CN106251649A - Based on alleviating the control strategy of intersection congestion under hypersaturated state - Google Patents

Abstract

The invention discloses a kind of based on alleviating the control strategy of intersection congestion under hypersaturated state, belong to the technical field of intelligent transportation.First described control method considers by setting up a kind of multi objective control model, thus is effectively improved traffic circulation efficiency.The relation connected each other for multiple optimization aim and mutually restrict, the combining adaptive method of weighting is the Comprehensive Control model that three optimization aim right of distribution recuperations are arrived, optimum timing is solved according to improved adaptive GA-IAGA, improve search capability and also ensure the accuracy of timing, it is achieved alleviate the traffic jam of destination object.For fixing the scheme of timing scheme and former genetic algorithm in prior art, this method optimizes control effect, improves traffic circulation efficiency.

Description

Based on alleviating the control strategy of intersection congestion under hypersaturated state

Technical field

The present invention relates to a kind of based on alleviating the control strategy of urban road intersection congestion under hypersaturated state, belong to Technical field in intelligent transportation.

Background technology

Along with the development of urban economy, vehicle guaranteeding organic quantity presents the state of quickly increase, and urban road increases limited And slowly, causing the normal traffic trip in city to be seriously obstructed, traffic congestion takes place frequently, and vehicle transport efficiency is remarkably decreased, impact The normal trip of urbanite, govern expanding economy.Meanwhile, incident vehicle accident, environmental pollution and the energy Shortage further governs the development in city.The essence of traffic congestion be the demand run of supply and the vehicle of urban road not Balance, is the demand contradiction that is more than supply, and the method solving this contradiction can be examined in terms of improving supply and demand for control two Consider.But, improving the supply of road infeasible, main cause has: (1) road infrastructure construction needs to take basis, city Space, and the basic space in city is rare limited resources, it is impossible to infinitely take；(2) the road infrastructure construction period is relatively Long, existing traffic jam issue can not be fast and effeciently alleviated in the construction relying on road；(3) construction of road infrastructure Relatively costly, due to economic restriction, it is impossible to the newly-built road of long-term investment.Based on above-mentioned analysis, by newly-built road with Improve supply and solve imbalance between supply and demand infeasible, therefore, be only capable of alleviating traffic congestion by demand for control, and control traffic The effective method of demand is traffic administration and control.Take effective signal control measure, can be from most starting to produce traffic congestion Crossing start with, identify its traffic congestion state, and take effective measures in time, alleviate the shape that blocks up of local crossing State, it is to avoid it has influence on upstream and downstream crossing.

In urban transport problems, 50%～80% results from crossing and surrounding place thereof, and motor vehicles is when urban district has 1/3 Between consume on signalized intersections, the delay time at stop of 80%～90% is caused by signalized crossing.By to signal lights Optimal control can improve operational efficiency, reduce the delay time at stop, avoids traffic to gather around, reduces air pollution and environmental disruption.Signal From the point of view of the purpose that crossing controls is exactly on the one hand to crossing user, delay to be made, stop, queue up the fewest；Another From the point of view of aspect is to the crossing efficiency of management, the traffic capacity of crossing to be made is the biggest.The most conventional signal timing dial is most Research is all under non-saturation conditions, to be delayed minimum to determine cycle duration or by traffic delay and stop frequency comprehensively for one Individual performance indications.Consider the real time status of crossing user and crossing herein, choose mean delay, queue length and The traffic capacity, as optimization aim, establishes the signalized intersections multi objective control model under supersaturated condition.

Traditional signal timing dial method is, by optimizing algorithm, specific crossing Controlling model is carried out optimizing, obtains The signal allocative decision of excellent timing.Conventional method has climbing method, particle cluster algorithm, genetic algorithm etc..Researcher is typically chosen Simplest timing method carries out signal timing dial, because road traffic condition is complex under supersaturation situation, utilizes timing method The timing result obtained can cause mean delay and queue length bigger than normal, and the traffic capacity the most significantly strengthens, it is impossible to have Effect alleviates blocking up of crossing；For overcoming the problems referred to above, choose genetic algorithm and Controlling model is carried out optimizing timing, genetic algorithm Complicated traffic flow data can be carried out overall situation repeated searching thus obtain the most accurate timing result.

Summary of the invention

The technical problem to be solved is to provide a kind of based on alleviating urban road intersection under hypersaturated state The control strategy of congestion.

The present invention solves above-mentioned technical problem by the following technical solutions:

The present invention provides a kind of based on alleviating the control method of intersection congestion under hypersaturated state, specifically walks Rapid as follows:

Step 1, according to intersection vehicles running orbit model, chooses mean delay, queue length and traffic capacity conduct Optimization aim, sets up crossing Comprehensive Control model, particularly as follows:

$\min f({\mathrm{\λ}}_i,T)=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\μ}}_i^1{d}_i+{\mathrm{\μ}}_i^2{N}_i+{\mathrm{\μ}}_i^3{Q}_i)$

s.t.t_min＜ t_i＜ t_max

λ_min＜ λ_i＜ λ_max

T_min＜ T ＜ T_max

$X_{min}<X_i=\frac{y_i}{{\mathrm{\&lambda;}}_i}<X_{max}$

In formula, f (λ_i, T) and represent control function, n represents number of phases；λ_iRepresent the split of i-th phase place, λ_minAnd λ_maxRespectively Represent λ_iMinimum and maximum split in restriction range；T represents the signal period duration of crossing,t_ssRepresent The lost time of phase place, T_minAnd T_maxRepresent the minimum and maximum duration in T restriction range respectively；d_iRepresent the average of i-th phase place It is delayed,D_iFor the total delay of i-th phase place, l_iRepresent the queuing vehicle number of i-th phase place initial time, q_iRepresent the vehicle arriving rate of i-th phase place, s_iRepresent i-th phase The saturation volume rate of position；N_iRepresent the queue length of i-th phase place,q_i(τ) when representing τ Carve the vehicle arriving rate of i-th phase place, s_i(τ) saturation volume rate of τ moment i-th phase place is represented；Q_iRepresent the logical of i-th phase place Row ability, Q_i=s_iλ_i；It is respectively i-th phase place correspondence mean delay, queue length and the power of the traffic capacity Weight；t_iRepresent the Effective Green Time duration of i-th phase place, t_minAnd t_maxRepresent t respectively_iMinimum and maximum in restriction range is effective Long green light time；X_iRepresent the saturation of i-th phase place, X_minAnd X_maxRepresent X respectively_iMinimum and maximum in restriction range is saturated Degree；

Step 2, the crossing Comprehensive Control model using genetic algorithm to obtain step 1 solves, and enters crossing Row signal timing dial.

As the further prioritization scheme of the present invention, described crossing is the crossing of cross four phase place.

As the further prioritization scheme of the present invention, according to the change of crossing each phase place flow-rate ratio, use self adaptation power Weight method is that mean delay, queue length and the traffic capacity carry out weight distribution.

As the further prioritization scheme of the present invention, i-th phase place correspondence mean delay, queue length and the traffic capacity WeightWherein,s_iRepresent i-th phase The saturation volume rate of position, y_iRepresenting the flow-rate ratio of i-th phase place, Y represents the flow-rate ratio sum of four phase places, and α represents the dimension factor.

As the further prioritization scheme of the present invention, use genetic algorithm that crossing Comprehensive Control model is solved Time, in a replication process, by comparing fitness size, select fitness high as requiredIndividual inheritance to of future generation.

The present invention uses above technical scheme compared with prior art, has following technical effect that

(1) Model for Multi-Objective Optimization complicated state in view of reality road traffic is set up, by Signal phase Scheme and the adjustment of timing, it is achieved the Comprehensive Control to urban traffic flow, using adaptive weighting method is that three optimization aim are divided Join weight, can automatically distribute weight according to the change of actual traffic stream, carry out between the optimal value of each sub-goal subsequently point Join, effectively overcome the coordination problem between multiple target；

(2), for compared to fixing the scheme of timing scheme and former genetic algorithm in prior art, this method optimizes Control effect, improve traffic circulation efficiency.

Accompanying drawing explanation

Fig. 1 is crossing physical model and crossing phase place phase sequence figure, and wherein, (a) is crossing physical model, (b) It it is crossing phase place phase sequence figure.

Fig. 2 is the intersection vehicles running orbit illustraton of model of multi-goal optimizing function in the derivation present invention.

Fig. 3 is the test function figure verifying improved adaptive GA-IAGA of the present invention.

Fig. 4 is to utilize test function checking difference in the present inventionThe simulated effect comparison diagram of improved adaptive GA-IAGA.

Fig. 5 is the control effectiveness comparison figure that in the application present invention, improved adaptive GA-IAGA and former genetic algorithm obtain.

Detailed description of the invention

Below in conjunction with the accompanying drawings and technical scheme is described in further detail by specific embodiment:

The crossing that crossing is typical cross four phase place of research in the present invention, four phase places are respectively A, B, C, D, The most corresponding i=1 ..., j ..., n, n=4.Each access mouth direction all have left-hand rotation, keep straight on, wagon flow of turning right, due to right-hand rotation wagon flow Typically no single signal controls phase place, does not the most consider right-hand rotation wagon flow.Crossing physical model and crossing phase place phase Sequence is as shown in Figure 1.

Because reality traffic conditions is complex, need to consider to choose suitably control target and more accurately reflect crossing situation. First considering the supersaturation situation of crossing, needing to increase the whole signal period expands the traffic capacity；Consider crossing simultaneously The interests of user, delay time at stop and queue length all should be less.Therefore, mean delay, queue length and the traffic capacity are chosen As optimization aim.Wherein, optimization object function is derived according to Fig. 2.

The analysis of oversaturated intersection is proceeded by, it is assumed that when initiateing with the green light of crossing phase place A from first cycle Carve as initial time, i.e. t=0.If the traffic data of the two of a phase place imports is identical, so the present invention only analyzes one The single direction traffic flow of phase place.Perform an analysis the period with phase place A Effective Green Time duration first below, with post analysis whole signal week Phase, derive optimization object function.

1) using phase place A Effective Green Time duration as analyzing the period:

It is analyzed as a example by the access mouth Through Lane of phase place A east, crossing.In the time period of [0, t], arrive vehicle Number isLeaving vehicle number is

At moment t (0 ＜ t ＜ t₁) time queuing vehicle number be:

$l_1+{\&Integral;}_0^t{q}_1\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}-{\&Integral;}_0^t{s}_1\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}$

In formula, l₁Queuing vehicle number for phase place A initial time.

Delay time at stop during green light is:

${\&Integral;}_0^{t_1}(l_1+{\&Integral;}_0^t{q}_1(\mathrm{\&tau;})d\mathrm{\&tau;}-{\&Integral;}_0^t{s}_1(\mathrm{\&tau;}\left)d\mathrm{\&tau;}\right)dt.$

Being detained delay time at stop of producing under its excess-three phase place of vehicle number is:

$(l_1+{\&Integral;}_0^{t_1}{q}_1(\mathrm{\&tau;})d\mathrm{\&tau;}-{\&Integral;}_0^{t_1}{s}_1(\mathrm{\&tau;}\left)d\mathrm{\&tau;}\right)(t_2+t_3+t_4)+{\&Integral;}_{t_1}^T{\&Integral;}_{t_1}^t{q}_{1}d\mathrm{\&tau;}dt.$

Therefore, can be at the total delay of phase place A after the whole signal period terminates:

$D_1=l_{1}T-{\&Integral;}_0^{t_1}{\&Integral;}_0^t{s}_1\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}dt-{\&Integral;}_0^{t_1}{s}_1\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}(T-t_1)+{\&Integral;}_0^T{\&Integral;}_0^t{q}_1\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}dt$

$D_1=l_{1}T+\frac{1}{2}{q}_1{T}^2+\frac{1}{2}{s}_1{{\mathrm{\&lambda;}}_1}^2{T}^2-s_1{\mathrm{\&lambda;}}_1{T}^2$

The crossing phase place A traffic capacity during the whole signal period:

Q₁=s₁λ₁；

Signal period terminates rear queue length:

$N_1=l_1+{\&Integral;}_0^T{q}_1\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}-{\&Integral;}_0^{t_1}{s}_1\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}.$

2) (four corresponding four kinds of scopes of phase place, corresponding moment t has four scopes (0, t to whole phase cycling₁)、(t₁, t₁+t₂)、(t₁+t₂,t₁+t₂+t₃)、(t₁+t₂+t₃,t₁+t₂+t₃+t₄), so moment t scope is)

In the momentTime queuing vehicle number be:

$l_i+{\&Integral;}_0^t{q}_i\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}-{\&Integral;}_0^{t-\underset{j=1}{\overset{i-1}{\mathrm{\&Sigma;}}}{t}_j}{s}_i\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}.$

Delay time at stop during green light is:

${\&Integral;}_{\underset{j=1}{\overset{i-1}{\mathrm{\&Sigma;}}}{t}_j}^{\underset{j=1}{\overset{i}{\mathrm{\&Sigma;}}}{t}_j}\&lsqb;l_i+{\&Integral;}_0^t{q}_i\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}-{\&Integral;}_0^{t-\underset{j=1}{\overset{i-1}{\mathrm{\&Sigma;}}}{t}_j}{s}_i\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}\&rsqb;dt$

Being detained delay time at stop of producing under its excess-three phase place of vehicle number is:

$\&lsqb;l_i+{\&Integral;}_0^{\underset{j=1}{\overset{i}{\mathrm{\&Sigma;}}}{t}_j}{q}_i\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}-{\&Integral;}_0^{t_i}{s}_i\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}\&rsqb;(T-\underset{j=1}{\overset{i}{\mathrm{\&Sigma;}}}{t}_j)+{\&Integral;}_{\underset{j=1}{\overset{i}{\mathrm{\&Sigma;}}}{t}_j}^T{\&Integral;}_{\underset{j=1}{\overset{i}{\mathrm{\&Sigma;}}}{t}_j}^t{q}_i\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}dt$

Therefore, can be at the total delay after the whole signal period terminates:

$D_i=l_{i}T-{\&Integral;}_0^{t_i}{s}_i\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}(T-\underset{j=1}{\overset{i}{\mathrm{\&Sigma;}}}{t}_j)-{\&Integral;}_{\underset{j=1}{\overset{i-1}{\mathrm{\&Sigma;}}}{t}_j}^{\underset{j=1}{\overset{i}{\mathrm{\&Sigma;}}}{t}_j}{\&Integral;}_0^{t-\underset{j=1}{\overset{i-1}{\mathrm{\&Sigma;}}}{t}_j}{s}_i\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}dt+{\&Integral;}_0^T{\&Integral;}_0^t{q}_i\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}dt$

$D_i=l_{i}T+\frac{1}{2}{q}_i{T}^2+\frac{1}{2}{s}_i{{\mathrm{\&lambda;}}_i}^2{T}^2+s_i\left(\right(\underset{j=1}{\overset{i-1}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_j\left){\mathrm{\&lambda;}}_i\right)T^2-s_i{\mathrm{\&lambda;}}_i{T}^2$

Mean delay:

$d_i=\frac{D_i}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{N}_i}$

The crossing phase place i traffic capacity during the whole signal period:

Q_i=s_iλ_i

Signal period terminates rear queue length:

$N_i=l_i+{\&Integral;}_0^T{q}_i\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}-{\&Integral;}_0^{t_i}{s}_i\left(\mathrm{\&tau;}\right)d\mathrm{\&tau;}$

Subsequently, design adaptive weighting method according to the change of crossing each phase place flow-rate ratio and be controlled the weight of target Distribution.

Consider the supersaturation situation of crossing, i.e. when the vehicle flowrate of crossing is bigger, need to increase the whole signal period Expand the traffic capacity；Consider the interests of crossing user simultaneously, i.e. when the vehicle flowrate of crossing is less, the delay time at stop and Queue length all should reduce.Therefore for the flow-rate ratio situation of phase place each in crossing, when the flow-rate ratio of certain phase place is relatively big, then table Bright phase place vehicle flowrate is relatively big, passes through efficiency for being effectively improved wagon flow, need to increase the phase place traffic capacity, distributes to current energy accordingly The weight of the control target of power is bigger, and the weight that mean delay, queue length are assigned to is less；Flow-rate ratio when certain phase place Less, on the contrary then consider.

The most corresponding mean delay, queue length and the weight of the traffic capacity, wherein, α represents the dimension factor, is constant.

The Comprehensive Control model simultaneously obtained under the object of four phase place Single Intersections need to meet following constraints, with Ensure the stability of whole system.The Effective Green Time duration summation of the most each phase place need to be equal to cycle duration, if each phase place is effective Long green light time summation is more than cycle duration, does not meets reality, and the most whole crossing system plays pendulum；As each in obtain The Effective Green Time duration summation of phase place is less than cycle duration, then it represents that four actual phase place crossings are long for lost time, vehicle Traffic efficiency is relatively low, causes whole crossing system to play pendulum.

Constraints formula is as follows:

$\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}(t_i+t_{ss})=T$

Wherein, t_ssRepresent the lost time of phase place.

Setting the error that e represents that the Effective Green Time duration of phase place and total cycle duration are compared, expression formula is as follows:

$e=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}({\mathrm{\&lambda;}}_{i}T+t_{ss})-T}{T}$

Choosing performance indications after analysis is:

$e^2=1-\frac{{\mathrm{\&mu;}}_i^1+{\mathrm{\&mu;}}_i^2}{(\mathrm{\&alpha;}+1)s_i{y}_i(1-Y)}$

When e more levels off to 0, and whole system tends towards stability.

After being three optimization aim distribution weights, the Comprehensive Control model obtained (includes control function and constraint as follows Condition):

$\min f({\mathrm{\&lambda;}}_i,T)=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}({\mathrm{\&mu;}}_i^1{d}_i+{\mathrm{\&mu;}}_i^2{N}_i+{\mathrm{\&mu;}}_i^3{Q}_i)$

s.t.t_min＜ t_i＜ t_max

λ_min＜ λ_i＜ λ_max

T_min＜ T ＜ T_max

$X_{min}<X_i=\frac{y_i}{{\mathrm{\&lambda;}}_i}<X_{max}$

After obtaining Comprehensive Control model, with optimized algorithm it need to be solved and carry out signal timing dial.

Choose simple timing method and carry out signal timing dial, because actual traffic stream situation is complex, utilize timing method The timing result obtained can cause mean delay and queue length bigger than normal, and the traffic capacity the most significantly strengthens, it is impossible to have Effect alleviates blocking up of crossing；Choose genetic algorithm for described problem and Controlling model is solved timing, it is possible to complexity Traffic flow data carry out the overall situation repeated searching thus obtain the most accurate timing result.

Genetic algorithm mainly includes parameter coding, solves fitness, replicates, intersects, makes a variation and the operation such as termination.The most first Using non-Revised genetic algorithum (representing with GA below) to carry out optimum signal timing, in GA, proportion of utilization selection method, two point are handed over Fork and uniform variation carry out replicating, intersecting and mutation operation respectively.Find that the ratio selection method in GA has certain defect herein, Make improvements the genetic algorithm after being improved (representing with MGA below) subsequently.

The basic thought of the ratio selection method in GA is based on individual selected probability and is directly proportional to its fitness size Relation.If setting in GA group size as M, the fitness of individual Ι is f_Ι, then individual probability selected for Ι is as follows:

$P_I=\frac{f_I}{\underset{I=1}{\overset{M}{\mathrm{\&Sigma;}}}{f}_I}$

The probability understanding the highest individuality of fitness selected is the biggest.Where there is selection defect, because method being random Choose, the individuality that fitness is high may be omitted in a replication process, cause the Select Error in reproduction process bigger.

Therefore, the present invention is that the problem overcoming the Select Error of ratio selection method bigger has made improvement, obtains elite choosing Selecting method, the elitist selection method after improvement is compared to former method can improve its accuracy selected, and reduces its Select Error, simultaneously Improve the MGA ability of searching optimum at signal timing dial.

This patent carries out method improvement on the basis of ratio selection method, in reproduction process, by comparing fitness size, Filter the individuality that in whole colony, part fitness is less, select the elite individual inheritance that residue fitness is high to of future generation.If In reproduction process, heredity to follow-on individuality isBelow with test function TF (Test Function), the property to MGA Can test.

The expression formula of test function is as follows:

f(x₁,x₂)=21.5+x₁sin(4πx₁)+x₂sin(20πx₂)

-3.0≤x₁≤12.1

-4.1≤x₂≤5.8

Wherein, f (x₁,x₂) represent test function, x₁And x₂Represent the variable in test function.

The fitness function of test function is as follows:

F(x₁,x₂)=c_max1-f(x₁,x₂)

Wherein, F (x₁,x₂) represent test function fitness function；c_max1Represent a sufficiently large constant, the present invention Middle selection c_max1=40.

As it is shown on figure 3, wherein, X-axis represents variable x to test function₁, Y-axis represents variable x₂, Z axis represents test function value.

By the genetic algorithm (MGA) after improving, test function is carried out optimizing, choose four hereinValue emulates Relatively,Being respectively 80%, 60%, 40% and 20%, simulated effect comparative result is as shown in Figure 4.

By the simulation result of Fig. 4, can be proper60% compared to working asWhen 80%, it emulates effect Really curve shows and can more quickly obtain optimal value.Again because the optimization problem of genetic algorithm is that object function maximum is asked Topic, fitness function value is the biggest i.e. the least when test function value, shows that the optimizing ability of algorithm is the strongest.In comparison diagram 2 four Simulation curve, when60%, the optimizing ability of the algorithm (MGA) after improvement compared to MGA (80%) Strengthen.To sum up analyze, whenTime, algorithm has stronger optimizing ability and more preferable rapidity, because of This chooses hereinIndividuality as retain elite individuality replicate.

Comprehensive Control model because obtaining above includes control function and constraints, adapts at genetic algorithm for solving Need to consider constraints when spending, using Means of Penalty Function Methods to convert Comprehensive Control function herein is unconstrained problem function, obtains Fitness function is as follows:

$F\left(x\right)=C_{max2}-\left(\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\right({\mathrm{\&mu;}}_i^1{d}_i+{\mathrm{\&mu;}}_i^2{N}_i+{\mathrm{\&mu;}}_i^3{Q}_i)+\mathrm{\&eta;}{\left(\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\left({\mathrm{\&lambda;}}_{i}T+t_{ss}\right)-T\right)}^2)$

Wherein, c_max2Being a sufficiently large constant, η represents penalty factor.

Fig. 5 is to utilize MATLAB software, the control obtained in conjunction with improved adaptive GA-IAGA as herein described and former genetic algorithm Effectiveness comparison figure processed.

As shown in table 1 is to utilize the genetic algorithm after improving in timing method, genetic algorithm and the present invention to carry out most preferably The comparison of timing result.

Genetic algorithm after table 1: timing method, genetic algorithm and improvement carries out the comparison of optimal timing result

According to simulation result, by three kinds of algorithm gained optimal timing results of contrast, the timing of improved adaptive GA-IAGA than Other two method more preferably, demonstrates the practicality of innovatory algorithm.

The technological merit of the present invention is as follows:

(1) Model for Multi-Objective Optimization complicated state in view of reality road traffic is set up, by Signal phase Scheme and the adjustment of timing, it is achieved the Comprehensive Control to urban traffic flow, using adaptive weighting method is that three optimization aim are divided Join weight, can automatically distribute weight according to the change of actual traffic stream, carry out between the optimal value of each sub-goal subsequently point Join, effectively overcome the coordination problem between multiple target；

(2), for compared to fixing the scheme of timing scheme and former genetic algorithm in prior art, this method optimizes Control effect, improve traffic circulation efficiency.

The above, the only detailed description of the invention in the present invention, but protection scope of the present invention is not limited thereto, and appoints What is familiar with the people of this technology in the technical scope that disclosed herein, it will be appreciated that the conversion expected or replacement, all should contain Within the scope of the comprising of the present invention, therefore, protection scope of the present invention should be as the criterion with the protection domain of claims.

Claims (5)

1. based on alleviating the control method of intersection congestion under hypersaturated state, it is characterised in that concrete steps are such as Under:

Step 1, according to intersection vehicles running orbit model, chooses mean delay, queue length and the traffic capacity as optimization Target, sets up crossing Comprehensive Control model, particularly as follows:

$\min f({\mathrm{\&lambda;}}_i,T)=\underset{i=1}{\overset{n}{\&Sigma;}}({\mathrm{\&mu;}}_i^1{d}_i+{\mathrm{\&mu;}}_i^2{N}_i+{\mathrm{\&mu;}}_i^3{Q}_i)$

s.t.t_min＜ t_i＜ t_max

λ_min＜ λ_i＜ λ_max

T_min＜ T ＜ T_max

$X_{min}<X_i=\frac{y_i}{{\mathrm{\&lambda;}}_i}<X_{max}$

In formula, f (λ_i, T) and represent control function, n represents number of phases；λ_iRepresent the split of i-th phase place, λ_minAnd λ_maxRepresent λ respectively_i Minimum and maximum split in restriction range；T represents the signal period duration of crossing,t_ssRepresent phase place Lost time, T_minAnd T_maxRepresent the minimum and maximum duration in T restriction range respectively；d_iRepresent the mean delay of i-th phase place,D_iFor the total delay of i-th phase place, l_iRepresent the queuing vehicle number of i-th phase place initial time, q_iRepresent the vehicle arriving rate of i-th phase place, s_iRepresent i-th phase The saturation volume rate of position；N_iRepresent the queue length of i-th phase place,q_i(τ) when representing τ Carve the vehicle arriving rate of i-th phase place, s_i(τ) saturation volume rate of τ moment i-th phase place is represented；Q_iRepresent the logical of i-th phase place Row ability, Q_i=s_iλ_i；It is respectively i-th phase place correspondence mean delay, queue length and the power of the traffic capacity Weight；t_iRepresent the Effective Green Time duration of i-th phase place, t_minAnd t_maxRepresent t respectively_iMinimum and maximum in restriction range is effective Long green light time；X_iRepresent the saturation of i-th phase place, X_minAnd X_maxRepresent X respectively_iMinimum and maximum in restriction range is saturated Degree；

Step 2, the crossing Comprehensive Control model using genetic algorithm to obtain step 1 solves, and crossing is carried out letter Number timing.

The most according to claim 1 based on alleviating the control method of intersection congestion under hypersaturated state, its Being characterised by, described crossing is the crossing of cross four phase place.

The most according to claim 1 based on alleviating the control method of intersection congestion under hypersaturated state, its It is characterised by, according to the change of crossing each phase place flow-rate ratio, uses adaptive weighting method to be mean delay, queue length and lead to Row ability carries out weight distribution.

The most according to claim 3 based on alleviating the control method of intersection congestion under hypersaturated state, its It is characterised by, i-th phase place correspondence mean delay, queue length and the weight of the traffic capacityWherein,s_iRepresent the saturation volume rate of i-th phase place, y_iRepresent i-th The flow-rate ratio of individual phase place, Y represents the flow-rate ratio sum of four phase places, and α represents the dimension factor.

The most according to claim 1 based on alleviating the control method of intersection congestion under hypersaturated state, its It is characterised by, when using genetic algorithm that crossing Comprehensive Control model is solved, in a replication process, by comparing adaptation Degree size, selects fitness high as requiredIndividual inheritance to of future generation.