CN113222382B - Method for determining passing capacity of heterogeneous traffic flow lane change influence road sections in Internet of vehicles environment - Google Patents

Method for determining passing capacity of heterogeneous traffic flow lane change influence road sections in Internet of vehicles environment Download PDF

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CN113222382B
CN113222382B CN202110481175.9A CN202110481175A CN113222382B CN 113222382 B CN113222382 B CN 113222382B CN 202110481175 A CN202110481175 A CN 202110481175A CN 113222382 B CN113222382 B CN 113222382B
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李锐
侍威
李诗洁
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Abstract

The invention discloses a method for determining the passing capacity of a road section affected by heterogeneous traffic flow lane changing in an Internet of vehicles environment, which divides the lane changing behavior of the heterogeneous traffic flow road section in the Internet of vehicles environment into an associated lane changing and an independent lane changing according to the following average head distance of various vehicle road sections of heterogeneous traffic flows and lane changing information; respectively calculating the road traffic space change values under the influence of independent lane change and associated lane change of heterogeneous traffic flow road sections in the Internet of vehicles environment; and calculating the passing capacity change value of the road section influenced by the heterogeneous traffic flow switching in the Internet of vehicles according to the road passing space change value under the influence of the independent switching and the associated switching. According to the invention, by quantifying the change value of the road section passing capacity influenced by heterogeneous vehicle flow switching in the Internet of vehicles environment, the running states of various vehicles on the road section are better mastered, and traffic jam is prevented, so that the road traffic running efficiency is improved.

Description

Method for determining passing capacity of heterogeneous traffic flow lane change influence road sections in Internet of vehicles environment
Technical Field
The invention relates to a method for analyzing the influence of vehicle lane changing in an Internet of vehicles environment, in particular to a method for determining the passing capacity of a heterogeneous vehicle flow lane changing influence road section in the Internet of vehicles environment, and belongs to the technical field of intelligent traffic management and control.
Background
With the rapid development of information technology and automobile industry technology, the intelligent traffic technology is emerging completely, the car networking technology realizes information interaction of cars, relieves traffic jam, improves traffic safety, improves traffic operation efficiency, provides a new technical means, and initiates revolutionary changes in management concepts, and the changes change the driving environment of traditional traffic.
However, in the process of completely converting manual driving into automatic driving, a transition period exists, namely, a period of hybrid driving of the manual driving automobile and the automatic driving automobile on a road exists, and the period of hybrid driving exists for a long time, and the traffic flow has some uncertainty.
Therefore, it is necessary to further study the heterogeneous traffic flow lane change behavior in the car networking environment, determine the change value of the various lane change behaviors on the road section passing capacity, and reflect the degree of influence of the vehicle lane change on the road operation characteristics, so as to quantify the change value of the heterogeneous traffic flow lane change influencing the road section passing capacity. The "heterogeneous traffic flow" refers to the traffic flow including manually driven cars, automatically driven cars, manually driven buses and automatically driven buses, and the "heterogeneous" refers to the traffic flow with different vehicle types (cars and buses) and different driving characteristics (manually driven and automatically driven).
Disclosure of Invention
The invention mainly aims to overcome the defects in the prior art, and provides a method for determining the passing capacity of a road section affected by heterogeneous traffic flow channel change in an internet of vehicles environment, so that the problem that the change value of the passing capacity of the road section affected by heterogeneous traffic flow channel change cannot be quantized at present is solved, the change value of the passing capacity of the road section affected by the behavior of associated channel change and independent channel change can be accurately determined, and the change value of the passing capacity of the road section affected by heterogeneous traffic flow channel change is further quantized, so that the traffic running state is better mastered, traffic jam is relieved, and the traffic running efficiency of a road is improved.
In order to achieve the purpose, the invention adopts the technical scheme that: a method for analyzing the passing capacity of a road section affected by heterogeneous vehicle flow switching in a vehicle networking environment comprises the following steps:
dividing the lane changing behavior of the heterogeneous vehicle flow road section in the Internet of vehicles environment into an associated lane changing behavior and an independent lane changing behavior according to the following average head distance and the lane changing information of various vehicle road sections of heterogeneous vehicle flows;
respectively calculating the road traffic space change values under the influence of independent lane change and associated lane change of heterogeneous traffic flow road sections in the Internet of vehicles environment;
and calculating the passing capacity change value of the road section influenced by the heterogeneous traffic flow switching in the Internet of vehicles according to the road passing space change values under the influence of the independent switching and the associated switching.
Further, the following average head distance of each vehicle section of the heterogeneous traffic flow comprises:
heterogeneous traffic stream autonomous car, autonomous bus, manually driven car and manually driven bus road section following average head distance
Figure GDA0003718113480000021
Figure GDA0003718113480000022
Wherein J is the total number of the automatic driving cars, the automatic driving buses, the manual driving cars or the manual driving buses,
Figure GDA0003718113480000023
respectively representing the number of head distances of an automatic driving car, an automatic driving bus, a manual driving car and a manual driving bus in the ith sub-period of the unit period;
Figure GDA0003718113480000024
respectively the head distances of the jth automatic driving car, the automatic driving bus, the manual driving car and the manual driving bus at the starting time of the ith sub-period of the unit period.
Further, the method for dividing the lane changing behavior of the heterogeneous vehicle flow road section in the vehicle networking environment into associated lane changing and independent lane changing according to the following average head distance and the lane changing information of various vehicle road sections of the heterogeneous vehicle flow comprises the following steps:
counting the channel change information of all vehicles in the peak time period, and calculating the time from the channel change vehicle to the kth process lane at the initial time of the channel change of the g-th channel change vehicle to the target lane, and the distance between the two vehicles before and after the channel change vehicle in each lane when the channel change vehicle reaches the target lane
Figure GDA0003718113480000031
Figure GDA0003718113480000032
The distance between the head of the front vehicle and the head of the rear vehicle of the initial lane at the initial time of the lane change in the process of changing the lane of the g-th lane change vehicle;
Figure GDA0003718113480000033
the vehicle-head distance between two vehicles before and after the kth process lane in the changing process of the g-th lane changing vehicle is represented by K, wherein K is 1,2, …, and K represents the total number of the process lanes;
Figure GDA0003718113480000034
the distance between the head of the g-th lane changing vehicle and the head of the two vehicles before and after the target lane in the lane changing process;
calculating the minimum head space required by the g-th lane-changing vehicle in the starting lane, the process lane and the target lane to implement the lane-changing process
Figure GDA0003718113480000035
Figure GDA0003718113480000036
Figure GDA0003718113480000037
The average head-to-head distance required by the g-th lane changing vehicle on the lane changing starting lane;
Figure GDA0003718113480000038
average head space required by a rear vehicle of the g-th lane changing vehicle on a lane changing starting lane;
Figure GDA0003718113480000039
the average head spacing required by the g lane changing vehicle on the lane in the k lane changing process;
Figure GDA00037181134800000310
average head spacing required by a rear vehicle of the g-th lane changing vehicle on a lane in the k-th lane changing process;
Figure GDA00037181134800000311
the average head-to-head distance required by the g-th lane changing vehicle on a lane changing target lane;
Figure GDA0003718113480000041
average head space required by a rear vehicle of the g-th lane changing vehicle on a lane changing target lane;
if the head distance between two vehicles before and after the lane change vehicle in any lane of the starting lane, the process lane and the target lane is smaller than the minimum head distance required by the g-th lane change vehicle in the lane change, and other lane change behaviors exist in the lane change time range of the corresponding lane front and rear vehicles, taking the lane change behavior of the lane change vehicle and the lane change behaviors of the front and rear vehicles in the lane change time range of the vehicle as a primary associated lane change, wherein the primary associated lane change comprises a plurality of times of sub lane changes, and the sub lane change is the lane change behavior of the lane change vehicle and the lane change behaviors of the front and rear vehicles in the lane change time range of the vehicle;
and other lane changing behaviors except the associated lane changing behavior in the road section in the unit time interval belong to independent lane changing.
Further, a road traffic space change value under the influence of independent road changing of heterogeneous traffic flow road sections in the Internet of vehicles environment is calculated, and the method comprises the following steps:
for the first independent lane change of the road section, the starting lane and the target lane are directly influenced, if the lane change vehicle
Figure GDA0003718113480000042
In the process of changing lanes from the k-1 th process lane to the k-th process lane, the following distance between the lane and the workshop behind the k-th process lane
Figure GDA0003718113480000043
If the distance between the road section of the lane changing vehicle and the average locomotive is smaller than the average locomotive distance, the kth process lane is influenced by the independent lane changing, and the independent lane changing is determined to have X influenced lanes;
calculating the average speed of the vehicle in front of the x-th lane influenced by the I-th independent lane change
Figure GDA0003718113480000044
Figure GDA0003718113480000045
In the formula (I), the compound is shown in the specification,
Figure GDA0003718113480000046
respectively representing the instant speed of the front vehicle of the xth lane influenced by the ith independent lane change at the t moment and the moment of leaving the lane,
Figure GDA0003718113480000047
representing the starting moment of changing the lane on the x-th affected lane by the I-th independent lane changing behavior;
calculating the traffic space change value of the x-th lane influenced by the I-th independent lane change
Figure GDA0003718113480000051
Figure GDA0003718113480000052
In the formula (I), the compound is shown in the specification,
Figure GDA0003718113480000053
respectively representing the instant speed of the vehicle behind the xth lane influenced by the ith independent lane change at the t moment and the moment of leaving the lane.
Further, a road traffic space change value under the influence of road switching associated with heterogeneous traffic flow road sections in the Internet of vehicles environment is calculated, and the method comprises the following steps:
if the following distance between the lane changing vehicle and a workshop behind the adjacent lane is smaller than the average head following distance of the road section of the lane changing vehicle in the process of changing the lane from one lane to the adjacent lane in the nth sub-lane in the mth associated lane, the adjacent lane is influenced by the associated lane changing; determining that the related lane change has Y affected lanes;
calculating the average speed of the front vehicle in the y-th lane influenced by the m-th associated lane change
Figure GDA0003718113480000054
Figure GDA0003718113480000055
In the formula (I), the compound is shown in the specification,
Figure GDA0003718113480000056
respectively representing the instant speed of the front vehicle of the y-th lane influenced by the m-th associated lane at the t-th time and the time of leaving the lane,
Figure GDA0003718113480000057
representing the starting moment of changing the lane on the y-th affected lane by the m-th associated lane changing behavior;
calculating the traffic space change value of the y-th lane influenced by the m-th time of associated lane change
Figure GDA0003718113480000058
Figure GDA0003718113480000059
In the formula (I), the compound is shown in the specification,
Figure GDA0003718113480000061
respectively representing the instant speed of the vehicle behind the y-th lane influenced by the m-th associated lane change at the t-th time and the time of leaving the lane.
Further, the calculating a value of the change of the passing capacity of the road section affected by the heterogeneous vehicle flow switching in the vehicle networking environment according to the value of the spatial change of the road passage under the influence of the independent switching and the associated switching comprises:
calculating the traffic space change value delta D under the influence of totally L times of independent lane change of the road section in unit time interval sc Traffic space change value delta D under influence of total M times of associated lane change rc Further, calculating a total change value delta D of the road traffic space of the road section affected by the road change;
Figure GDA0003718113480000062
Figure GDA0003718113480000063
ΔD＝ΔD sc +ΔD rc
and converting a road traffic space change value under the influence of road section change into a vehicle passing quantity change value according to the distance between the manual/automatic driving car or the manual/automatic driving bus road section and the driving average head, and obtaining the road section passing capacity in unit time period by adding the actual passing capacity of the road section.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the aforementioned vehicle networking environment heterogeneous vehicle interchange impact road segment throughput capability analysis methods.
A computing device, characterized by: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the aforementioned vehicle networking environment heterogeneous traffic lane change impact segment throughput capability analysis methods.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a method for analyzing the passing capacity of road sections influenced by heterogeneous traffic flow lane changing in an internet of vehicles environment, which is characterized in that the acquired traffic flow running characteristic data of the road sections in the internet of vehicles environment are used for determining the following average locomotive distance of 4 types of vehicle road sections of manual/automatic driving cars and manual/automatic driving buses, analyzing the independent lane changing and associated lane changing characteristics, determining the change value of various lane changing behaviors on the passing capacity of the road sections, and reflecting the influence degree of the vehicle lane changing on the road running characteristics, thereby quantifying the change value of the heterogeneous traffic flow lane changing on the passing capacity of the road sections. The method can better master the running state of heterogeneous traffic flow in the car networking environment, improve the running efficiency of road traffic, relieve traffic jam and has very important significance.
Drawings
FIG. 1 is a flow chart in an embodiment of the invention;
FIG. 2 is a schematic illustration of a vehicle section following a vehicle nose separation in an embodiment of the invention;
fig. 3 is a schematic diagram of the distance between the car head and the car head of 4 types of vehicles, namely, a manual/automatic driving car and a manual/automatic driving bus, in a road section in the embodiment of the invention;
FIG. 4 is a schematic diagram of an associated lane change in an embodiment of the present invention;
FIG. 5 is a schematic view of a lead vehicle in an affected lane in accordance with an embodiment of the present invention;
FIG. 6 is a schematic diagram of a vehicle behind a lane change vehicle in an affected lane in an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the accompanying drawings.
Example 1:
as shown in fig. 1, a method for determining a lane change influence of a vehicle in a vehicle-road cooperative environment includes the following steps:
step 1, calculating the following average head distance of various vehicle sections of heterogeneous traffic flows according to collected traffic flow operation characteristic data of the sections under the Internet of vehicles environment;
according to the collected traffic flow running characteristic data of the road section in the Internet of vehicles environment, a data set of the following vehicle head distances of 4 types of vehicles in the unit time interval, namely manual/automatic driving cars and manual/automatic driving buses, on the road section is constructed, the vehicle head distances are the distances from the tail of the vehicle to the tail of the vehicle in front, and as shown in fig. 2, the following average vehicle head distances of various types of vehicles in heterogeneous traffic flows on the road section are calculated;
in particular to a method for preparing a high-performance nano-silver alloy,
1-1) constructing a following vehicle head distance data set of various vehicle sections in a unit time interval;
selecting video data of peak hours (generally, 7 days can be continuously taken) in a historical period of the road section (generally, 7:30-8:30 can be taken), equally dividing the peak hours in the historical data into I sub-periods (generally, one sub-period can be divided every minute, namely, I is 60) every day, and respectively counting the time sections at the starting time of each sub-periodAll the head distances of various vehicles are respectively constructed, and a data set of head distance between the automatically-driven cars, the automatically-driven buses, the manually-driven cars and the manually-driven buses at the starting points of all the sub-periods in a unit period at the time is respectively constructed
Figure GDA0003718113480000081
Figure GDA0003718113480000082
Respectively, the head distances of the jth automatic driving car, the automatic driving bus, the manual driving car and the manual driving bus at the starting time of the ith sub-period of the unit period, as shown in fig. 3;
1-2) calculating the following average head interval of various vehicle sections of heterogeneous traffic flows according to the following head interval data sets of various vehicle sections;
calculating the following average head interval of heterogeneous traffic flows of automatic driving cars, automatic driving buses, manual driving cars and manual driving buses by combining the following head interval data sets of various vehicle sections
Figure GDA0003718113480000091
Figure GDA0003718113480000092
Wherein J is the total number of autonomous cars, autonomous buses, manually driven cars or manually driven buses, e.g. in the formula
Figure GDA0003718113480000093
Where J denotes the total number of autonomous cars,
Figure GDA0003718113480000094
respectively representing the number of the head distances of the automatic driving cars, the automatic driving buses, the manual driving cars and the manual driving buses in the ith sub-period of the unit period.
Step 2, dividing the lane changing behavior of the heterogeneous vehicle flow road section in the Internet of vehicles environment into an associated lane changing and an independent lane changing according to the following average head distance of various vehicle road sections of the heterogeneous vehicle flow and the lane changing information;
dividing the statistical lane changing behavior into an associated lane changing behavior and an independent lane changing behavior according to the difference of the associated characteristics between the lane changing behaviors of the heterogeneous traffic flow road sections in the Internet of vehicles environment;
in particular to a method for preparing a high-performance nano-silver alloy,
2-1) determining the associated lane change of the heterogeneous traffic flow road section in the Internet of vehicles environment according to the distance between the heads of the lane change vehicles;
as shown in fig. 4, the lane change information (including the time-space information of each lane change occurrence process) of all vehicles during the peak hours of the day is counted by combining the traffic flow running video of the road section during the peak hours of the day<I.e. position information of the vehicle per second>) The g-th lane change vehicle (i.e. the g-th lane change vehicle) is used for carrying out the analysis of the lane change process, and the initial time of the lane change is calculated
Figure GDA0003718113480000095
Time when a lane change vehicle arrives at the kth (K ═ 1, 2.. K) course lane
Figure GDA0003718113480000101
Time when lane-changing vehicle arrives at target lane
Figure GDA0003718113480000102
When the lane-changing vehicle changes the front and the back two vehicles (namely the associated vehicles of the lane-changing vehicle) of the lane-changing vehicle in each lane
Figure GDA0003718113480000103
Figure GDA0003718113480000104
At the initial time of lane change in the course of lane change for the g-th lane change vehicle
Figure GDA0003718113480000105
The vehicle-head distance between the front vehicle and the rear vehicle of the starting lane;
Figure GDA0003718113480000106
during the course of changing lanes for the g-th lane-changing vehicle (i.e., during the course of changing lanes
Figure GDA0003718113480000107
Time) between two vehicles before and after the kth process lane, k being 1, 2. . . K, K represents the total number of course lanes;
Figure GDA0003718113480000108
during the course of changing lanes for the g-th lane-changing vehicle (i.e., during the course of changing lanes
Figure GDA0003718113480000109
Time) the headway distance between two vehicles before and after the target lane;
calculating the minimum head space required by the g-th lane-changing vehicle in the starting lane, the process lane and the target lane to implement the lane-changing process
Figure GDA00037181134800001010
Figure GDA00037181134800001011
Figure GDA00037181134800001012
The average head-to-head distance required by the g-th lane changing vehicle on the lane changing starting lane;
Figure GDA00037181134800001013
average head space required by a rear vehicle of the g-th lane changing vehicle on a lane changing starting lane;
Figure GDA00037181134800001014
average headway distance required by the g-th lane change vehicle on the k-th lane change course lane, the value and "
Figure GDA00037181134800001015
"same;
Figure GDA00037181134800001016
average head spacing required by a rear vehicle of the g-th lane changing vehicle on a lane in the k-th lane changing process;
Figure GDA00037181134800001017
average headway distance, numerical value and required by the g-th lane changing vehicle on the lane changing target lane "
Figure GDA00037181134800001018
"same;
Figure GDA00037181134800001019
average vehicle head distance required by a rear vehicle of the g-th lane changing vehicle on a lane changing target lane; the six average headway distances described above are dependent on vehicle type, from "
Figure GDA00037181134800001020
"is selected.
If the headway distance between two vehicles before and after the lane-changing vehicle in any one lane of the starting lane, the process lane and the target lane is less than the minimum headway distance required by the g-th lane-changing vehicle in the lane-changing and other lane-changing behaviors exist in the time range of the lane-changing of the corresponding vehicle before and after the lane, the lane-changing behavior of the lane-changing vehicle and the lane-changing behavior of the front and the rear vehicles in the time range of the lane-changing of the vehicle are taken as a primary associated lane-changing, the primary associated lane-changing comprises a plurality of secondary lane-changing, the secondary lane-changing behavior is the lane-changing behavior of the lane-changing vehicle and the lane-changing behavior of the front and the rear vehicles in the time range of the lane-changing of the vehicle, as shown in FIG. 4, in the process lane 1, the headway distance between the two vehicles before and after the lane-changing vehicle is less than the minimum headway distance, and the lane-changing behavior occurs in the rear vehicle, so that the primary associated lane-changing behavior is taken as a primary associated lane-changing, the secondary associated lane-changing behavior comprises two secondary lane-changing behaviors, namely: lane changing behavior of lane changing vehicles and lane changing behavior of rear vehicles in the process lane 1;
after a lane change of a vehicle, due to the distance between the heads of the vehicles, the fact that the actual distance is smaller than the minimum distance causes the associated vehicles of the lane change vehicle (the first vehicles in front of and behind each lane in the lane change process of the lane change vehicle) to also change lanes when passing through the road section, and the related lane change behaviors are all associated lane changes once, so that a plurality of times of sub lane changes may exist in the lane change process of the lane change vehicle.
2-2) determining that the heterogeneous traffic flow road sections of the Internet of vehicles environment independently change roads;
and other lane changing behaviors except the associated lane changing behavior in the road section in the unit time interval belong to independent lane changing.
Step 3, calculating a road traffic space change value under the influence of independent road changing of heterogeneous traffic flow road sections in the Internet of vehicles environment;
determining the number of affected lanes of a road section under the action of independent lane changing, and further calculating the traffic space change value of each lane affected by independent lane changing according to the average speed of vehicles ahead of the lane changing vehicles of each affected lane, so as to reflect the degree of influence of independent lane changing on each lane;
in particular to a method for preparing a high-performance nano-silver alloy,
3-1) determining the affected lane of the independent lane change of the road section according to the following distance of the vehicles behind the lane change vehicle and the road section following average head distance in the lane change process;
for the first independent lane change of the road section, the initial lane and the target lane are directly influenced, whether the lanes of the first independent lane change in the related K processes are influenced or not is analyzed in an important way, and if the lane change vehicles are influenced
Figure GDA0003718113480000121
In the process of changing lanes from the k-1 th process lane to the k-th process lane, the following distance between the lane and the workshop behind the k-th process lane
Figure GDA0003718113480000122
If the distance between the road section of the lane changing vehicle and the average locomotive is smaller than the average locomotive distance, the kth process lane is influenced by the independent lane changing, and the independent lane changing is determined to have X influenced lanes;
3-2) determining the average speed of the front vehicle affected by the independent lane change according to the time-varying speed of the front vehicle of the lane change vehicle;
by analyzing the first vehicle ahead of the lane change vehicle in the affected lane (defined as "aheadVehicle ", as shown in fig. 5) at the time-varying speed of the road section, calculating the average speed of the vehicle before the x-th lane influenced by the I-th independent lane change
Figure GDA0003718113480000123
Figure GDA0003718113480000124
In the formula (I), the compound is shown in the specification,
Figure GDA0003718113480000125
respectively representing the instant speed of the front vehicle of the xth lane influenced by the ith independent lane change at the t moment and the moment of leaving the lane,
Figure GDA0003718113480000126
representing the starting moment of changing the lane on the x-th affected lane by the I-th independent lane changing behavior;
3-3) calculating a traffic space change value of the lane under the influence of the independent lane change according to the average speed of the front vehicle and the vehicle speed when the rear vehicle is changed, wherein the average speed of the front vehicle and the vehicle speed are influenced by the independent lane change, and determining the influenced degree of each lane under the influence of the independent lane change;
calculating the spatial variation value of the x-th lane passing space influenced by the I-th independent lane change by analyzing the time-varying speed of the first vehicle (defined as the 'rear vehicle', as shown in figure 6) behind the lane change vehicle in each influenced lane on the road section
Figure GDA0003718113480000131
And reflecting the affected degree of the lane by using the change value of the traffic space of the x-th lane:
Figure GDA0003718113480000132
in the formula (I), the compound is shown in the specification,
Figure GDA0003718113480000133
respectively indicate the first time of independent lane changeThe instant speed of the vehicle behind the affected xth lane at time t and the time of departure from the lane.
The vehicle lane changing process is a process of making full use of the road (pressing the free road space in front more densely). After the vehicles change lanes, the original vehicle space is left, and more vehicles can pass by the road space, so that the road passing capacity is increased.
Step 4, calculating a road traffic space change value under the influence of road switching associated with heterogeneous traffic flow road sections in the Internet of vehicles environment;
determining the number of affected lanes of a road section under the action of associated lane changing, determining vehicles ahead of the affected lanes, analyzing the average speed of the vehicles ahead, determining vehicles behind the affected lanes, and calculating the traffic space change value of each lane so as to reflect the degree of influence of the associated lane changing on each lane;
in particular to a method for preparing a high-performance nano-silver alloy,
4-1) determining the road section associated lane change influenced lane according to the following distance of the vehicles behind the process lane and the average head following distance of the road section;
analyzing the influence condition of the lane in the nth sub lane change process in the mth associated lane change, specifically as follows: if the following distance between the lane changing vehicle and a workshop behind the adjacent lane is smaller than the average head following distance of the road section of the lane changing vehicle in the process of changing the lane from one lane to the adjacent lane in the nth sub-lane in the mth associated lane, the adjacent lane is influenced by the associated lane changing; determining that the related lane change has Y affected lanes;
4-2) determining the average speed of the front vehicle affected by the associated lane change according to the time-varying speed of the front vehicle of the lane change vehicle;
counting the front vehicles on each affected lane in all the N times of sub-lane changing processes in the mth associated lane changing, wherein one front vehicle, which is closest to a lane changing vehicle, in the y-th lane front vehicles affected by the mth associated lane changing is the front vehicle of the y-th affected lane under the influence of the mth associated lane changing;
calculating the mth time affected by the mth time associated lane change by analyzing the time-varying speed of the vehicle in front of the lane change vehicle in the affected laneAverage speed of front vehicle of y lanes
Figure GDA0003718113480000141
Figure GDA0003718113480000142
In the formula (I), the compound is shown in the specification,
Figure GDA0003718113480000143
respectively representing the instant speed of the front vehicle of the y-th lane influenced by the m-th associated lane at the t-th time and the time of leaving the lane,
Figure GDA0003718113480000144
representing the starting moment of changing the lane on the y-th affected lane by the m-th associated lane changing behavior;
4-3) determining the influenced degree of each lane influenced by the associated lane change according to the traffic space change value of the vehicle behind the lane change vehicle;
counting the rear vehicles on each affected lane in all the N times of sub-lane changing processes in the mth associated lane changing, wherein one rear vehicle which is the most close to the lane changing vehicle in the rear vehicles on the yth lane is the rear vehicle on the yth affected lane under the influence of the mth associated lane changing;
calculating the traffic space change value of the y-th lane influenced by the m-th time of associated lane change by analyzing the time-varying speed of the vehicle behind the lane change vehicle in each influenced lane on the road section
Figure GDA0003718113480000145
And reflecting the affected degree of the lane by using the y-th lane passing space change value:
Figure GDA0003718113480000151
in the formula (I), the compound is shown in the specification,
Figure GDA0003718113480000152
respectively representing the instant speed of the vehicle behind the y-th lane influenced by the m-th associated lane at the t-th time and the time of leaving the lane.
Step 5, calculating a road section passing capacity change value influenced by heterogeneous traffic flow switching in the Internet of vehicles environment according to the road passage space change value under the influence of independent switching and associated switching;
the total change value of the traffic space of the road section affected by lane changing is calculated, the change value of the number of cars which can be served by the changed traffic space is measured by manually driving the car section to follow the average head distance, and the change value of the traffic capacity of the road section affected by heterogeneous traffic flow lane changing is calculated.
In particular to a method for preparing a high-performance nano-silver alloy,
5-1) determining a total change value of the road traffic space of the road section influenced by the lane change according to the change values of the road traffic space of the individual lane change and the associated lane change;
calculating the traffic space change value delta D under the influence of totally L times of independent lane change of the road section in unit time interval sc Traffic space change value delta D under influence of total M times of associated lane change rc Further, calculating a total change value delta D of the road traffic space of the road section affected by the road change;
Figure GDA0003718113480000153
Figure GDA0003718113480000154
ΔD＝ΔD sc +ΔD rc
5-2) calculating a change value of the passing capacity of the road section influenced by the heterogeneous traffic flow lane change according to the total change value of the passing space of the road section influenced by the lane change and the average head distance of the manually-driven car road section;
according to the average distance between the manual/automatic driving cars (or manual/automatic driving buses) and the average head of the cars, the change value of the road passing space under the influence of the road section by the road changing is converted into the change value delta Q of the car passing quantity, and the change value of the vehicle passing capacity of the road section influenced by the heterogeneous traffic flow change in unit time period is reflected by the change value.
For example, the variation value Δ Q is calculated according to the average headway distance followed by the manually-driven car section:
Figure GDA0003718113480000161
the change value of the vehicle passing capacity is the change value generated by the influence of the traffic lane change on the road segment vehicle passing capacity. The invention firstly calculates the influence of vehicle lane change on the road traffic space, and then converts the newly increased/reduced road traffic space into the number of vehicles which can pass, thereby determining the change value of the vehicle passing capacity.
5-3) calculating the vehicle passing capacity of the road section in unit time period according to the change value of the vehicle passing capacity of the road section influenced by the heterogeneous vehicle flow change;
and according to the average head distance between the manually driven car road section and the car, converting the vehicle passing space change value of the road section influenced by lane change into the change value of the car passing number, and determining and calculating the road section passing capacity Q in unit time period.
Q＝Q C +ΔQ
Wherein Q is C Indicating the actual capacity of the road segment.
The invention can calculate the passing capacity of the road section and the residual road space in real time, thereby obtaining the passing capacity of the whole road network, if the information is uploaded to APP such as a Baidu map, a drip car and the like, a traffic manager can integrate the information, correctly guide part of drivers to drive to the non-congested road, and reasonably distribute the number of vehicles according to the residual road space. The traffic jam can be relieved to a certain extent, the road traffic operation efficiency is improved, and the method has very important practical significance.
Example 2:
a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the aforementioned vehicle networking environment heterogeneous vehicle interchange impact road segment throughput capability analysis methods.
A computing device, characterized by: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the foregoing internet of vehicles environment heterogeneous traffic lane change affected segment throughput capability analysis methods.
Example 3:
the method for analyzing the passing capacity of the road sections influenced by the heterogeneous vehicle flow switching in the Internet of vehicles environment is further explained by taking the method into consideration by an example, and the specific steps of the method for analyzing the passing capacity of the road sections influenced by the heterogeneous vehicle flow switching in the Internet of vehicles environment are considered below.
S1: and calculating the following average head distance of various vehicle sections of the heterogeneous traffic flows.
S11: and (3) surveying the following head distances and the like of 4 types of vehicles, namely manual/automatic driving cars and manual/automatic driving buses, in a circle of a road section of a certain three lane at a ratio of 7:30-8:30, and selecting the road section without bus stops and intersections as a data acquisition area. Dividing the peak hour of the investigation into 60 sub-periods, and constructing a following vehicle head interval data set of various vehicle type road sections in a unit period, specifically the head interval of the automatic driving car
Figure GDA0003718113480000171
Automatic drive bus locomotive spacing
Figure GDA0003718113480000172
Head spacing of manually driven car
Figure GDA0003718113480000181
Manual driving bus head spacing
Figure GDA0003718113480000182
As shown in table 1 (enumerate part of the data).
TABLE 1 headway distance table for various vehicles
Figure GDA0003718113480000183
S12: and calculating the following average head distances of different vehicle sections of 10 meters, 12 meters, 15 meters and 18 meters respectively for the automatic driving cars, the automatic driving buses, the manual driving cars and the manual driving buses according to the following head distance data sets of various vehicle sections.
S2: and dividing the lane changing types of the heterogeneous traffic flow road sections in the Internet of vehicles environment.
Counting the head space between the front and the rear vehicles of the lane-changing vehicle in each lane at the initial time of implementing the lane-changing in the initial lane, the process lane and the target lane
Figure GDA0003718113480000184
As shown in table 2 (listing partial data), the minimum headway distances required for implementing the lane change process of the 1 st lane change vehicle in the starting lane, the process lane and the target lane are respectively 10 meters, 12 meters and 14 meters, the headway distance between two vehicles before and after the 1 st lane change vehicle in the target lane is smaller than the minimum headway distance required for changing the lane of the 1 st lane change vehicle in the lane, and other lane change behaviors of the vehicles before and after the target lane exist in the lane change time range of the vehicles, so that the lane change behavior is taken as a correlated lane change, and the lane change behaviors of the starting lane and the process lane belong to independent lane change.
TABLE 2 vehicle headway distance table for vehicles around each lane of lane changing vehicle
Figure GDA0003718113480000191
S3: and analyzing the influence of independent lane change of heterogeneous traffic flow road sections in the Internet of vehicles environment.
S31: and determining the affected lane of the independent lane change of the road section.
And (3) surveying the following distance between the vehicles after the process lane of the independent lane change, as shown in table 3 (listing partial data), wherein the following distance between the vehicles after the process lane of the 3 rd independent lane change is 9 meters and is less than the average following distance of the road sections of the lane-changing vehicles by 10 meters, and 3 affected lanes are determined to be in total for the 3 rd independent lane change, and 2 affected lanes are determined to be in total for the 1 st and 2 nd independent lane changes.
TABLE 3 following distance of vehicle and average following distance of road section after lane changing process
Number of passes Distance between heel and heel of rear workshop (Rice) Average distance between the highway section and the car
1 11 10
2 12 10
3 9 10
S32: calculating the average speed of the front vehicle affected by independent lane change
Investigating the time-varying speed of the vehicle in front of the lane-changing vehicle in the affected lane, specifically the instantaneous speed of the vehicle in front of the lane affected by the independent lane change
Figure GDA0003718113480000201
And the time of departure from the lane
Figure GDA0003718113480000202
Starting time of lane change on independent lane change behavior lane
Figure GDA0003718113480000203
As shown in table 4 (enumerate part of the data).
TABLE 4 starting and leaving times of the vehicle ahead of the independent lane change and instantaneous vehicle speed
Figure GDA0003718113480000204
And determining the average speed of the vehicle in the 2 nd lane influenced by the 3 rd independent lane change to be 34 km/h.
S33: analyzing the influence degree of each lane influenced by independent lane change
Investigating the operating characteristics of a first vehicle (defined as "rear vehicle") behind a lane-change vehicle in each affected lane, in particular the instantaneous speed of the rear vehicle in the lane affected by the lane change independently
Figure GDA0003718113480000205
And the time of departure from the lane
Figure GDA0003718113480000206
As shown in table 5 (enumerate part of the data).
TABLE 5 starting and departure times of the vehicle after independent lane change and instantaneous vehicle speed
Figure GDA0003718113480000207
And determining the traffic space change value of the 2 nd lane influenced by the 3 rd independent lane change to be 0.4 m.
S4: and analyzing the road changing influence associated with the heterogeneous traffic flow road sections in the Internet of vehicles environment.
S41: and determining the road section-associated lane change affected lane.
The following distance between vehicles behind the process lane of the associated lane change is investigated, as shown in table 6 (listing partial data), the following distance between vehicles behind the process lane of the 2 nd independent lane change is 9 meters and is 10 meters smaller than the average following distance of the road section of the lane change vehicle, 3 affected lanes are determined for the 3 rd independent lane change, and 2 affected lanes are determined for the 1 st and 3 rd independent lane changes.
TABLE 6 procedure following distance of vehicle to the lane and whether lane change occurred and average distance to the road following
Figure GDA0003718113480000211
S42: and calculating the average speed of the front vehicle of the lane influenced by the associated lane change.
Counting the previous vehicles on each affected lane in all sub-lane changing processes in the associated lane changing, specifically the instantaneous speed of the previous vehicle on the affected lane in the associated lane changing
Figure GDA0003718113480000212
And the time of departure from the lane
Figure GDA0003718113480000213
Starting time of lane change on associated lane change behavior lane
Figure GDA0003718113480000214
As shown in table 7 (enumerate part of the data).
TABLE 7 associates the start and departure times of the lead vehicle and the instantaneous vehicle speed
Figure GDA0003718113480000215
And determining the average speed of the vehicle ahead of the 2 nd lane influenced by the 2 nd associated lane change to be 31 km/h.
S43: and analyzing the influence degree of each lane influenced by the associated lane change.
Counting front vehicles on each affected lane in all sub lane changing processes in the associated lane changing, specifically rear vehicles on the affected lanesInstantaneous vehicle speed of
Figure GDA0003718113480000216
And the time of departure from the lane
Figure GDA0003718113480000217
As shown in table 8 (enumerate part of the data).
TABLE 8 associates the start and departure times of the vehicle following the lane change and the instantaneous vehicle speed
Figure GDA0003718113480000218
And determining that the 2 nd lane passing space change value influenced by the 3 rd time associated lane change is 0.6 m.
S5: and calculating the change value of the road section passing capacity influenced by the heterogeneous vehicle flow change in the vehicle networking environment.
S51: and calculating the total change value of the passing space of the road section influenced by lane change.
Accumulating the traffic space change values under the influence of all the independent lane changes and the traffic space change values under the influence of all the associated lane changes in the road section in a unit time interval, and determining that the traffic space change value of the road section under the influence of the lane changes is 750 meters.
S52: and calculating a change value of the road section passing capacity influenced by the heterogeneous traffic flow switching.
The change value of the passing number of the cars is determined to be 50 by means of the average head distance of the manually driven car section following and the change value of the passing space of the section under the influence of lane change.
S53: and calculating the vehicle passing capacity of the road section in the unit time period.
And (3) converting a vehicle passing space change value into a change value of the passing number of the cars under the influence of lane change on the road section according to the average head distance between the manually driven car road section and the car, and determining that the passing capacity of the road section in unit time interval is 1850.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (5)

1. A method for determining the passing capacity of a road section affected by heterogeneous vehicle flow switching in a vehicle networking environment is characterized by comprising the following steps:
dividing the lane changing behavior of the heterogeneous vehicle flow road section in the Internet of vehicles environment into an associated lane changing behavior and an independent lane changing behavior according to the following average head distance and the lane changing information of various vehicle road sections of heterogeneous vehicle flows;
respectively calculating the road traffic space change values under the influence of independent lane change and associated lane change of heterogeneous traffic flow road sections in the Internet of vehicles environment;
calculating a road passage capacity change value influenced by heterogeneous traffic flow channel change in the Internet of vehicles according to the road passage space change value under the influence of independent channel change and associated channel change;
the method comprises the following steps of calculating a road traffic space change value under the influence of independent road changing of heterogeneous traffic flow road sections in the Internet of vehicles environment, wherein the method comprises the following steps:
for the first independent lane change of the road section, the starting lane and the target lane are directly influenced, if the lane change vehicle
Figure FDA0003718113470000011
In the process of changing lanes from the k-1 th process lane to the k-th process lane, the following distance between the lane and the workshop behind the k-th process lane
Figure FDA0003718113470000012
If the distance between the road section of the lane changing vehicle and the average locomotive is smaller than the average locomotive distance, the kth process lane is influenced by the independent lane changing, and the independent lane changing is determined to have X influenced lanes;
calculating the average speed of the front vehicle in the x-th lane influenced by the independent lane change for the first time
Figure FDA0003718113470000013
Figure FDA0003718113470000014
In the formula (I), the compound is shown in the specification,
Figure FDA0003718113470000015
respectively representing the instant speed of the front vehicle of the xth lane influenced by the ith independent lane change at the t moment and the moment of leaving the lane,
Figure FDA0003718113470000016
representing the starting moment of changing the lane on the x-th affected lane by the I-th independent lane changing behavior;
calculating the traffic space change value of the x-th lane influenced by the I-th independent lane change
Figure FDA0003718113470000021
Figure FDA0003718113470000022
In the formula (I), the compound is shown in the specification,
Figure FDA0003718113470000023
respectively representing the instant speed of the rear vehicle of the xth lane influenced by the ith independent lane change at the t moment and the moment of leaving the lane;
the method for calculating the road traffic space change value under the influence of road switching associated with heterogeneous traffic flow road sections in the Internet of vehicles environment comprises the following steps:
if the following distance between the lane changing vehicle and a workshop behind the adjacent lane is smaller than the average head following distance of the road section of the lane changing vehicle in the process of changing the lane from one lane to the adjacent lane in the nth sub-lane in the mth associated lane, the adjacent lane is influenced by the associated lane changing; determining that the related lane change has Y affected lanes;
calculating the average speed of the front vehicle in the y-th lane influenced by the m-th associated lane change
Figure FDA0003718113470000024
Figure FDA0003718113470000025
In the formula (I), the compound is shown in the specification,
Figure FDA0003718113470000026
respectively representing the instant speed of the front vehicle of the y-th lane influenced by the m-th associated lane at the t-th time and the time of leaving the lane,
Figure FDA0003718113470000027
representing the starting moment of changing the lane on the y-th affected lane by the m-th associated lane changing behavior;
calculating the traffic space change value of the y-th lane influenced by the m-th time of associated lane change
Figure FDA0003718113470000028
Figure FDA0003718113470000029
In the formula (I), the compound is shown in the specification,
Figure FDA00037181134700000210
respectively representing the instant speed of a rear vehicle of the y-th lane influenced by the m-th associated lane change at the t-th time and the time of leaving the lane;
the method for calculating the passing capacity change value of the road section influenced by the heterogeneous traffic flow switching in the Internet of vehicles environment according to the road passage space change value under the influence of the independent switching and the associated switching comprises the following steps:
calculating the traffic space change value delta D under the influence of totally L times of independent lane change of the road section in unit time interval sc Traffic space change value delta D under influence of total M times of associated lane change rc Then, further countCalculating a total change value delta D of a road traffic space of a road section affected by road changing;
Figure FDA0003718113470000031
Figure FDA0003718113470000032
ΔD＝ΔD sc +ΔD rc
and converting a road traffic space change value under the influence of road section change into a vehicle passing quantity change value according to the distance between the manual/automatic driving car or the manual/automatic driving bus road section and the driving average head, and obtaining the road section passing capacity in unit time period by adding the actual passing capacity of the road section.
2. The method for determining the road section passing capacity influenced by heterogeneous vehicle flow lane change in the vehicle networking environment according to claim 1, wherein the following average head distance between vehicle sections of heterogeneous vehicle flows comprises:
heterogeneous traffic stream autonomous car, autonomous bus, manually driven car and manually driven bus road section following average head distance
Figure FDA0003718113470000033
Figure FDA0003718113470000034
Wherein J is the total number of the automatic driving cars, the automatic driving buses, the manual driving cars or the manual driving buses,
Figure FDA0003718113470000035
respectively representing the number of head distances of an automatic driving car, an automatic driving bus, a manual driving car and a manual driving bus in the ith sub-period of the unit period;
Figure FDA0003718113470000041
respectively the head distances of the jth automatic driving car, the automatic driving bus, the manual driving car and the manual driving bus at the starting time of the ith sub-period of the unit period.
3. The method for determining the passage capacity of the road sections affected by the heterogeneous traffic flow road change in the Internet of vehicles environment according to claim 2, wherein the road change behavior of the heterogeneous traffic flow road sections in the Internet of vehicles environment is divided into associated road change and independent road change according to the following average head distance and the road change information of various vehicle road sections in the heterogeneous traffic flow, and the method comprises the following steps:
counting the channel change information of all vehicles in the peak time period, and calculating the time from the channel change vehicle to the kth process lane at the initial time of the channel change of the g-th channel change vehicle to the target lane, and the distance between the two vehicles before and after the channel change vehicle in each lane when the channel change vehicle reaches the target lane
Figure FDA0003718113470000042
Figure FDA0003718113470000043
The distance between the head of the front vehicle and the head of the rear vehicle of the initial lane at the initial time of the lane change in the process of changing the lane of the g-th lane change vehicle;
Figure FDA0003718113470000044
the method comprises the steps that the head distance between two vehicles before and after a K process lane in the g lane changing process of a g lane changing vehicle is defined, wherein K is 1,2,.
Figure FDA0003718113470000045
The distance between the head of the g-th lane changing vehicle and the head of the two vehicles before and after the target lane in the lane changing process;
calculating implementation of the g-th lane-changing vehicle in the starting lane, the process lane and the target laneMinimum headway distance required for a lane-changing process
Figure FDA0003718113470000046
Figure FDA0003718113470000047
Figure FDA0003718113470000048
The average head distance required by the g-th lane changing vehicle on the lane changing starting lane;
Figure FDA0003718113470000049
average head space required by a rear vehicle of the g-th lane changing vehicle on a lane changing starting lane;
Figure FDA0003718113470000051
the average head spacing required by the g lane changing vehicle on the lane in the k lane changing process;
Figure FDA0003718113470000052
average head spacing required by a rear vehicle of the g-th lane changing vehicle on a lane in the k-th lane changing process;
Figure FDA0003718113470000053
the average head-to-head distance required by the g-th lane changing vehicle on a lane changing target lane;
Figure FDA0003718113470000054
average head space required by a rear vehicle of the g-th lane changing vehicle on a lane changing target lane;
if the head distance between two vehicles before and after the lane change vehicle in any lane of the starting lane, the process lane and the target lane is smaller than the minimum head distance required by the g-th lane change vehicle in the lane change, and other lane change behaviors exist in the lane change time range of the corresponding lane front and rear vehicles, taking the lane change behavior of the lane change vehicle and the lane change behaviors of the front and rear vehicles in the lane change time range of the vehicle as a primary associated lane change, wherein the primary associated lane change comprises a plurality of times of sub lane changes, and the sub lane change is the lane change behavior of the lane change vehicle and the lane change behaviors of the front and rear vehicles in the lane change time range of the vehicle;
and other lane changing behaviors except the associated lane changing behavior in the road section in the unit time interval belong to independent lane changing.
4. A computer readable storage medium storing one or more programs, characterized in that: the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the vehicle networking environment heterogeneous vehicle diversion road segment passing capability determination methods of claims 1-3.
5. A computing device, characterized by: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the network of vehicles environment heterogeneous vehicular flow switching affected road segment passing capability determination methods of claims 1-3.
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