CN106384521A - Single-intersection traffic signal optimization control method based on public transport priority - Google Patents

Abstract

The invention discloses a single-intersection traffic signal optimization control method based on public transport priority, and the method comprises the steps: S1, building a single-intersection traffic signal timing optimization model based on the public transport priority, and calculating a target function; S2, initializing the position and speed of each particle in a particle swarm through employing a chaotic strategy, calculating the adaptability value of the particles according to the target function, and setting the overall initial optimal solution of the current particle swarm and the initial optimal solution of the particle individuals; S3, judging whether the position and speed of each particle in a solution space of the target function are convergent or not according to the concentration degree of the particle swarm: outputting the optimal solution in the solution space if the position and speed of each particle in the solution space of the target function are convergent; S4, updating the position and speed of each particle according to an adaptive inertia weight if the position and speed of each particle in the solution space of the target function are not convergent, carrying out the iterative updating of the overall optimal solution of the current particle swarm and the initial optimal solution of the particle individuals, returning to and carrying out step S3 till the position and speed of each particle in the solution space of the target function are convergent, and outputting the optimal solution; S5, carrying out the single-intersection traffic optimization control based on public transport priority according to the priority. The method enables the delay of a bus to be minimized.

Description

A kind of Single Intersection traffic signal optimization control method based on public traffic in priority

Technical field

A kind of the present invention relates in technical field of control over intelligent traffic, more particularly, it relates to list based on public traffic in priority Intersection traffic signal optimal control method.

Background technology

With the continuous propulsion of Development of China's Urbanization, urban transport problems becomes puzzlement urban development, restriction urban economy is built If key factor, resident trip hardly possible problem more and more prominent.Crossing is the traffic bottlenecks of road, and the traffic capacity is little, often The delay especially severe that the peak of occurrence period produces when running.By first developing urban public transport and using intelligent transportation skill Art, to improve traffic resource utilization ratio, to alleviate traffic congestion, is the important means vigorously advocated and implement of country.

Traffic signal optimization control method adopts fixed light timing scheme, the timing of the method to traditional presentate with one voice Scheme can not carry out self-adaptative adjustment to real-time traffic stream information, and the delay of public transit vehicle and public vehicles is larger, especially public Hand over the delay especially severe that car produces when running the traffic peak period.

Therefore, buses delay minimum how is made to be ability while improving the traffic efficiency of Single Intersection vehicle The field technique personnel urgency technical issues that need to address.

Content of the invention

For solving above-mentioned technical problem, the present invention provides a kind of Single Intersection traffic signal optimization control based on public traffic in priority Method processed, makes buses delay minimize while the traffic efficiency improving Single Intersection vehicle.

For achieving the above object, the present invention provides following technical scheme：

A kind of Single Intersection traffic signal optimization control method based on public traffic in priority, including：

S1：Set up the Single Intersection Traffic Signal Timing Optimized model based on public traffic in priority, calculating target function；

S2：Initialize position and the speed of each particle in population using chaos strategy, according to described object function Calculate the fitness value of described particle, set the initial optimal solution of the overall situation and the individual initial optimal solution of particle of current particle group；

S3：According to the position of each particle and speed described in the concentration class judgement of described population in described object function Whether restrain in solution space, if convergence, export the optimal solution in described solution space；

S4：If do not restrained, update position and the speed of each particle described according to self adaptation inertia weight, iteration is more The globally optimal solution of new particle group and particle individual optimal solution, return execution step S3, until restraining and exporting optimal solution；

S5：Control according to the Single Intersection traffic optimization that described optimal solution to carry out public traffic in priority.

2nd, the method for claim 1 is it is characterised in that the described Single Intersection traffic signal based on public traffic in priority Timing designing mathematical model is：

$\underset{D_r,D_p}{\min }D={\mathrm{XD}}_r\left(T\right)+(1-X)D_p\left(T\right);$

Wherein, D_r(T) it is all vehicles average delay times, D_p(T) be passenger on all vehicles the mean delay time, Real number X is crossing total flow saturation, 0<X<1.

Preferably, in the above-mentioned methods, described calculating target function specifically includes：

The vehicle flowrate data of detection Single Intersection, described vehicle flowrate data includes public vehicles arrival rate q_ij, public transit vehicle Arrival rate Q_ij, the saturation volume s of public vehicles_ijAnd the saturation volume bs of public transit vehicle_ij, the carrying number P of buses_b、 Public vehicles carrying number P_v, wherein, phase bit number i=1,2 ... n, j=1,2 ...；

Calculate the weights omega of kth vehicle at Single Intersection_k, according to the weights omega of described kth vehicle_kAnd described vehicle flowrate Data calculates the public vehicles total delay time in the signal periodIn one signal period during public transit vehicle total delay BetweenPublic vehicles total delay time D^v(T), bus passenger total delay time D^b(T)；

According to described public vehicles arrival rate q_ij, described public transit vehicle arrival rate Q_ij, society in one signal period Vehicle total delayAnd public transit vehicle total delay in one signal periodCalculate all vehicles average delay Time D_r(T)：

$D_r\left(T\right)=\frac{D_r^b\left(T\right)+D_r^v\left(T\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}(q_{ij}+Q_{ij})};$

According to described public vehicles arrival rate q_ij, described public transit vehicle arrival rate Q_ij, the described public vehicles total delay time D^v(T), bus passenger total delay time D^b(T), the carrying number P of described buses_b, public vehicles carrying number P_vMeter Calculate the mean delay time D of passenger on all vehicles_p(T)：

$D_p\left(T\right)=\frac{D^b\left(T\right)+D^v\left(T\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}(P_v{q}_{ij}+P_b{Q}_{ij})};$

By all vehicles average delay time D_r(T) the mean delay time D of passenger and on all vehicles_p(T)

Based in the Single Intersection Traffic Signal Timing Optimized model of public traffic in priority described in substituting into, obtain object function：

$\underset{D_r,D_p}{\min }D={\mathrm{XD}}_r\left(T\right)+(1-X)D_p\left(T\right)=X\frac{D_r^b\left(T\right)+D_r^v\left(T\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}(q_{ij}+Q_{ij})}+(1-X)\frac{D^b\left(T\right)+D^v\left(T\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}(P_v{q}_{ij}+P_b{Q}_{ij})}$

Wherein, X is crossing total flow saturation (0<X<1 real number).

Preferably, in the above-mentioned methods, described employing chaos strategy initialize the position of each particle in population and Speed, specifically includes：

Initialization algorithm parameter, described algorithm parameter includes：Population size N, Studying factors c₁And c₂, inertia weight coefficient ω, maximum iteration time E, current iteration number of times k, wherein, c₁>0、c₂>0、ω>0, c₁、c₂, ω be real number, E, k, N be just whole Number；

Feature is solved according to the described Single Intersection Traffic Signal Timing Optimized model based on public traffic in priority and carries out particle volume Code, described particle coding includes particle position coding x_i=(t_i1,t_i2,…,t_in) and particle rapidity coding v_i=(v_i1, v_i2,…,v_in)；

Particle initial position x is produced by chaos strategy_i=(t_i1,t_i2,…,t_in), and initialize particle rapidity for v_i= (0.5,0.5,0.5,0.5) is so that the particle of population can be uniformly distributed in solution space.

Preferably, in the above-mentioned methods, step S4 specifically includes：

Update speed and the position of described particle, the iteration of the described particle after being updated according to self adaptation inertia weight Speed and iterative position, wherein, described self adaptation inertia weight ω formula is：

ω=1.0-Pspeed* ω_h+Ptogether*ω_s

Pspeed is the speed evolution factor, and Ptogether is the concentration class factor, ω_hValue is 0.4 to 0.6, ω_sValue For 0.05 to 0.20；

Described iteration speed formula is：

Described iterative position formula is：

X_ij(t+1)=X_ij(t)+V_ij(t+1)

Wherein, positive integer N is population total number of particles, i=1, and 2 ..., N, i number for particle, and positive integer M is particle speed Degree or the length of position vector, j=1,2 ..., M, j number for particle coordinate, and positive integer t is current iteration number of times, real number V_ij T () is the velocity amplitude of particle, real number X_ijT () is the positional value of particle, real number P_ijT () is the individual optimal solution of particle, real number G_ij T () is the globally optimal solution of population population, real number c₁With real number c₂For Studying factors, real number r_1j(t) and real number r_2jT () is area Between random number between scope (0,1)；

Update the individual optimal solution of each particle described and population according to described iteration speed and described iterative position Globally optimal solution；

Judge whether current Dynamic iterations number of times k is more than maximum iteration time E, if being more than E, initialization algorithm ginseng again Number；If being less than E, return execution step S3.

From technique scheme as can be seen that the Single Intersection traffic signal based on public traffic in priority provided by the present invention are excellent Change control method, including：S1：Set up the Single Intersection Traffic Signal Timing Optimized model based on public traffic in priority, calculate target letter Number；S2：Initialize position and the speed of each particle in population using chaos strategy, calculate institute according to described object function State the fitness value of particle, set the initial optimal solution of the overall situation and the individual initial optimal solution of particle of current particle group；S3：According to institute The concentration class stating population judges whether the position of each particle described and speed restrain in the solution space of described object function, If convergence, export the optimal solution in described solution space；S4：If do not restrained, update institute according to self adaptation inertia weight State position and the speed of each particle, and calculate the fitness value of described particle, iteration update population globally optimal solution and Particle individual optimal solution, returns execution step S3, until restraining and exporting optimal solution；S5：To carry out public affairs according to described optimal solution Preferential Single Intersection traffic optimization is handed over to control.

The application passes through the quality of the Traffic Signal Timing optimization aim model evaluation signal time distributing conception of single crossing, The result that this model formation draws is the evaluation of estimate of the buses delay time at stop of crossing, however, timing scheme has many kinds Combination, in order to obtain optimal timing scheme then by particle cluster algorithm come solution, understood it is contemplated that not homophase by object function The traffic flow of position, carries out signal timing dial according to traffic flow, just can improve traffic efficiency.Grain is initialized using chaos strategy Sub- position and speed, can effectively improve the multiformity of primary so that during chaos intialization, particle can uniformly divide It is distributed in solution space.In population iteration searching process, more solution spaces can be traveled through, obtain more preferable globally optimal solution. Obtain a more preferable optimal solution, then can get a more preferable traffic signal accessories scheme, this scheme is compared to its other party Case can more improve crossing communication efficiency so that buses delay minimizes.Self adaptation inertia weight update described each The position of particle and speed, convergence rate can not only be improved additionally it is possible to improve convergence precision so that whole efficiency of algorithm more Height, the optimal solution of output is more accurate.

Brief description

In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing Have technology description in required use accompanying drawing be briefly described it should be apparent that, drawings in the following description be only this Inventive embodiment, for those of ordinary skill in the art, on the premise of not paying creative work, can also basis The accompanying drawing providing obtains other accompanying drawings.

Fig. 1 is a kind of Single Intersection traffic signal optimization control method based on public traffic in priority provided in an embodiment of the present invention Schematic diagram；

Fig. 2 Single Intersection provided in an embodiment of the present invention schematic diagram；

Fig. 3 Single Intersection provided in an embodiment of the present invention phase place schematic diagram；

Fig. 4 experimental result provided in an embodiment of the present invention contrast table；

The algorithm optimization result figure of Fig. 5 passenger provided in an embodiment of the present invention mean delay time；

The algorithm optimization result figure of Fig. 6 public vehicles provided in an embodiment of the present invention mean delay time.

Specific embodiment

Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete Site preparation description is it is clear that described embodiment is only a part of embodiment of the present invention, rather than whole embodiments.It is based on Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of not making creative work Embodiment, broadly falls into the scope of protection of the invention.

Refer to, a kind of Single Intersection traffic signal optimization controlling party based on public traffic in priority that Fig. 1 provides for the present invention Method schematic diagram；Fig. 2 is Single Intersection schematic diagram provided in an embodiment of the present invention；Fig. 3 is Characteristics for Single Staggered provided in an embodiment of the present invention Mouth phase place schematic diagram；Fig. 4 is experimental result contrast table provided in an embodiment of the present invention；Fig. 5 passenger provided in an embodiment of the present invention The algorithm optimization result figure of mean delay time；The algorithm of Fig. 6 public vehicles provided in an embodiment of the present invention mean delay time Optimum results figure.

Wherein, Webster：Fixing timing scheme is the timing scheme of timing signal；PSO：Standard particle group's algorithm；With mark Quasi particle group's algorithm is solving the traffic signal optimization model of Single Intersection public traffic in priority；DACPSO：Based on dynamic self-adapting Chaos particle swarm optimization algorithm；This patent proposes to solve Single Intersection public traffic in priority for the Chaos particle swarm optimization algorithm of dynamic self-adapting Traffic signal optimization Controlling model.

In a kind of specific embodiment, there is provided a kind of Single Intersection traffic signal optimization control based on public traffic in priority Method processed, including：

Step S1：Set up the Single Intersection Traffic Signal Timing Optimized model based on public traffic in priority, calculating target function.

Wherein, described based on the Single Intersection Traffic Signal Timing optimized mathematical model of public traffic in priority it is：

$\underset{D_r,D_p}{\min }D={\mathrm{XD}}_r\left(T\right)+(1-X)D_p\left(T\right);---\left(1\right)$

Wherein, real number X is crossing total flow saturation, 0<X<1, T is intersection signal timing scheme T=(t₁, t₂,…,t_n), D_r(T) it is all vehicles average delay times (second), D_p(T) be passenger on all vehicles the mean delay time (second).Above-mentioned model meet the constraint condition：

$\left\{\begin{array}{c}{t}_{i,\min }\&le;t_i\&le;t_{i,\max }\\ C=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{t}_i+L\\ \frac{q_{ij}C}{s_{ij}{t}_i}\&le;0.9\\ {\mathrm{bx}}_{ij}=\frac{Q_{ij}C}{{\mathrm{bs}}_{ij}{t}_i}\&le;0.8\end{array}\right.---\left(2\right)$

Wherein, phase place i=1,2 ... n, t_iFor phase place i long green light time (second), t_i,minFor phase place minimum green time (second)； t_i,maxFor phase place maximum green time (second)；C is cycle duration (second), and positive integer n is crossing number of phases, and L is that each phase place is total Lost time (second)；J is the lane number of phase place i, j=1,2 ... n, q_ijFor public vehicles arrival rate (/ hour), s_ijFor The saturation volume (/ hour) of public vehicles；Q_ijFor public transit vehicle arrival rate (/ hour), bs_ijSaturation for public transit vehicle Flow (/ hour).

Two delay time at stop parameters in this model, all vehicles average delay time, one is on all vehicles The mean delay time of passenger, the passenger on buses several times more than the passenger in public vehicles, then in mathematical model, passenger The proportion of delay time at stop will be above public vehicles, thus embodies public traffic in priority.

Step S2：Initialize position and the speed of each particle in population using chaos strategy, according to described target Function calculates the fitness value of described particle, sets globally optimal solution and the particle individual optimal solution of current particle group；

Wherein, above-mentioned model is solved using improved Chaos particle swarm optimization algorithm, particle initial solution is produced by chaos strategy, The particle enabling population is uniformly distributed in the solution space of object function, and chaos strategy initialization particle initial solution can be effective Improve the multiformity of primary.

Because the difference of particle position, the fitness function value that it is calculated by object function is also different, by than To determine optimal solution compared with the size of particle fitness value, the more little corresponding particle result of fitness value is more excellent.Wherein iterate to and work as In front all particles, the minimum solution of fitness is globally optimal solution；Same particle current iteration all obtain suitable Answer in angle value minimum for individual optimal solution.

Step S3：According to the position of each particle and speed described in the concentration class judgement of described population in described target letter Whether restrain in the solution space of number, if convergence, export the optimal solution in described solution space；

Wherein, the concentration class of population is embodied by the concentration class factor calculating population, if convergence, output Optimal solution in solution space.Optimal solution refers specifically to for optimal Single Intersection Traffic Signal Timing scheme, now object function Fitness is minimum, and specifically, particle is iterated optimizing in solution space, and particle passes through target letter in the position of solution space and speed The available fitness value of number, after iteration optimizing terminates, the speed of the minimum particle position of fitness and particle It is solution space optimal solution.

Step S4：Position and the speed of each particle described if do not restrained, is updated according to self adaptation inertia weight, and Calculate the fitness value of described particle, iteration updates globally optimal solution and the particle individuality optimal value of population, returns and executes step Rapid S3, until restraining and exporting optimal solution；

Self adaptation inertia weight updates position and the speed of each particle described, can make current according to particle when mobile The fitness value of position to adjust population translational speed with the difference of current global optimum.In particle moving process, when From globally optimal solution farther out when can be larger speed, when particle is close to globally optimal solution with less speed move, then Population has speed faster at the optimizing initial stage, has preferable precision in the optimizing later stage.Self adaptation inertia weight updates described each The position of individual particle and speed, convergence rate can not only be improved additionally it is possible to improve convergence precision so that whole efficiency of algorithm more Height, the optimal solution of output is more accurate.

Step S5：Control according to the Single Intersection traffic optimization that described optimal solution to carry out public traffic in priority.

Obtain an optimal solution, that is, obtained the signal time distributing conception of a Single Intersection, such as the signal of four phase places controls Crossing then can get four green time length, respectively for the green time of the one two three four phase place.This green time Obtain it is contemplated that the vehicle flowrate of current crossing buses and public vehicles, and because with the minimum target of passenger delay, and The handling capacity of passengers of buses is higher than public vehicles, just achieves public traffic in priority.

The application passes through the quality of the Traffic Signal Timing optimization aim model evaluation signal time distributing conception of single crossing, The result that this model formation draws is the evaluation of estimate of the buses delay time at stop of crossing, however, timing scheme has many kinds Combination, in order to obtain optimal timing scheme then by particle cluster algorithm come solution, understood it is contemplated that not homophase by object function The traffic flow of position, carries out signal timing dial according to traffic flow, just can improve traffic efficiency.Grain is initialized using chaos strategy Sub- position and speed, can effectively improve the multiformity of primary so that during chaos intialization, particle can uniformly divide It is distributed in solution space.In population iteration searching process, more solution spaces can be traveled through, obtain more preferable globally optimal solution. Obtain a more preferable optimal solution, then can get a more preferable traffic signal accessories scheme, this scheme is compared to its other party Case can more improve crossing communication efficiency so that buses delay minimizes.Self adaptation inertia weight update described each The position of particle and speed, convergence rate can not only be improved additionally it is possible to improve convergence precision so that whole efficiency of algorithm more Height, the optimal solution of output is more accurate.

Based on technique scheme, described calculating target function specifically includes：

Step S11：The vehicle flowrate data of detection Single Intersection, described vehicle flowrate data includes public vehicles arrival rate q_ij、 Public transit vehicle arrival rate Q_ij, the saturation volume s of public vehicles_ijAnd the saturation volume bs of public transit vehicle_ij, buses Carrying number P_b, public vehicles carrying number P_v, wherein, phase bit number i=1,2 ... n, j=1,2 ...；

Step S12：Calculate the weights omega of kth vehicle at Single Intersection_k, according to the weights omega of described kth vehicle_kAnd institute State vehicle flowrate data and calculate the public vehicles total delay time in the signal periodPublic transit vehicle in one signal period The total delay timePublic vehicles total delay time D^v(T), bus passenger total delay time D^b(T)；

Wherein, ω_kFor the weight of kth vehicle, ω_kIt is calculated as follows：

${\mathrm{\&omega;}}_k=\left\{\begin{array}{cc}{p}_k& d_k<\mathrm{\&beta;}\\ p_k\{1+\&lsqb;k\left(t\right)-d_k\&rsqb;\}& d_k\&GreaterEqual;\mathrm{\&beta;}\end{array}\right.,---\left(3\right)$

Wherein, p_kFor the ridership of kth vehicle, k (t) is the penalty coefficient of t, d_kFor kth buses operation when Carve table deviation value, if the car detecting is public vehicles, take d_kDeviate the compensation of state weight for 0, β for the public transport operation moment to face Dividing value.

It is calculated as follows：

$D_r^v\left(T\right)=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}\left\{\&lsqb;\frac{C{(1-\frac{t_i}{C})}^2}{2(1-\frac{q_{ij}}{s_{ij}})}+\frac{{\left(C\frac{{\mathrm{\&omega;}}_k{q}_{ij}}{t_i{s}_{ij}}\right)}^2}{2q_{ij}(1-C\frac{q_{ij}}{t_i{s}_{ij}})}\&rsqb;\&times;q_{ij}\right\}---\left(4\right)$

It is calculated as follows：

$D_r^b\left(T\right)=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}\underset{k=1}{\overset{Q_{ij}}{\mathrm{\&Sigma;}}}\&lsqb;\frac{C{(1-\frac{t_i}{C})}^2}{2(1-\frac{{\mathrm{\&omega;}}_k{Q}_{ij}}{t_i})}+\frac{{\left(C\frac{{\mathrm{\&omega;}}_k{Q}_{ij}}{t_i{\mathrm{bs}}_{ij}}\right)}^2}{2Q_{ij}(1-C\frac{{\mathrm{\&omega;}}_k{Q}_{ij}}{t_i{\mathrm{bs}}_{ij}})}\&rsqb;---\left(5\right)$

D^b(T) it is calculated as follows：

$D^b\left(T\right)=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}\&lsqb;\frac{C{(1-\frac{t_i}{C})}^2}{2(1-\frac{t_i}{C}{\mathrm{bx}}_{ij})}+\frac{x_{ij}^2}{2Q_{ij}(1-{\mathrm{bx}}_{ij})}\&rsqb;Q_{ij}{P}_b---\left(6\right)$

D^v(T) it is calculated as follows：

$D^v\left(T\right)=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}\&lsqb;\frac{C{(1-\frac{t_i}{C})}^2}{2(1-\frac{t_i}{C}{x}_{ij})}+\frac{x_{ij}^2}{2q_{ij}(1-x_{ij})}\&rsqb;q_{ij}{P}_v---\left(7\right)$

Wherein, P_bFor the carrying number of buses, P_vFor public vehicles carrying number,For in the signal period Public vehicles total delay,For public transit vehicle total delay in the signal period, D^vFor public vehicles total delay time, D^b (T) it is the bus passenger total delay time, positive integer m_iEntrance driveway number for phase place i.

Step S13：According to described public vehicles arrival rate q_ij, described public transit vehicle arrival rate Q_ij, one signal week Public vehicles total delay in phaseAnd public transit vehicle total delay in one signal periodCalculate all vehicles Mean delay time D_r(T)：

$D_r\left(T\right)=\frac{D_r^b\left(T\right)+D_r^v\left(T\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}(q_{ij}+Q_{ij})};$

Step S14：According to described public vehicles arrival rate q_ij, described public transit vehicle arrival rate Q_ij, described public vehicles total Delay time at stop D^v(T), bus passenger total delay time D^b(T), the carrying number P of described buses_b, public vehicles carry Objective number P_vCalculate the mean delay time D of passenger on all vehicles_p(T)：

$D_p\left(T\right)=\frac{D^b\left(T\right)+D^v\left(T\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}(P_v{q}_{ij}+P_b{Q}_{ij})};$

Step S15：By all vehicles average delay time D_r(T) the mean delay time D of passenger and on all vehicles_p(T) Based in the Single Intersection Traffic Signal Timing Optimized model of public traffic in priority described in substituting into, obtain object function：

$\underset{D_r,D_p}{\min }D={\mathrm{XD}}_r\left(T\right)+(1-X)D_p\left(T\right)=X\frac{D_r^b\left(T\right)+D_r^v\left(T\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}(q_{ij}+Q_{ij})}+(1-X)\frac{D^b\left(T\right)+D^v\left(T\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}(P_v{q}_{ij}+P_b{Q}_{ij})}$

Wherein, real number X is crossing total flow saturation, 0<X<1.

Based on technique scheme, described employing chaos strategy initializes the position of each particle and speed in population Degree, specifically includes：

Step S21：Initialization algorithm parameter, described algorithm parameter includes：Population size N, Studying factors c₁And c₂, inertia Weight coefficient ω, maximum iteration time E, current iteration number of times k, wherein, c₁>0、c₂>0、ω>0, c₁、c₂, ω be real number, E, k, N is positive integer；

In the present embodiment, following algorithm parameter can be set：Population size N=20, Studying factors c₁=c₂=1, it is used to Property weight coefficient ω=0.8, maximum iteration time E=1000, current iteration number of times k=1.

Step S22：Solve feature according to the described Single Intersection Traffic Signal Timing Optimized model based on public traffic in priority to enter Row particle encodes, and described particle coding includes particle position coding x_i=(t_i1,t_i2,…,t_in) and particle rapidity coding v_i= (v_i1,v_i2,…,v_in)；

Step S23：Particle initial position x is produced by chaos strategy_i=(t_i1,t_i2,…,t_in), and initialize particle speed Spend for v_i=(0.5,0.5,0.5,0.5) is so that the particle of population can be uniformly distributed in solution space.

Solve the Traffic Signal Timing scheme of single crossing, solve feature and be intersection signal number of phases.As one The signalized intersections of four phase places, then the particle position vector of particle cataloged procedure is then 4 dimension spaces.The initialization of standard particle group Process is the process of random distribution, and that is, particle can be randomly dispersed in solution space.If adding chaos strategy in initialization procedure, Then enable the particles to more be evenly distributed in solution space, particle speed of searching optimization and precision can be improved.

Based on technique scheme, step S4 specifically includes：

Step S41：Update speed and the position of described particle, the described grain after being updated according to self adaptation inertia weight The iteration speed of son and iterative position, wherein, described self adaptation inertia weight ω formula is：

ω=1.0-Pspeed* ω_h+Ptogether*ω_s

Pspeed is the speed evolution factor, and Ptogether is the concentration class factor, ω_hValue is 0.4 to 0.6, ω_sValue For 0.05 to 0.20；

Described iteration speed formula is：

Described iterative position formula is：

X_ij(t+1)=X_ij(t)+V_ij(t+1)

Wherein, positive integer N is population total number of particles, i=1, and 2 ..., N, i number for particle, and positive integer M is particle speed Degree or the length of position vector, j=1,2 ..., M, j number for particle coordinate, and positive integer t is current iteration number of times, real number V_ij T () is the velocity amplitude of particle, real number X_ijT () is the positional value of particle, real number P_ijT () is the individual optimal solution of particle, real number G_ij T () is the globally optimal solution of population population, real number c₁With real number c₂For Studying factors, real number r_1j(t) and real number r_2jT () is area Between random number between scope (0,1).

Wherein, bias toward the local search ability playing particle cluster algorithm during inertia weight ω very little, inertia weight is very big When will bias toward play particle cluster algorithm ability of searching optimum.

Step S42：According to described iteration speed and described iterative position update each particle described individual optimal solution and The globally optimal solution of population；

Step S43：Judge whether current Dynamic iterations number of times k is more than maximum iteration time E, if being more than E, initial again Change algorithm parameter；If being less than E, return execution step S3.

After completing above implementation steps, can get the experimental result contrast table shown in Fig. 4, its broken line diagram form such as Fig. 5 and Improvement particle cluster algorithm (DACPSO) based on dynamic self-adapting solves the signal time distributing conception of gained shown in Fig. 6, and traditional Webster is fixing, and timing scheme is compared with standard particle group's algorithm (PSO), and model solution algorithm proposed by the present invention can make intersection All effectively reduced per capita by delay time at stop and public vehicles delay time at stop for mouth.Can be seen that from result, institute of the present invention extracting method pair Improve the public transit vehicle of roadway sign crossing and public vehicles traffic efficiency has preferable effect.

In this specification, each embodiment is described by the way of going forward one by one, and what each embodiment stressed is and other The difference of embodiment, between each embodiment identical similar portion mutually referring to.

Described above to the disclosed embodiments, makes professional and technical personnel in the field be capable of or uses the present invention. Multiple modifications to these embodiments will be apparent from for those skilled in the art, as defined herein General Principle can be realized without departing from the spirit or scope of the present invention in other embodiments.Therefore, the present invention It is not intended to be limited to the embodiments shown herein, and be to fit to and principles disclosed herein and features of novelty phase one The scope the widest causing.

Claims (5)

1. a kind of Single Intersection traffic signal optimization control method based on public traffic in priority is it is characterised in that include：

S1：Set up the Single Intersection Traffic Signal Timing Optimized model based on public traffic in priority, calculating target function；

S2：Initialize position and the speed of each particle in population using chaos strategy, calculate according to described object function The fitness value of described particle, sets the initial optimal solution of the overall situation and the individual initial optimal solution of particle of current particle group；

S3：Judge that the position of each particle described and speed are empty in the solution of described object function according to the concentration class of described population Between in whether restrain, if convergence, export the optimal solution in described solution space；

S4：If do not restrained, update position and the speed of each particle described according to self adaptation inertia weight, iteration updates grain The globally optimal solution of subgroup and particle individual optimal solution, return execution step S3, until restraining and exporting optimal solution；

S5：Control according to the Single Intersection traffic optimization that described optimal solution to carry out public traffic in priority.

2. the method for claim 1 is it is characterised in that the described Single Intersection Traffic Signal Timing based on public traffic in priority Optimized mathematical model is：

$\underset{D_r,D_p}{\min }D={\mathrm{XD}}_r\left(T\right)+(1-X)D_p\left(T\right);$

Wherein, D_r(T) it is all vehicles average delay times, D_p(T) be passenger on all vehicles mean delay time, real number X For crossing total flow saturation, 0<X<1.

3. method as claimed in claim 2 is it is characterised in that described calculating target function specifically includes：

The vehicle flowrate data of detection Single Intersection, described vehicle flowrate data includes public vehicles arrival rate qi_j, public transit vehicle reach Rate Q_ij, the saturation volume s of public vehicles_ijAnd the saturation volume bs of public transit vehicle_ij, the carrying number P of buses_b, society Vehicle carrying number P_v, wherein, phase bit number i=1,2 ... n, j=1,2 ...；

Calculate the weights omega of kth vehicle at Single Intersection_k, weights omega k according to described kth vehicle and described vehicle flowrate data Calculate the public vehicles total delay time in the signal periodThe public transit vehicle total delay time in one signal periodPublic vehicles total delay time D^v(T), bus passenger total delay time D^b(T)；

According to described public vehicles arrival rate q_ij, described public transit vehicle arrival rate Q_ij, public vehicles in one signal period Total delayAnd public transit vehicle total delay in one signal periodCalculate all vehicles average delay times D_r(T)：

$D_r\left(T\right)=\frac{D_r^b\left(T\right)+D_r^v\left(T\right)}{\underset{i=1}{\overset{n}{\&Sigma;}}\underset{j=1}{\overset{m_i}{\&Sigma;}}(q_{ij}+Q_{ij})};$

According to described public vehicles arrival rate q_ij, described public transit vehicle arrival rate Q_ij, described public vehicles total delay time D^v (T), bus passenger total delay time D^b(T), the carrying number P of described buses_b, public vehicles carrying number P_vCalculate The mean delay time D of passenger on all vehicles_p(T)：

$D_p\left(T\right)=\frac{D^b\left(T\right)+D^v\left(T\right)}{\underset{i=1}{\overset{n}{\&Sigma;}}\underset{j=1}{\overset{m_i}{\&Sigma;}}(P_v{q}_{ij}+P_b{Q}_{ij})};$

By all vehicles average delay time D_r(T) the mean delay time D of passenger and on all vehicles_p(T)

Based in the Single Intersection Traffic Signal Timing Optimized model of public traffic in priority described in substituting into, obtain object function：

$\underset{D_r,D_p}{\min }D={\mathrm{XD}}_r\left(T\right)+(1-X)D_p\left(T\right)=X\frac{D_r^b\left(T\right)+D_r^v\left(T\right)}{\underset{i=1}{\overset{n}{\&Sigma;}}\underset{j=1}{\overset{m_i}{\&Sigma;}}(q_{ij}+Q_{ij})}+(1-X)\frac{D^b\left(T\right)+D^v\left(T\right)}{\underset{i=1}{\overset{n}{\&Sigma;}}\underset{j=1}{\overset{m_i}{\&Sigma;}}(P_v{q}_{ij}+P_b{Q}_{ij})}$

Wherein, real number X is crossing total flow saturation, 0<X<1.

4. profit requires the method as described in 3 it is characterised in that described employing chaos strategy initializes each particle in population Position and speed, specifically include：

Initialization algorithm parameter, described algorithm parameter includes：Population size N, Studying factors c₁And c₂, inertia weight coefficient ω, Big iterationses E, current iteration number of times k, wherein, c₁>0、c₂>0、ω>0, c₁、c₂, ω be real number, E, k, N be positive integer；

Feature is solved according to the described Single Intersection Traffic Signal Timing Optimized model based on public traffic in priority and carries out particle coding, institute State particle coding and include particle position coding x_i=(t_i1,t_i2,…,t_in) and particle rapidity coding v_i=(v_i1,v_i2,…, v_in)；

Particle initial position x is produced by chaos strategy_i=(t_i1,t_i2,…,t_in), and initialize particle rapidity for v_i=(0.5, 0.5,0.5,0.5) so that the particle of population can be uniformly distributed in solution space.

5. profit requires the method as described in 4 it is characterised in that step S4 specifically includes：

Update speed and the position of described particle, the iteration speed of the described particle after being updated according to self adaptation inertia weight And iterative position, wherein, described self adaptation inertia weight ω formula is：

ω=1.0-Pspeed* ω_h+Ptogether*ω_s

Pspeed is the speed evolution factor, and Ptogether is the concentration class factor, ω_hValue is 0.4 to 0.6, ω_sValue be 0.05 to 0.20；

Described iteration speed formula is：

V_ij(t+1)=ω V_ij(t)+c₁·r_1j(t)·[P_ij(t)-X_ij(t)]+c₂·r_2j(t)·[G_ij(t)-X_ij(t)]

Described iterative position formula is：

X_ij(t+1)=X_ij(t)+V_ij(t+1)

Wherein, positive integer N is population total number of particles, i=1, and 2 ..., N, i are that particle is numbered, positive integer M for particle rapidity or The length of position vector, j=1,2 ..., M, j number for particle coordinate, and positive integer t is current iteration number of times, real number V_ijT () is The velocity amplitude of particle, real number X_ijT () is the positional value of particle, real number P_ijT () is the individual optimal solution of particle, real number G_ijT () is The globally optimal solution of population population, real number c₁With real number c₂For Studying factors, real number r_1j(t) and real number r_2jT () is interval model Enclose the random number between (0,1)；

Update the described individual optimal solution of each particle and the overall situation of population according to described iteration speed and described iterative position Optimal solution；

Judge whether current Dynamic iterations number of times k is more than maximum iteration time E, if being more than E, initialization algorithm parameter again； If being less than E, return execution step S3.