CN112927525B - Signal lamp phase distribution method and device under intelligent networking environment and storage medium - Google Patents

Signal lamp phase distribution method and device under intelligent networking environment and storage medium Download PDF

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CN112927525B
CN112927525B CN202110508955.8A CN202110508955A CN112927525B CN 112927525 B CN112927525 B CN 112927525B CN 202110508955 A CN202110508955 A CN 202110508955A CN 112927525 B CN112927525 B CN 112927525B
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phase
green light
time
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dynamic priority
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CN112927525A (en
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何书贤
童厚健
任学锋
邱志军
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Ismartways Wuhan Technology Co ltd
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    • G—PHYSICS
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    • G08G1/081—Plural intersections under common control
    • G08G1/083—Controlling the allocation of time between phases of a cycle
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    • G08G1/00—Traffic control systems for road vehicles
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Abstract

The invention relates to a signal lamp phase distribution method, a device and a computer readable storage medium under an intelligent networking environment, wherein the method comprises the following steps: acquiring the minimum green light time, the maximum green light time and the yellow light time of a phase of a signal lamp, the residual green light time of a current phase, the minimum green light time of the current phase, the yellow light time and the maximum red light time of the current phase; determining the dynamic priority traffic weight of the bus under the effective green light scene, and determining the total value of the dynamic priority traffic weight of the bus and the social bus under the effective green light scene; and determining the dynamic priority traffic weight of the bus under the effective red light scene, and determining the total value of the dynamic priority traffic weight of the bus and the social bus under the effective red light scene, thereby determining the distribution of the phase. The signal lamp phase distribution method under the intelligent networking environment can calculate the real-time priority weight of the vehicle to determine the distribution of the phase, thereby meeting the requirement of real-time signal control.

Description

Signal lamp phase distribution method and device under intelligent networking environment and storage medium
Technical Field
The invention relates to the technical field of intelligent network connection, in particular to a signal lamp phase distribution method and device in an intelligent network connection environment and a computer readable storage medium.
Background
With the accelerated landing of the intelligent internet demonstration area in the open road scene in China, the V2X internet buses, the automatic driving buses and even the intelligent internet buses gradually enter the public-oriented open commissioning phase. At present, the signal priority passing scheme of the bus mainly comprises a road priority passing right and a signal priority passing right. The road priority right of way assignment mainly adopts and sets up the special lane of bus, the special traffic phase place two kinds of modes of bus lane; the signal priority traffic is divided into absolute priority traffic and relative priority traffic.
The bus absolute priority right of passage distribution under the networking environment is based on two types of information of passive bus detection signals or active priority passage requests, and the color state of the direction signal light of the driving route is adjusted to be green light by driving a signal machine to execute a control strategy. The method is easy to cause green light resource waste and intersection paralysis in the peak period. The relative priority right of way is comprehensively analyzed and decided on the basis of considering parameters such as bus arrival time, right of way of other social vehicles at the intersection, signal lamp timing at the current intersection, minimum signal timing switching, maximum green lamp protection and the like, and priority right of way distribution is carried out. The related research focuses on the aspects of maximizing the number of vehicles passing through the intersection, minimizing the traffic delay, maximizing the traffic capacity and the like, fixed weight proportion and permeability are respectively given to the social vehicles and the buses on the basis of historical data analysis, and then phase distribution is carried out. The methods can also improve the intersection operation efficiency while ensuring the priority right of the bus to a certain extent, but historical data cannot reflect future time-varying traffic characteristics, fixed weight and permeability parameters often cannot meet the requirements of real-time signal control, and the traffic capacity evaluation standard taking the number of passing vehicles as the reference cannot truly reflect the per-capita delay.
Disclosure of Invention
In view of the above, there is a need to provide a method and an apparatus for assigning signal phases in an intelligent networking environment, and a computer readable storage medium, so as to solve the problem in the prior art that the requirement of real-time signal control cannot be met.
The invention provides a signal lamp phase distribution method in an intelligent networking environment, which comprises the following steps:
acquiring the minimum green light time, the maximum green light time and the yellow light time of a phase of a signal lamp, the residual green light time of a current phase, the minimum green light time of the current phase, the yellow light time and the maximum red light time of the current phase;
determining the dynamic priority traffic weight of the single vehicle under the effective green light scene according to the phase minimum green light time, the maximum green light time, the yellow light time and the residual green light time of the current phase, and determining the total dynamic priority traffic weight value of the bus and the social vehicle under the effective green light scene;
determining the dynamic priority traffic weight of the bicycle under the effective red light scene according to the phase minimum green light time, the current phase yellow light time and the maximum red light time, and determining the total value of the dynamic priority traffic weight of the bus and the social vehicle under the effective red light scene;
and determining the distribution of the phases according to the total dynamic priority traffic weight value of the buses and the social vehicles under the effective green light scene and the total dynamic priority traffic weight value of the buses and the social vehicles under the effective red light scene.
Further, determining a bicycle dynamic priority traffic weight in an effective green light scene according to the phase minimum green light time, the phase maximum green light time, the yellow light time and the current phase green light remaining time, specifically comprising:
determining the dynamic priority traffic weight of the bicycle under the effective green light scene according to the phase minimum green light time, the maximum green light time, the yellow light time, the current phase green light residual time and the dynamic priority weight distribution model of the bicycle under the effective green light condition, wherein the dynamic priority weight distribution model of the bicycle under the effective green light condition is
Figure 727360DEST_PATH_IMAGE001
The above-mentioned
Figure 154799DEST_PATH_IMAGE002
For the dynamic priority traffic weight of the bicycle under the effective green light scene,
Figure 292388DEST_PATH_IMAGE003
the time is prolonged for the green light,
Figure 581419DEST_PATH_IMAGE004
the remaining time period for the current phase green light,
Figure 730640DEST_PATH_IMAGE005
is the phase minimum green time,
Figure 86839DEST_PATH_IMAGE006
The maximum duration of the green light is set,Ythe length of the yellow light is the length of the yellow light,
Figure 196877DEST_PATH_IMAGE007
is a dirac function.
Further, the green light extension time is obtained through a green light extension time calculation formula which is
Figure 414232DEST_PATH_IMAGE008
Wherein,
Figure 277015DEST_PATH_IMAGE009
as a function of the arrival rate of the vehicle,
Figure 921623DEST_PATH_IMAGE010
is the average arrival rate of the vehicles,Sin order to achieve the intersection saturated traffic flow rate,
Figure 253378DEST_PATH_IMAGE011
for the distance from the vehicle to the stop line,
Figure 8844DEST_PATH_IMAGE012
and the green light time length is remained at the receiving moment of the prior passing request of the signal machine.
Further, determining a total value of dynamic priority traffic weights of the buses and the social vehicles in the effective green light scene specifically comprises:
according to a dynamic priority passing weighted total value model under an effective green light scene, determining a bus and social vehicle dynamic priority passing weighted total value under the effective green light scene, wherein the dynamic priority passing weighted total value model is
Figure 991713DEST_PATH_IMAGE013
Wherein,
Figure 72801DEST_PATH_IMAGE014
is as followsiThe bus and the social vehicle dynamic priority traffic weight total value under the effective green light scene in the phase,
Figure 891853DEST_PATH_IMAGE015
is a factor of the passenger carrying rate of the public transport,
Figure 841223DEST_PATH_IMAGE016
first, theiThe number of buses at the current time in phase,
Figure 553964DEST_PATH_IMAGE017
is as followsiNumber of social vehicles in the phase effective green light scene.
Further, the first step is obtained by a social vehicle number calculation formulaiSocial vehicle number in effective green light scene during phase
Figure 681320DEST_PATH_IMAGE018
The social vehicle number calculation formula is
Figure 236935DEST_PATH_IMAGE019
Wherein,
Figure 334204DEST_PATH_IMAGE020
the maximum red light duration is set as the maximum red light duration,
Figure 42397DEST_PATH_IMAGE021
the average permeability of the social vehicles is shown,
Figure 589922DEST_PATH_IMAGE022
is as followsiThe average number of vehicles arriving at society per unit time in phase,
Figure 242620DEST_PATH_IMAGE023
is as followsiAnd the standard deviation of the number of social vehicles is reached in unit time at the phase.
Further, determining the dynamic priority traffic weight of the bicycle under the effective red light scene according to the phase minimum green light time, the current priority phase yellow light time and the maximum red light time, specifically comprising:
determining the dynamic priority traffic weight of the single vehicle under the effective red light scene according to the phase minimum green light time, the current priority phase yellow light time and the maximum red light time and the dynamic priority traffic weight distribution model under the effective red light scene, wherein the dynamic priority traffic weight distribution model under the effective red light scene is
Figure 284526DEST_PATH_IMAGE024
Wherein,
Figure 971859DEST_PATH_IMAGE025
for the dynamic priority traffic weight of the bicycle under the effective red light scene,
Figure 690285DEST_PATH_IMAGE026
the remaining green time is the reception time of the traffic signal priority pass request,
Figure 705646DEST_PATH_IMAGE027
for the current phase the minimum green light duration,
Figure 410296DEST_PATH_IMAGE028
the maximum green light duration for the current phase,
Figure 76770DEST_PATH_IMAGE029
the remaining green light duration for the current phase,
Figure 716830DEST_PATH_IMAGE030
the current priority phase yellow lamp duration.
Further, determining a total value of dynamic priority traffic weights of the buses and the social vehicles under the effective red light scene specifically comprises the following steps:
according to a dynamic priority passing weighted total value model under an effective red light scene, determining a bus and social vehicle dynamic priority passing weighted total value under the effective red light scene, wherein the dynamic priority passing weighted total value model under the effective red light scene is
Figure 609700DEST_PATH_IMAGE031
Wherein,
Figure 977096DEST_PATH_IMAGE032
and the dynamic priority traffic weighted total value is under the effective red light scene.
Further, determining the distribution of the phases according to the total dynamic priority traffic weight value of the bus and the social vehicle in the effective green light scene and the total dynamic priority traffic weight value of the bus and the social vehicle in the effective red light scene specifically comprises:
acquiring a phase distribution function according to the total dynamic priority traffic weight value of the bus and the social vehicle in the effective green light scene and the total dynamic priority traffic weight value of the bus and the social vehicle in the effective red light scene, and determining the distribution of the phase according to the phase distribution function;
the phase distribution function is
Figure 779967DEST_PATH_IMAGE033
，j1 or 2, when the traffic direction waiting time is greater than the set threshold value,
Figure 449983DEST_PATH_IMAGE034
getxOtherwise, 1 is selected; the above-mentionedxIs a positive integer greater than 1 and is,
Figure 954782DEST_PATH_IMAGE035
is as followsiThe bus and the social vehicle dynamic priority traffic weight total value under the effective green light scene in the phase,
Figure 142181DEST_PATH_IMAGE036
and the dynamic priority traffic weighted total value is under the effective red light scene.
The invention also provides a signal lamp phase allocation device in the intelligent network connection environment, which comprises a processor and a memory, wherein the memory is stored with a computer program, and when the computer program is executed by the processor, the signal lamp phase allocation method in the intelligent network connection environment is realized according to any one of the technical schemes.
The invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for assigning the phase of the signal lamp in the intelligent networking environment according to any one of the above technical solutions is implemented.
Compared with the prior art, the invention has the beneficial effects that: obtaining the minimum green light time, the maximum green light time and the yellow light time of a phase of a signal lamp, the residual green light time of a current phase, the minimum green light time of the current phase, the yellow light time and the maximum red light time of the current phase; determining the dynamic priority traffic weight of the single vehicle under the effective green light scene according to the phase minimum green light time, the maximum green light time, the yellow light time and the residual green light time of the current phase, and determining the total dynamic priority traffic weight value of the bus and the social vehicle under the effective green light scene; determining the dynamic priority traffic weight of the bicycle under the effective red light scene according to the phase minimum green light time, the current phase yellow light time and the maximum red light time, and determining the total value of the dynamic priority traffic weight of the bus and the social vehicle under the effective red light scene; real-time priorities of the vehicles can be calculated to determine the allocation of phases to meet the requirements of real-time signal control.
Drawings
Fig. 1 is a schematic flow chart of a signal lamp phase assignment method in an intelligent networking environment according to the present invention;
FIG. 2 is a schematic diagram of the crossing traffic direction under the equivalent green light condition provided by the present invention;
FIG. 3 is a schematic diagram of the direction of the crossing under the equivalent red light condition provided by the present invention;
FIG. 4 is a schematic diagram of the traffic direction at the intersection under the yellow light condition provided by the present invention;
FIG. 5 is a schematic diagram of a multi-directional priority traffic request scenario in a mixed traffic flow environment according to the present invention;
FIG. 6 is a schematic diagram of a priority assignment model under an effective green light scene according to the present invention;
FIG. 7 is a schematic diagram of a priority assignment model under an effective red light scene according to the present invention;
FIG. 8 is a schematic diagram of a queue dissipation process during green light periods provided by the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
Example 1
The embodiment of the invention provides a signal lamp phase allocation method in an intelligent networking environment, which has a flow diagram, as shown in fig. 1, and comprises the following steps:
s1, acquiring the minimum green light time, the maximum green light time, the yellow light time, the residual green light time of the current phase, the minimum green light time of the current phase, the yellow light time and the maximum red light time of the current phase;
s2, determining the dynamic priority traffic weight of the single vehicle under the effective green light scene according to the phase minimum green light time, the maximum green light time, the yellow light time and the residual green light time of the current phase, and determining the total value of the dynamic priority traffic weight of the bus and the social vehicle under the effective green light scene;
s3, determining the dynamic priority traffic weight of the bus under the effective red light scene according to the phase minimum green light duration, the current phase yellow light duration and the maximum red light duration, and determining the total value of the dynamic priority traffic weight of the bus and the social bus under the effective red light scene;
and S4, determining the distribution of the phases according to the total dynamic priority traffic weight value of the bus and the social vehicle in the effective green light scene and the total dynamic priority traffic weight value of the bus and the social vehicle in the effective red light scene.
Preferably, the determining of the dynamic priority traffic weight of the bicycle under the effective green light scene according to the phase minimum green light time, the phase maximum green light time, the yellow light time and the remaining phase green light time specifically includes:
determining the dynamic priority traffic weight of the bicycle under the effective green light scene according to the phase minimum green light time, the maximum green light time, the yellow light time, the current phase green light residual time and the dynamic priority weight distribution model of the bicycle under the effective green light condition, wherein the dynamic priority weight distribution model of the bicycle under the effective green light condition is
Figure 314405DEST_PATH_IMAGE037
The above-mentioned
Figure 296268DEST_PATH_IMAGE038
For the dynamic priority traffic weight of the bicycle under the effective green light scene,
Figure 632571DEST_PATH_IMAGE039
the time is prolonged for the green light,
Figure 341770DEST_PATH_IMAGE040
the remaining time period for the current phase green light,
Figure 712708DEST_PATH_IMAGE041
is the phase minimum green time,
Figure 599893DEST_PATH_IMAGE042
The maximum duration of the green light is set,Ythe length of the yellow light is the length of the yellow light,
Figure 689072DEST_PATH_IMAGE043
is a dirac function.
In a specific embodiment, the schematic diagrams of the crossing traffic directions under the equivalent green light condition, the equivalent red light condition and the yellow light condition are respectively shown in fig. 2-4, and the schematic diagrams of the multi-direction priority traffic request scene under the mixed traffic flow environment are shown in fig. 5, wherein the numbers in the circles in fig. 2-5 represent the numbers of signal lamps; in FIG. 5, the dark circle is a red light and the light circle is a light colorThe color circle is a green light; equating the traffic direction represented in fig. 2 as the green light and yellow light scenes as the effective green light scene, and considering the minimum green light duration
Figure 936382DEST_PATH_IMAGE044
Maximum green time
Figure 161827DEST_PATH_IMAGE045
And duration of yellow lightYIntroducing a Dirac function based on the minimum green light time length protection principle
Figure 485492DEST_PATH_IMAGE046
Then the dynamic priority traffic weight of the bicycle (including the bus and the social vehicle) in the effective green light scene can be expressed as
Figure 796388DEST_PATH_IMAGE047
Wherein,
Figure 847389DEST_PATH_IMAGE048
indicating the Time To Change (TTC) remaining Time of the green light for the current phase.
Based on the green light extension control principle, the extension time is introduced
Figure 458499DEST_PATH_IMAGE049
In order to ensure that the buses (vehicles) do not stop passing through the intersection, the buses are cleared by switching front-queue vehicles during the signal timing of the bus passing direction, and the vehicles arriving at the intersection are prevented from forming secondary queue, then
Figure 687487DEST_PATH_IMAGE050
The remaining green time length at the time of receiving the signal machine priority passage request (
Figure 485678DEST_PATH_IMAGE051
) Is related, i.e.
Figure 340371DEST_PATH_IMAGE052
(ii) a LetterThe residual green light time and the actual effective green light time of the number machine at the moment of receiving the priority passing request from the bus are respectively
Figure 540408DEST_PATH_IMAGE053
And
Figure 940296DEST_PATH_IMAGE054
。
a schematic diagram of the priority assignment model in an effective green light scene, as shown in fig. 6, a schematic diagram of the priority assignment model in an effective red light scene, as shown in fig. 7, and a schematic diagram of the queue dissipation process in a green light period, as shown in fig. 8;
Figure 960205DEST_PATH_IMAGE055
the green light is divided into two parts, namely the green light time of the bus passing the intersection without stopping is ensured to be prolonged
Figure 618588DEST_PATH_IMAGE056
The green light prolonging time for preventing the subsequent arriving vehicles in the passing direction of the bus from blocking to form secondary queuing is avoided
Figure 673132DEST_PATH_IMAGE057
(ii) a Due to bus-to-stop line distance (
Figure 243922DEST_PATH_IMAGE058
) (also can be expressed as vehicle queue length) can be known, and the crossing saturation traffic flow rate is combinedSAnd average arrival rate of vehicles
Figure 16706DEST_PATH_IMAGE059
，
Figure 213201DEST_PATH_IMAGE060
Can be expressed as
Figure 122251DEST_PATH_IMAGE061
Wherein,
Figure 129521DEST_PATH_IMAGE062
representing a vehicle arrival rate function, follows a gaussian distribution, and is expected to be relatively constant with variance during peak, peak-to-average, and trough periods.
By using
Figure 124022DEST_PATH_IMAGE063
Replacement of
Figure 733995DEST_PATH_IMAGE064
Obtaining a Dynamic Priority assignment model (DCP) of the bicycle under the condition of effective green light
Figure 622185DEST_PATH_IMAGE065
Preferably, the green light extension time is obtained through a green light extension time calculation formula which is
Figure 65936DEST_PATH_IMAGE066
Wherein,
Figure 282154DEST_PATH_IMAGE067
as a function of the arrival rate of the vehicle,
Figure 695817DEST_PATH_IMAGE068
is the average arrival rate of the vehicles,Sin order to achieve the intersection saturated traffic flow rate,
Figure 704094DEST_PATH_IMAGE069
vehicle-to-stop line distance.
Preferably, determining the total value of the dynamic priority traffic weight of the bus and the social vehicle in the effective green light scene specifically comprises:
according to a dynamic priority passing weighted total value model under an effective green light scene, determining a bus and social vehicle dynamic priority passing weighted total value under the effective green light scene, wherein the dynamic priority passing weighted total value model is
Figure 177800DEST_PATH_IMAGE013
Wherein,
Figure 22260DEST_PATH_IMAGE070
is as followsiThe bus and the social vehicle dynamic priority traffic weight total value under the effective green light scene in the phase,
Figure 239614DEST_PATH_IMAGE071
is a factor of the passenger carrying rate of the public transport,
Figure 102397DEST_PATH_IMAGE072
first, theiThe number of buses at the current time in phase,
Figure 481426DEST_PATH_IMAGE073
is as followsiNumber of social vehicles in the phase effective green light scene.
In one specific embodiment, a linear relation model of the dynamic weight of the single vehicle and the remaining duration is established according to different light color state scenes corresponding to the passing direction of the vehicle, and in order to measure the dynamic total weight of a certain phase, a weighted DCP model is established to calculate the weight value
Figure 813181DEST_PATH_IMAGE074
. Taking into account phaseiCorresponding to the number of buses in the passing direction
Figure 568647DEST_PATH_IMAGE075
And social vehicle
Figure 817095DEST_PATH_IMAGE076
And corresponding weighted value
Figure 101446DEST_PATH_IMAGE077
And
Figure 920497DEST_PATH_IMAGE078
. In order to measure the average weight of people, a public transport passenger carrying rate factor is introduced
Figure 479655DEST_PATH_IMAGE079
；
For the
Figure 317029DEST_PATH_IMAGE080
Since the intelligent Road Side Unit (RSU) can record the request sent by the bus intelligent On Board Unit (OBU), it is equivalent to the request sent by the bus intelligent on board Unit (RSU)
Figure 37861DEST_PATH_IMAGE081
A process of accumulation; for the
Figure 203263DEST_PATH_IMAGE082
If the intersection can not obtain the number of real-time social vehicles, the number of the social vehicles can be solved by analyzing the arrival distribution of the vehicles according to historical track data; thus, the weighted DCP model derivation can be divided into three steps: (1) solving a DCP weighted value of the bus; (2) solving the DCP weighted value of the social vehicles based on the real-time social vehicle number or the social vehicle arrival distribution; (3) and solving the weighted total value of the DCP. And further deducing a weighted DCP model:
Figure 175898DEST_PATH_IMAGE083
as described above, the vehicle passing directions respectively correspond to the effective green light scene and the effective red light scene; the DCP weighted values of different vehicle types can be respectively solved according to scene division.
Preferably, the first step is obtained by a social vehicle number calculation formulaiSocial vehicle number in effective green light scene during phase
Figure 8725DEST_PATH_IMAGE084
The social vehicle number calculation formula is
Figure 290671DEST_PATH_IMAGE019
Wherein,
Figure 677790DEST_PATH_IMAGE085
the maximum red light duration is set as the maximum red light duration,
Figure 454116DEST_PATH_IMAGE086
the average permeability of the social vehicles is shown,
Figure 141449DEST_PATH_IMAGE087
is as followsiThe average number of vehicles arriving at society per unit time in phase,
Figure 594296DEST_PATH_IMAGE088
is as followsiAnd the standard deviation of the number of social vehicles is reached in unit time at the phase.
In one embodiment, the bus DCP weighting value can be solved according to the number of buses and the DCP model per bus, wherein the number of buses and the DCP model per bus are
Figure 734290DEST_PATH_IMAGE089
To obtain the social vehicle DCP weighted value, first, the number of social vehicles is calculated, the number of social vehicles is composed of two parts, the social vehicles arriving during the red light period and the actual green light period and the social vehicles passing through the intersection during the actual green light period, under the condition of vehicle arrival distribution and queue dissipation speed under the effective red light scene, the number of social vehicles is
Figure 907783DEST_PATH_IMAGE019
Solving the total weighted value of DCP as
Figure 324989DEST_PATH_IMAGE090
Preferably, the determining of the dynamic priority passing weight of the bicycle under the effective red light scene according to the phase minimum green light time, the current priority phase yellow light time and the maximum red light time specifically comprises:
determining the dynamic priority traffic weight of the single vehicle under the effective red light scene according to the phase minimum green light time, the current priority phase yellow light time and the maximum red light time and the dynamic priority traffic weight distribution model under the effective red light scene, wherein the dynamic priority traffic weight distribution model under the effective red light scene is
Figure 824103DEST_PATH_IMAGE024
Wherein,
Figure 576027DEST_PATH_IMAGE091
for the dynamic priority traffic weight of the bicycle under the effective red light scene,
Figure 818790DEST_PATH_IMAGE092
the remaining green time is the reception time of the traffic signal priority pass request,
Figure 90502DEST_PATH_IMAGE027
for the current phase the minimum green light duration,
Figure 494939DEST_PATH_IMAGE028
the maximum green light duration for the current phase,
Figure 734159DEST_PATH_IMAGE093
the remaining green light duration for the current phase,
Figure 780612DEST_PATH_IMAGE030
the current priority phase yellow lamp duration.
In one embodiment, the traffic direction illustrated in FIG. 3 is a red light scene; when not in storageDuring signal control, if a bus needs to be stopped and wait to be switched to a green light for passing when the bus enters a stop line of an intersection, the waiting time is closely related to the urgency degree of the priority passing requirement, and the longer the waiting time is, the more urgent the priority communication requirement is; thus, the remaining red light duration and the maximum red light duration are considered
Figure 906831DEST_PATH_IMAGE094
Constructing the dynamic priority traffic weight of the bicycle under the effective red light scene,
Figure 747748DEST_PATH_IMAGE095
considering the early-off control of the red light under the effective red light scene, according to the minimum green light time length protection principle, whether the early-off control of the red light can respond in real time or not is determined by the remaining green light time length of the current phase
Figure 474265DEST_PATH_IMAGE096
Then according to
Figure 58830DEST_PATH_IMAGE097
The difference is that the consumption time of the green light in the current phase is less than that of the green light in the current phase when the signal machine receives the priority passing request
Figure 164189DEST_PATH_IMAGE098
(ii) a The consumption time of the green light of the current phase is longer than the minimum green light time of the current phase
Figure 51374DEST_PATH_IMAGE099
。
For the first case, when a phase priority pass request is received, it is satisfied
Figure 140553DEST_PATH_IMAGE100
（
Figure 387863DEST_PATH_IMAGE101
Indicating that the current phase is the priority phase maximum green duration), and responsive to that phase being excellentPass through the request first
Figure 613308DEST_PATH_IMAGE102
（
Figure 936973DEST_PATH_IMAGE103
Indicating the current priority phase yellow light duration) time the red light is turned off to green, so the actual red light duration for that priority request phase would be
Figure 982290DEST_PATH_IMAGE104
(ii) a Defining the actual red light duration function in response to priority traffic requests
Figure 298870DEST_PATH_IMAGE105
；
For the second case, when a phase priority pass request is received, it is satisfied
Figure 378822DEST_PATH_IMAGE106
At this time, the red light cut-off control can be performed without waiting, and the actual red light duration of the phase is
Figure 607809DEST_PATH_IMAGE107
；
Therefore, a DCP model under a uniform effective red light scene is established, and the TTC is defined as
Figure 406001DEST_PATH_IMAGE108
And then the bicycle dynamic priority weight distribution model under the effective red light scene
Figure 260693DEST_PATH_IMAGE024
Preferably, determining the total value of the dynamic priority traffic weight of the bus and the social vehicle under the effective red light scene specifically comprises:
according to a dynamic priority passing weighted total value model under an effective red light scene, determining a bus and social vehicle dynamic priority passing weighted total value under the effective red light scene, wherein the dynamic priority passing weighted total value model under the effective red light scene is
Figure 195151DEST_PATH_IMAGE031
Wherein,
Figure 860619DEST_PATH_IMAGE109
and the dynamic priority traffic weighted total value is under the effective red light scene.
Preferably, the determining the distribution of the phases according to the total value of the dynamic priority traffic weight of the bus and the social vehicle in the effective green light scene and the total value of the dynamic priority traffic weight of the bus and the social vehicle in the effective red light scene specifically comprises:
acquiring a phase distribution function according to the total dynamic priority traffic weight value of the bus and the social vehicle in the effective green light scene and the total dynamic priority traffic weight value of the bus and the social vehicle in the effective red light scene, and determining the distribution of the phase according to the phase distribution function;
the phase distribution function is
Figure 880527DEST_PATH_IMAGE110
，j1 or 2, when the traffic direction waiting time is greater than the set threshold value,
Figure 148698DEST_PATH_IMAGE111
getxOtherwise, 1 is selected; the above-mentionedxIs a positive integer greater than 1 and is,
Figure 327875DEST_PATH_IMAGE112
is as followsiThe bus and the social vehicle dynamic priority traffic weight total value under the effective green light scene in the phase,
Figure 288878DEST_PATH_IMAGE113
and the dynamic priority traffic weighted total value is under the effective red light scene.
In one embodiment, sinceThe traffic flow at the intersection is likely to have the condition of uneven distribution, so that the total weighted value of the DCP in the traffic direction with small flow is easy to be continuously lower, and the traffic direction cannot acquire the phase distribution weight for a long time, so that in order to ensure that the traffic direction with low flow has a certain phase distribution weight and the traffic direction reaches the vehicle for a limited waiting time, a phase counter function is designed
Figure 937028DEST_PATH_IMAGE114
Satisfy the following requirements
Figure 743310DEST_PATH_IMAGE115
Waiting time in a certain traffic direction
Figure 42573DEST_PATH_IMAGE116
Greater than a threshold value
Figure 908898DEST_PATH_IMAGE117
，
Figure 778765DEST_PATH_IMAGE118
Is 100 at this time
Figure 388738DEST_PATH_IMAGE119
The reset value is 0, so as to avoid the situation that no vehicle arrives in a certain traffic direction for a long time, therefore, only when the traffic direction is detected to have the vehicle arrive,
Figure 378445DEST_PATH_IMAGE120
the basic principle of starting counting and phase priority assignment is to assign a priority to each phase at the end of a certain phase to determine the next phase and establish a phase assignment function
Figure 681250DEST_PATH_IMAGE121
Solving the phase parameter of the maximum weighted DCP value to satisfy
Figure 897468DEST_PATH_IMAGE122
。
In specific implementation, based on simulation software, a single intersection simulation environment is set up, the effective range of an intersection is set to be 500 meters, the inter-vehicle distance is set to be 8 meters when vehicles are parked, queued and waited, when each phase of a signal lamp is fixed, the traffic flow of the corresponding traffic direction of the phase, the traffic saturation flow rate of the phase, the permeability of the intelligent internet-connected bus, the passenger carrying rate configuration and the parameter configuration are shown in table 1,
TABLE 1
Figure 452077DEST_PATH_IMAGE123
Based on the experimental data, the results of the analysis were compared, as shown in table 2,
TABLE 2
Figure 70141DEST_PATH_IMAGE124
According to the table 2, the signal lamp phase distribution method in the intelligent networking environment can improve the operation efficiency of the intersection, and the average number of queued vehicles and the passing percentage of the vehicles in each phase are greatly improved.
Example 2
The embodiment of the invention provides a signal lamp phase allocation device in an intelligent network connection environment, which comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the signal lamp phase allocation method in the intelligent network connection environment is realized as in embodiment 1.
Example 3
An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for assigning signal lamp phases in an intelligent networking environment according to embodiment 1.
The invention discloses a signal lamp phase distribution method, a device and a computer readable storage medium in an intelligent networking environment, wherein the minimum green lamp time, the maximum green lamp time, the yellow lamp time, the residual green lamp time of the current phase, the minimum green lamp time of the current phase, the yellow lamp time and the maximum red lamp time of the current phase are obtained; determining the dynamic priority traffic weight of the single vehicle under the effective green light scene according to the phase minimum green light time, the maximum green light time, the yellow light time and the residual green light time of the current phase, and determining the total dynamic priority traffic weight value of the bus and the social vehicle under the effective green light scene; determining the dynamic priority traffic weight of the bicycle under the effective red light scene according to the phase minimum green light time, the current phase yellow light time and the maximum red light time, and determining the total value of the dynamic priority traffic weight of the bus and the social vehicle under the effective red light scene; determining the distribution of phases according to the total dynamic priority traffic weight values of the buses and the social vehicles under the effective green light scene and the total dynamic priority traffic weight values of the buses and the social vehicles under the effective red light scene; real-time priorities of the vehicles can be obtained to determine the allocation of the phases so as to meet the requirements of real-time signal control.
The technical scheme of the invention constructs the bus priority weight distribution model based on real-time data, avoids the problems of universality and rationality of the model constructed based on historical data, respectively considers the conditions of effective red light and effective green light, constructs the single-bus traffic weight distribution model by taking the remaining Time To Change (TTC) as a core input parameter, and simplifies the use process of real-time SPAT message data; on the basis of a single-vehicle traffic weight distribution model, bus passenger carrying rate parameters are introduced to support intersection running state evaluation taking the people average delay as an evaluation index, so that the people average delay can be reduced; the dissipation time of the secondary queuing is considered, and on the basis of meeting the minimum green light protection time, the emptying of the vehicles in the queue before switching in signal timing based on the model can be ensured, the secondary queuing is avoided, and the priority weight distribution is more reasonable.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims (6)

1. A signal lamp phase distribution method in an intelligent networking environment is characterized by comprising the following steps:
acquiring the minimum green light time, the maximum green light time and the yellow light time of a phase of a signal lamp, the residual green light time of a current phase, the minimum green light time of the current phase, the yellow light time and the maximum red light time of the current phase;
determining the dynamic priority traffic weight of the bicycle under the effective green light scene according to the phase minimum green light time, the maximum green light time, the yellow light time, the current phase green light residual time and the dynamic priority weight distribution model of the bicycle under the effective green light condition, wherein the dynamic priority weight distribution model of the bicycle under the effective green light condition is
Figure 182204DEST_PATH_IMAGE001
The above-mentioned
Figure 619002DEST_PATH_IMAGE002
For the dynamic priority traffic weight of the bicycle under the effective green light scene,
Figure 89035DEST_PATH_IMAGE003
the time is prolonged for the green light,
Figure 236858DEST_PATH_IMAGE004
the remaining time period for the current phase green light,
Figure 757838DEST_PATH_IMAGE005
is the phase minimum green time,
Figure 818066DEST_PATH_IMAGE006
The maximum duration of the green light is set,Ythe length of the yellow light is the length of the yellow light,
Figure 870336DEST_PATH_IMAGE007
the method comprises the steps of determining a bus and social vehicle dynamic priority passing weight total value under an effective green light scene according to a dynamic priority passing weight total value model under the effective green light scene by a dirac function, wherein the dynamic priority passing weight total value model is
Figure 307003DEST_PATH_IMAGE008
Wherein,
Figure 229959DEST_PATH_IMAGE009
is as followsiThe bus and the social vehicle dynamic priority traffic weight total value under the effective green light scene in the phase,
Figure 461089DEST_PATH_IMAGE010
is a factor of the passenger carrying rate of the public transport,
Figure 203917DEST_PATH_IMAGE011
first, theiThe number of buses at the current time in phase,
Figure 788482DEST_PATH_IMAGE012
is as followsiThe number of social vehicles in the phase effective green light scene;
determining the dynamic priority traffic weight of the single vehicle under the effective red light scene according to the phase minimum green light time, the current priority phase yellow light time and the maximum red light time and the dynamic priority traffic weight distribution model under the effective red light scene, wherein the dynamic priority traffic weight distribution model under the effective red light scene is
Figure 549634DEST_PATH_IMAGE013
Wherein,
Figure 702398DEST_PATH_IMAGE014
for the dynamic priority traffic weight of the bicycle under the effective red light scene,
Figure 791576DEST_PATH_IMAGE015
the remaining green time is the reception time of the traffic signal priority pass request,
Figure 570045DEST_PATH_IMAGE016
for the current phase the minimum green light duration,
Figure 670857DEST_PATH_IMAGE017
the maximum green light duration for the current phase,
Figure 119156DEST_PATH_IMAGE018
the remaining green light duration for the current phase,
Figure 85843DEST_PATH_IMAGE019
determining the total dynamic priority traffic weight value of buses and social vehicles under the effective red light scene according to the dynamic priority traffic weighted total value model under the effective red light scene for the yellow light duration of the current priority phase, wherein the dynamic priority traffic weighted total value model under the effective red light scene is
Figure 153157DEST_PATH_IMAGE020
Wherein,
Figure 623321DEST_PATH_IMAGE021
for dynamic priority traffic weighted total in a valid red light scene,
Figure 508100DEST_PATH_IMAGE022
the maximum red light duration is set as the maximum red light duration,
Figure 712817DEST_PATH_IMAGE023
the average permeability of the social vehicles is shown,
Figure 301930DEST_PATH_IMAGE024
is as followsiThe average number of vehicles arriving at society per unit time in phase,
Figure 642913DEST_PATH_IMAGE025
is as followsiThe standard deviation of the number of the social vehicles arriving at unit time in the phase;
and determining the distribution of the phases according to the total dynamic priority traffic weight value of the buses and the social vehicles under the effective green light scene and the total dynamic priority traffic weight value of the buses and the social vehicles under the effective red light scene.
2. The method for assigning signal light phases in an intelligent networking environment according to claim 1, wherein the green light extension time is obtained through a green light extension time calculation formula
Figure 698593DEST_PATH_IMAGE026
Wherein,
Figure 374294DEST_PATH_IMAGE027
as a function of the arrival rate of the vehicle,
Figure 783410DEST_PATH_IMAGE028
is the average arrival rate of the vehicles,Sin order to achieve the intersection saturated traffic flow rate,
Figure 228167DEST_PATH_IMAGE029
for the distance from the vehicle to the stop line,
Figure 923590DEST_PATH_IMAGE030
and the green light time length is remained at the receiving moment of the prior passing request of the signal machine.
3. The signal lamp phase assignment method in the intelligent networking environment according to claim 2, wherein the first signal lamp phase assignment method is obtained through a social vehicle number calculation formulaiSocial vehicle number in effective green light scene during phase
Figure 837319DEST_PATH_IMAGE031
The social vehicle number calculation formula is
Figure 909181DEST_PATH_IMAGE032
。
4. The method for distributing the phase of the signal lamp in the intelligent networking environment according to claim 1, wherein the distribution of the phase is determined according to the total dynamic priority traffic weight value of the bus and the social vehicle in the effective green light scene and the total dynamic priority traffic weight value of the bus and the social vehicle in the effective red light scene, and specifically comprises the following steps:
acquiring a phase distribution function according to the total dynamic priority traffic weight value of the bus and the social vehicle in the effective green light scene and the total dynamic priority traffic weight value of the bus and the social vehicle in the effective red light scene, and determining the distribution of the phase according to the phase distribution function;
the phase distribution function is
Figure 208444DEST_PATH_IMAGE033
，j1 or 2, when the traffic direction waiting time is greater than the set threshold value,
Figure 215714DEST_PATH_IMAGE034
getxOtherwise, 1 is selected; the above-mentionedxIs a positive integer greater than 1 and is,
Figure 866007DEST_PATH_IMAGE035
is as followsiThe bus and the social vehicle dynamic priority traffic weight total value under the effective green light scene in the phase,
Figure 148084DEST_PATH_IMAGE036
and the dynamic priority traffic weighted total value is under the effective red light scene.
5. A traffic light phase assignment device in an intelligent networking environment, comprising a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the traffic light phase assignment device in the intelligent networking environment implements the traffic light phase assignment method in any one of claims 1 to 4.
6. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method for assigning phases to beacon signals in an intelligent networking environment according to any of claims 1 to 4.
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