CN113947911A - Method for determining correction factor and conversion coefficient of network connection automatic automobile traffic capacity - Google Patents

Method for determining correction factor and conversion coefficient of network connection automatic automobile traffic capacity Download PDF

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CN113947911A
CN113947911A CN202111118941.1A CN202111118941A CN113947911A CN 113947911 A CN113947911 A CN 113947911A CN 202111118941 A CN202111118941 A CN 202111118941A CN 113947911 A CN113947911 A CN 113947911A
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江泽浩
刘聪健
陈珂
骆汉宾
丁烈云
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Huazhong University of Science and Technology
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    • 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
    • G08G1/015—Detecting movement of traffic to be counted or controlled with provision for distinguishing between two or more types of vehicles, e.g. between motor-cars and cycles
    • 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

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Abstract

The invention relates to a method for determining a correction factor and a conversion coefficient of network connection automatic automobile traffic capacity, which comprises the following steps: s1 setting three types of motor vehicles including human-driven vehicles, single-vehicle intelligent automatic driving vehicles and intelligent network automatic driving vehicles in the road section traffic flow; s2, counting the road sections of the research object to obtain the occupation ratio of HDV, AV and CAV; s3, calculating an expression of the traffic capacity correction factor of AV and CAV; s4 defining the traffic capacity conversion coefficient of AV and CAV; s5 actually measuring and obtaining the deceleration, the perception reaction time, the vehicle speed and the vehicle length of the HDV, the AV and the CAV; s6 calculating to obtain an expression of the saturated headway time when the rear vehicle follows the front vehicle by taking the HDV, the AV and the CAV as the expressions; s7, calculating to obtain an expected value of the saturated headway when the rear vehicle follows the front vehicle by taking the HDV, the AV and the CAV as the expected values; s8 calculates the traffic capacity conversion coefficient of AV and CAV. On one hand, the invention considers various automatic driving systems and provides a concept of a combined correction factor; on the other hand, the calculation method is simplified, the calculation efficiency is improved, and engineering application is facilitated.

Description

Method for determining correction factor and conversion coefficient of network connection automatic automobile traffic capacity
Technical Field
The invention relates to the technical field of traffic system analysis of road vehicles, in particular to a method for determining a correction factor and a conversion coefficient of network connection automatic automobile traffic capacity under a free flow field scene.
Background
Traffic capacity is one of the core concepts of traffic engineering and is the basic theory for analyzing traffic systems. With the rapid development of technologies such as transportation and automobiles, Autonomous vehicles will gradually blend into the traffic flow of Human Driving Vehicles (HDV), and particularly, Autonomous vehicles can be divided into two categories, namely, Autonomous Vehicle (AV) and intelligent internet Autonomous Vehicle (CAV). Because the highway is free flow and the traffic environment is single compared with the urban road, the popularization of the automatic driving vehicle at present is usually started from the highway.
In heterogeneous traffic flows, in order to enable different types of vehicles to be analyzed under the same standard and have comparability, when the traffic capacity and the service level are analyzed and calculated, the traffic volume of various types of vehicles needs to be converted into a standard vehicle equivalent, namely, a traffic capacity correction factor and a conversion coefficient need to be used. The traffic capacity correction factor and the conversion coefficient are basic parameters for analyzing the traffic capacity of the facility. However, there is no correction factor that can easily convert the traffic capacities of AV and CAV to HDV, and particularly, there is no conversion factor that can simultaneously consider the mixing of vehicles of two automatic driving systems.
Disclosure of Invention
The invention aims to solve the technical problem of providing a simple and convenient method for determining a correction factor and a conversion coefficient of the network connection automatic automobile traffic capacity under the free flow field scene, which can adapt to automatic driving vehicles of different systems.
A method for determining a traffic capacity correction factor and a conversion coefficient of an online automatic automobile is constructed, wherein the online automatic automobile comprises two types of vehicles, namely a single intelligent automatic driving vehicle and an intelligent online automatic driving vehicle, and the acquisition method comprises the following steps:
s1, defining vehicles in the traffic flow as human-driven vehicles, single-vehicle intelligent automatic driving vehicles and intelligent network automatic driving vehicles;
the Human-driven Vehicle is Human Vehicle, HDV for short, the single-Vehicle intelligent automatic driving Vehicle is Autonomous Vehicle, AV for short, and the intelligent networked automatic driving Vehicle is Connected Autonomous Vehicle, CAV for short;
s2, selecting peak time in the road section of the research object, and counting that the occupation rates of HDV, AV and CAV are PHDV、PAV、PCAV；
Figure BDA0003276237430000021
Figure BDA0003276237430000022
Figure BDA0003276237430000023
In the formula, a traffic peak time is selected, a reference section is set on a road section of a research object, and the number of vehicles passing through the reference section in HDV, AV and CAV peak time is VHDV、VAVAnd VCAV。
S3, calculating an expression of the traffic capacity correction factor of the AV and the CAV:
capacity correction factor for AV:
Figure BDA0003276237430000024
traffic capacity correction factor for CAV:
Figure BDA0003276237430000025
joint correction factor for AV and CAV:
Figure BDA0003276237430000026
in the formula, PHDV、PAV、PCAVHDV, AV and CAV occupancy rates; eAV、ECAVRespectively the traffic capacity conversion coefficients of AV and CAV; f. ofAV、fCAVPass capacity correction factors of AV and CAV respectively; f. ofAV,CAVIs a joint correction factor for AV and CAV.
S4, defining a traffic capacity conversion coefficient of AV and CAV;
traffic capacity conversion factor of AV:
Figure BDA0003276237430000027
defining the traffic capacity conversion coefficient of the CAV:
Figure BDA0003276237430000028
in the formula, hHDV、hAV、hCAVHDV, AV and CAV are respectively used as the saturated head time interval when the rear vehicle follows the front vehicle.
S5, actually measuring and obtaining the HDV as the deceleration d of the rear vehicleHDVPerception-reaction time tHDVV running speed vHDVAnd length l of vehicleHDVThe operation index of (1); actual measurement is carried out to obtain AV as deceleration d when a rear vehicle is drivenAVPerception-reaction time tAVV running speed vAVAnd length l of vehicleAVThe operation index of (1); actually measuring and obtaining CAV as deceleration d when rear vehicleCAVPerception-reaction time tCAVV running speed vCAVAnd length l of vehicleCAVThe operation index of (1);
s6, calculating the traffic capacity conversion coefficient of AV and CAV by the following formula;
HDV is as expression of saturated locomotive headway when rear vehicle follows front vehicle
Figure BDA0003276237430000031
AV as expression of saturated headway when following front vehicle by rear vehicle
Figure BDA0003276237430000032
Expression of saturated headway time when CAV is used as following front vehicle of rear vehicle
Figure BDA0003276237430000033
S7, calculating to obtain an expected value of the saturated headway when the rear vehicle follows the front vehicle, wherein the expected value is HDV, AV and CAV;
HDV as the expected value of the saturated headway when the following vehicle follows the preceding vehicle:
Figure BDA0003276237430000034
the AV is taken as an expected value of a saturated headway when the following vehicle follows the preceding vehicle:
Figure BDA0003276237430000035
the CAV is taken as an expected value of the saturated head time distance when the rear vehicle follows the front vehicle:
Figure BDA0003276237430000036
s8, calculating to obtain a traffic capacity conversion coefficient of AV and CAV;
traffic capacity conversion factor of AV:
Figure BDA0003276237430000037
traffic capacity conversion factor of CAV:
Figure BDA0003276237430000038
the method for determining the network connection automatic automobile traffic capacity correction factor and the conversion coefficient has the following beneficial effects:
1. according to the invention, the following vehicle head time distance and the occupancy of HDV, AV and CAV are obtained through observation or calculation, so that the traffic capacity correction factor and the conversion coefficient determining method can be obtained, the calculation method is simplified, the calculation efficiency is improved, and the engineering application is facilitated.
2. In the invention, in the aspect of traffic capacity calculation, the complex combination of three types of vehicles in space is simplified by utilizing knowledge and experience of traffic engineering, and the traffic capacity correction factor and the conversion coefficient determining method can be obtained by only selecting five easily observed parameters of HDV, AV and CAV as deceleration, perception-reaction time, driving speed, vehicle length, occupancy and the like in the rear vehicle.
3. The invention provides a concept of a combined correction factor in consideration of two categories of automatic driving vehicles, namely single-vehicle intelligent automatic driving vehicles and intelligent networked automatic driving vehicles, and has important theoretical significance and engineering value in a simple method for calculating the correction factor and the conversion coefficient of the traffic capacity of the highway lane mixed by the automatic driving vehicles of different systems.
Drawings
FIG. 1 is a schematic diagram of a vehicle following vehicle head time distance in a method for determining a network connection automatic vehicle traffic capacity correction factor and a conversion coefficient under a free flow field scene according to the invention;
FIG. 2 is a schematic diagram of an average headway in a lane in the method for determining the correction factor and the conversion coefficient of the network connection automatic automobile traffic capacity under the free flow field scene;
FIG. 3 is a flow chart of the method for determining the correction factor and the conversion coefficient of the network connection automatic automobile traffic capacity under the free flow field scene.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
As shown in fig. 1-3, in the embodiment of the method for determining the correction factor and the conversion coefficient of the internet automatic vehicle traffic capacity under the free flow field scene, the method comprises the following steps:
and S1, defining vehicles in the traffic flow as Human vehicles (HDV), single intelligent automatic driving vehicles (AV) and intelligent internet automatic driving vehicles (CAV) when carrying out traffic capacity correction and calculating the conversion coefficient.
So-called HDV, i.e. the perception of the external environment by humans and the manipulation of vehicles performing relevant actions; the AV refers to a vehicle which senses the external environment by a vehicle-mounted sensor, operates and executes related actions, but does not communicate with other vehicles and cooperates with the other vehicles; CAV refers to a vehicle which senses the external environment by a vehicle-mounted sensor, operates and executes related actions, and communicates and cooperates with other vehicles.
S2, selecting peak hours in the road section of the research object, and counting that the occupation rates of HDV, AV and CAV are alpha respectivelyHDV、αAV、αCAV。
Wherein, step S2 specifically includes the following steps:
s201: selecting 1 hour of a traffic peak, such as 17: 00-18: 00 of a late peak, as a research period;
s202: setting a reference section on the road section of the object to be researched, and counting the number V of vehicles passing through the reference section within 1 hour according to the vehicle types, namely HDV, AV and CAVHDV、VAVAnd VCAV；
S203: the HDV, AV and CAV occupancy is calculated using the following equations:
Figure BDA0003276237430000051
Figure BDA0003276237430000052
Figure BDA0003276237430000053
in this embodiment, a reference cross section is set up for a certain highway at the late peak, and the number of vehicles passing through the reference cross section in 1 hour is respectively 600, 400 and 200 through HDV, AV and CAV statistics, so the occupancy rates of HDV, AV and CAV are respectively 50.00%, 33.33% and 16.67%.
And S3, calculating the traffic capacity correction factor of the AV and the CAV and the expression of the joint correction factor.
Wherein, step S3 specifically includes the following contents:
calculating the average headway in the lane using the following formula
Figure BDA0003276237430000054
Figure BDA0003276237430000055
Defining the correction factor f of the AV by using the following formulaAV：
CM＝fAV·CHV
Defining the correction factor f of the CAV using the following formulaCAV：
CM＝fCAV·CHV
Defining a joint correction factor f for AV and CAV using the following formulaAV,CAV：
CM＝fAV,CAV·CHV
The traffic capacity correction factor of the AV is calculated by the following formula:
Figure BDA0003276237430000056
calculating the traffic capacity correction factor of the CAV by using the following formula:
Figure BDA0003276237430000061
calculating a joint correction factor of AV and CAV by using the following formula:
Figure BDA0003276237430000062
and S4, defining the traffic capacity conversion coefficient of the AV and the CAV.
Wherein, step S4 specifically includes the following contents:
the AV traffic capacity conversion factor is defined using the following equation:
Figure BDA0003276237430000063
the traffic capacity conversion factor for CAV is defined using the following equation:
Figure BDA0003276237430000064
and S5, actually measuring and acquiring the deceleration, the perceived reaction time, the vehicle speed and the vehicle length of the HDV, the AV and the CAV.
Wherein, step S5 specifically includes the following steps:
s501, hovering the unmanned aerial vehicle at a height of 150m above the road section, and shooting a live-action video running on the road section in peak hours;
s502, establishing a plane rectangular coordinate system in the road section, and dispersing the road section space into grid points of 1m multiplied by 1 m;
s503, recording the position of the vehicle in the plane rectangular coordinate system by taking 1S as a time step;
s504, according to the parameter definition, calculating to obtainHDV as deceleration d when rear vehicleHDVPerception-reaction time tHDVV running speed vHDVAnd length l of vehicleHDVWaiting for operation indexes;
s505, calculating AV as deceleration d when the vehicle is at the rear according to the parameter definitionAVPerception-reaction time tAVV running speed vAVAnd length l of vehicleAVWaiting for operation indexes;
s506, calculating to obtain CAV as deceleration d when the vehicle is at the rear according to parameter definitionCAVPerception-reaction time tCAVV running speed vCACAnd length l of vehicleCAVWaiting for operation indexes;
and S6, substituting the parameters obtained by actual measurement or calculation into an expression for obtaining the saturated headway when the HDV, the AV and the CAV are used as the following front vehicles.
Wherein, step S6 specifically includes the following contents:
calculating the saturated headway of the HDV when the rear vehicle follows the front vehicle by using the following formula:
Figure BDA0003276237430000065
calculating the AV as the saturated headway when the following vehicle follows the front vehicle by using the following formula:
Figure BDA0003276237430000071
calculating the saturated headway of the CAV when the rear vehicle follows the front vehicle by using the following formula:
Figure BDA0003276237430000072
and S7, calculating to obtain the HDV, the AV and the CAV as expected values of the saturated headway when the rear vehicle follows the front vehicle.
Wherein, step S7 specifically includes the following contents:
calculating the expected value of the saturation headway when the rear vehicle follows the front vehicle by using the following formula:
Figure BDA0003276237430000073
calculating the AV as the expected value of the saturated headway when the following vehicle follows the front vehicle by using the following formula:
Figure BDA0003276237430000074
calculating the CAV as the expected value of the saturated headway when the rear vehicle follows the front vehicle by using the following formula:
Figure BDA0003276237430000075
and S8, calculating the traffic capacity conversion coefficient of the AV and the CAV.
Wherein, step S8 specifically includes the following contents:
the traffic capacity conversion factor of the AV is calculated by the following formula:
Figure BDA0003276237430000076
calculating the traffic capacity conversion coefficient of the CAV by the following formula:
Figure BDA0003276237430000077
in this embodiment, the obtained parameters by actual measurement or calculation are substituted into the calculated traffic capacity conversion coefficients of AV and CAV, which are 0.62 and 0.35, respectively. Substituting the coefficients into a formula, obtaining the expressions of the traffic capacity correction factor and the joint correction factor of the AV and the CAV as follows:
Figure BDA0003276237430000078
Figure BDA0003276237430000079
Figure BDA00032762374300000710
while the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (2)

1. A method for determining a network connection automatic automobile traffic capacity correction factor and a conversion coefficient is characterized in that the network connection automatic automobile comprises two types of vehicles, namely a single intelligent automatic driving vehicle and an intelligent network connection automatic driving vehicle, and the obtaining method comprises the following steps:
s1, defining vehicles in the traffic flow into three categories of human-driven vehicles, single-vehicle intelligent automatic driving vehicles, intelligent network connection automatic driving vehicles and the like;
the Human-driven Vehicle is Human Vehicle, HDV for short, the single-Vehicle intelligent automatic driving Vehicle is Autonomous Vehicle, AV for short, and the intelligent networked automatic driving Vehicle is Connected Autonomous Vehicle, CAV for short;
s2, selecting peak time in the road section of the research object, and counting that the occupation rates of HDV, AV and CAV are PHDV、PAV、PCAV；
S3, calculating an expression of the traffic capacity correction factor of the AV and the CAV:
capacity correction factor for AV:
Figure FDA0003276237420000011
traffic capacity correction factor for CAV:
Figure FDA0003276237420000012
joint correction factor for AV and CAV:
Figure FDA0003276237420000013
in the formula, PHDV、PAV、PCAVHDV, AV and CAV occupancy rates; eAV、ECAVRespectively the traffic capacity conversion coefficients of AV and CAV; f. ofAV、fCAVPass capacity correction factors of AV and CAV respectively; f. ofAV,CAVIs a joint correction factor for AV and CAV.
S4, defining a traffic capacity conversion coefficient of AV and CAV;
traffic capacity conversion factor of AV:
Figure FDA0003276237420000014
defining the traffic capacity conversion coefficient of the CAV:
Figure FDA0003276237420000015
in the formula, hHDV、hAV、hCAVHDV, AV and CAV are respectively used as the saturated head time interval when the rear vehicle follows the front vehicle.
S5, actually measuring and obtaining the HDV as the deceleration d of the rear vehicleHDVPerception-reaction time tHDVV running speed vHDVAnd length l of vehicleHDVThe operation index of (1); actual measurement to obtain AV asDeceleration d at rear vehicleAVPerception-reaction time tAVV running speed vAVAnd length l of vehicleAVThe operation index of (1); actually measuring and obtaining CAV as deceleration d when rear vehicleCAVPerception-reaction time tCAVV running speed vCAVAnd length l of vehicleCAVThe operation index of (1);
s6, calculating to obtain an expression of the saturated headway time when the rear vehicle follows the front vehicle by taking the HDV, the AV and the CAV as the saturated headway time when the rear vehicle follows the front vehicle;
HDV is as expression of saturated locomotive headway when rear vehicle follows front vehicle
Figure FDA0003276237420000021
AV as expression of saturated headway when following front vehicle by rear vehicle
Figure FDA0003276237420000022
Expression of saturated headway time when CAV is used as following front vehicle of rear vehicle
Figure FDA0003276237420000023
S7, calculating to obtain an expected value of the saturated headway when the rear vehicle follows the front vehicle, wherein the expected value is HDV, AV and CAV;
HDV as the expected value of the saturated headway when the following vehicle follows the preceding vehicle:
Figure FDA0003276237420000024
the AV is taken as an expected value of a saturated headway when the following vehicle follows the preceding vehicle:
Figure FDA0003276237420000025
the CAV is taken as an expected value of the saturated head time distance when the rear vehicle follows the front vehicle:
Figure FDA0003276237420000026
s8, calculating to obtain a traffic capacity conversion coefficient of AV and CAV;
traffic capacity conversion factor of AV:
Figure FDA0003276237420000027
traffic capacity conversion factor of CAV:
Figure FDA0003276237420000028
2. the method for determining the internet automatic vehicle traffic capacity correction factor and the conversion factor according to claim 1, wherein in step S2, the occupation ratios of HDV, AV and CAV are:
Figure FDA0003276237420000031
Figure FDA0003276237420000032
Figure FDA0003276237420000033
selecting traffic peak time, setting a reference section on the road section of the object to be researched, and passing through the reference section in HDV, AV and CAV peak timeRespectively is VHDV、VAVAnd VCAV。
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