CN111383470A - System and method for realizing intelligent dynamic green wave control based on traffic big data - Google Patents
System and method for realizing intelligent dynamic green wave control based on traffic big data Download PDFInfo
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- CN111383470A CN111383470A CN201811620838.5A CN201811620838A CN111383470A CN 111383470 A CN111383470 A CN 111383470A CN 201811620838 A CN201811620838 A CN 201811620838A CN 111383470 A CN111383470 A CN 111383470A
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Abstract
The invention relates to a system for realizing intelligent dynamic green wave control based on traffic big data, which comprises a scheme selection module, a green wave band speed control module and a traffic big data control module, wherein the scheme selection module is used for dynamically matching a preset static green wave sub-scheme with the green wave band speed close to the vehicle driving speed according to different road vehicle driving speeds; the intersection signal fine-tuning function module is used for adjusting signal phase timing of intersections in real time according to traffic collection data of each intersection; and the program control module is used for starting the command, inquiring the current control mode command and recovering the autonomous control command. The invention also relates to a method for realizing intelligent dynamic green wave control based on traffic big data. By adopting the system and the method, the green wave scheme can be dynamically adjusted according to the actual traffic condition of the road, and the effect of coordinating trunk signals is maximally met while vehicles at the intersection pass; the method can ensure that the main road straight-going motorcade has high running speed and few parking times, and improves the road service level; the smoothness of traffic flow running and the actual traffic capacity of the road can be improved.
Description
Technical Field
The invention relates to the field of urban traffic, in particular to the field of urban traffic intelligent control, and specifically relates to a system and a method for realizing intelligent dynamic green wave control based on traffic big data.
Background
The green wave coordination control of the trunk road is an important control mode preferentially selected by an urban traffic signal control system, has obvious characteristics and obvious advantages, and can ensure that a trunk road fleet has high running speed, few parking times and high service level; the smoothness of traffic flow running and the actual traffic capacity of a road can be improved; the running speed of the vehicles on the road section can be adjusted, and the consistency of the running speed of the vehicles is enhanced; the system can promote drivers and pedestrians to better obey traffic signals, reduce traffic accidents at intersections, further improve the attraction and priority of urban trunk roads, and is more favorable for obtaining good signal coordination control effect.
At present, the green wave coordination control of the trunk road is mainly divided into two control modes of static green wave and dynamic green wave. The dynamic green wave has all the characteristics of the static green wave and also has the self-adaptive characteristic, the signal timing parameters can be dynamically adjusted according to the actual traffic condition, and the problem that the bidirectional green wave scheme realized by adopting the static green wave mode cannot be well adapted to the actual traffic condition of the road can be solved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a system and a method for realizing intelligent dynamic green wave control based on traffic big data, which meet the requirements of signal coordination, improve the traffic flow driving smoothness and improve the actual traffic capacity of a road.
In order to achieve the purpose, the system and the method for realizing intelligent dynamic green wave control based on traffic big data are as follows:
the method for realizing intelligent dynamic green wave control based on traffic big data is mainly characterized in that the system comprises:
the scheme selection module is used for dynamically matching a preset static green wave sub-scheme with the green wave band speed similar to the vehicle driving speed according to different road vehicle driving speeds;
the intersection signal fine-tuning function module is connected with the scheme selection module and is used for adjusting the signal phase timing of the intersections in real time according to the traffic acquisition data of each intersection;
and the program control module is connected with the intersection signal fine-tuning function module and used for inquiring the current control mode command and recovering the autonomous control command according to the issued dynamic green wave starting command.
The method for realizing intelligent dynamic green wave control based on traffic big data based on the system is mainly characterized by comprising the following steps:
(1) the scheme selection module dynamically matches a preset static green wave sub-scheme with the green wave band speed similar to the vehicle driving speed according to the different road vehicle driving speeds;
(2) the intersection signal fine-tuning function module adjusts signal phase timing of intersections in real time according to traffic collection data of each intersection;
(3) and the program control module issues a dynamic green wave starting command according to the result, inquires the current control mode command and recovers the autonomous control command.
Preferably, the step (1) specifically comprises the following steps:
(1.1) calculating the average speed of each vehicle in the section where the electric alarm point is located and the vehicle travel speed in the electric alarm section according to the vehicle passing data;
(1.2) judging whether the moment crosses a large scheme time interval, if so, continuing the step (1.3); otherwise, executing a default sub-scheme;
(1.3) judging whether a specific sub scheme is matched, if so, extracting the scheme in the time period and executing; otherwise, executing the default sub-scheme in the new big scheme.
Preferably, the step (1.1) specifically comprises the following steps:
(1.1.1) calculating the average speed of each vehicle in the section where the electric alarm point is located according to the vehicle passing data;
(1.1.2) carrying out speed molecular set counting treatment, and dividing the vehicles into different speed subsets;
and (1.1.3) recording the supremum speed of the speed subset corresponding to the largest number of vehicles as the vehicle travel speed of the electric warning interval.
Preferably, the step (2) specifically comprises the following steps:
(2.1) acquiring intersection data, and calculating the occupancy rate of each phase according to a Webster timing method;
(2.2) judgment of GzWhether or not greater than GZ0If yes, calculating the time after the main phase is increased, the secondary phase needing to be prolonged and the secondary phase needing to be reduced; otherwise, continue step (2.1).
Preferably, the step (3) specifically includes the following steps:
(3.1) judging whether the execution condition is met or not at present, if so, executing a sub-scheme to send a front-end signal machine; otherwise, continuing the step (1).
By adopting the system and the method for realizing intelligent dynamic green wave control based on traffic big data, the green wave scheme can be dynamically adjusted according to the actual traffic condition of the road, and the effect of coordinating trunk signals is maximally met while vehicles at the intersection pass; the method can ensure that the main road straight-going motorcade has high running speed and few parking times, and improves the road service level; the smoothness of traffic flow running and the actual traffic capacity of the road can be improved.
Drawings
Fig. 1 is a data thread diagram of the method for implementing intelligent dynamic green wave control based on traffic big data according to the present invention.
Fig. 2 is a flowchart of a scheme selection of a method for implementing intelligent dynamic green wave control based on traffic big data according to the present invention.
Fig. 3 is a flow chart of a fine-tuning scheme of the method for implementing intelligent dynamic green wave control based on traffic big data according to the present invention.
Fig. 4 is a program control flow chart of the method for implementing intelligent dynamic green wave control based on traffic big data according to the present invention.
Detailed Description
In order to more clearly describe the technical contents of the present invention, the following further description is given in conjunction with specific embodiments.
The system for realizing intelligent dynamic green wave control based on traffic big data comprises:
the scheme selection module is used for dynamically matching a preset static green wave sub-scheme with the green wave band speed similar to the vehicle driving speed according to different road vehicle driving speeds;
the intersection signal fine-tuning function module is connected with the scheme selection module and is used for adjusting the signal phase timing of the intersections in real time according to the traffic acquisition data of each intersection;
and the program control module is connected with the intersection signal fine-tuning function module and used for inquiring the current control mode command and recovering the autonomous control command according to the issued dynamic green wave starting command.
The method for realizing intelligent dynamic green wave control based on traffic big data based on the system comprises the following steps:
(1) the scheme selection module dynamically matches a preset static green wave sub-scheme with the green wave band speed similar to the vehicle driving speed according to the different road vehicle driving speeds;
(1.1) calculating the average speed of each vehicle in the section where the electric alarm point is located and the vehicle travel speed in the electric alarm section according to the vehicle passing data;
(1.1.1) calculating the average speed of each vehicle in the section where the electric alarm point is located according to the vehicle passing data;
(1.1.2) carrying out speed molecular set counting treatment, and dividing the vehicles into different speed subsets;
(1.1.3) recording the supremum speed of the speed subset with the maximum number of corresponding vehicles as the vehicle travel speed in the electric police district;
(1.2) judging whether the moment crosses a large scheme time interval, if so, continuing the step (1.3); otherwise, executing a default sub-scheme;
(1.3) judging whether a specific sub scheme is matched, if so, extracting the scheme in the time period and executing; otherwise, executing a default sub-scheme in the new scheme;
(2) the intersection signal fine-tuning function module adjusts signal phase timing of intersections in real time according to traffic collection data of each intersection;
(2.1) acquiring intersection data, and calculating the occupancy rate of each phase according to a Webster timing method;
(2.2) judgment of GzWhether or not greater than GZ0If yes, calculating the time after the main phase is increased, the secondary phase needing to be prolonged and the secondary phase needing to be reduced; otherwise, continuing the step (2.1);
(3) the program control module issues a dynamic green wave starting command according to the result, inquires the current control mode command and recovers the autonomous control command;
(3.1) judging whether the execution condition is met or not at present, if so, executing a sub-scheme to send a front-end signal machine; otherwise, continuing the step (1).
In the specific implementation mode of the invention, the dynamic green wave control method is based on the existing single-point multi-time-period timing scheme of the intersection, a plurality of static green wave sub-schemes are configured in each time period of the whole day, the static green wave sub-schemes with the green wave band speed close to the vehicle driving speed are dynamically matched in each time period according to the difference of the road vehicle driving speed, and then on the basis, the signal phase timing of the intersection is adjusted in real time based on the traffic acquisition data of each intersection, so that the green wave bandwidth acquisition rate is ensured, the actual traffic requirements of the intersection are met, and the effect of main line coordination between the intersections is maximally met.
The whole intelligent dynamic green wave control is realized through a scheme selection module, an intersection signal fine adjustment module and a program control module:
scheme selection module
The module dynamically matches a preset static green wave sub-scheme with the green wave band speed similar to the vehicle driving speed according to the difference of the road vehicle driving speed in each time interval on the basis of the existing single-point multi-time interval timing scheme of the intersection.
Step 1: the speed of extraction. And calculating the average speed of each vehicle in the interval where the electric alarm point is located by using the vehicle passing data acquired by the electric alarm in a data collision mode. Then, speed sub-set counting processing is carried out, namely, the speed is divided into a plurality of speed sub-sets, each vehicle is respectively recorded into different speed sub-sets according to different speeds, and the supreme speed of the speed sub-set with the maximum vehicle number corresponding to the speed sub-sections is taken to represent the vehicle travel speed of the two electric alarm sections;
step 2: judging whether the moment crosses a large scheme time interval, if so, executing according to the matched scheme, and if not, executing by using a default sub-scheme in the new large scheme (the sub-scheme with the closest acquired speed); otherwise, directly extracting the scheme in the time interval, if the extraction is successful, according to the extracted scheme, otherwise, not performing any operation;
second, crossing signal fine-tuning functional module
On the basis of a static green wave sub scheme matched with the scheme selection module, the signal phase timing of each intersection is adjusted in real time based on traffic acquisition data of each intersection, so that the green wave bandwidth acquisition rate is ensured, the actual traffic requirements of the intersections are met, and the effect of main line coordination among the intersections is maximally met.
Description of the parameters:
i: the number of phases;
j: the number of secondary phases;
t: a green wave period;
Gz: the primary phase requires an increased split;
Gc: the secondary phase requires a reduced split;
G′c: the secondary phase requires an increased split;
GZ0: a primary phase initial split;
GZm: the green signal ratio of the main phase after the mth adjustment;
G′Z0: the initial split ratio of the main phase is a certain proportion;
G′c0: the initial split ratio of the secondary phase is a certain proportion;
tmax: maximum green;
tmin: minimum green
And step 3: acquiring intersection data, and calculating the occupancy of each phase by using a Webster (Webster) timing method;
and 4, step 4: judgment GzAnd GZ0In the context of (a) or (b),when G isz＞GZ0If so, then 1), 2), and 3) are performed; otherwise, executing step 3;
1) when the time required for the primary phase to increase is greater than the time that the secondary phases can decrease in total, i.e., Mix { (G)z-Gz0)*T，G′z0*T，tmax-Gz0*T}＞∑Max{(Gc0-Gc)*T,Gc′0*T,Gc0*T-tmin}, then
a. The time after the increase of the main phase is:
∑Max{(Gc0-Gc)*T,G′c0*T,Gc0*T-tmin}+Gz0*T；
b. the secondary phase, which requires an extended time, remains unchanged;
c. the secondary phase times that require time reduction are:
Gc0*T-Max{(Gc0-Gc)*T,G′c0*T,Gc0*T-tmin}
2) when the time required for the main phase to increase is less than or equal to the time required for the secondary phase to decrease, and the time required for the secondary phase to decrease is less than or equal to the time required for the main phase and the secondary phase to increase, that is
Mix{(Gz-Gz0)*T，G′z0*T，tmax-Gz0*T}≤∑Max{(Gc0-Gc)*T,G′c0*T,Gc0*T-tmin}≤Mix{(Gz-Gz0)*T，G′c0*T}+Mix{(Gc-Gc0)*T,G′c0*T,tmax-Gz0*T}，
Then
a. The time after the increase of the main phase is:
Mix{(Gz-Gz0)*T，G′z0*T，tmax-Gz0*T}+Gz0*T；
b. a secondary phase requiring a prolonged period of time;
∑Max{(Gc0-Gc)*T,G′c0*T,Gc0*T-tmin}-
Mix{(Gz-Gz0)*T，G′z0*T，tmax-Gz0*T}+G′c0*T
c. the secondary phase times that require time reduction are:
Gc0*T-Max{(Gc0-Gc)*T,G′c0*T,Gc0*T-tmin}
3) when the total amount of time that the primary and secondary phases need to be added is less than the total time that the secondary phases can decrease, i.e., Mix { (G)z-Gz0)*T，G′z0*T，tmax-Gz0*T}+Mix{(Gc-Gc0)*T,G′c0*T,tmax-Gz0*T}＜∑Max{(Gc0-Gc)*T,G′c0*T,Gc0*T-tmin}, then
a. The time after the increase of the main phase is:
Mix{(Gz-Gz0)*T，G′z0*T，tmax-Gz0*T}+Gz0*T；
b. a secondary phase requiring a prolonged period of time;
Mix{(Gc-Gc0)*T,G′c0*T,tmax-Gc0*T}+Gc0*T
c. the secondary phase times that require time reduction are:
Gc0*T-Max{(Gc0-Gc)*T,G′c0*T,Gc0*T-tmin}
+(Gci/∑Gci)*{∑Max{(Gc0-Gc)*T,G′c0*T,Gc0*T-tmin}
-Mix{(Gz-Gz0)*T，G′z0*T，tmax-Gz0*T}-Mix{(Gc-Gc0)*T,G′c0*T,tmax-Gz0*T}}
third, program control module
This block specifies the release limits and conditional information for intelligent dynamic green wave control. The module directly communicates with the central machine by means of the link of the optimizing machine. And the results of the dynamic green wave selection module and the intersection signal fine adjustment module are used as input to issue a dynamic green wave starting command, inquire a current control mode command and recover an autonomous control command.
And 5: and judging whether the execution condition is met currently or not according to the extracted execution sub-scheme (the sub-scheme cannot be issued twice continuously within 10 minutes). If the sub-scheme is satisfied, the sub-scheme is issued to a front end signal machine for execution; if not, returning to the step 1.
By adopting the system and the method for realizing intelligent dynamic green wave control based on traffic big data, the green wave scheme can be dynamically adjusted according to the actual traffic condition of the road, and the effect of coordinating trunk signals is maximally met while vehicles at the intersection pass; the method can ensure that the main road straight-going motorcade has high running speed and few parking times, and improves the road service level; the smoothness of traffic flow running and the actual traffic capacity of the road can be improved.
In this specification, the invention has been described with reference to specific embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.
Claims (6)
1. A system for realizing intelligent dynamic green wave control based on traffic big data is characterized by comprising:
the scheme selection module is used for dynamically matching a preset static green wave sub-scheme with the green wave band speed similar to the vehicle driving speed according to different road vehicle driving speeds;
the intersection signal fine-tuning function module is connected with the scheme selection module and is used for adjusting the signal phase timing of the intersections in real time according to the traffic acquisition data of each intersection;
and the program control module is connected with the intersection signal fine-tuning function module and used for inquiring the current control mode command and recovering the autonomous control command according to the issued dynamic green wave starting command.
2. A method for implementing intelligent dynamic green wave control based on traffic big data by using the system of claim 1, wherein the method comprises the following steps:
(1) the scheme selection module dynamically matches a preset static green wave sub-scheme with the green wave band speed similar to the vehicle driving speed according to the different road vehicle driving speeds;
(2) the intersection signal fine-tuning function module adjusts signal phase timing of intersections in real time according to traffic collection data of each intersection;
(3) and the program control module issues a dynamic green wave starting command according to the result, inquires the current control mode command and recovers the autonomous control command.
3. The method for implementing intelligent dynamic green wave control based on traffic big data as claimed in claim 3, wherein the step (1) specifically comprises the following steps:
(1.1) calculating the average speed of each vehicle in the section where the electric alarm point is located and the vehicle travel speed in the electric alarm section according to the vehicle passing data;
(1.2) judging whether the moment crosses a large scheme time interval, if so, continuing the step (1.3); otherwise, executing a default sub-scheme;
(1.3) judging whether a specific sub scheme is matched, if so, extracting the scheme in the time period and executing; otherwise, executing the default sub-scheme in the new big scheme.
4. The method for implementing intelligent dynamic green wave control based on traffic big data as claimed in claim 3, wherein the step (1.1) specifically comprises the following steps:
(1.1.1) calculating the average speed of each vehicle in the section where the electric alarm point is located according to the vehicle passing data;
(1.1.2) carrying out speed molecular set counting treatment, and dividing the vehicles into different speed subsets;
and (1.1.3) recording the supremum speed of the speed subset corresponding to the largest number of vehicles as the vehicle travel speed of the electric warning interval.
5. The method for implementing intelligent dynamic green wave control based on traffic big data as claimed in claim 3, wherein said step (2) specifically comprises the following steps:
(2.1) acquiring intersection data, and calculating the occupancy rate of each phase according to a Webster timing method;
(2.2) judgment of GzWhether or not greater than GZ0If yes, calculating the time after the main phase is increased, the secondary phase needing to be prolonged and the secondary phase needing to be reduced; otherwise, continue step (2.1).
6. The method for implementing intelligent dynamic green wave control based on traffic big data according to claim 1, wherein the step (3) specifically comprises the following steps:
(3.1) judging whether the execution condition is met or not at present, if so, executing a sub-scheme to send a front-end signal machine; otherwise, continuing the step (1).
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Application publication date: 20200707 |