CN107680393A - Intelligent control method of crossroad traffic signal lamp based on time-varying domain - Google Patents
Intelligent control method of crossroad traffic signal lamp based on time-varying domain Download PDFInfo
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Abstract
The invention discloses an intelligent control method for a crossroad traffic signal lamp based on a time-varying domain. Firstly, collecting traffic data by using a detector of a crossroad; evaluating the congestion condition of the crossroad according to the maximum queuing length of the phase in the current green light direction and the queuing length data of each phase in the red light direction, and obtaining a domain of the cycle length under the current traffic flow condition; under the cycle length domain, taking the maximum queuing length in the phase of the current green light direction and the average parking times of each vehicle as input, taking the green light time length distributed by the phase of the current green light direction as output, and listing dynamic fuzzy rules; performing clearness calculation by using a gravity center method to obtain the duration of a green light; and comprehensively considering the traffic flow of the crossroad and the traffic safety of drivers, setting a limiting condition, obtaining the final green light time length, and finishing the optimization of the timing scheme. The invention has the advantages of effectively enhancing the traffic capacity of the road intersection and reducing the vehicle delay time.
Description
Technical field
The invention belongs to intelligent transport system field, more particularly to a kind of traffic lights real-time time-mixed method.
Background technology
Timing signal timing method, mainly there is Robert Webster (Webster) timing method of Britain in the world, on its basis
On, Australian ARRB (Australian Road Research Board) timing method considers over-saturation traffic conditions,
It is the amendment and extension of Webster Delay Models.HCM (Highway Capacity Man) the timing method in the U.S. is used also more
Extensively.There are conflict point method, critical lane method, estimation algorithm, Shanghai Comprehensive algorithm in China.Wherein Shanghai Comprehensive algorithm be
On the basis of domestic and international existing signal timing dial method, proposed with reference to China's traffic feature, its timing delay estimation and U.S. HCM
Algorithm is identical.
With rapid development of economy, vehicles number sharply increases, and transport need expands rapidly, timing signal timing
Method is unable to reach the requirement for efficiently improving urban traffic blocking situation.
With the expansion of intelligent transportation system application and increasing for application region, the real-time control of traffic signals and excellent
Change, cause the concern of increasing domestic and foreign scholars.But because City road traffic system randomness is extremely strong, even in compared with
In short time, traffic parameter can also have greatly changed, and the real-time time-mixed to traffic lights brings very big difficulty.And retouching
When stating the state of things, the situation that changes over time and change for domain, when variable universe and dynamic fuzzy rule, solve people
The problem of being difficult to the membership function of ambiguity in definition set, also provide method to analysis of complex system.The present invention is based on
When variable universe, using dynamic fuzzy rule, dynamic adjustment traffic lights Cycle Length and long green light time, complete four crossway oral sex in real time
The real-time time-mixed of ventilating signal lamp, effectively strengthen the road junction traffic capacity and reduce the vehicle delay time at stop.
The content of the invention
The present invention gives it is a kind of based on when variable universe crossroad access signal lamp intelligent control method, this method
Comprise the following steps：
Step S1：Using the detector of intersection, traffic data needed for collection；
Step S2：With the data obtained, with the maximum queue length in the phase of current green light direction and current red light direction
Queue length data in each phase are foundation, to evaluate intersection congestion level, are drawn in the case of current flows, the cycle
The domain of length；
Step S3：Using current period length as domain, with the maximum queue length in the phase of current green light direction and each
The average stop frequency of car is input, and the long green light time of current green light direction phase assignments is output, lists dynamic fuzzy rule；
Step S4：Sharpening calculating is carried out using gravity model appoach, draws long green light time；
Step S5：Gained long green light time is judged, constraints is set, completes timing scheme.
Wherein described step S1 is further：Required traffic data includes the maximum queuing in the phase of current green light direction
Queue length rL on length gL, the current each phase in red light directionjAnd the average stop frequency of each carEach car is averagely stopped
Train number number is that the vehicle for rolling intersection in the phase of current green light direction away from averagely runs into the number of red light.
Wherein, the step S2 further comprises the steps：
Step S21：Cycle Length c 40~180s of OK range, if the domain of Cycle Length is Ω (t), by time-varying
Cycle Length c domain is divided into consecutive hours variable universe sequence under domain：{Ωk(t) }, (k ∈ N), Ωk(t)=[0,40+
20k], wherein k=1,2 ... ..6,7；
Step S22：The 4 grades of service levels suggested using urban planning and design academy of Beijing, to be averaged during red light therein
Queue length item is foundation, to evaluate intersection congestion level.According to 4 grades of service levels of its suggestion, queue length L during red light
It is unobstructed VS less than 50 meters, between 50 meters and 100 meters be more unobstructed NS, is compared with congestion NJ, super between 100 meters and 150 meters
It is congestion VJ to cross 150 meters；By obtained gL and rLjThe digital form of data, according to the 4 of suggestion grades of service levels, be converted to word
VS, NS, NJ, VJ form；
Step S23：According to the combined situation of keyword, draw in the case of current flows, the domain of Cycle Length.
Wherein, the step S3 further comprises the steps：
Step S31：Input quantity gL,Output quantity TlBlurring；
Step S32：Cycle Length domain is consecutive hours variable universe sequence：{Ωk(t) }, (k ∈ N), when domain is sent out with the time
During changing, the membership function for the fuzzy set being defined on domain can also change with the time, respectively in each Ωk(t)
Upper setting input quantity gL and rLj, output quantityMembership function corresponding to each Linguistic Value；
Step S33：Consider all possible situation, when listing under variable universe, the domain of Cycle Length is ΩkWhen (t)
Dynamic fuzzy rule Rk。
Wherein, the step S4 further comprises the steps：
Step S41：Fuzzy relationship matrix r is obtained by dynamic fuzzy rulei, total fuzzy rule is R；
Step S42：The fuzzy set of corresponding output variable is obtained according to total fuzzy relation and push-pull picklingline；
Step S43：The fuzzy quantity that fuzzy reasoning is obtained is converted into the clear amount in present domain, carries out sharpening meter
Calculate, draw long green light time.
Wherein, the step S5 further comprises the steps：
Step S51：Consider intersection traffic flow and the current safety of driver, alleviation traffic congestion and reduction are rushed red
Lamp phenomenon, set and remind, if certain phase red light duration, more than 130s, system receives prompting 1；
Step S52：When system receives prompting 1, by the prediction of short-term traffic flow, become according to the change of the traffic flow of prediction
Gesture, adjust the transit time of current green light phase；
Step S53：Tl≤Nmax, NmaxFor the maximum of transit time, set according to the magnitude of traffic flow at actual crossing, the value
Preferably not more than 80s, set and remind, if long green light time is more than Nmax, then system receive prompting 2；
Step S54：When system receives prompting 2, current green light phase transit time is adjusted, sets it maximum for transit time
Value.
The beneficial effects of the invention are as follows：Distribute by dynamic adjustment traffic lights Cycle Length and in real time the length of long green light time
It is short, make to add by the vehicle fleet size of intersection, maximum queue length and average queue length are shortened, and being averaged for day part is prolonged
Mistake all have dropped, and the timing scheme effectively alleviates intersection congestion.
Brief description of the drawings
Fig. 1 is intersection traffic signal lamp real-time time-mixed method frame figure of the present invention；
Fig. 2 is step S2 flow charts of the present invention；
Fig. 3 is this intersection Four-phase control schematic diagram；
Fig. 4 is that gL in Cycle Length domain is Ω1And Ω (t)2(t) membership function when；
Fig. 5 is that gL in Cycle Length domain is Ω3And Ω (t)4(t) membership function when.
Embodiment
The present invention is described in detail below in conjunction with accompanying drawing, it is noted that described embodiment is only intended to just
In the understanding of the present invention, and any restriction effect is not played to it.
The present invention provides a kind of intersection traffic signal lamp real-time time-mixed method.As shown in figure 1, specifically, this method bag
Include following steps：
Step S1：Using the detector of intersection, traffic data needed for collection；
The data that this method needs to gather include the maximum queue length gL in the phase of current green light direction, current red light side
To the queue length rL in each phasejAnd the average stop frequency of each carThe average stop frequency of each car is current green light
The vehicle for rolling intersection in the phase of direction away from averagely runs into the number of red light.
Step S2：With the data obtained, with the maximum queue length in the phase of current green light direction and current red light direction
Queue length data in each phase are foundation, to evaluate intersection congestion level, are drawn in the case of current flows, the cycle
The domain of length, the process flow diagram flow chart are as shown in Figure 2；
Step S21：Cycle Length c 40~180s of OK range, if the domain of Cycle Length is Ω (t), time-varying opinion
Cycle Length c domain is divided into consecutive hours variable universe sequence under domain：{Ωk(t) }, (k ∈ N), Ωk(t)=[0,40+20k],
Wherein k=1,2 ... ..6,7；
Step S22：The 4 grades of service levels suggested using urban planning and design academy of Beijing, to be averaged during red light therein
Queue length item is foundation, to evaluate intersection congestion level.According to 4 grades of service levels of its suggestion, queue length L during red light
It is unobstructed VS less than 50 meters, between 50 meters and 100 meters be more unobstructed NS, is compared with congestion NJ, super between 100 meters and 150 meters
It is congestion VJ to cross 150 meters；By obtained gL and rLjThe digital form of data, according to the 4 of suggestion grades of service levels, be converted to word
VS, NS, NJ, VJ form；
Step S23：According to the combined situation of keyword, draw in the case of current flows, the domain of Cycle Length；With ten
Exemplified by the Four-phase control at word crossing, as shown in Figure 3.Situation during the keyword combination of four phases has 35, when keyword combining form
For VS, VS, VS, VS//VS, VS, VS, NS when, Cycle Length domain is Ω1(t)；Combining form be VS, VS, VS, NJ//VS,
When VS, VS, VJ//VS, VS, NS, NS//VS, VS, NS, NJ//VS, VS, NS, VJ//VS, VS, NJ, VJ, Cycle Length domain is
Ω2(t)；The like, draw corresponding length domain in the case of each combination of keyword.
Step S3：Under the Cycle Length domain, with the maximum queue length in the phase of current green light direction and each
The average stop frequency of car is input, and the long green light time of current green light direction phase assignments is output, lists dynamic fuzzy rule；
Step S31：Input quantity gL,Output quantity TlBlurring；The experience controlled according to actual traffic takes the change for determining gL
Change scope.If the excursion that input quantity is is 0~200, domain is { 0,1,2,3,4,5,6,7,8,9,10 }, on its domain
7 fuzzy subsets are defined, corresponding language value is { l1(very short), l2(short), l3(shorter), l4(medium), l5(longer), l6(length),
l7(very long) }；If input quantityExcursion be 0~4, domain be { 0,1,2,3,4,5 }, on its domain definition 5 moulds
Subset is pasted, corresponding Linguistic Value is { n1(small), n2(smaller), n3(medium), n4(larger), n5(big) }；If output quantity TlChange
It is 0~40+20k, k=1,2 to change scope, 3,4,5,6,7, domain is that { 0,1,2,3,4,5,6,7,8,9,10 } is fixed on its domain
Adopted 5 fuzzy subsets, corresponding Linguistic Value is { t1(short), t2(shorter), t3(medium), t4(longer), t5(length) }；
Step S32：Cycle Length domain is consecutive hours variable universe sequence：{Ωk(t) }, (k ∈ N), when domain is sent out with the time
During changing, the membership function for the fuzzy set being defined on domain can also change with the time, respectively in each Ωk(t)
Upper setting input quantity gL,Output quantity TlEach Linguistic Value corresponding to membership function；If Fig. 4 is when Cycle Length domain is
Ω1And Ω (t)2(t) when, membership function corresponding to each Linguistic Values of input quantity gL；If Fig. 5 is when Cycle Length domain is Ω3
And Ω (t)4(t) when, membership function corresponding to each Linguistic Values of input quantity gL；Similarly when Cycle Length domain is Ω5(t)、
Ω6And Ω (t)7(t) when, membership function corresponding to each Linguistic Values of input quantity gL is similar with Fig. 4, Fig. 5 respectively；It is similar, it is fixed
Adopted input quantityOutput quantity TlIn each Ωk(t) membership function corresponding to each Linguistic Value on；
Step S33：Consider all possible situation, when listing under variable universe, the domain of Cycle Length is ΩkWhen (t)
Dynamic fuzzy rule Rk。
Step S4：Sharpening calculating is carried out using gravity model appoach, draws long green light time；
Step S41：Fuzzy relationship matrix r is obtained by dynamic fuzzy rulei, total fuzzy rule is R；The language of fuzzy language
In method, the clause expression of general if (regular former piece) then (conclusion) of rule.Corresponding one of every Linguistic control law is fuzzy to close
SystemFuzzy relationship matrix r can be obtained by fuzzy control rulei, total fuzzy relation is
Step S42：The fuzzy set of corresponding output variable is obtained according to total fuzzy relation and push-pull picklingline：
Step S43：The fuzzy quantity that fuzzy reasoning is obtained is converted into the clear amount in present domain, carries out sharpening meter
Calculate, draw long green light time.
Step S5：Gained long green light time is judged, constraints is set, completes timing scheme.
Step S51：Consider intersection traffic flow and the current safety of driver, alleviation traffic congestion and reduction are rushed red
Lamp phenomenon, set and remind, if certain phase red light duration, more than 130s, system receives prompting 1；
Step S52：When system receives prompting 1, by the prediction of short-term traffic flow, become according to the change of the traffic flow of prediction
Gesture, adjust the transit time of current green light phase；
Step S53：Tl≤Nmax, NmaxFor the maximum of transit time, set according to the magnitude of traffic flow at actual crossing, the value
Preferably not more than 80s, set and remind, if long green light time is more than Nmax, then system receive prompting 2；
Step S54：When system receives prompting 2, current green light phase transit time is adjusted, sets it maximum for transit time
Value.
It is described above, it is only the embodiment in the present invention, but protection scope of the present invention is not limited thereto, and is appointed
What be familiar with the people of the technology disclosed herein technical scope in, it will be appreciated that the conversion or replacement expected, should all cover
Within the scope of the present invention.Therefore, protection scope of the present invention should be defined by the protection domain of claims.
Claims (6)
1. it is a kind of based on when variable universe crossroad access signal lamp intelligent control method, it is characterised in that this method includes
Following steps：
Step S1：Using the detector of crossroad, traffic data needed for collection；
Step S2：With the data obtained, with the maximum queue length in the phase of current green light direction and the current each phase in red light direction
Queue length data on position are foundation, to evaluate intersection congestion level, are drawn in the case of current flows, Cycle Length
Domain；
Step S3：Under the Cycle Length domain, with the maximum queue length in the phase of current green light direction and each car
Average stop frequency is input, and the long green light time of current green light direction phase assignments is output, lists dynamic fuzzy rule；
Step S4：Sharpening calculating is carried out using gravity model appoach, draws long green light time；
Step S5：Gained long green light time is judged, constraints is set, completes timing scheme.
2. according to the method for claim 1, it is characterised in that the step S1 is further：Required traffic data bag
Include the maximum queue length gL in the phase of current green light direction, the queue length rL in the current each phase in red light directionjIt is and every
The average stop frequency of car
3. according to the method for claim 1, it is characterised in that the step S2 further comprises the steps：
Step S21：Cycle Length c 40~180s of OK range, if the domain of Cycle Length is Ω (t), by when variable universe
Lower Cycle Length c domain is divided into consecutive hours variable universe sequence：{Ωk(t)},(k∈N)；
Step S22：The 4 grades of service levels suggested using urban planning and design academy of Beijing, to be averagely lined up during red light therein
Length item is foundation, to evaluate intersection congestion level；By obtained gL and rLjThe digital form of data, according to the 4 of suggestion grades
Service level, be converted to word VS, NS, NJ, VJ form；
Step S23：According to the combined situation of keyword, draw in the case of current flows, the domain of Cycle Length.
4. according to the method for claim 1, it is characterised in that the step S3 further comprises the steps：
Step S31：Input quantity gL,Output quantity TlBlurring；
Step S32：Cycle Length domain is consecutive hours variable universe sequence：{Ωk(t) }, (k ∈ N), when domain becomes with the time
During change, the membership function for the fuzzy set being defined on domain can also change with the time, respectively in each Ωk(t) set on
Put input quantity gL and rLj, output quantityMembership function corresponding to each Linguistic Value；
Step S33：Consider all possible situation, when listing under variable universe, the domain of Cycle Length is Ωk(t) dynamic analog when
Paste regular Rk。
5. according to the method for claim 1, it is characterised in that the step S4 further comprises the steps：
Step S41：Fuzzy relationship matrix r is obtained by dynamic fuzzy rulei, total fuzzy rule is R；
Step S42：The fuzzy set of corresponding output variable is obtained according to total fuzzy relation and push-pull picklingline；
Step S43：The fuzzy quantity that fuzzy reasoning is obtained is converted into the clear amount in present domain, carries out sharpening calculating, obtains
Go out long green light time.
6. according to the method for claim 1, it is characterised in that the step S5 further comprises the steps：
Step S51：Consider crossroad access flow and the current safety of driver, alleviate traffic congestion and reduce and make a dash across the red light
Phenomenon, set and remind, if certain phase red light duration, more than 130s, system receives prompting 1；
Step S52：When system receives prompting 1, by the prediction of short-term traffic flow, according to the variation tendency of the traffic flow of prediction,
Adjust the transit time of current green light phase；
Step S53：Tl≤Nmax, NmaxFor the maximum of transit time, set according to the magnitude of traffic flow at actual crossing, the value is best
80s is not exceeded, sets and reminds, if long green light time is more than Nmax, then system receive prompting 2；
Step S54：When system receives prompting 2, current green light phase transit time is adjusted, it is transit time maximum to set it.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111243300A (en) * | 2018-11-28 | 2020-06-05 | 阿里巴巴集团控股有限公司 | Method and device for acquiring loss duration |
| CN111951576A (en) * | 2020-08-17 | 2020-11-17 | 国为(南京)软件科技有限公司 | Traffic light control system based on vehicle identification and method thereof |
| CN112581778A (en) * | 2020-12-02 | 2021-03-30 | 张鹏 | Traffic signal intelligent guiding system based on bus priority mechanism |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101604479A (en) * | 2009-07-14 | 2009-12-16 | 北京交通大学 | The evaluation method of service level of plane signal intersection under mixed traffic environment |
| CN102592464A (en) * | 2012-03-12 | 2012-07-18 | 山东大学 | Video-based phase difference fuzzy reasoning determining method |
| CN103778791A (en) * | 2012-10-26 | 2014-05-07 | 中兴通讯股份有限公司 | Traffic adaptive control method and traffic adaptive control device |
| US8903636B1 (en) * | 2013-12-02 | 2014-12-02 | Abdualrahman Abdullah Mohammad Al Kandari | Accident detection system and method for accident detection |
| CN105279982A (en) * | 2015-11-13 | 2016-01-27 | 北京航空航天大学 | Single intersection dynamic traffic signal control method based on data driving |
| CN105632198A (en) * | 2016-01-26 | 2016-06-01 | 新誉集团有限公司 | City area road traffic coordination control method and city area road traffic coordination system based on fuzzy control |
| CN105788302A (en) * | 2016-04-08 | 2016-07-20 | 华北电力大学（保定） | Dual-target-optimization-based dynamic timing method for urban traffic signal lamp |
| CN105869417A (en) * | 2016-06-16 | 2016-08-17 | 兰州理工大学 | Traffic signal control method and system based on combined control |
| CN106408961A (en) * | 2016-11-14 | 2017-02-15 | 华北电力大学（保定） | Two-level universal logic intelligent control method and control system for signal lamps of single intersection |
| CN107067759A (en) * | 2017-03-20 | 2017-08-18 | 长沙理工大学 | A kind of parallel traffic lights real-time control method based on ACP |
-
2017
- 2017-11-07 CN CN201711082059.XA patent/CN107680393B/en not_active Expired - Fee Related
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101604479A (en) * | 2009-07-14 | 2009-12-16 | 北京交通大学 | The evaluation method of service level of plane signal intersection under mixed traffic environment |
| CN102592464A (en) * | 2012-03-12 | 2012-07-18 | 山东大学 | Video-based phase difference fuzzy reasoning determining method |
| CN103778791A (en) * | 2012-10-26 | 2014-05-07 | 中兴通讯股份有限公司 | Traffic adaptive control method and traffic adaptive control device |
| US8903636B1 (en) * | 2013-12-02 | 2014-12-02 | Abdualrahman Abdullah Mohammad Al Kandari | Accident detection system and method for accident detection |
| CN105279982A (en) * | 2015-11-13 | 2016-01-27 | 北京航空航天大学 | Single intersection dynamic traffic signal control method based on data driving |
| CN105632198A (en) * | 2016-01-26 | 2016-06-01 | 新誉集团有限公司 | City area road traffic coordination control method and city area road traffic coordination system based on fuzzy control |
| CN105788302A (en) * | 2016-04-08 | 2016-07-20 | 华北电力大学（保定） | Dual-target-optimization-based dynamic timing method for urban traffic signal lamp |
| CN105869417A (en) * | 2016-06-16 | 2016-08-17 | 兰州理工大学 | Traffic signal control method and system based on combined control |
| CN106408961A (en) * | 2016-11-14 | 2017-02-15 | 华北电力大学（保定） | Two-level universal logic intelligent control method and control system for signal lamps of single intersection |
| CN107067759A (en) * | 2017-03-20 | 2017-08-18 | 长沙理工大学 | A kind of parallel traffic lights real-time control method based on ACP |
Non-Patent Citations (1)
| Title |
|---|
| 莫红: "时变论域下红绿灯配时的语言动力学分析", 《自动化学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111243300A (en) * | 2018-11-28 | 2020-06-05 | 阿里巴巴集团控股有限公司 | Method and device for acquiring loss duration |
| CN111951576A (en) * | 2020-08-17 | 2020-11-17 | 国为(南京)软件科技有限公司 | Traffic light control system based on vehicle identification and method thereof |
| CN112581778A (en) * | 2020-12-02 | 2021-03-30 | 张鹏 | Traffic signal intelligent guiding system based on bus priority mechanism |
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| CN107680393B (en) | 2021-07-09 |
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