CN110992676B - Road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method - Google Patents

Road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method Download PDF

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CN110992676B
CN110992676B CN201910978887.4A CN201910978887A CN110992676B CN 110992676 B CN110992676 B CN 110992676B CN 201910978887 A CN201910978887 A CN 201910978887A CN 110992676 B CN110992676 B CN 110992676B
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automatic driving
driving vehicle
vehicles
alpha
average value
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CN110992676A (en
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马万经
林启恒
赫子亮
王玲
俞春辉
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Tongji University
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/22—Platooning, i.e. convoy of communicating vehicles
    • 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
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles

Abstract

The invention relates to a road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method, which comprises the following steps: obtaining a saturated headway average value and a layout type: obtaining the number of the networked automatic driving vehicles in the research range; obtaining the number of queues formed by the networked automatic driving vehicles; obtaining a mixed saturated traffic flow headway average value in a specific arrangement form; calculating the probability of a specific arrangement form through a probability mass function; obtaining the average value of the locomotive time intervals of the final mixed saturated traffic flow; obtaining road traffic capacity
Figure DDA0002234546420000011
And the equivalent coefficient PCE of the networked automatic driving vehicle. Compared with the prior art, the method effectively simplifies the calculation, and simultaneously makes up the weakness that the networked automatic driving vehicle formation strategy is generally ignored in the current research.

Description

Road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method
Technical Field
The invention relates to the field of road traffic planning and management oriented to the internet automatic driving technology, in particular to a road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method.
Background
The internet automatic driving vehicle can fundamentally improve the road traffic capacity, the stability of traffic flow and the traffic safety. The internet automatic driving vehicle obtains running state information such as real-time speed, position and the like of a plurality of internet automatic driving vehicles in front through vehicle-to-vehicle (V2V) communication or a vehicle-road cooperative system. A plurality of networked automatic driving vehicles can form a motorcade by sharing the information in real time, and the significantly lower time interval of the vehicle head compared with the time interval between human driving vehicles is maintained inside the motorcade, so that the road traffic capacity is improved. When the networked automatic driving vehicles form formation, different formation strategies can be adopted to form networked automatic driving vehicle fleets with different lengths and positions, so that the traffic capacity, the fuel economy and the traffic safety are further improved. On the other hand, the popularization of the internet automatic driving technology is still in a preliminary stage, the permeability of the internet automatic driving vehicle in a road network is gradually increased, and a novel mixed traffic flow consisting of the internet automatic driving vehicle and human driving vehicles exists for a long time. Therefore, the influence of the internet automatic driving technology on the road traffic capacity needs to be quantified by considering the permeability of the internet automatic driving vehicle and the formation strategy under the condition of a novel mixed traffic flow. However, existing research at home and abroad attempting to quantify this effect has generally ignored the effect of the formation strategy of networked autonomous vehicles. In addition, because the setting of the behaviors of the networked automatic driving vehicle and the human driving vehicle in each study is different, the calculation result of the traffic capacity improvement degree is not consistent, and the calculation result is not consistent with the running state of the actual novel mixed traffic flow.
The vehicle model equivalent coefficient is an important tool for researching mixed traffic flow, and the function of the vehicle model equivalent coefficient is to convert the mixed traffic flow into the traffic flow of a standard vehicle (usually a passenger car), so that the mixed traffic flow with different compositions has comparability. At present, no matter in theoretical research or engineering practice, the target vehicle types of the equivalence coefficient are mainly concentrated on large-sized vehicles such as trucks and buses, and small-sized vehicles such as non-motor vehicles and motorcycles are considered, so that the novel mixed traffic flow which is served for processing the traditional mixed traffic flow and the novel mixed traffic flow which introduces the internet automatic driving technology cannot be processed. Only Bujanovic and Lochrane in the United states study the equivalence factor of networked automatic driving vehicles, but the study also does not relate to the formation strategy of the networked automatic driving vehicles, and only limits the fleet length of the networked automatic driving vehicles according to the communication range of V2V.
The problems existing at present are as follows: 1. neglecting a formation strategy of the networked automatic driving vehicles when the road traffic capacity of the mixed traffic flow is quantified; 2. when the equivalent coefficient of the networked automatic driving vehicle is obtained, the formation strategy of the networked automatic driving vehicle is not involved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method.
The purpose of the invention can be realized by the following technical scheme:
a road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method comprises the following steps:
step S1: obtaining a saturated headway average value and a layout type;
step S2: obtaining the number N of the networked automatic driving vehicles in the research range based on the permeability eta and the total number N of the vehicles in the research rangeA；
Step S3: based on total number N of vehicles and number N of networked automatic driving vehiclesAObtaining the number alpha of queues formed by the networked automatic driving vehicles;
step S4: obtaining the average value of the headway of the mixed saturated traffic flow in a specific arrangement form based on the number alpha of the queues, the layout types and the average value of the saturated headway
Figure BDA0002234546400000021
Step S5: queue-based number alpha, layout type, total number of vehicles N and number of networked automatic driving vehicles NACalculating the probability Pr ((alpha, J) | (N, N) of a specific arrangement form through a probability mass functionA))；
Step S6: traffic flow headway average based on mixed saturationThe value and the probability are obtained to obtain the average value of the locomotive time distance of the final mixed saturated traffic flow
Figure BDA0002234546400000022
Step S7: vehicle head time interval average value based on final mixed saturated traffic flow
Figure BDA0002234546400000023
Obtaining road traffic capacity
Figure BDA0002234546400000024
And the equivalent coefficient PCE of the networked automatic driving vehicle.
The number N of the networked automatic driving vehiclesAComprises the following steps:
NA＝N·η。
the saturated headway average includes a saturated headway average of a human-driven vehicle following the human-driven vehicle
Figure BDA0002234546400000025
Saturated headway time average value of human-driven vehicle following net-connected automatic driving vehicle
Figure BDA0002234546400000026
Saturated headway average value of network connection automatic driving vehicle and human driving vehicle
Figure BDA0002234546400000027
Saturated headway average value of grid-connected automatic driving vehicle and following grid-connected automatic driving vehicle
Figure BDA0002234546400000031
Said
Figure BDA0002234546400000032
Obtained by counting the saturated traffic flow of pure human-driven vehicles, the method comprises the following steps
Figure BDA0002234546400000033
Automatic driving vehicle through internet connectionObtaining experimental data of a closed or open field, said
Figure BDA0002234546400000034
And
Figure BDA0002234546400000035
the method is obtained by carrying out simulation experiments on the saturated traffic flow of the pure network connection automatic driving vehicle.
The layout types comprise an E layout type that a head queue and a tail queue are human-driven vehicles, the head queue is an automatic internet-connected driving vehicle, the tail queue is an F layout type that the human-driven vehicles are human-driven vehicles, the head queue is a G layout type that the human-driven vehicles are internet-connected automatic driving vehicles, and the head queue and the tail queue are H layout types that the automatic internet-connected driving vehicles are.
The average value of the locomotive time distances of the mixed saturated traffic flow in the specific arrangement form
Figure BDA0002234546400000036
Comprises the following steps:
Figure BDA0002234546400000037
if the formation strategy of arbitrary formation of human-driven vehicles and networked automatic driving vehicles is adopted, the probability Pr ((alpha, J) | (N, N)A) Include Pr ((α, E) | (N, N)A))、Pr((α,F)|(N,NA))、Pr((α,G)|(N,NA) And Pr ((α, H) | (N, N)A))：
Figure BDA0002234546400000038
Figure BDA0002234546400000039
Figure BDA00022345464000000310
Wherein, Pr ((alpha, E) | (N, N)A) Probability of arrangement form of E layout type and number of queues of alpha, Pr ((alpha, F) | (N, N)A) Probability of arrangement form of F layout type and number of queues of alpha, Pr ((alpha, G) | (N, N)A) Probability of arrangement form of G layout type and number of queues of alpha, Pr ((alpha, H) | (N, N)A) Probability of an arrangement form of H layout type and the number of queues is α;
if a formation strategy of fixing the length of the queue formed by the networked automatic driving vehicles is adopted, and the length of the queue is limited to lambda0The probability Pr ((alpha, J) | (N, N)A) ) is:
Figure BDA0002234546400000041
wherein J is a layout type,
Figure BDA0002234546400000042
Included
Figure BDA0002234546400000043
and
Figure BDA0002234546400000044
Figure BDA0002234546400000045
Figure BDA0002234546400000046
Figure BDA0002234546400000047
Figure BDA0002234546400000048
comprises the following steps:
Figure BDA0002234546400000049
the average time headway value of the final mixed saturated traffic flow
Figure BDA00022345464000000410
Comprises the following steps:
Figure BDA00022345464000000411
the road traffic capacity
Figure BDA00022345464000000412
Comprises the following steps:
Figure BDA00022345464000000413
the equivalent coefficient PCE of the networked automatic driving vehicle is as follows:
Figure BDA0002234546400000051
compared with the prior art, the invention has the following advantages:
(1) the saturated headway average value obtained in the saturated traffic flow statistics, closed or open field experiments and simulation experiments of the pure human-driven vehicles is used as model input, the actual running state of the novel mixed traffic flow is effectively reflected, the method is suitable for the running environment of the actual traffic flow, the data obtaining method has operability, the reliability of the estimation result is high, and the estimation method is convenient to popularize and apply.
(2) According to a queue formation strategy adopted by the networked automatic driving vehicle (including a formation strategy adopting human driving vehicles and any formation of the networked automatic driving vehicle and a formation strategy adopting the length of a queue formed by fixed networked automatic driving vehicles), a probability mass function is adopted to reflect the influence of the formation strategy on the time-distance average value of the locomotive of the final mixed saturated traffic flow, and modeling is directly carried out from the result of the formation strategy without relating to a specific algorithm for forming the formation, so that the calculation is effectively simplified, and meanwhile, the weakness that the networked automatic driving vehicle formation strategy is generally ignored in the current research is overcome.
(3) The obtained road traffic capacity and the equivalent coefficient of the internet automatic driving vehicle can be used for guiding the traffic planning and management practice of the internet automatic driving technology facing the internet automatic driving technology, such as the setting of special roads of the internet automatic driving vehicle, the setting of newly-built or reconstructed road vehicle roads and the like, the practical requirements are met, the popularization and application of the internet automatic driving technology are promoted, and the operation efficiency and the safety of the national public road network and the urban road network are further improved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic view of the headway category of the present invention;
fig. 3 is a schematic diagram of the layout type of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
The embodiment provides a road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method, which comprises the following steps:
step S1: obtaining a saturated headway average and a layout type, the saturated headway average of a human-driven vehicle following the human-driven vehicle
Figure BDA0002234546400000061
Saturated headway time average value of human-driven vehicle following net-connected automatic driving vehicle
Figure BDA0002234546400000062
Saturated locomotive of network connection automatic driving vehicle following human driving vehicleMean value of time distance
Figure BDA0002234546400000063
Saturated headway average value of grid-connected automatic driving vehicle and following grid-connected automatic driving vehicle
Figure BDA0002234546400000064
Step S2: obtaining the number N of the networked automatic driving vehicles in the research range based on the permeability eta and the total number N of the vehicles in the research rangeA；
Step S3: based on total number N of vehicles and number N of networked automatic driving vehiclesAObtaining the number alpha of queues formed by the networked automatic driving vehicles;
step S4: based on the number of queues alpha, the layout type,
Figure BDA0002234546400000065
And
Figure BDA0002234546400000066
obtaining the average value of the locomotive time distances of the mixed saturated traffic flow in a specific arrangement form
Figure BDA0002234546400000067
Step S5: queue-based number alpha, layout type, total number of vehicles N and number of networked automatic driving vehicles NACalculating the probability Pr ((alpha, J) | (N, N) of a specific arrangement form through a probability mass functionA))；
Step S6: obtaining the final average value of the headway of the mixed saturated traffic flow based on the average value and the probability of the headway of the mixed saturated traffic flow
Figure BDA0002234546400000068
Step S7: vehicle head time interval average value based on final mixed saturated traffic flow
Figure BDA0002234546400000069
Obtaining road traffic capacity
Figure BDA00022345464000000610
And the equivalent coefficient PCE of the networked automatic driving vehicle.
Specifically, the method comprises the following steps:
in step S1
Figure BDA00022345464000000611
Obtained by counting the saturated traffic flow of pure human-driven vehicles,
Figure BDA00022345464000000612
the experimental data of the closed or open site of the automatic driving vehicle connected with the internet is obtained, and the assumption can also be made
Figure BDA00022345464000000613
And
Figure BDA00022345464000000614
the phase of the two phases is equal to each other,
Figure BDA00022345464000000615
and
Figure BDA00022345464000000616
the method is obtained by carrying out simulation experiments on the saturated traffic flow of the pure network connection automatic driving vehicle.
The layout types comprise four types, namely an E layout type that a head queue and a tail queue are human-driven vehicles respectively, an F layout type that the head queue is an internet automatic driving vehicle and the tail queue is a human-driven vehicle, a G layout type that the head queue is a human-driven vehicle and the tail queue is an internet automatic driving vehicle, and an H layout type that the head queue and the tail queue are internet automatic driving vehicles respectively. If the traffic flow within the scope of the study has only one queue for a particular vehicle type, then the queue is considered to be both a head queue and a tail queue.
The number N of networked automatic driving vehicles in step S2AComprises the following steps:
NA＝N·η
and N is selected so that the change of the road traffic capacity and the equivalent coefficient of the network connection automatic driving vehicle is smaller than a preset threshold value when the research range is continuously enlarged, namely the road traffic capacity and the equivalent coefficient of the network connection automatic driving vehicle estimated in the research range can reflect the real running state of the mixed traffic flow.
The number α of queues in step S3 multiplied by the length of each queue is not greater than N.
Mixed saturated traffic flow headway average value in specific arrangement form in step S4
Figure BDA0002234546400000071
Comprises the following steps:
Figure BDA0002234546400000072
in step S5, if the formation strategy of arbitrary formation of human-driven vehicles and internet automatic driving vehicles is adopted, the probability Pr ((α, J) | (N, N)A) Include Pr ((α, E) | (N, N)A))、Pr((α,F)|(N,NA))、Pr((α,G)|(N,NA) And Pr ((α, H) | (N, N)A))：
Figure BDA0002234546400000073
Figure BDA0002234546400000074
Figure BDA0002234546400000075
Wherein, Pr ((alpha, E) | (N, N)A) Probability of arrangement form of E layout type and number of queues of alpha, Pr ((alpha, F) | (N, N)A) Probability of arrangement form of F layout type and number of queues of alpha, Pr ((alpha, G) | (N, N)A) Probability of arrangement form of G layout type and number of queues of alpha, Pr ((alpha, H) | (N, N)A) Probability of an arrangement form of H layout type and the number of queues is α;
if a formation strategy of fixing the length of the queue formed by the networked automatic driving vehicles is adopted, and the length of the queue is limited to lambda0，λ0The value of (A) is predetermined, and the probability Pr ((alpha, J) | (N, N)A) ) is:
Figure BDA0002234546400000076
wherein J is a layout type,
Figure BDA0002234546400000077
the total number of the vehicles is N, and the number of the networked automatic driving vehicles is NAThen, forming the number of arrangement forms of the layout type J;
Figure BDA0002234546400000081
the total number of the vehicles is N, and the number of the networked automatic driving vehicles is NAThen, a layout type J is formed, and the number of the arrangement forms of the alpha networking automatic driving vehicle queues is included
Figure BDA0002234546400000082
Figure BDA0002234546400000083
And
Figure BDA0002234546400000084
Figure BDA0002234546400000085
Figure BDA0002234546400000086
Figure BDA0002234546400000087
Figure BDA0002234546400000088
comprises the following steps:
Figure BDA0002234546400000089
step S6 is finally carried out on the average value of the headway time of the mixed saturated traffic flow
Figure BDA00022345464000000810
Comprises the following steps:
Figure BDA00022345464000000811
road traffic capability in step S7
Figure BDA00022345464000000812
Comprises the following steps:
Figure BDA00022345464000000813
the equivalent coefficient PCE of the networked automatic driving vehicle is dimensionless and is expressed as follows:
Figure BDA00022345464000000814
the following is set forth with reference to a specific example:
1) as shown in FIG. 2, the saturated traffic flow statistics of the pure human-driven vehicles at the intersection entrance lane is obtained
Figure BDA00022345464000000815
Figure BDA0002234546400000091
Is taken from
Figure BDA0002234546400000092
Equal;
Figure BDA0002234546400000093
and
Figure BDA0002234546400000094
the values are obtained by simulating the saturated traffic flow of the networked automatic driving vehicle through a computer simulation platform, and the specific values are shown in table 1, and the unit is second: the layout types can be classified into E, F, G, H types of 4 types according to the vehicle types to which the head queue and the tail queue belong, as shown in fig. 3.
TABLE 1
Figure BDA0002234546400000095
And
Figure BDA0002234546400000096
specific value of
Figure BDA0002234546400000097
2) In the present example, the permeability η is 0.5, and the road traffic capacity and the grid-connected autonomous driving vehicle equivalence factor at this permeability are calculated.
3) A continuous section is taken as a research range in the mixed traffic flow, as shown in fig. 2, the scale is N equal to 100, and the number of the networked automatic driving vehicles in the research range is NA＝N·η＝50。
4) Within the research range in the step 3), the arrangement forms of the networked automatic driving vehicle queues are shared
Figure BDA0002234546400000098
And (4) seed preparation.
5) And calculating the average value of the locomotive time distances of the mixed saturated traffic flows in different arrangement forms. For example, calculating the average value of the headway time of the mixed saturated traffic flow in the arrangement form of the F layout types of the 2 networked automatic driving vehicle queues, wherein the calculation process is shown as the following formula:
Figure BDA0002234546400000099
the arrangement forms of the other layout types and the queue numbers of different networked automatic driving vehicles adopt the same algorithm, and the calculation results are not listed in a row.
6) In this example, a formation strategy of arbitrary formation of human-driven vehicles and networked automatic driving vehicles is adopted, and then the probability of generating a queue F layout type with 2 networked automatic driving vehicles is calculated as follows:
Figure BDA00022345464000000910
and calculating the probability by adopting the same method in the arrangement modes of other layout types and the queue numbers of different networked automatic driving vehicles, and not counting the calculation results in a row.
7) Calculating the average value and the probability of the headway of the mixed saturated traffic flow according to the steps 5) and 6), and calculating the average value of the headway of the final mixed saturated traffic flow according to the following formula
Figure BDA00022345464000000911
Figure BDA00022345464000000912
8) Road traffic capacity is calculated according to the following formula:
Figure BDA0002234546400000101
calculating the equivalent coefficient of the networked automatic driving vehicle according to the following formula:
Figure BDA0002234546400000102
the result shows that under the formation strategy conditions that the permeability of the networked automatic driving vehicles is 0.5 and human-driven vehicles and networked automatic driving vehicles are randomly formed, the road traffic capacity is 1964veh/(h · ln), the equivalent coefficient of the networked automatic driving vehicles is 0.53, and the networked automatic driving vehicles can be used for converting the mixed traffic flow into the traffic flow of standard vehicles (usually passenger cars), so that the mixed traffic flows with different compositions have comparability.

Claims (4)

1. A road traffic capacity and internet automatic driving vehicle equivalence coefficient estimation method is characterized by comprising the following steps:
step S1: obtaining the average value of the saturated headway and the layout type,
step S2: obtaining the number N of the networked automatic driving vehicles in the research range based on the permeability eta and the total number N of the vehicles in the research rangeA，
Step S3: based on total number N of vehicles and number N of networked automatic driving vehiclesAObtaining the number alpha of queues formed by the networked automatic driving vehicles,
step S4: obtaining the average value of the headway of the mixed saturated traffic flow in a specific arrangement form based on the number alpha of the queues, the layout types and the average value of the saturated headway
Figure FDA0002985391020000011
Step S5: queue-based number alpha, layout type, total number of vehicles N and number of networked automatic driving vehicles NACalculating the probability Pr ((alpha, J) | (N, N) of a specific arrangement form through a probability mass functionA))，
Step S6: obtaining the final average value of the headway of the mixed saturated traffic flow based on the average value and the probability of the headway of the mixed saturated traffic flow
Figure FDA0002985391020000012
Step S7: vehicle head time interval average value based on final mixed saturated traffic flow
Figure FDA0002985391020000013
Obtaining road traffic capacity
Figure FDA0002985391020000014
The equivalent coefficient PCE of the networked automatic driving vehicle;
the saturated headway average includes a saturated headway average of a human-driven vehicle following the human-driven vehicle
Figure FDA0002985391020000015
Saturated headway time average value of human-driven vehicle following net-connected automatic driving vehicle
Figure FDA0002985391020000016
Saturated headway average value of network connection automatic driving vehicle and human driving vehicle
Figure FDA0002985391020000017
Saturated headway average value of grid-connected automatic driving vehicle and following grid-connected automatic driving vehicle
Figure FDA0002985391020000018
Said
Figure FDA0002985391020000019
Obtained by counting the saturated traffic flow of pure human-driven vehicles, the method comprises the following steps
Figure FDA00029853910200000110
Obtaining experimental data of closed or open site of the automatic driving vehicle through internet connection, wherein the experimental data are obtained through the automatic driving vehicle
Figure FDA00029853910200000111
And
Figure FDA00029853910200000112
the method is obtained by carrying out simulation experiments on the saturated traffic flow of the pure network connection automatic driving vehicle;
the layout types comprise a head queue and a tail queue which are E layout types of human-driven vehicles, the head queue is an automatic internet-connected driving vehicle, the tail queue is an F layout type of the human-driven vehicles, the head queue is a G layout type of the human-driven vehicles, the tail queue is a G layout type of the automatic internet-connected driving vehicle, and the head queue and the tail queue are H layout types of the automatic internet-connected driving vehicle;
the average value of the locomotive time distances of the mixed saturated traffic flow in the specific arrangement form
Figure FDA00029853910200000113
Comprises the following steps:
Figure FDA0002985391020000021
if the formation strategy of arbitrary formation of human-driven vehicles and networked automatic driving vehicles is adopted, the probability Pr ((alpha, J) | (N, N)A) Include Pr ((α, E) | (N, N)A))、Pr((α,F)|(N,NA))、Pr((α,G)|(N,NA) And Pr ((α, H) | (N, N)A))：
Figure FDA0002985391020000022
Figure FDA0002985391020000023
Figure FDA0002985391020000024
Wherein, Pr ((alpha, E) | (N, N)A) Probability of arrangement form of E layout type and number of queues of alpha, Pr ((alpha, F) | (N, N)A) Probability of arrangement form of F layout type and number of queues of alpha, Pr ((alpha, G) | (N, N)A) Probability of arrangement form of G layout type and number of queues of alpha, Pr ((alpha, H) | (N, N)A) Probability of an arrangement form of H layout type and the number of queues is α;
if a formation strategy of fixing the length of the queue formed by the networked automatic driving vehicles is adopted, and the length of the queue is limited to lambda0The probability Pr ((alpha, J) | (N, N)A) ) is:
Figure FDA0002985391020000025
wherein J is a layout type,
Figure FDA0002985391020000026
Included
Figure FDA0002985391020000027
and
Figure FDA0002985391020000028
Figure FDA0002985391020000031
Figure FDA0002985391020000032
Figure FDA0002985391020000033
Figure FDA0002985391020000034
comprises the following steps:
Figure FDA0002985391020000035
2. according to claim1 the road traffic capacity and networking automatic driving vehicle equivalent coefficient estimation method is characterized in that the number N of networking automatic driving vehiclesAComprises the following steps:
NA＝N·η。
3. the method of claim 1, wherein the vehicle headway average value of the final mixed saturated traffic flow is obtained by a method of estimating road traffic capacity and internet automatic driving vehicle equivalence coefficient
Figure FDA0002985391020000036
Comprises the following steps:
Figure FDA0002985391020000037
4. the method of claim 3, wherein the road traffic capacity and the equivalent coefficient of the Internet-connected automatic driving vehicle are estimated according to the road traffic capacity and the equivalent coefficient of the Internet-connected automatic driving vehicle
Figure FDA0002985391020000038
Comprises the following steps:
Figure FDA0002985391020000039
the equivalent coefficient PCE of the networked automatic driving vehicle is as follows:
Figure FDA00029853910200000310
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