CN108171997A - Traffic signals iteration control optimization method based on macroscopical parent map - Google Patents
Traffic signals iteration control optimization method based on macroscopical parent map Download PDFInfo
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
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Abstract
The present invention relates to road traffic signal control fields, traffic signals iteration control optimization method specially based on macroscopical parent map, solve existing traffic signal control scheme inefficiency at present, and it cannot effectively be combined with road network actual conditions, cause traffic hardware facility that cannot fully efficiently use, the problem of road network traffic efficiency is low, scheme：First, it determines road network import and numbers, obtain import number set R Enterance { R1,R2,…,Rm,…}；2nd, import is combined into the collection of the vehicle flowrate changing value of timing node：Rj‑Enterance{R1j,R2j,…,Rmj,…}；3rd, the dissipation rate V in each sectionm=Smgm(t)；4th, the vehicle number in the m articles section：hmj=hmj‑1+Rmj‑Vm；5th, traffic densityTraffic density error y (t)=x1(t)‑x2(t)；6th, with reference to following phase restriction conditionObtain g1(t)=c l g2(t)；7th, using p-type iterative learning control methods gk+1(t)=gk(t)+Γ[yd‑yk(t+1)] y, is enabledd=0, obtain green time.Advantage：According to real-time vehicle density case, traffic signals are made timely to regulate and control, effectively improve traffic efficiency, solved peak period traffic congestion situation, facilitate trip.
Description
Technical field
The present invention relates to road traffic signal control fields, specially the traffic signals iteration control based on macroscopical parent map
Optimization method.
Background technology
With the development of society and the propulsion of urbanization paces, China's transport need constantly increases, while the traffic faced
Problem is more and more prominent, preferably to be asked using the existing traffic traffic efficiency of existing traffic infrastructure solution is relatively low
Topic, people have paid countless effort in terms of intelligent, efficient urban traffic control system.Existing traffic letter at present
Number control program inefficiency, and signal timing plan cannot effectively be combined with road network actual conditions, lead to traffic hardware facility
It cannot fully efficiently use, road network traffic efficiency is low, and a road often easily occurs and blocks, adjacent road is spacious
Situation without vehicle.Inventor can effectively be reacted by macroscopical parent map (MFD) of relationship between research description each element of road network
The attribute of road network operating status with reference to the characteristic that daily Macro-traffic Flow similitude is distributed, proposes that one kind can effectively combine road
Net actual conditions, make full use of traffic hardware facility, maximize and play the adjacent road traffic capacity, effectively improve road network and pass through effect
The traffic signals iteration control optimization method based on macroscopical parent map of rate.
Invention content
The present invention solves existing traffic signal control scheme inefficiency, and cannot be effective with road network actual conditions at present
With reference to causing traffic hardware facility that cannot fully efficiently use, the problem of road network traffic efficiency is low provides a kind of based on macroscopic view
The traffic signals iteration control optimization method of parent map.
The present invention is realized by following operating procedure：Traffic signals iteration control optimization side based on macroscopical parent map
Method, including following operating procedure：
First, according to the concrete condition of road network A in practice, the vehicle import of each intersection in road network A is determined, and right
Number is established in all vehicle imports, then can obtain vehicle import number set, represent as follows：
R-Enterance{R1,R2,…,Rm...,
Wherein RmRepresent the vehicle import in the m articles section；It determines Fixed Time Interval Δ t, summarizes all timing nodes simultaneously
Settling time node set, is expressed as：
T-interval{t0,t1,…tj...,
Wherein tj=tj-1+Δt；
2nd, on the basis of step 1, the corresponding vehicle flow quantitative change at each timing node by each vehicle import
The set expression of change value is：
Rj-Enterance{R1j,R2j,…,Rmj...,
Wherein RmjThe vehicle import in the m articles section is represented, in tj-1To tjThe vehicle number counted in period；
3rd, the dissipation rate in each section is determined, wherein the dissipation rate in the m articles section is by VmIt represents, and VmCalculation formula such as
Under：
Vm=Smgm(t),
Wherein S represents the saturation volume rate in each section, SmRepresent the saturation volume rate in the m articles section, gm(t) the m articles section is represented
Corresponding green time；
4th, the R obtained to step 2mjThe V obtained with step 3mThe vehicle number that data processing obtains the m articles section is carried out,
Its expression formula is as follows：
hmj=hmj-1+Rmj-Vm,
Wherein hmjIt is m sections in tj-1To tjThe vehicle number counted in period；hmj-1It is the m articles section in tj-2Extremely
tj-1The vehicle number counted in period；
5th, x is set1(t),x2(t) traffic density of certain intersection two phase place corresponding road section, the wherein intersection are represented respectively
1 corresponding road section m of phase, then the data h by being obtained in above-mentioned steps fourmjIt can be calculated, 1 corresponding road section m of phase is in tj-1To tj
Traffic density in period, expression formula are：
Wherein emFor the length of section m, hmjIt is section m in tj-1To tjThe vehicle number counted in period；It can similarly obtain
The traffic density of 2 corresponding road section of intersection phase is x2(t)；It can then show that same intersection two phase place corresponds to traffic density
Traffic density error y (t), expression formula is：
Y (t)=x1(t)-x2(t)；
6th, the green time in section corresponding to phase 1 in the intersection is set again as g1(t), section corresponding to phase 2
Green time is g2(t), then with reference to following phase restriction condition：
It can obtain：g1(t)=c-l-g2(t),
Wherein c is the signal period, and l is the loss time；
7th, based on step 5 and step 6, adjustment is iterated, and described to each phase green time of each intersection
Iteration adjustment uses p-type iterative learning control methods, and expression formula is as follows：
gk+1(t)=gk(t)+Γ[yd-yk(t+1)],
Wherein Γ is learning gains, specific learning gains it is big neglect road network situation depending on, ydFor the corresponding vehicle of two phase place
Density error, enables yd=0, then it can obtain green times of a certain intersection phase k+1 under current wagon flow traffic conditions.
According to the characteristic that traffic repeats, according under road network traffic density equilibrium state, the traffic efficiency of road network is highest, therefore when same
The corresponding traffic density difference of two phase place of intersection is smaller, illustrates that the traffic density of two phase place is more balanced, road network at this time
Traffic efficiency is highest, i.e. ydDuring for desired value, the traffic efficiency highest of intersection at this time, therefore enable yd=0.
It is of the invention to be had the following advantages compared with existing static traffic signal control method：It is of the invention effectively to have used repeatedly
For learning control method, on the basis of existing transportation condition is not changed, the real-time vehicle density feelings that are fed back to by each section
Condition makes timely regulating and controlling to traffic signals on each section, can effectively improve the traffic efficiency in each section, largely
It solves the traffic congestion situation of peak time morning and evening in road network, facilitates the trip of people.
Figure of description
Fig. 1 is operational flowchart when control method of the present invention is specifically used；
Fig. 2 is region intersection road network schematic diagram；
Fig. 3 is the specific passage situation schematic diagram in each intersection of road network；
Fig. 4 is the Regional Road Network macroscopic view parent map MFD of static traffic control methods；
Fig. 5 is using the Regional Road Network macroscopic view parent map MFD after present invention control optimization method.By comparing Fig. 4 with
Fig. 5 can be seen that the point position being distributed in Fig. 5 is more uniform than Fig. 4 and compact, illustrate using the road after the method for the present invention control
Traffic traffic efficiency higher, while prove the validity and feasibility of the method for the present invention.
Specific embodiment
It in order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to the skill of the present invention
Art scheme is described in detail.
Now a kind of iteration of traffic signals provided by the invention regulation and control method is illustrated.Basic ideas are according to road network
The characteristic that middle vehicular traffic repeats, according under road network traffic density equilibrium state, the traffic efficiency of road network is highest, proposes to hand over
Messenger iteration regulates and controls method, so as to improve traffic efficiency.In the examples below, inspection is installed first in road network on each section
Device is surveyed, various data is collected and draws the macroscopical parent map MFD for the road network A that static traffic signal control methods control and with utilizing this hair
Macroscopical parent map MFD of the lower road network A of bright the method control is compared, to verify the feasibility of the control method and effective
Property.
Traffic signals iteration control optimization method based on macroscopical parent map, including following operating procedure：
First, according to the concrete condition of road network A in practice, the vehicle import of each intersection in road network A is determined, and right
Number is established in all vehicle imports, then can obtain vehicle import number set, represent as follows：
R-Enterance{R1,R2,…,Rm...,
Wherein RmRepresent the vehicle import in the m articles section；It determines Fixed Time Interval Δ t, summarizes all timing nodes simultaneously
Settling time node set, is expressed as：
T-interval{t0,t1,…tj...,
Wherein tj=tj-1+Δt；
2nd, on the basis of step 1, the corresponding vehicle flow quantitative change at each timing node by each vehicle import
The set expression of change value is：
Rj-Enterance{R1j,R2j,…,Rmj...,
Wherein RmjThe vehicle import in the m articles section is represented, in tj-1To tjThe vehicle number counted in period；
3rd, the dissipation rate in each section is determined, wherein the dissipation rate in the m articles section is by VmIt represents, and VmCalculation formula such as
Under：
Vm=Smgm(t),
Wherein S represents the saturation volume rate in each section, SmRepresent the saturation volume rate in the m articles section, gm(t) the m articles section is represented
Corresponding green time；
4th, the R obtained to step 2mjThe V obtained with step 3mThe vehicle number that data processing obtains the m articles section is carried out,
Its expression formula is as follows：
hmj=hmj-1+Rmj-Vm,
Wherein hmjIt is m sections in tj-1To tjThe vehicle number counted in period；hmj-1It is the m articles section in tj-2Extremely
tj-1The vehicle number counted in period；
5th, x is set1(t),x2(t) traffic density of certain intersection two phase place corresponding road section, the wherein intersection are represented respectively
1 corresponding road section m of phase, then the data h by being obtained in above-mentioned steps fourmjIt can be calculated, 1 corresponding road section m of phase is in tj-1To tj
Traffic density in period, expression formula are：
Wherein emFor the length of section m, hmjIt is section m in tj-1To tjThe vehicle number counted in period；It can similarly obtain
The traffic density of 2 corresponding road section of intersection phase is x2(t)；It can then show that same intersection two phase place corresponds to traffic density
Traffic density error y (t), expression formula is：
Y (t)=x1(t)-x2(t)；
6th, the green time in section corresponding to phase 1 in the intersection is set again as g1(t), section corresponding to phase 2
Green time is g2(t), then with reference to following phase restriction condition：
It can obtain：g1(t)=c-l-g2(t),
Wherein c is the signal period, and l is the loss time；
7th, based on step 5 and step 6, adjustment is iterated, and described to each phase green time of each intersection
Iteration adjustment uses p-type iterative learning control methods, and expression formula is as follows：
gk+1(t)=gk(t)+Γ[yd-yk(t+1)],
Wherein Γ is learning gains, specific learning gains it is big neglect road network situation depending on, ydFor the corresponding vehicle of two phase place
Density error, enables yd=0, then it can obtain green times of a certain intersection phase k+1 under current wagon flow traffic conditions.
Static traffic control methods, i.e., the traffic lights at each crossing in road network are not done with the jam situation in section
Adjustment, the practical jam situation of line segments is not how, and the controlling cycle duration of traffic lights is constant.Dynamic traffic control
The controlling cycle of preparation method, i.e. traffic lights can make with the jam situation in section and timely adjust.Of the present invention one
The iteration regulation and control method of kind traffic signals is a kind of dynamic control method, and traffic signals are in control method of the present invention
Under control, the time of green light in respective stretch can be adjusted with traffic density different on each section, so as to play dynamic
Control traffic signals maximally utilize road traffic hardware facility, improve the purpose of traffic efficiency.
The actual conditions design installation detector of road network A is primarily based on, and obtains the road network structure form of road network A, road
Geometry, crossing signals setting and vehicle pass-through requirement etc., the actual traffic data based on acquisition, and imitated using microcosmic traffic
The various means such as true software, least square method draw out macroscopical parent map MFD (such as attached drawings of the road network A under static traffic control
4).A kind of iteration of traffic signals of the present invention is recycled to regulate and control two-phase of the method to all intersections in road network A later
The green time of position is iterated adjustment, iteration adjustment and then the detector using the installation of each section, collects related data,
Macroscopic view parent map MFD (such as attached drawing 5) is drawn, two macroscopic view parent map MFD before iteration adjustment and after adjustment are compared, from
In macroscopical parent map MFD after iteration adjustment as can be seen that after being iterated adjustment to the green light of each intersection, each road
The traffic efficiency of section greatly improves, it is clear that the iteration of this traffic signals regulation and control method has a feasibility, stability and efficiently
Property.
Claims (1)
1. a kind of traffic signals iteration control optimization method based on macroscopical parent map, including following operating procedure：
First, according to the concrete condition of road network A in practice, the vehicle import of each intersection in road network A is determined, and to all
Number is established in vehicle import, then can obtain vehicle import number set, represent as follows：
R-Enterance{R1,R2,…,Rm...,
Wherein RmRepresent the vehicle import in the m articles section；When determining Fixed Time Interval Δ t, summarize all timing nodes and establishing
Segmentum intercalaris point set, is expressed as：
T-interval{t0,t1,…tj...,
Wherein tj=tj-1+Δt；
2nd, on the basis of step 1, by each vehicle import at each timing node corresponding vehicle flow changing value
Set expression be：
Rj-Enterance{R1j,R2j,…,Rmj...,
Wherein RmjThe vehicle import in the m articles section is represented, in tj-1To tjThe vehicle number counted in period；
3rd, the dissipation rate in each section is determined, wherein the dissipation rate in the m articles section is by VmIt represents, and VmCalculation formula it is as follows：
Vm=Smgm(t),
Wherein S represents the saturation volume rate in each section, SmRepresent the saturation volume rate in the m articles section, gm(t) represent that the m articles section corresponds to
Green time；
4th, the R obtained to step 2mjThe V obtained with step 3mCarry out the vehicle number that data processing obtains the m articles section, table
It is as follows up to formula：
hmj=hmj-1+Rmj-Vm,
Wherein hmjIt is m sections in tj-1To tjThe vehicle number counted in period；hmj-1It is the m articles section in tj-2To tj-1Time
The vehicle number counted in section；
5th, x is set1(t),x2(t) traffic density of certain intersection two phase place corresponding road section is represented respectively, wherein the intersection phase 1
Corresponding road section m, then the data h by being obtained in above-mentioned steps fourmjIt can be calculated, 1 corresponding road section m of phase is in tj-1To tjTime
Traffic density in section, expression formula are：
Wherein emFor the length of section m, hmjIt is section m in tj-1To tjThe vehicle number counted in period；It can similarly obtain the friendship
The traffic density of 2 corresponding road section of prong phase is x2(t)；It can then show that same intersection two phase place corresponds to the vehicle of traffic density
Density error y (t), expression formula are：
Y (t)=x1(t)-x2(t)；
6th, the green time in section corresponding to phase 1 in the intersection is set again as g1(t), the green light in section corresponding to phase 2
Time is g2(t), then with reference to following phase restriction condition：
It can obtain：g1(t)=c-l-g2(t),
Wherein c is the signal period, and l is the loss time；
7th, based on step 5 and step 6, adjustment, and the iteration are iterated to each phase green time of each intersection
Adjustment uses p-type iterative learning control methods, and expression formula is as follows：gk+1(t)=gk(t)+Γ[yd-yk(t+1)], wherein Γ is learns
Practise gain, specific learning gains it is big neglect road network situation depending on, ydFor the corresponding traffic density error of two phase place, y is enabledd=0,
It can then obtain green times of a certain intersection phase k+1 under current wagon flow traffic conditions.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109767632A (en) * | 2019-03-02 | 2019-05-17 | 太原理工大学 | A kind of traffic signals mixing control method based on iterative learning and Model Predictive Control |
| CN109872538A (en) * | 2019-04-16 | 2019-06-11 | 广东交通职业技术学院 | Saturation intersection group multilayer frontier iterative learning control method and device based on MFD |
| CN111429733A (en) * | 2020-03-24 | 2020-07-17 | 浙江工业大学 | Road network traffic signal control method based on macroscopic basic graph |
-
2018
- 2018-02-09 CN CN201810138807.XA patent/CN108171997A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109767632A (en) * | 2019-03-02 | 2019-05-17 | 太原理工大学 | A kind of traffic signals mixing control method based on iterative learning and Model Predictive Control |
| CN109767632B (en) * | 2019-03-02 | 2021-07-16 | 太原理工大学 | Traffic signal hybrid control method based on iterative learning and model predictive control |
| CN109872538A (en) * | 2019-04-16 | 2019-06-11 | 广东交通职业技术学院 | Saturation intersection group multilayer frontier iterative learning control method and device based on MFD |
| CN111429733A (en) * | 2020-03-24 | 2020-07-17 | 浙江工业大学 | Road network traffic signal control method based on macroscopic basic graph |
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