CN116665442B - Intelligent networking special lane design method considering mixed flow theoretical traffic capacity - Google Patents
Intelligent networking special lane design method considering mixed flow theoretical traffic capacity Download PDFInfo
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Abstract
The invention discloses an intelligent networking special lane design method considering the traffic capacity of a mixed flow theory, which comprises the following steps: the method comprises the steps of determining basic traffic parameter ranges such as traffic demands, market permeability and the like of a road scene in which intelligent networking special lanes are required to be laid, designing an intelligent networking special lane layout scheme from the combination of the number of lanes, the positions of the lanes and road right rules, analyzing the traffic capacity of single-lane and multi-lane mixed traffic flows, comprehensively calculating the total traffic of the road, recording the optimal layout form of the intelligent networking special lanes under each traffic parameter combination with the highest traffic as a target, and dynamically adjusting the special lane layout scheme according to the current traffic demands, the market permeability and other traffic parameters. The invention can ensure the high-efficiency operation of the mixed traffic flow, shortens the data collection process of the actual traffic environment by a theoretical analysis method, and realizes the dynamic continuous update of the lane management measures.
Description
Technical Field
The invention relates to the field of intelligent traffic and traffic design, in particular to an intelligent network special lane design method considering the traffic capacity of a mixed flow theory.
Background
The intelligent network-connected vehicle can realize the functions of complex environment sensing, information real-time interaction, intelligent driving control and the like, and has potential advantages in the aspects of relieving road congestion, ensuring traffic safety, reducing oil consumption, pollutant emission and the like. However, in future traffic flows, intelligent networked vehicles inevitably interact with manually driven vehicles to form a mixed traffic flow. On one hand, the uncertainty and randomness exist in the motion of the manual driving vehicle, so that the intelligent network-connected vehicle is interfered, and the performance of the intelligent network-connected vehicle is damaged; on the other hand, the intelligent network vehicles keep a short distance when forming a queue to run, and the operation such as lane changing and converging of the manual driving vehicles is difficult. To ensure efficient operation of the mixed traffic flow, it is necessary to design new management and control strategies.
Intelligent networked vehicle-specific lane designs are considered to be one of the effective approaches to solve the above-described problems. Through separation in time or space, intelligent network vehicles can form a queue to run on a special lane, so that a homogeneous traffic flow is formed, and the road traffic capacity is improved. However, the reservation of the special lanes will occupy the original road resources, resulting in the reduction of the number of conventional lanes, and unreasonable planning and design will lead to negative benefits such as traffic flow congestion. This means that the setting conditions and layout form of the dedicated lane have threshold ranges for parameters such as market penetration, intelligent network vehicle performance, etc.
The existing intelligent network special lane design focuses on application scenes of double lanes or three lanes, and lacks a more universal special lane design method under a multi-lane scene. In addition, the related invention adopts a simulation method to analyze and manage the optimal layout scheme of the lane, a large amount of traffic state data needs to be collected, and the analysis process takes a long time. Therefore, in order to shorten the data collection process of the actual traffic environment and realize the dynamic continuous updating of the lane management measures, the intelligent network-connected special lane design method based on theoretical analysis has important practical significance.
Disclosure of Invention
The purpose of the invention is that: the intelligent network special lane design method considering the mixed flow theoretical traffic capacity is provided, the mixed flow traffic capacity in a special lane scene is analyzed from the theoretical level, and the application ranges of different special lane layout schemes on traffic parameters such as traffic demands, market permeability and the like are obtained, so that the dynamic real-time adjustment of intelligent network special lane management is realized.
In order to achieve the functions, the invention designs an intelligent network special lane design method considering the mixed flow theoretical traffic capacity, which comprises the following steps S1-S6, and the design of the layout scheme of the special lane is completed aiming at the target road scene of intelligent network vehicles and manual driving vehicles.
Step S1: aiming at a target road scene needing to be provided with a special lane of the intelligent network-connected vehicle, acquiring various basic traffic parameters of the target road scene, and determining the range of the various basic traffic parameters, wherein the various basic traffic parameters comprise the number of lanes, traffic demand flow and market permeability of the intelligent network-connected vehicle;
Step S2: according to the number combination of lanes of the general lane, the special lane of the intelligent network vehicle and the special lane for manual driving, the lane position setting and the right-of-way rule, the layout scheme of each group of the special lanes of the intelligent network vehicle is designed;
Step S3: calculating single-lane mixed traffic flow capacity of three lane types according to a general lane, an intelligent network-connected vehicle special lane and a manual driving special lane respectively;
Step S4: determining the distribution situation of vehicles in each lane type, calculating the traffic capacity of the multi-lane mixed traffic flow by combining the traffic capacity of the single-lane mixed traffic flow of the three lanes of the general lane, the intelligent network-connected vehicle special lane and the manual driving special lane, and further calculating the total traffic of the road;
Step S5: based on different basic traffic parameter combinations, calculating the total road flow under the layout scheme of each group of intelligent network vehicle special lanes, wherein the layout scheme of the intelligent network vehicle special lane with the highest total road flow is used as the optimal layout scheme;
Step S6: and adjusting and applying an optimal layout scheme according to the current basic traffic parameters of the target road scene to complete the layout scheme design of the special lane.
As a preferred technical scheme of the invention: the basic traffic parameters in step S1 include the number of lanes N, the traffic demand flow D, which is in the range of [ D min,Dmax ], the market penetration p of the intelligent network vehicle, which is in the range of [ p min,pmax ], the vehicle length l, and the free flow speed v 0.
As a preferred technical scheme of the invention: the specific steps of step S2 are as follows:
Step S2.1: determining the number combination of different lane types, including a general lane, an intelligent network special lane and a manual driving special lane;
step S2.2: determining the arrangement position of an intelligent network special lane, wherein the arrangement position comprises three modes of arrangement along an outer lane, arrangement along a middle lane and arrangement along an inner lane;
Step S2.3: setting a vehicle passing right, and aiming at intelligent network vehicles, dividing passing rules into: the intelligent network-connected vehicle can only run on the special lane; the intelligent network-connected vehicle runs on a general lane, but needs to be degraded to a manual driving state; the intelligent network-connected vehicle freely selects to run on a special lane or a general lane, and allows to keep an intelligent network-connected driving state on the general lane;
Step S2.4: and obtaining the special lane layout scheme of each intelligent network according to the lane number combination, the lane position setting and the traffic right rule combination.
As a preferred technical scheme of the invention: the specific steps of step S3 are as follows:
step S3.1: the vehicle following model is constructed, an intelligent driver model is adopted for the manual driving vehicle, and the calculation formula is as follows:
Wherein v (T) represents a vehicle speed, v 0 represents a free flow speed, s 0 represents a minimum parking pitch, T represents a safe headway, Δv represents a difference in front-rear vehicle speed, a represents a maximum acceleration, b represents a comfortable deceleration, s (T) represents a headway, and l represents a vehicle length;
A vehicle following model constructed for an intelligent network vehicle adopts a cooperative self-adaptive cruise control model, and the calculation formula is as follows:
Where v prev denotes a speed at a time on the vehicle, e denotes a vehicle-to-vehicle distance error, t e denotes a desired headway, and k p and k d denote control coefficients;
when the intelligent network-connected vehicle runs behind the manual driving vehicle, the intelligent network-connected vehicle is degraded into an intelligent vehicle, a vehicle following model constructed for the intelligent vehicle is an adaptive cruise control model, and the calculation formula is as follows:
Where t a represents a desired headway, k 1 and k 2 represent control coefficients;
step S3.2: according to the following models of the vehicles constructed in the step S3.1, the average head distance calculation formula of the vehicles of various types in the state that the acceleration is 0 is as follows:
sCAV＝s0+l+tev
sAV＝s0+l+tav
Wherein s HV represents the manual driving vehicle headway, s CAV represents the intelligent network vehicle headway, and s AV represents the intelligent vehicle headway;
step S3.3: deriving the proportion of intelligent network vehicles on the universal lane degraded into intelligent vehicles, wherein the actual proportion of different types of vehicles on the universal lane is as follows:
pHV＝1-p
pCAV＝p-(1-p)p＝p2
pAV＝1-p2-(1-p)＝p(1-p)
Wherein, p is the market permeability of the intelligent network-connected vehicle, and p HV、pCAV、pAV is the actual proportion of the manual driving vehicle, the intelligent network-connected vehicle and the intelligent vehicle on the general lane respectively;
Step S3.4: according to the actual proportion of different types of vehicles and the corresponding headway, the mixed traffic flow under the market permeability p of the intelligent network-connected vehicles is obtained as follows:
In the method, in the process of the invention, Representing an average locomotive spacing;
The general lane traffic capacity C mix,1 is calculated, and the calculation formula is as follows:
Cmix,1＝(Qmix,1)max＝Cmix,1(p)
Step S3.5: according to a general lane traffic capacity calculation formula, calculating the traffic capacity of the special lane, specifically, when the market permeability p=0 of the intelligent network-connected vehicle, the traffic capacity C HV＝Cmix,1 (0) of the pure manual driving vehicle is calculated, and when p=1, the traffic capacity C CAV＝Cmix,1 (1) of the pure intelligent network-connected vehicle is calculated.
As a preferred technical scheme of the invention: the specific steps of step S4 are as follows:
Step S4.1: determining the distribution condition of vehicles, wherein on a multi-lane expressway provided with a special lane, the vehicles preferentially select the corresponding special lane, and when the traffic flow on the special lane reaches a preset traffic capacity threshold value, the rest vehicles are distributed to the general lanes adjacent to the special lane;
Step S4.2: the traffic flow of the special lane is distributed, and the traffic demand flow D is distributed to the traffic of the special lane, so that the traffic capacity of the special lane is not exceeded:
Wherein q CAV and m respectively represent the average traffic and the number of lanes of the intelligent network-connected special lanes, and q HV and n respectively represent the average traffic and the number of lanes of the manual driving special lanes;
Step S4.3: calculating traffic flow of a general lane, and after distribution, changing the proportion of various types of vehicles on the general lane, wherein the theoretical proportion p g of intelligent network vehicles is as follows:
The average flow of the general lane is:
Wherein q mix and N-m-N represent the average flow rate and the number of lanes of the general lane, respectively;
Step S4.4: according to the average flow of different types of lanes, calculating the total flow of the road as follows:
Wherein Q mix,N is the total road flow, and N is the number of lanes.
The beneficial effects are that: the advantages of the present invention over the prior art include:
1. By adopting the theoretical traffic capacity analysis method, the application threshold range of all possible intelligent network special lane layout schemes is obtained according to fewer basic traffic parameters, so that a large amount of time spent on collecting data by a simulation means or in an actual traffic environment can be shortened, and the scheme comparison efficiency is obviously improved.
2. The method has the advantages that the dynamic control of lane management is realized, the optimal layout scheme under all traffic parameter combinations is pre-determined before the layout of the special lanes, the continuous updating of the scheme is completed according to the specific change of the traffic state, the control delay condition is avoided, and meanwhile, the high-efficiency traffic efficiency of the special lanes and the whole road is ensured.
Drawings
Fig. 1 is a flowchart of an intelligent network-connected special lane design method considering a mixed flow theoretical traffic capacity according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a lane layout scheme provided in accordance with an embodiment of the present invention;
FIG. 3 is a basic diagram of a single lane mixed traffic flow and a traffic capacity result diagram provided according to an embodiment of the present invention;
FIG. 4 is a graph of flow results for different layout schemes under multiple lanes according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Referring to fig. 1, the method for designing the intelligent network-connected special lane taking the mixed flow theoretical traffic capacity into consideration provided by the embodiment of the invention comprises the following steps S1-S6, and the design of the layout scheme of the special lane is completed aiming at the target road scene of intelligent network-connected vehicles and manual driving vehicles.
Step S1: aiming at a target road scene needing to be provided with a special lane of the intelligent network-connected vehicle, acquiring various basic traffic parameters of the target road scene, and determining the range of the various basic traffic parameters, wherein the various basic traffic parameters comprise the number of lanes, traffic demand flow and market permeability of the intelligent network-connected vehicle;
The basic traffic parameters include the number of lanes N, the traffic demand flow D, which ranges from [ D min,Dmax ], the market penetration p of intelligent networked vehicles, which ranges from [ p min,pmax ], the vehicle length l, the free flow speed v 0.
In one embodiment, the basic traffic parameters take the values of: the number of lanes N=3, the traffic demand range is 1000veh/h < D <4000veh/h/ln, the step size is 1000veh/h, the intelligent network market permeability range is 0< p <1, the vehicle length l=5m, and the free flow speed v 0 =100 km/h.
Step S2: according to the number combination of lanes of the general lane, the special lane of the intelligent network vehicle and the special lane for manual driving, the lane position setting and the right-of-way rule, the layout scheme of each group of the special lanes of the intelligent network vehicle is designed;
The specific steps of step S2 are as follows:
Step S2.1: determining the number combination of different lane types, including a general lane, an intelligent network special lane and a manual driving special lane; in a mixed traffic flow environment, the influence of the independent arrangement of the special manual driving lane on the traffic capacity is small, so that the intelligent network-connected special lane is mainly considered, and the special manual driving lane is used as an auxiliary lane management measure;
step S2.2: determining the arrangement position of an intelligent network special lane, wherein the arrangement position comprises three modes of arrangement along an outer lane, arrangement along a middle lane and arrangement along an inner lane; in order to ensure the traffic efficiency of the special lanes, all the special lanes are arranged along the inner side of the road, and the intelligent network special lanes are positioned at the innermost side when different types of special lanes exist;
Step S2.3: setting a vehicle passing right, and aiming at intelligent network vehicles, dividing passing rules into: the intelligent network-connected vehicle can only run on the special lane; the intelligent network-connected vehicle runs on a general lane, but needs to be degraded to a manual driving state; the intelligent network-connected vehicle freely selects to run on a special lane or a general lane, and allows to keep an intelligent network-connected driving state on the general lane;
for simplicity of analysis, the invention assumes that intelligent networked vehicles can freely choose to travel in their dedicated or common lanes and only degenerate to intelligent vehicles in the case of a preceding vehicle being a manually driven vehicle, irrespective of the situation of degenerate to a manually driven vehicle;
Step S2.4: and obtaining the layout scheme of each intelligent network special lane according to the combination of the number of lanes, the position setting of the lanes and the rule combination of the right of way, wherein 6 intelligent network special lanes obtained by the arrangement and combination are shown in a figure 2, CAV special lanes represent intelligent network special lanes in the figure, and HV special lanes represent manual driving special lanes.
Step S3: and calculating the single-lane mixed traffic flow capacity of the three lane types according to the general lane, the intelligent network-connected vehicle special lane and the manual driving special lane respectively.
The specific steps of step S3 are as follows:
Step S3.1: a vehicle following model is built, an Intelligent Driver Model (IDM) is adopted for a manually driven vehicle, and the calculation formula is as follows:
Wherein v (T) represents a vehicle speed, v 0 represents a free flow speed, s 0 represents a minimum parking pitch, T represents a safe headway, Δv represents a difference in front-rear vehicle speed, a represents a maximum acceleration, b represents a comfortable deceleration, s (T) represents a headway, and l represents a vehicle length;
in this embodiment, parameters related to the manual driving of the vehicle are selected as shown in table 1 below:
TABLE 1
| v0/(m/s) | s0/m | T/s | a/(m/s2) | b/(m/s2) | l/m |
| 29 | 2 | 1.5 | 1 | 2.8 | 5 |
A vehicle following model constructed for an intelligent network-connected vehicle adopts a cooperative self-adaptive cruise control model (CACC), and the calculation formula is as follows:
Where v prev denotes a speed at a time on the vehicle, e denotes a vehicle-to-vehicle distance error, t e denotes a desired headway, and k p and k d denote control coefficients;
In this embodiment, the relevant parameters of the intelligent network-connected vehicle are selected as follows in table 2:
TABLE 2
| te/s | l/m | s0/m | kp | kd |
| 0.6 | 5 | 2 | 0.45 | 0.25 |
When the intelligent network-connected vehicle runs behind the manual driving vehicle, the intelligent network-connected vehicle is degraded into an intelligent vehicle, a vehicle following model constructed for the intelligent vehicle is an adaptive cruise control model (ACC), and the calculation formula is as follows:
Where t a represents a desired headway, k 1 and k 2 represent control coefficients;
In this embodiment, the relevant parameters of the intelligent vehicle are selected as shown in table 3 below:
TABLE 3 Table 3
| ta/s | l/m | s0/m | k1/(s-2) | k2/(s-1) |
| 1.1 | 5 | 2 | 0.23 | 0.07 |
Step S3.2: according to the following models of the vehicles constructed in the step S3.1, the average head distance calculation formula of the vehicles of various types in the state that the acceleration is 0 is as follows:
sCAV＝s0+l+tev
sAV＝s0+l+tav
Wherein s HV represents the manual driving vehicle headway, s CAV represents the intelligent network vehicle headway, and s AV represents the intelligent vehicle headway;
Step S3.3: deriving the proportion of intelligent networked vehicles degenerated into intelligent vehicles on a general lane, wherein the quantity of the intelligent vehicles depends on whether following vehicles behind manually driven vehicles are intelligent networked vehicles or not, and considering the degeneration process, the actual proportion of different types of vehicles on the general lane is as follows:
pHV＝1-p
pCAV＝p-(1-p)p＝p2
pAV＝1-p2-(1-p)＝p(1-p)
Wherein, p is the market permeability of the intelligent network-connected vehicle, and p HV、pCAV、pAV is the actual proportion of the manual driving vehicle, the intelligent network-connected vehicle and the intelligent vehicle on the general lane respectively;
Step S3.4: according to the actual proportion of different types of vehicles and the corresponding headway, the mixed traffic flow under the market permeability p of the intelligent network-connected vehicles is obtained as follows:
In the method, in the process of the invention, Representing an average locomotive spacing;
The general lane traffic capacity C mix,1 is calculated, and the calculation formula is as follows:
Cmix,1＝(Qmix,1)max＝Cmix,1(p)
Step S3.5: calculating the traffic capacity of the special lane according to a general lane traffic capacity calculation formula, wherein the traffic capacity is the traffic capacity C HV＝Cmix,1 (0) of the purely manual driving vehicle when the market permeability p=0 of the intelligent network vehicle, and the traffic capacity C CAV＝Cmix,1 (1) of the purely intelligent network vehicle when p=1; the basic diagram of the mixed traffic flow and the traffic capacity in the single-lane scene refer to fig. 3.
Step S4: and determining the distribution condition of vehicles in each lane type, and calculating the multi-lane mixed traffic flow capacity by combining the single-lane mixed traffic flow capacity of the three lanes of the general lane, the intelligent network-connected vehicle special lane and the manual driving special lane, so as to further calculate the total road flow.
The specific steps of step S4 are as follows:
Step S4.1: determining the distribution situation of vehicles, wherein in order to pursue higher running efficiency on a multi-lane expressway provided with a special lane, the vehicles preferentially select the corresponding special lane, and when the traffic flow on the special lane reaches a preset traffic capacity threshold value, the rest vehicles are distributed to the general lanes adjacent to the special lane;
Step S4.2: the traffic flow of the special lane is distributed, and the traffic demand flow D is distributed to the traffic of the special lane, so that the traffic capacity of the special lane is not exceeded:
Wherein q CAV and m respectively represent the average traffic and the number of lanes of the intelligent network-connected special lanes, and q HV and n respectively represent the average traffic and the number of lanes of the manual driving special lanes;
Step S4.3: calculating traffic flow of a general lane, and after distribution, changing the proportion of various types of vehicles on the general lane, wherein the theoretical proportion p g of intelligent network vehicles is as follows:
The average flow of the general lane is:
Wherein q mix and N-m-N represent the average flow rate and the number of lanes of the general lane, respectively;
Step S4.4: according to the average flow of different types of lanes, calculating the total flow of the road as follows:
Wherein Q mix,N is the total road flow, and N is the number of lanes.
Step S5: based on different basic traffic parameter combinations, calculating the total road flow under the layout scheme of each group of intelligent network vehicle special lanes, wherein the layout scheme of the intelligent network vehicle special lane with the highest total road flow is used as the optimal layout scheme.
The change of the total road flow along with the permeability under different traffic demands is shown in fig. 4, and the optimal layout scheme of the intelligent network special lanes for analyzing and obtaining the traffic parameter conditions is shown in the following table 4:
Step S6: and adjusting and applying an optimal layout scheme according to the current basic traffic parameters of the target road scene to complete the layout scheme design of the special lane.
Judging whether each basic traffic parameter at the current moment changes or not, if not, applying the current optimal layout scheme, and if so, returning to execute the step S5, and reselecting the optimal layout scheme.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present invention.
Claims (1)
1. The intelligent network special lane design method considering the mixed flow theoretical traffic capacity is characterized by comprising the following steps S1-S6, and the design of the special lane layout scheme is completed aiming at the intelligent network vehicles and the target road scene of manual driving vehicles traffic:
Step S1: aiming at a target road scene needing to be provided with a special lane of the intelligent network-connected vehicle, acquiring various basic traffic parameters of the target road scene, and determining the range of the various basic traffic parameters, wherein the various basic traffic parameters comprise the number of lanes, traffic demand flow and market permeability of the intelligent network-connected vehicle;
The basic traffic parameters in the step S1 comprise the number of lanes N and the traffic demand flow D, wherein the range of the traffic demand flow D is [ D min,Dmax ], the market permeability p of the intelligent network-connected vehicle is [ p min,pmax ], and the basic traffic parameters also comprise the vehicle length l and the free flow speed v 0;
Step S2: according to the number combination of lanes of the general lane, the special lane of the intelligent network vehicle and the special lane for manual driving, the lane position setting and the right-of-way rule, the layout scheme of each group of the special lanes of the intelligent network vehicle is designed;
The specific steps of step S2 are as follows:
Step S2.1: determining the number combination of different lane types, including a general lane, an intelligent network special lane and a manual driving special lane;
step S2.2: determining the arrangement position of an intelligent network special lane, wherein the arrangement position comprises three modes of arrangement along an outer lane, arrangement along a middle lane and arrangement along an inner lane;
Step S2.3: setting a vehicle passing right, and aiming at intelligent network vehicles, dividing passing rules into: the intelligent network-connected vehicle can only run on the special lane; the intelligent network-connected vehicle runs on a general lane, but needs to be degraded to a manual driving state; the intelligent network-connected vehicle freely selects to run on a special lane or a general lane, and allows to keep an intelligent network-connected driving state on the general lane;
step S2.4: obtaining a special lane layout scheme of each intelligent network according to the number combination of lanes, the position setting of lanes and the rule combination of traffic rights;
Step S3: calculating single-lane mixed traffic flow capacity of three lane types according to a general lane, an intelligent network-connected vehicle special lane and a manual driving special lane respectively;
The specific steps of step S3 are as follows:
step S3.1: the vehicle following model is constructed, an intelligent driver model is adopted for the manual driving vehicle, and the calculation formula is as follows:
Wherein v (T) represents a vehicle speed, v 0 represents a free flow speed, s 0 represents a minimum parking pitch, T represents a safe headway, Δv represents a difference in front-rear vehicle speed, a represents a maximum acceleration, b represents a comfortable deceleration, s (T) represents a headway, and l represents a vehicle length;
A vehicle following model constructed for an intelligent network vehicle adopts a cooperative self-adaptive cruise control model, and the calculation formula is as follows:
Where v prev denotes a speed at a time on the vehicle, e denotes a vehicle-to-vehicle distance error, t e denotes a desired headway, and k p and k d denote control coefficients;
when the intelligent network-connected vehicle runs behind the manual driving vehicle, the intelligent network-connected vehicle is degraded into an intelligent vehicle, a vehicle following model constructed for the intelligent vehicle is an adaptive cruise control model, and the calculation formula is as follows:
Where t a represents a desired headway, k 1 and k 2 represent control coefficients;
step S3.2: according to the following models of the vehicles constructed in the step S3.1, the average head distance calculation formula of the vehicles of various types in the state that the acceleration is 0 is as follows:
sCAV＝s0+l+tev
sAV＝s0+l+tav
Wherein s HV represents the manual driving vehicle headway, s CAV represents the intelligent network vehicle headway, and s AV represents the intelligent vehicle headway;
step S3.3: deriving the proportion of intelligent network vehicles on the universal lane degraded into intelligent vehicles, wherein the actual proportion of different types of vehicles on the universal lane is as follows:
pHV＝1-p
pCAV＝p-(1-p)p＝p2
pAV＝1-p2-(1-p)＝p(1-p)
Wherein, p is the market permeability of the intelligent network-connected vehicle, and p HV、pCAV、pAV is the actual proportion of the manual driving vehicle, the intelligent network-connected vehicle and the intelligent vehicle on the general lane respectively;
Step S3.4: according to the actual proportion of different types of vehicles and the corresponding headway, the mixed traffic flow under the market permeability p of the intelligent network-connected vehicles is obtained as follows:
In the method, in the process of the invention, Representing an average locomotive spacing;
the general lane traffic capacity C mix,1 is calculated, and the calculation formula is as follows:
C mix,1＝(Qmix,1)max＝C mix,1(p)
Step S3.5: calculating the traffic capacity of the special lane according to a general lane traffic capacity calculation formula, wherein the traffic capacity is the traffic capacity C HV＝Cmix,1 (0) of the purely manual driving vehicle when the market permeability p=0 of the intelligent network vehicle, and the traffic capacity C CAV＝Cmix,1 (1) of the purely intelligent network vehicle when p=1;
Step S4: determining the distribution situation of vehicles in each lane type, calculating the traffic capacity of the multi-lane mixed traffic flow by combining the traffic capacity of the single-lane mixed traffic flow of the three lanes of the general lane, the intelligent network-connected vehicle special lane and the manual driving special lane, and further calculating the total traffic of the road;
Step S5: based on different basic traffic parameter combinations, calculating the total road flow under the layout scheme of each group of intelligent network vehicle special lanes, wherein the layout scheme of the intelligent network vehicle special lane with the highest total road flow is used as the optimal layout scheme;
the specific steps of step S4 are as follows:
Step S4.1: determining the distribution condition of vehicles, wherein on a multi-lane expressway provided with a special lane, the vehicles preferentially select the corresponding special lane, and when the traffic flow on the special lane reaches a preset traffic capacity threshold value, the rest vehicles are distributed to the general lanes adjacent to the special lane;
Step S4.2: the traffic flow of the special lane is distributed, and the traffic demand flow D is distributed to the traffic of the special lane, so that the traffic capacity of the special lane is not exceeded:
Wherein q CAV and m respectively represent the average traffic and the number of lanes of the intelligent network-connected special lanes, and q HV and n respectively represent the average traffic and the number of lanes of the manual driving special lanes;
Step S4.3: calculating traffic flow of a general lane, and after distribution, changing the proportion of various types of vehicles on the general lane, wherein the theoretical proportion p g of intelligent network vehicles is as follows:
The average flow of the general lane is:
Wherein q mix and (N-m-N) represent the average flow rate of the general lanes and the number of lanes, respectively;
Step S4.4: according to the average flow of different types of lanes, calculating the total flow of the road as follows:
Wherein Q mix,N is the total flow of the road, and N is the number of lanes;
Step S6: and adjusting and applying an optimal layout scheme according to the current basic traffic parameters of the target road scene to complete the layout scheme design of the special lane.
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