CN205959418U - Traffic signals control system based on combination control - Google Patents
Traffic signals control system based on combination control Download PDFInfo
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- CN205959418U CN205959418U CN201620588159.4U CN201620588159U CN205959418U CN 205959418 U CN205959418 U CN 205959418U CN 201620588159 U CN201620588159 U CN 201620588159U CN 205959418 U CN205959418 U CN 205959418U
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Abstract
The utility model relates to a traffic signals control system based on combination control, the system includes host system, AD conversion module, IO expander circuit, storage module, drive module, display module, power module and key entry module, host system connects respectively IO expander circuit, storage module, drive module, display module, power module and key entry module, host system passes through ethernet connection control platform, the control signal who is used for the reception control platform to send transmits real -time traffic data to control cabinet simultaneously, the detector passes through AD conversion module and connects IO expander circuit, drive module connects the signal lamp. Utilizing the vehicle test data, adopting SOM neural network to discern crossing traffic state, according to crossing traffic state, fuzzy controller structure that dynamic selection is suitable has reduced the average delay of vehicle effectively, characteristics such as have that adaptability is good, control performance is excellent, with low costs.
Description
Technical field
This utility model belong to intelligent transportation optimization control field and in particular to a kind of based on combination control traffic signal
Control system.
Background technology
Crossing, as the important component part in urban traffic network, is to realize urban transportation by putting and line optimizes to face
The important foundation ingredient controlling.In view of the complexity of urban transportation, strong dynamic, traditional traffic control mode is very
Difficulty meets the requirement of transport development.Intelligent control technology is to solve traffic problems to provide effective guarantee.At present to traffic signal
Intelligent optimal control, adopts the policy optimization crossing phase sequence of " jump phase " more, and phase place change frequently, easily misleads pedestrian and driver,
It is difficult to promote the use of.Single the two-stage fuzzy controller or single-stage fuzzy control model are it is impossible to well adapt to the change of vehicle flowrate
Change.When crossing wagon flow is larger, using the two-stage fuzzy controller, effectively reduce the mean delay of vehicle, control effect is good.Low
In the case of wagon flow, entrance driveway vehicle flowrate very little, so that the two-stage fuzzy controller is equal to minimum period control, leads to newly-increased vehicle
Need to wait for passing through, average stop frequency increases, control performance is deteriorated.In the case of saturation flow, the mean delay of vehicle is very
Greatly, before and after optimization, fitness function change is very small, and two-stage fuzzy controller updates unsuccessfully.
Utility model content
In order to solve the problems referred to above of prior art presence, this utility model provides a kind of traffic controlling based on combination
Whistle control system.Using SOM neutral net can effectively identify intersection traffic rheology, determine the traffic shape of crossing
State.Using combination control thought, according to the traffic behavior at crossing, select single-stage fuzzy control under low wagon flow or saturation flow
Device, selects single-stage fuzzy controller under middle wagon flow or high wagon flow, is dynamically selected control strategy, implementation strategy self adaptation is cut
Change.
The technical scheme that this utility model is adopted is：
A kind of traffic signal control system being controlled based on combination, it thes improvement is that：Described system includes master control mould
Block, A/D modular converter, I/O expanded circuit, memory module, drive module, display module, power module and input through keyboard module；
Described main control module connects described I/O expanded circuit, memory module, drive module, display module, power supply mould respectively
Block and input through keyboard module；Described main control module connects control station by Ethernet；For receiving the control letter that control station sends
Number, real time traffic data is transferred to control station simultaneously；
Described detector connects I/O expanded circuit by A/D modular converter；Described drive module connects signal lighties.
Further, described main control module adopts S3C6410 processor.
Further, described main control module is provided with hand switch.
The beneficial effects of the utility model are：
This utility model utilizes vehicle detection data, using SOM neutral net, intersection traffic state is identified, according to
According to traffic state at road cross, the suitable structure of fuzzy controller of dynamic select, effectively reduce the mean delay of vehicle, have suitable
Answering property is good, control performance is excellent, low cost the features such as.
Two sets and above many sets control system are combined by this utility model using combination control tactics, retain each set
The advantage of control system, overcomes respective shortcoming, so that the performance of system reaches optimum.By single-stage fuzzy control and two-stage mould
Paste controls two kinds of strategies to combine, and adopts corresponding control strategy to different traffic behaviors, can retain two kinds of tactful advantages.
Brief description
The traffic signal control system structural representation being controlled based on combination that Fig. 1 provides for this utility model；
The traffic signalization schematic flow sheet being controlled based on combination that Fig. 2 provides for this utility model；
The integrative design intersection principle schematic that Fig. 3 provides for this utility model；
The traffic behavior clustering recognition schematic diagram based on SOM that Fig. 4 provides for this utility model；
The two-stage fuzzy controller structural representation that Fig. 5 provides for this utility model；
The two-stage fuzzy controller Optimizing Flow schematic diagram that Fig. 6 provides for this utility model.
Specific embodiment
As shown in figure 1, this utility model also includes a kind of traffic signal control system controlling based on combination, described system
Including main control module, A/D modular converter, I/O expanded circuit, memory module, drive module, display module, power module and key
Disk input module；
Described main control module connects described I/O expanded circuit, memory module, drive module, display module, power supply mould respectively
Block and input through keyboard module；Described main control module connects control station by Ethernet；For receiving the control letter that control station sends
Number, real time traffic data is transferred to control station simultaneously；
Described detector connects I/O expanded circuit by A/D modular converter；Described drive module connects signal lighties.
Described main control module adopts S3C6410 processor.
Described main control module is provided with hand switch.
As shown in Figure 2：A kind of traffic signal control system being controlled based on combination of this utility model, its concrete operations mode
For：
Step 1：Set the maximum queuing vehicle number Q of each phase place in 4 phase placesmax, maximum entrance vehicle flowrate λmax, the shortest green
Lamp time GminWith maximum green perild Gmax；
Step 2：As shown in figure 3, crossing is 4 phase controlling of standard, do not consider right turn wagon flow, between with 150m being
Away from arranging that on every track former and later two sense detection coil, record queuing vehicle number, inlet flow rate and leave vehicle；
Step 3：As shown in figure 4, clustering recognition is carried out to the historical traffic flow data of crossing using SOM network, select
, as the parameter of the traffic status identification of crossing, wherein average speed is crossing in a cycle for average speed and vehicle flowrate
The average speed of all motor vehicles, unit is km/h, and the number of vehicles that vehicle flowrate refers to each vehicle in crossing in a cycle is multiplied by
Sum after corresponding vehicle proportion, unit is pcu/h；By jam level is divided to historical traffic stream Parameter Clustering, it is divided into
Low wagon flow, middle wagon flow, high wagon flow and 4 grades of saturation flow；Low wagon flow, middle wagon flow, high wagon flow and saturation flow are respectively with number
Word 1,2,3,4 replaces；
Step 4：The traffic flow parameter being obtained according to Real-time Collection, using receding horizon, each cycle internal controller is held
The traffic behavior at next cycle crossing after row 55s, is determined using SOM neutral net；
Step 5：If step 4 draws intersection in middle wagon flow or high wagon flow state, if selecting two-stage fuzzy controller, such as
Fig. 5 show two-stage fuzzy controller structural representation, and the 1st grade of 2 parallel controller green lights crossed by two-stage fuzzy controller bag
The decision-making module of degree of urgency determination module, next phase place red light degree of urgency determination module and the 2nd grade, the 1st grade of 2 parallel control
Device is used for judging the degree of urgency of each phase place, and the 2nd grade of decision-making module is used for determining the prolongation time of current green light, each obscures
Controller has two input variables and an output variable, is all divided into 7 fuzzy subsets MS, VS, S, M, L, VL, ML, choosing
Select Triangleshape grade of membership function, then execution step 6；If step 4 show that intersection, in low wagon flow or saturation flow state, is selected
Select single-stage fuzzy controller, using the queuing vehicle number of green light phase place and next red light phase place as input, controller has two
Input variable and an output variable, are all divided into 7 fuzzy subsets MS, VS, S, M, L, VL, ML, select triangle degree of membership
Function；Execution step 7；
Step 6：Determine the prolongation time of green light phase place and to two-stage fuzzy controller optimization；
(1) give initial green light time G of current clearance wagon flow phase place ii=Gmin, wherein, GminFor Minimum Green Time；
(2) green light terminates front 2s, the traffic flow parameter being arrived according to detection coil real-time detection, true by two-stage fuzzy controller
Determine green extension Δ Gi.If Δ Gi+Gi＞ Gmax, then Δ Gi=Gmax-Gi；Wherein, GmaxFor maximum green time；
(3) if Δ GiMore than 8s, current green light phase place enters line delay, return to step (6.2)；If Δ GiLess than 8s, then phase place
Switching, current wagon flow stops letting pass, and next phase place wagon flow starts to let pass, and return to step (6.1), when 4 phase places all run knot
Shu Shi, current demand signal end cycle, return to step 4 starts next signal period, and two-stage fuzzy controller parameter is carried out excellent
Change, execution step (6.4)；Wherein, the Minimum Green Time of 4 phase places is Gmin=15s, straight trip phase place 1 and phase place 3 are
Big green time is Gmax=70s, the maximum green time G of left turn phase 2 and 4max=50s；
(4) calculate the mean delay of vehicle in this cycle, using the inverse of the mean delay of vehicle as individual adaptation degree letter
Number, using improving the Chaos Genetic Algorithm membership function of optimal controller and control rule simultaneously, belongs to coding using real, as schemed
Shown in 6, optimization process includes；
1) initialize：Setting population scale, evolutionary generation, the initial value of Logistic chaos sequence, intersect and make a variation is general
Rate；
2) evaluate：The inverse selecting vehicles average delay, as the fitness function of algorithm, takes into account the average of vehicle simultaneously
Stop frequency, calculates individual adaptation degree by formula (3).
Fit (d)=1/d (3)
hi=0.9 (1- λi)/(1-xi) (4)
In formula：The mean delay of vehicle in 1 cycle of d；The average stop frequency of vehicle in 1 cycle of h；diThe
The mean delay of i phase place vehicle；hiThe average stop frequency of the i-th phase place vehicle；C cycle duration；λiI-th phase place green
Letter ratio；qiI-th phase place entrance driveway vehicle arriving rate；xiThe saturation of the i-th phase place.
3) select：Selected using roulette robin.
4) chaotic crossover：The population generating through selection operation is randomly choosed two individual pairings, crossover operation divides two
Duan Jinhang, carries out arithmetic crossover in membership function coding section and control rule encoding section according to chaotic crossover rule respectively, until
Produce new population.
5) chaotic mutation：To the new individual producing through chaotic crossover, carry out mutation operation, two sections of variation point is carried out, point
Do not carry out multipoint random variation in membership function coding section and control rule encoding section according to chaotic mutation rule；
6) elite retention strategy：Worst 5 individualities in population of new generation are replaced with fitness value highest in parent population
5 individualities；
7) evaluation algorithm end condition：If cycle-index is less than maximum algebraically, return 2)；Otherwise, output optimum individual and
Minimum average B configuration delay value；
Step 7：Determine the duration G of current clearance wagon flow phase place ii；
(1) the queuing vehicle number l according to current green light phase placeGQueuing vehicle number l with next red light phase placeR, using single-stage
The rule that controls of fuzzy controller determines the duration G of current clearance wagon flow phase place ii, control rule as shown in table 1；
Table 1 single-stage fuzzy Control rule
(2) if Gi<Gmin, then Gi=GminIf, Gi>Gmax, then Gi=Gmax, current green time arrival GiWhen, current wagon flow
Stop letting pass, next phase place starts to let pass, return to step 7 (1)；
(3) when the whole end of run of 4 phase places, current demand signal end cycle, return to step 4 starts next signal week
Phase.
This utility model is not limited to above-mentioned preferred forms, and anyone can draw under enlightenment of the present utility model
Other various forms of products, however, making any change in its shape or structure, every have same as the present application or phase
Approximate technical scheme, all falls within protection domain of the present utility model.
Claims (3)
1. a kind of based on combination control traffic signal control system it is characterised in that：Described system includes main control module, A/D
Modular converter, I/O expanded circuit, memory module, drive module, display module, power module and input through keyboard module；
Described main control module connect respectively described I/O expanded circuit, memory module, drive module, display module, power module and
Input through keyboard module；Described main control module connects control station by Ethernet；For receiving the control signal that control station sends, with
When real time traffic data is transferred to control station；
Detector connects I/O expanded circuit by A/D modular converter；Described drive module connects signal lighties.
2. according to claim 1 a kind of based on combination control traffic signal control system it is characterised in that：Described master
Control module adopts S3C6410 processor.
3. according to claim 2 a kind of based on combination control traffic signal control system it is characterised in that：Described master
Control module is provided with hand switch.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201620588159.4U CN205959418U (en) | 2016-06-16 | 2016-06-16 | Traffic signals control system based on combination control |
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| CN201620588159.4U CN205959418U (en) | 2016-06-16 | 2016-06-16 | Traffic signals control system based on combination control |
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| CN205959418U true CN205959418U (en) | 2017-02-15 |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105869417A (en) * | 2016-06-16 | 2016-08-17 | 兰州理工大学 | Traffic signal control method and system based on combined control |
| CN109509357A (en) * | 2018-12-25 | 2019-03-22 | 上海慧昌智能交通系统有限公司 | A kind of traffic control method and equipment |
| CN110136443A (en) * | 2019-05-24 | 2019-08-16 | 辽宁工业大学 | A kind of traffic lights optimization method based on vehicle running state |
| CN111583672A (en) * | 2020-04-09 | 2020-08-25 | 江苏中科院智能科学技术应用研究院 | Intelligent traffic light control method, system and device |
| CN111739284A (en) * | 2020-05-06 | 2020-10-02 | 东华大学 | Traffic signal lamp intelligent timing method based on genetic algorithm optimization fuzzy control |
| CN116189459A (en) * | 2023-04-26 | 2023-05-30 | 西南民族大学 | Intersection traffic signal lamp timing method |
-
2016
- 2016-06-16 CN CN201620588159.4U patent/CN205959418U/en not_active Expired - Fee Related
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105869417A (en) * | 2016-06-16 | 2016-08-17 | 兰州理工大学 | Traffic signal control method and system based on combined control |
| CN105869417B (en) * | 2016-06-16 | 2019-07-23 | 兰州理工大学 | A kind of traffic signal control method and system based on combination control |
| CN109509357A (en) * | 2018-12-25 | 2019-03-22 | 上海慧昌智能交通系统有限公司 | A kind of traffic control method and equipment |
| CN109509357B (en) * | 2018-12-25 | 2021-01-01 | 上海慧昌智能交通系统有限公司 | Traffic control method and equipment |
| CN110136443A (en) * | 2019-05-24 | 2019-08-16 | 辽宁工业大学 | A kind of traffic lights optimization method based on vehicle running state |
| CN110136443B (en) * | 2019-05-24 | 2020-09-29 | 辽宁工业大学 | Traffic signal lamp optimization method based on vehicle driving state |
| CN111583672A (en) * | 2020-04-09 | 2020-08-25 | 江苏中科院智能科学技术应用研究院 | Intelligent traffic light control method, system and device |
| CN111583672B (en) * | 2020-04-09 | 2021-11-12 | 江苏中科院智能科学技术应用研究院 | Intelligent traffic light control method, system and device |
| CN111739284A (en) * | 2020-05-06 | 2020-10-02 | 东华大学 | Traffic signal lamp intelligent timing method based on genetic algorithm optimization fuzzy control |
| CN116189459A (en) * | 2023-04-26 | 2023-05-30 | 西南民族大学 | Intersection traffic signal lamp timing method |
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Legal Events
| Date | Code | Title | Description |
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| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20170215 Termination date: 20190616 |
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| CF01 | Termination of patent right due to non-payment of annual fee |