1 内容介绍
城市交通拥堵阻碍城市发展:(1)减少市民可用于工作时间;(2)造成环境污染;(3)难以应变道路紧急情况.特种车辆在城市中执行紧急任务时,由于现阶段灯控系统未能对其做出区分,无法动态引导特种车辆到路口之间的交通流,并在其到达路口时设置绿灯,造成特种车辆通行过程中常常遇到阻碍.红绿灯作为城市交通管理的工具,根据感知到的路口周边车辆调整红灯时间和绿灯时间,可以优化交通控制,解决路口交通拥堵以及实现特种车辆执行紧急任务时一路"绿灯"畅行.对于灯控路口拥堵问题的研究.
2 仿真代码
%% Starting point, clear everything in matlab
tic;
clear all;
close all;
clc;
%% Problem Formulation
FitnessFunction=@(C,g,x,c) TDi(C,g,x,c); % FitnessFunction
nLights=4; % Number of Traffic Lights
nIntersections=1; % Number of Intersections (static as 1 intersection)
VarSize=[1 nIntersections*nLights]; % Decision Chromosome genes based on number of Intersections
greenMin= 10; % Lower bound of GREEN LIGHT
greenMax= 60; % Upper bound of GREEN LIGHT
Cyclemin=60; % Lower bound of CYCLE
Cyclemax=180 ;
RoadcapacityNSWE=[20,20,20,20]; % Road Capacity for NSWE respectivelly
CarsNSWE=[20,20,11,17];
RoadCongestion1NSWE=RoadcapacityNSWE-CarsNSWE; % congestion according to free road spaces
RoadCongestionNSWE=RoadCongestion1NSWE./RoadcapacityNSWE; % Volume/Capacity RATIO
carpass=5;
%% Genetic Algorithm Parameters
MaxIt=25; % Maximum Number of Iterations
nPop=400; % Population Size
pc=0.5; % Crossover Percentage
nc=2*round(pc*nPop/2); % Number of Offsprings (parents)
pm=0.02; % Mutation Percentage
nm=round(pm*nPop); % Number of Mutants
mu=0.1; % Mutation Rate
pinv=0.2;
ninv=round(pinv*nPop);
beta=8; % Selection Pressure
%% Initialization
% Individual Structure
empty_individual.GreenNSWE=[];
empty_individual.TotalDelay=[];
% Population Structure
pop=repmat(empty_individual,nPop,1);
% Initialize Population
i=1;
current_cycle=160-12; %estw kiklos 160 seconds - 12 seconds gia ;
disp(['FIRST Population..........Best TotalDelay = ' num2str(BestDelay)]);
fprintf('\n')
disp('Green Timings in seconds:');
disp([' North Green time = ' num2str(BestSol.GreenNSWE(1))]);
fprintf('\n')
disp([' South Green time = ' num2str(BestSol.GreenNSWE(2))]);
fprintf('\n')
disp([' West Green time = ' num2str(BestSol.GreenNSWE(3))]);
fprintf('\n')
disp([' East Green time = ' num2str(BestSol.GreenNSWE(4))]);
fprintf('\n')
%% Loop For Number of Iterations
count=0;
for it=1:MaxIt
% TERMINATION CRITERIA IF NEEDED
% if(it>2)
% if(BestDelay(it-1)==BestDelay(it-2))
% count=count+1;
% else
% count=0;
% end
% end
% if(count>5)
% disp('5 Generations without evolution. Process Stopped !');
% break;
% end
% Calculate Selection Probabilities
P=exp(-beta*TotalDelay/WorstDelay);
P=P/sum(P);
%% Crossover
popc=repmat(empty_individual,nc/2,2);
k=1;
while k<=nc/2
% Select Parents Indices from roulette wheel
i1=RouletteWheelSelection(P);
i2=RouletteWheelSelection(P);
% Select Parents
p1=pop(i1);
p2=pop(i2);
popc(k,1).GreenNSWE=p1.GreenNSWE;
popc(k,2).GreenNSWE=p2.GreenNSWE;
popc(k,1).TotalDelay=p1.TotalDelay;
popc(k,2).TotalDelay=p2.TotalDelay;
% Select random crossover point
i=randi([1 3]);
% crossover randomness
if(i==1)
popc1=popc(k,1).GreenNSWE(2);
popc(k,1).GreenNSWE(2)= popc(k,2).GreenNSWE(2);
popc(k,2).GreenNSWE(2)=popc1;
popc1=popc(k,1).GreenNSWE(3);
popc(k,1).GreenNSWE(3)= popc(k,2).GreenNSWE(3);
popc(k,2).GreenNSWE(3)=popc1;
popc1=popc(k,1).GreenNSWE(4);
popc(k,1).GreenNSWE(4)= popc(k,2).GreenNSWE(4);
popc(k,2).GreenNSWE(4)=popc1;
elseif(i==2)
popc1=popc(k,1).GreenNSWE(3);
popc(k,1).GreenNSWE(3)= popc(k,2).GreenNSWE(3);
popc(k,2).GreenNSWE(3)=popc1;
popc1=popc(k,1).GreenNSWE(4);
popc(k,1).GreenNSWE(4)= popc(k,2).GreenNSWE(4);
popc(k,2).GreenNSWE(4)=popc1;
else
popc1=popc(k,1).GreenNSWE(4);
popc(k,1).GreenNSWE(4)= popc(k,2).GreenNSWE(4);
popc(k,2).GreenNSWE(4)=popc1;
end
% check if new green times are out constraints 10-60s. If it is
% get it to closer min or max
popc(k,1).GreenNSWE=max(popc(k,1).GreenNSWE,greenMin);
popc(k,1).GreenNSWE=min(popc(k,1).GreenNSWE,greenMax);
popc(k,2).GreenNSWE=max(popc(k,2).GreenNSWE,greenMin);
popc(k,2).GreenNSWE=min(popc(k,2).GreenNSWE,greenMax);
if(sum(popc(k,1).GreenNSWE)>current_cycle || sum(popc(k,2).GreenNSWE)>current_cycle)
continue;
end
% Evaluate Generated Offsprings for each traffic light according to
% the corresponding traffic congestion
for j=1:nLights
popc(k,1).TotalDelay(j)=FitnessFunction(current_cycle, popc(k,1).GreenNSWE(j), RoadCongestionNSWE(j),RoadcapacityNSWE(j));
popc(k,2).TotalDelay(j)=FitnessFunction(current_cycle, popc(k,2).GreenNSWE(j), RoadCongestionNSWE(j),RoadcapacityNSWE(j));
end
% TOTAL DELAY which correspongs to the summation of of 4 lights quotients
popc(k,1).TotalDelay= real(sum(popc(k,1).TotalDelay));
popc(k,2).TotalDelay= real(sum(popc(k,2).TotalDelay));
k=k+1; %step
end
% Make 2 rows 1
popc=popc(:);
% Sort popc matrix according to TotalDelay
TotalDelay=[popc.TotalDelay];
[TotalDelay, SortOrder]=sort(TotalDelay);
popc=popc(SortOrder);
%% Mutation
% Create empty Matrix with length the number of mutants
popm=repmat(empty_individual,nm,1);
k=1;
while k<=nm
% Select Parent population
i=randi([1 nPop]); %nPop value 100
p=pop(i);
% Apply Mutation
nVar=4;
nmu=ceil(mu*nVar);
j=randi([1 nVar]);
prosimo=randi([-1 1]);
sigma=prosimo*0.02*(greenMax-greenMin);
mutated=p.GreenNSWE(j)+sigma;
popm(k).GreenNSWE = p.GreenNSWE;
popm(k).GreenNSWE(j)=mutated;
popm(k).GreenNSWE=max(popm(k).GreenNSWE,greenMin);
popm(k).GreenNSWE=min(popm(k).GreenNSWE,greenMax);
if(sum(popm(k).GreenNSWE)>current_cycle)
continue;
end
for j=1:nLights
% Evaluate Mutant
popm(k).TotalDelay(j)=FitnessFunction(current_cycle, popm(k).GreenNSWE(j), RoadCongestionNSWE(j),RoadcapacityNSWE(j));
end
% Summation of delay quotients
popm(k).TotalDelay=real(sum(popm(k).TotalDelay));
k=k+1; %step
end
%% INVERSION
% Create empty Matrix
popinv=repmat(empty_individual,nm,1);
k=1;
while k<=ninv
% Select Parent population
i=randi([1 nPop]);
p=pop(i);
% Apply Inversion
nVar=numel(p.GreenNSWE);
randomgene1=randi([1 4]);
randomgene2=randi([1 4]);
y=p.GreenNSWE;
popinv(k).GreenNSWE=y;
x=popinv(k).GreenNSWE(randomgene1);
popinv(k).GreenNSWE(randomgene1)=popinv(k).GreenNSWE(randomgene2);
popinv(k).GreenNSWE(randomgene2)=x;
popinv(k).GreenNSWE=max(popinv(k).GreenNSWE,greenMin);
popinv(k).GreenNSWE=min(popinv(k).GreenNSWE,greenMax);
if(sum(popinv(k).GreenNSWE)>current_cycle)
continue;
end
for j=1:nLights
% Evaluate Mutant
popinv(k).TotalDelay(j)=FitnessFunction(current_cycle, popinv(k).GreenNSWE(j), RoadCongestionNSWE(j),RoadcapacityNSWE(j));
end
% Summation of delay quotients
popinv(k).TotalDelay=real(sum(popinv(k).TotalDelay));
k=k+1;
end
% Make 2 rows 1
popinv=popinv(:);
%% Merge Population
pop=[pop
popc
popm
popinv]; %#ok
% Sort New Population according to TotalDelay
TotalDelay=[pop.TotalDelay];
[TotalDelay, SortOrder]=sort(TotalDelay);
pop=pop(SortOrder);
% Update Worst Cost
WorstDelay=max(WorstDelay,pop(end).TotalDelay);
% Keep the Best Population from the given number
pop=pop(1:nPop);
TotalDelay=TotalDelay(1:nPop);
% Store Best Solution Ever Found
BestSol=pop(1);
% Store Best Cost Ever Found
BestDelay(it)=BestSol.TotalDelay;
% Show Iteration Information
disp([' Iteration ' num2str(it) ': Best TotalDelay = ' num2str(BestDelay(it))]);
fprintf('\n')
disp('Green Timings:');
fprintf('\n')
disp([' North Green time = ' num2str(BestSol.GreenNSWE(1))'' ' seconds']);
fprintf('\n')
disp([' South Green time = ' num2str(BestSol.GreenNSWE(2))'' ' seconds']);
fprintf('\n')
disp([' West Green time = ' num2str(BestSol.GreenNSWE(3))'' ' seconds']);
fprintf('\n')
disp([' East Green time = ' num2str(BestSol.GreenNSWE(4))'' ' seconds']);
fprintf('\n')
%end of generation
end
disp(' ****************************************************************' );
disp(' CASE: Every 5 seconds 2 vehicles leaves the corresponding road ' );
disp(' Expected vehicles left through North road' );
disp(round(2*BestSol.GreenNSWE(1)/carpass));
disp(' Expected vehicles left through South road' );
disp(round(2*BestSol.GreenNSWE(2)/carpass));
disp(' Expected vehicles left through West road' );
disp(round(2*BestSol.GreenNSWE(3)/carpass));
disp(' Expected vehicles left through East road' );
disp(round(2*BestSol.GreenNSWE(4)/carpass));
fprintf('\n')
disp(' ****************************************************************' );
disp(['Cycle Time = ' num2str(current_cycle)'' ' seconds']);
%% Results / Plots
figure(1);
semilogy(BestDelay,'LineWidth',2);
% plot(BestCost,'LineWidth',2);
xlabel('Iteration');
ylabel('Total Delay');
grid on;
toc
3 运行结果
4 参考文献
[1]赵功勋, 郭海滨, 苏利. 基于遗传算法的工程项目资源均衡优化及其MATLAB实现[J]. 工程经济, 2016, 26(12):6.
[2]叶文斌. 基于红绿灯优化城市交通控制设计与仿真[D]. 华东师范大学, 2015.
