m in arc segment a, v a is the traffic flow of arc segment a, Z is the objective function value, is the travel cost of travel mode m through arc segment a, ω is the traffic flow variable of the travel cost function of arc segment a; Step S4.3: According to the UE conditions, when the urban traffic system reaches a stable state, the path flows of HVs and CAVs meet the following constraints: in, represents the traffic flow of travel mode m on path k between OD pair (r, s), represents the travel cost of travel mode m on path k between OD pair (r, s), u rs,m It represents the minimum travel cost of travel mode m between the OD pair (r, s).

6. A double-layer optimization design method for CAV dedicated lane deployment strategy according to claim 5, characterized in that: The deployment constraints of CAV dedicated lanes are introduced into the constructed two-layer optimization model of traffic network carrying capacity. The deployment constraints of CAV dedicated lanes include CAV market share, potential unfair factors for HV users and construction costs of CAV dedicated lanes, forming a two-layer optimization model of CAV dedicated lane deployment strategy; the details are as follows: Step S5.1: Construct the constraints of CAV market share: in, and They represent the CAV and HV traffic volumes of OD pair (r, s), ρ represents the market share of CAV; Step S5.2: Construct constraints for potential unfairness factors for HV users: in, represents the travel cost of HV users passing through the OD pair (r, s) after the deployment of CAV dedicated lanes, represents the travel cost of HV users passing through the OD pair (r, s) when CAV dedicated lanes are not deployed, and λ represents the degree of unfair impact of deploying CAV dedicated lanes on HV users; Step S5.3: Constructing the constraints of the construction cost of CAV dedicated lanes Among them, y a is a binary 0-1 variable, indicating the deployment of CAV dedicated lanes in arc a, y a =1 means that arc a is equipped with a CAV dedicated lane, otherwise y a =0,L a represents the length of arc segment a, d represents the unit mileage construction cost of the CAV dedicated lane, represents the total budget cost of CAV dedicated lane deployment.

7. A double-layer optimization design method for CAV dedicated lane deployment strategy according to claim 6, characterized in that: By introducing sufficiently large values ​​and binary 0-1 variables, the nonlinear user equilibrium UE traffic assignment model is transformed into a linear user equilibrium UE traffic assignment model; the details are as follows: Among them, U is a sufficiently large value, is a binary 0-1 variable representing the relationship between the path and the travel mode. Indicates that the traffic flow of travel mode m on path k between OD pair (r, s) is non-zero, otherwise 8. A double-layer optimization design method for CAV dedicated lane deployment strategy according to claim 7, characterized in that: The nonlinear road impedance function is linearized to obtain a two-level optimization model for CAV dedicated lane deployment strategy in the form of mixed integer linear programming. Specifically, it includes: Step S7.1: Introduce auxiliary variable w a and l a : Among them, w a The lower bound of the range w a and upper bound They are w a =0 and is the basic capacity of arc segment a∈A, is the HV traffic flow of arc segment a∈A; Step S7.2: Using auxiliary variables and Convert the link impedance function into a univariate polynomial: Step S7.3, in the auxiliary variable w a The value range of Set N+1 breakpoints in the The pth breakpoint is p is an integer, P is a set of integers, and N is any positive integer; Step S7.4: Introduce variables Indicates breakpoint The weight coefficient of The variable w in a Expressed as is the p+1th breakpoint, For breakpoint The weight coefficient of , according to the partition linear approximation method, obtains the following linear constraints: in, Indicates l a The piecewise linear approximation of ; Step S7.5: According to the special order set second type SOS2 constraint, there are at most two adjacent is an integer, and its constraints are expressed as: in, is a binary 0-1 variable, express otherwise All are parameters; Step S7.6: Introduce auxiliary variable ω a = l a y a , its value range is: Linearize the product formula of continuous variables and binary variables to obtain the linear expression formula of the road section impedance function: At this point, the two-level optimization model of the CAV dedicated lane deployment strategy is reconstructed into a mixed integer linear programming.

Images