CN113382064A - Traffic capacity estimation method and device considering CAV (vehicle Access control) special lane of intelligent internet vehicle - Google Patents

Traffic capacity estimation method and device considering CAV (vehicle Access control) special lane of intelligent internet vehicle Download PDF

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CN113382064A
CN113382064A CN202110637576.9A CN202110637576A CN113382064A CN 113382064 A CN113382064 A CN 113382064A CN 202110637576 A CN202110637576 A CN 202110637576A CN 113382064 A CN113382064 A CN 113382064A
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cav
lane
traffic capacity
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孙棣华
赵敏
蒋婷
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Chongqing University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed

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Abstract

The invention discloses a traffic capacity estimation method considering a special lane for intelligent internet connection, which comprises the following steps: acquiring the overall traffic demand of a road; calculating the proportion of the intelligent Internet connection vehicle and the common people to drive to obtain the permeability of the intelligent Internet connection vehicle and the permeability of the common people to drive; modeling the mixed traffic flow by adopting a discrete Markov model; acquiring a vehicle head spacing distribution matrix in a single lane scene; the traffic capacity of the special lane for the intelligent internet connection with the intelligent internet connection accounting for 100% is obtained by using a CACC model and based on a vehicle head spacing distribution matrix of vehicles on the special lane; calculating the permeability and traffic capacity of a common mixed single lane under a multi-lane scene; and calculating the integral traffic capacity in a multi-lane scene after the special lane for the intelligent internet vehicle is opened. The method can estimate the traffic capacity under the conditions of different permeabilities, different traffic demands and random vehicle distribution, and can provide reference for the setting conditions of the CAV special lane of the intelligent internet vehicle under the novel mixed traffic scene.

Description

Traffic capacity estimation method and device considering CAV (vehicle Access control) special lane of intelligent internet vehicle
Technical Field
The invention belongs to the field of intelligent transportation, and particularly relates to a traffic capacity estimation method and device under the condition of considering an intelligent networked vehicle CAV (connected and automatic vehicle) special lane scene.
Background
With the development of social economy and transportation industry, the automobile keeping quantity is continuously increased, and the transportation mode of a transportation system is expanded from a single transportation system to a comprehensive transportation system. In recent years, the number of vehicles on roads is increasing, so that the increasing traffic demand is not matched with the road section traffic capacity, a series of problems such as traffic jam and the like are generated, and the problems also cause wide attention of government departments, scholars and technicians of various countries.
The occupancy rate of intelligent networked automobiles as a future development form of automobiles is gradually increased in traffic composition. Under the mixed traffic scene, set up the special lane for intelligent networking car, be thought to reduce the conflict between the different intelligent degree vehicles, improve traffic efficiency. However, the conventional research is insufficient in exploring the change mechanism of the traffic capacity in a special lane scene, and the critical point of the increase of the traffic capacity of the road section is not fully researched.
Disclosure of Invention
In view of the above, the present invention provides a traffic capacity estimation method and device considering an intelligent internet vehicle CAV dedicated lane, which are used to solve at least one defect in the prior art.
The purpose of the invention is realized by the following technical scheme: a traffic capacity estimation method considering CAV (vehicle Access control) special lanes of an intelligent internet vehicle comprises the following steps:
obtaining the current time and the whole road traffic demand Qinput；
Calculating the proportion of the intelligent network-connected CAV and the HDV for driving by common people based on the vehicle distribution conditions of different types of vehicles in the current traffic flow to obtain the permeability of the intelligent network-connected CAV and the permeability of the HDV for driving by common people;
according to the vehicle distribution conditions of different types of vehicles, a discrete Markov model is adopted to model the mixed traffic flow;
acquiring a vehicle head spacing distribution matrix in a single lane scene;
calculating a variation with speed based on the speed of the vehicle on the exclusive lane using a CACC modelThe dynamic vehicle head distance of the vehicle head obtains a vehicle head distance distribution matrix on the special lane, so that the traffic capacity C of the special lane of the intelligent network connection vehicle CAV with the intelligent network connection vehicle CAV accounting for 100 percent of the distance is obtainedML；
Obtaining the permeability of the common mixed single lane under the multi-lane scene according to the vehicle head distance distribution matrix or the average vehicle head distance of the common mixed single lane, the vehicle distribution condition, the probability of each condition and the traffic capacity of the intelligent internet vehicle CAV special lane
Figure BDA0003106404560000021
And traffic capacity Cmix；
According to the traffic capacity C of the common mixed single lanemixThe traffic capacity of the CAV special lane of the intelligent internet vehicle is calculated, and the integral traffic capacity Q under the multi-lane scene after the CAV special lane of the intelligent internet vehicle is opened is calculatedtotal。
Optionally, permeability P of CAV of intelligent Internet vehicle1：
Figure BDA0003106404560000022
Wherein: a. thenIs the equation of state of the vehicle, AnE {0,1}, when the vehicle is connected with the intelligent network, A n1 is ═ 1; when the vehicle is driven by ordinary people, A n0; n is the number of vehicles, including intelligent networked CAV and HDV for driving by ordinary people;
permeability P of HDV for driving by ordinary people0：
Figure BDA0003106404560000023
Optionally, the modeling the mixed traffic flow by using a discrete markov model according to the vehicle distribution of different types of vehicles includes:
equation of state A of the vehiclenAs the state variable of the nth step, the state space is:
S＝{s1,...,sn}＝{1,0}
in the case of a single lane, the initial state of the Markov chain is π and the transition matrix is A:
π＝[P1,P0]
Figure BDA0003106404560000024
Figure BDA0003106404560000025
wherein: a isijRepresenting the transition matrix in different following modes; si、sjBelongs to a state space, and the value of the state space represents the types of a vehicle and a front vehicle;
calculating the transfer probability under different permeabilities and queue strengths according to the vehicle distribution conditions of different types of vehicles:
when i is 1 and j is 0,
Figure BDA0003106404560000031
(ii) when i is 1 and j is 1,
a11(P1,O)＝1-a10(P1,O),
(iii) when i is 0, j is 1,
Figure BDA0003106404560000032
when i is 0 and j is 0,
a00(P1,O)＝1-a01(P1,O).
and O is the queue intensity of the mixed fleet under different distribution conditions.
Optionally, the vehicle head interval distribution matrix T is:
T＝[t11,t10,t00,t01]
wherein: t is t11The front vehicle is an intelligent internet vehicle CAV, and the rear vehicle is the distance between the heads of the intelligent internet vehicle CAV; t is t10The front vehicle is an intelligent internet vehicle CAV, and the rear vehicle is the head spacing of an HDV driven by a common person; t is t00The front vehicle is a vehicle head interval of HDV driven by common people, and the rear vehicle is also a vehicle head interval of HDV driven by common people; t is t01The front vehicle is HDV driven by ordinary people, and the rear vehicle is the distance between the heads of CAVs of the intelligent Internet vehicles.
Optionally, the vehicle head interval distribution matrix T is:
Figure BDA0003106404560000033
optionally, the obtaining of the traffic capacity of the intelligent internet vehicle CAV dedicated lane with the intelligent internet vehicle CAV accounting for 100% by using the CACC model and based on the vehicle headway distribution matrix of the vehicle on the dedicated lane includes:
obtaining a CACC model, wherein the CACC model is as follows:
Figure BDA0003106404560000034
wherein the content of the first and second substances,
Figure BDA0003106404560000035
) Respectively representing the acceleration of the vehicles n and n-1 at the time t; x is the number ofn(t)、xn-1(t) represents the displacement of the vehicles n, n-1 at time t, respectively; l is the vehicle length; k is a radical of1、k2And k3Is a coefficient; s0A safe parking space; t is tgTo a desired headway, vn(t)、vn-1(t) represents the speed of the vehicles n, n-1 at time t, respectively;
the distance between the car heads which dynamically changes along with the speed is obtained as follows:
Figure BDA0003106404560000041
wherein h isd(t) is the distance between the car heads; v. of0Representing a current vehicle speed;
calculating the traffic capacity of the intelligent internet vehicle CAV special lane with the intelligent internet vehicle CAV accounting for 100 percent:
Figure BDA0003106404560000042
optionally, the permeability of the common mixed single lane in the multi-lane scene
Figure BDA0003106404560000043
Comprises the following steps:
Figure BDA0003106404560000044
the traffic capacity CmixComprises the following steps:
Figure BDA0003106404560000045
wherein: lMLIndicating the number of lanes set as exclusive lanes among all lanes;
Figure BDA0003106404560000046
the mean headway in different following situations is indicated.
Optionally, the overall traffic capacity Q in the multi-lane scenariototalComprises the following steps:
Qtotal＝lML·CML+(L-lML)Cmix
wherein L represents the total number of roads; lMLIndicating the number of lanes in which the exclusive lane is set among all lanes.
The purpose of the invention is realized by the following technical scheme: a traffic capacity estimation apparatus considering an intelligent internet vehicle CAV-dedicated lane, the apparatus comprising:
traffic demand acquisition moduleFor obtaining the whole traffic demand Q of the road at the current momentinput；
The permeability calculation module is used for calculating the proportion of the intelligent network connection CAV and the HDV for driving by common people based on the vehicle distribution conditions of different types of vehicles in the current traffic flow to obtain the permeability of the intelligent network connection CAV and the permeability of the HDV for driving by common people;
the model building module is used for modeling the mixed traffic flow by adopting a discrete Markov model according to the vehicle distribution conditions of different types of vehicles;
the vehicle head interval acquisition module is used for acquiring a vehicle head interval distribution matrix in a single lane scene;
the intelligent internet vehicle CAV special lane traffic capacity calculation module calculates the dynamic head distance changing along with the speed by using a CACC model and based on the vehicle speed of the vehicle on the special lane to obtain a head distance distribution matrix on the special lane, so that the traffic capacity of the intelligent internet vehicle CAV special lane with the intelligent internet vehicle CAV accounting for 100% is obtained by the distance;
the common mixed single lane traffic capacity calculation module is used for obtaining the permeability of the common mixed single lane under the multi-lane scene according to the vehicle head interval distribution matrix or the average vehicle head interval of the common mixed single lane, the vehicle distribution condition, the probability of each condition and the traffic capacity of the intelligent internet CAV special lane
Figure BDA0003106404560000051
And traffic capacity Cmix；
The integral traffic capacity calculation module under the multi-lane scene is used for calculating the traffic capacity C according to the common mixed single lanemixThe traffic capacity of the CAV special lane of the intelligent internet vehicle is calculated, and the integral traffic capacity Q under the multi-lane scene after the CAV special lane of the intelligent internet vehicle is opened is calculatedtotal。
Due to the adoption of the technical scheme, the invention has the following advantages:
in the invention, under the scene of a special traffic lane of CAV (vehicle-associated vehicle) of hybrid traffic intelligent network, the macroscopic traffic flow information (permeability of CAV of intelligent network, input traffic flow and the like) of the current road is utilized to classify the road and estimate the traffic capacity of the multi-lane road under the scene of the special traffic lane; and (5) restoring the vehicle distribution condition of the hybrid fleet and the following state. The method can estimate the traffic capacity under the conditions of different permeabilities, different traffic demands and random vehicle distribution, and can provide reference for the setting conditions of the intelligent internet vehicle CAV (vehicle access vehicle) special lane in a novel mixed traffic scene.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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The drawings of the present invention are described below.
FIG. 1 is a schematic diagram of a multi-lane road classification according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of vehicle distribution at different fleet strengths in accordance with one embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating vehicle headway classification according to an embodiment of the present invention;
fig. 4 is a flowchart of a traffic capacity estimation method considering an intelligent internet vehicle CAV dedicated lane according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a traffic capacity estimation device considering an intelligent internet vehicle CAV dedicated lane according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application. On the contrary, the embodiments of the application include all changes, modifications and equivalents coming within the spirit and terms of the claims appended hereto.
As shown in fig. 4, an embodiment of the present application provides a traffic capacity estimation method considering a dedicated lane of a CAV of an intelligent internet vehicle, including the following steps:
s1 obtaining the current time and the whole road traffic demand Qinput；
Specifically, as shown in fig. 1, the types of roads include a smart internet vehicle CAV dedicated lane and a hybrid vehicle lane.
S2, calculating the proportion of the intelligent network connection CAV and the HDV for driving by common people based on the vehicle distribution condition of different types of vehicles in the current traffic flow to obtain the permeability of the intelligent network connection CAV and the permeability of the HDV for driving by common people;
under the single lane scene, considering the traffic flow of N vehicles, dividing the vehicles into HDV (high-level data video) for driving by common people and CAV (intelligent Internet access vehicle) for connecting the vehicles according to the types of the vehicles, and calculating the proportion of each type of the vehicles to obtain the permeability of the CAV for connecting the vehicles by the intelligent Internet and the permeability of the HDV for driving by the common people;
wherein, according to the quantity of the intelligent network connection CAVs in the traffic flow, the permeability P of the intelligent network connection CAV is calculated1：
Figure BDA0003106404560000061
Wherein: a. thenIs the equation of state of the vehicle, AnE {0,1}, when the vehicle is connected with the intelligent network, An1 is ═ 1; when the vehicle is driven by ordinary people, An＝0。
Calculating the permeability P of the HDV driven by the ordinary people according to the quantity of the HDV driven by the ordinary people in the traffic flow0：
Figure BDA0003106404560000062
S3, modeling the mixed traffic flow by adopting a discrete Markov model according to the vehicle distribution condition of different types of vehicles;
S31equation of state A of the vehiclenAs the state variable of the nth step, the state space is:
S＝{s1,...,sn}＝{1,0}
s32, under the condition of a single lane, obtaining an initial state pi and a transfer matrix A of the Markov chain:
π＝[P1,P0]
Figure BDA0003106404560000063
Figure BDA0003106404560000064
wherein: a isijRepresenting the transition matrix in different following modes; si、sjThe vehicle-following state space belongs to a state space, the value of the state space represents the types of the vehicle and the front vehicle, and different vehicle-following states can generate different vehicle-head distances, as shown in fig. 2.
S33, according to different distribution conditions of the mixed fleet, considering the mutual influence of the vehicles, generating different queue strengths, and calculating the transfer probabilities under different permeabilities and queue strengths:
when i is 1 and j is 0,
Figure BDA0003106404560000071
(ii) when i is 1 and j is 1,
a11(P1,O)＝1-a10(P1,O),
(iii) when i is 0, j is 1,
Figure BDA0003106404560000072
when i is 0 and j is 0,
a00(P1,O)＝1-a01(P1,O).
in the above expression: and O is the queue intensity under different distribution conditions of the mixed fleet.
S4, acquiring a vehicle head space distribution matrix in a single lane scene;
in a single lane scene, a schematic diagram of the distribution of the distance between the vehicle heads in the traffic flow is shown in fig. 3, and a distribution matrix of the distance between the vehicle heads is as follows:
T＝[t11,t10,t00,t01]
wherein: t is t11The front vehicle is an intelligent internet vehicle CAV, and the rear vehicle is the distance between the heads of the intelligent internet vehicle CAV; t is t10The front vehicle is an intelligent internet vehicle CAV, and the rear vehicle is the head spacing of an HDV driven by a common person; t is t00The front vehicle is a vehicle head interval of HDV driven by common people, and the rear vehicle is also a vehicle head interval of HDV driven by common people; t is t01The front vehicle is HDV driven by ordinary people, and the rear vehicle is the distance between the heads of CAVs of the intelligent Internet vehicles.
It should be noted that this classification method only represents that the distances between the heads of the different following methods of the vehicles are different, but the distances between the heads of the vehicles in a single following method may also be changed. Thus, in this case, the average headway of the normal mixed single lane is calculated as follows:
Figure BDA0003106404560000073
s5, obtaining the traffic capacity of the special lane of the intelligent internet vehicle CAV with the ratio of the intelligent internet vehicle CAV being 100% by using the CACC model and based on the vehicle headway distribution matrix of the vehicle on the special lane;
cooperative Adaptive Cruise Control (CACC) is generally used as a model of an intelligent internet vehicle (CAV), the CACC model is used, the vehicle speed of a vehicle on a special lane is considered, the distribution of the distance between the vehicle heads which dynamically changes along with the speed is calculated, and the traffic capacity C of the special lane of the intelligent internet vehicle (CAV) with the ratio of the intelligent internet vehicle (CAV) being 100% can be obtainedMLThe method specifically comprises the following steps:
the CACC model structure is as follows:
Figure BDA0003106404560000088
wherein the content of the first and second substances,
Figure BDA0003106404560000089
respectively representing the acceleration of the vehicles n and n-1 at the time t; x is the number ofn(t)、xn-1(t) represents the displacement of the vehicles n, n-1 at time t, respectively; l is the vehicle length; k is a radical of1、k2And k3As coefficients, take k respectively1＝1.0,k2＝0.2,k3The value of 3.0 can also be set according to actual conditions; s0A safe parking space; t is tgThe desired headway distance.
S51 considers the headway distance dynamically changing with speed as:
Figure BDA0003106404560000081
wherein h isd(t) is the distance between the car heads; v. of0Indicating the current vehicle speed.
S52, calculating the traffic capacity of the intelligent internet vehicle CAV special lane with the ratio of the intelligent internet vehicle CAV being 100%:
Figure BDA0003106404560000082
s6 obtaining traffic capacity C of CAV special lane of intelligent internet vehicle according to the vehicle headway distribution matrix or average vehicle headway of common mixed single lane obtained in step S4, the vehicle distribution situation and the probability of each situation obtained in step S3, and step S5MLObtaining the permeability of the common mixed single lane under the multi-lane scene
Figure BDA0003106404560000083
And traffic capacity CmixThe method specifically comprises the following steps:
s61 calculating permeability of common mixed single lane under multi-lane scene
Figure BDA0003106404560000084
Figure BDA0003106404560000085
S62 calculating the traffic capacity C of the common mixed single lane according to the following formulamix：
Figure BDA0003106404560000086
In the above expression: lMLIndicating the number of pieces set as exclusive lanes among all lanes; qinputIs the input traffic flow;
Figure BDA0003106404560000087
the mean headway in different following situations is indicated.
S7 passing ability C of the ordinary mixed single lane obtained according to the step S5)mixAnd S6) obtaining the traffic capacity C of the CAV special lane of the intelligent internet vehicleMLCalculating the integral traffic capacity Q under the multi-lane scene after the CAV special lane of the intelligent internet vehicle is openedtotal。
Qtotal＝lML·CML+(L-lML)Cmix
Wherein L represents the total number of roads; lMLIndicating the number of lanes in which the exclusive lane is set among all lanes.
As shown in fig. 5, an embodiment of the present application provides a traffic capacity estimation device considering a dedicated lane of a smart internet vehicle CAV, including:
a traffic demand acquisition module for acquiring the whole traffic demand Q of the road at the current momentinput；
The permeability calculation module is used for calculating the proportion of the intelligent network connection CAV and the HDV for driving by common people based on the vehicle distribution conditions of different types of vehicles in the current traffic flow to obtain the permeability of the intelligent network connection CAV and the permeability of the HDV for driving by common people;
the model building module is used for modeling the mixed traffic flow by adopting a discrete Markov model according to the vehicle distribution conditions of different types of vehicles;
the vehicle head interval acquisition module is used for acquiring a vehicle head interval distribution matrix in a single lane scene;
the traffic capacity calculation module is used for obtaining the traffic capacity of the intelligent internet vehicle CAV special lane with the intelligent internet vehicle CAV accounting for 100% by using a CACC model and based on a vehicle head spacing distribution matrix of vehicles on the special lane;
the common mixed single lane traffic capacity calculation module is used for obtaining the permeability of the common mixed single lane under the multi-lane scene according to the vehicle head interval distribution matrix or the average vehicle head interval of the common mixed single lane, the vehicle distribution condition, the probability of each condition and the traffic capacity of the intelligent internet CAV special lane
Figure BDA0003106404560000091
And traffic capacity Cmix；
The integral traffic capacity calculation module under the multi-lane scene is used for calculating the traffic capacity C according to the common mixed single lanemixThe traffic capacity of the CAV special lane of the intelligent internet vehicle is calculated, and the integral traffic capacity Q under the multi-lane scene after the CAV special lane of the intelligent internet vehicle is opened is calculatedtotal。
It should be noted that the explanation of the embodiment of the method in the foregoing embodiments of fig. 1 to 4 also applies to the apparatus proposed in this embodiment, and the implementation principle thereof is similar and will not be described herein again.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present application, "a plurality" means two or more unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.

Claims (9)

1. A traffic capacity estimation method considering CAV (vehicle Access control) special lanes of an intelligent internet vehicle is characterized by comprising the following steps:
obtaining the current time and the whole road traffic demand Qinput；
Calculating the proportion of the intelligent network-connected CAV and the HDV for driving by common people based on the vehicle distribution conditions of different types of vehicles in the current traffic flow to obtain the permeability of the intelligent network-connected CAV and the permeability of the HDV for driving by common people;
according to the vehicle distribution conditions of different types of vehicles, a discrete Markov model is adopted to model the mixed traffic flow;
acquiring a vehicle head spacing distribution matrix in a single lane scene;
calculating the dynamic vehicle head distance changing along with the speed by using a CACC model and based on the vehicle speed of the vehicle on the special lane to obtain a vehicle head distance distribution matrix on the special lane, thereby obtaining the traffic capacity C of the intelligent network connection CAV special lane with the intelligent network connection CAV accounting for 100 percentML；
According to the vehicle-head spacing distribution matrix or average of common mixed single lanesHead distance, vehicle distribution condition, probability of each condition and traffic capacity C of intelligent internet vehicle CAV (vehicle access control) special laneMLObtaining the permeability of the common mixed single lane under the multi-lane scene
Figure FDA0003106404550000013
And traffic capacity Cmix；
According to the traffic capacity C of the common mixed single lanemixTraffic capacity C of CAV (vehicle Access control) special lane of intelligent internet vehicleMLCalculating the integral traffic capacity Q under the multi-lane scene after the CAV special lane of the intelligent internet vehicle is openedtotal。
2. The traffic capacity estimation method considering the CAV (lane specific for intelligent networked vehicle) according to claim 1, wherein the permeability P of the CAV is1：
Figure FDA0003106404550000011
Wherein: a. thenIs the equation of state of the vehicle, AnE {0,1}, when the vehicle is connected with the intelligent network, An1 is ═ 1; when the vehicle is driven by ordinary people, An0; n is the number of vehicles, including intelligent networked CAV and HDV for driving by ordinary people;
permeability P of HDV for driving by ordinary people0：
Figure FDA0003106404550000012
3. The traffic capacity estimation method considering the CAV (vehicle Access vehicle) special lane of the intelligent internet vehicle according to claim 2, wherein the modeling of the mixed traffic flow by adopting the discrete Markov model according to the vehicle distribution of different types of vehicles comprises the following steps:
equation of state A of the vehiclenAs the state variable of the nth step, the state space is:
S＝{s1,...,sn}＝{1,0}
in the case of a single lane, the initial state of the Markov chain is π and the transition matrix is A:
π＝[P1,P0]
Figure FDA0003106404550000021
Figure FDA0003106404550000022
wherein: a isijRepresenting the transition matrix in different following modes; si、sjBelongs to a state space, and the value of the state space represents the types of a vehicle and a front vehicle;
calculating the transfer probability under different permeabilities and queue strengths according to the vehicle distribution conditions of different types of vehicles:
when i is 1 and j is 0,
Figure FDA0003106404550000023
(ii) when i is 1 and j is 1,
a11(P1,O)＝1-a10(P1,O),
(iii) when i is 0, j is 1,
Figure FDA0003106404550000024
when i is 0 and j is 0,
a00(P1,O)＝1-a01(P1,O).
and O is the queue intensity of the mixed fleet under different distribution conditions.
4. The traffic capacity estimation method considering the CAV (vehicle Access Voltage) special lane of the intelligent internet vehicle as claimed in claim 2, wherein the vehicle headway distribution matrix T is as follows:
T＝[t11,t10,t00,t01]
wherein: t is t11The front vehicle is an intelligent internet vehicle CAV, and the rear vehicle is the distance between the heads of the intelligent internet vehicle CAV; t is t10The front vehicle is an intelligent internet vehicle CAV, and the rear vehicle is the head spacing of an HDV driven by a common person; t is t00The front vehicle is a vehicle head interval of HDV driven by common people, and the rear vehicle is also a vehicle head interval of HDV driven by common people; t is t01The front vehicle is HDV driven by ordinary people, and the rear vehicle is the distance between the heads of CAVs of the intelligent Internet vehicles.
5. The traffic capacity estimation method considering the CAV (vehicle Access Voltage) special lane of the intelligent networked vehicle according to claim 2, wherein the average headway distance of the common mixed single lane is as follows:
Figure FDA0003106404550000031
6. the traffic capacity estimation method considering the CAV (vehicle-to-vehicle) special lane of the intelligent network connection vehicle as claimed in claim 3, wherein the traffic capacity C of the CAV special lane of the intelligent network connection vehicle with the CACC model of 100% is obtained based on the head-to-head distance distribution matrix of the vehicle on the special laneMLThe method comprises the following steps:
obtaining a CACC model, wherein the CACC model is as follows:
Figure FDA0003106404550000032
wherein the content of the first and second substances,
Figure FDA0003106404550000033
respectively representing the acceleration of the vehicles n and n-1 at the time t; x is the number ofn(t)、xn-1(t) represents the displacement of the vehicles n, n-1 at time t, respectively; l is the vehicle length; k is a radical of1、k2And k3Is a coefficient; s0A safe parking space; t is tgTo a desired headway, vn(t)、vn-1(t) represents the speed of the vehicles n, n-1 at time t, respectively;
the distance between the car heads which dynamically changes along with the speed is obtained as follows:
Figure FDA0003106404550000034
wherein h isd(t) is the distance between the car heads; v. of0Representing a current vehicle speed;
calculating the traffic capacity C of the CAV special lane of the intelligent network connection vehicle with the CAV of 100 percentML：
Figure FDA0003106404550000035
7. The traffic capacity estimation method considering CAV (vehicle Access vehicle) special lanes for intelligent networked vehicles according to claim 6, wherein the permeability of a common mixed single lane in the multi-lane scene is the permeability of a common mixed single lane
Figure FDA0003106404550000036
Comprises the following steps:
Figure FDA0003106404550000037
the traffic capacity CmixComprises the following steps:
Figure FDA0003106404550000038
wherein：lMLIndicating the number of lanes set as exclusive lanes among all lanes;
Figure FDA0003106404550000039
the mean headway in different following situations is indicated.
8. The traffic capacity estimation method considering CAV (vehicle Access video) special lanes for intelligent networked vehicles according to claim 7, wherein the overall traffic capacity Q in the multi-lane scenetotalComprises the following steps:
Qtotal＝lML·CML+(L-lML)Cmix
wherein L represents the total number of roads; lMLIndicating the number of lanes in which the exclusive lane is set among all lanes.
9. A traffic capacity estimation device considering an intelligent Internet vehicle CAV exclusive lane, characterized by comprising:
a traffic demand acquisition module for acquiring the whole traffic demand Q of the road at the current momentinput；
The permeability calculation module is used for calculating the proportion of the intelligent network connection CAV and the HDV for driving by common people based on the vehicle distribution conditions of different types of vehicles in the current traffic flow to obtain the permeability of the intelligent network connection CAV and the permeability of the HDV for driving by common people;
the model building module is used for modeling the mixed traffic flow by adopting a discrete Markov model according to the vehicle distribution conditions of different types of vehicles;
the vehicle head interval acquisition module is used for acquiring a vehicle head interval distribution matrix in a single lane scene;
the traffic capacity calculation module of the CAV special lane of the intelligent internet vehicle is used for calculating the dynamic head distance changing along with the speed by using the CACC model and the CACC model based on the vehicle speed of the vehicle on the special lane to obtain a head distance distribution matrix on the special lane, so that the distance of the intelligent internet vehicle CAV special vehicle with the CAV of the intelligent internet vehicle accounting for 100 percent is obtainedTraffic capacity of a road CML；
A common mixed single lane traffic capacity calculation module used for calculating the traffic capacity C of the intelligent network connection CAV special lane according to the vehicle head distance distribution matrix or the average vehicle head distance of the common mixed single lane, the vehicle distribution condition and the probability of each conditionMLObtaining the permeability of the common mixed single lane under the multi-lane scene
Figure FDA0003106404550000041
And traffic capacity Cmix；
The integral traffic capacity calculation module under the multi-lane scene is used for calculating the traffic capacity C according to the common mixed single lanemixTraffic capacity C of CAV (vehicle Access control) special lane of intelligent internet vehicleMLCalculating the integral traffic capacity Q under the multi-lane scene after the CAV special lane of the intelligent internet vehicle is openedtotal。
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114613144A (en) * 2022-04-07 2022-06-10 重庆大学 Method for depicting hybrid vehicle group motion evolution law based on Embedding-CNN
CN115148016A (en) * 2022-06-27 2022-10-04 云控智行(上海)汽车科技有限公司 Highway CAV vehicle-specific lane control algorithm and system based on intelligent internet automobile permeability monitoring
CN115188189A (en) * 2022-07-07 2022-10-14 清华大学 Special lane dynamic setting method for supporting automatic driving truck vehicle queue operation
CN116665442A (en) * 2023-05-31 2023-08-29 东南大学 Intelligent networking special lane design method considering mixed flow theoretical traffic capacity

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111586130A (en) * 2020-04-29 2020-08-25 清华大学 Performance evaluation method and device of longitudinal control algorithm in mixed traffic scene
CN112116822A (en) * 2020-09-21 2020-12-22 长沙理工大学 Expressway traffic capacity cooperative regulation and control method based on CAVs mixed traffic flow lane dynamic allocation
CN112614344A (en) * 2020-12-14 2021-04-06 中汽研汽车试验场股份有限公司 Hybrid traffic system efficiency evaluation method for automatic driving automobile participation

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111586130A (en) * 2020-04-29 2020-08-25 清华大学 Performance evaluation method and device of longitudinal control algorithm in mixed traffic scene
CN112116822A (en) * 2020-09-21 2020-12-22 长沙理工大学 Expressway traffic capacity cooperative regulation and control method based on CAVs mixed traffic flow lane dynamic allocation
CN112614344A (en) * 2020-12-14 2021-04-06 中汽研汽车试验场股份有限公司 Hybrid traffic system efficiency evaluation method for automatic driving automobile participation

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
于佳丽: "基于马尔科夫链的自动驾驶-手动驾驶混合通行能力研究", 《万方》 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114613144A (en) * 2022-04-07 2022-06-10 重庆大学 Method for depicting hybrid vehicle group motion evolution law based on Embedding-CNN
CN114613144B (en) * 2022-04-07 2024-04-30 重庆大学 Method for describing motion evolution law of hybrid vehicle group based on Embedding-CNN
CN115148016A (en) * 2022-06-27 2022-10-04 云控智行(上海)汽车科技有限公司 Highway CAV vehicle-specific lane control algorithm and system based on intelligent internet automobile permeability monitoring
CN115188189A (en) * 2022-07-07 2022-10-14 清华大学 Special lane dynamic setting method for supporting automatic driving truck vehicle queue operation
CN116665442A (en) * 2023-05-31 2023-08-29 东南大学 Intelligent networking special lane design method considering mixed flow theoretical traffic capacity
CN116665442B (en) * 2023-05-31 2024-05-10 东南大学 Intelligent networking special lane design method considering mixed flow theoretical traffic capacity

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