十字路口交通信號(hào)和車(chē)輛軌跡協(xié)同優(yōu)化主要方案帶有信號(hào)燈的十字路口是城市交通環(huán)境中常見(jiàn)的一種場(chǎng)景,而十字路口很容易產(chǎn)生大面積的車(chē)輛擁堵。十字路口常用的定時(shí)信號(hào)控制方法在一方面會(huì)引起大量車(chē)輛的停車(chē)等待造成延誤,另一方面,車(chē)輛因?yàn)榧t燈造成的頻繁啟停和怠速停車(chē),會(huì)帶來(lái)額外的燃油消耗。智能交通以及智能網(wǎng)聯(lián)汽車(chē)的發(fā)展為解決十字路口的擁堵問(wèn)題帶來(lái)了新的方法。

本文針對(duì)十字路口處的交通環(huán)境,提出了信號(hào)燈的配時(shí)和車(chē)輛軌跡協(xié)同系統(tǒng)優(yōu)化方法以及車(chē)道上的隊(duì)列劃分方法,可以對(duì)路口前的大量輸入車(chē)流展開(kāi)優(yōu)化。首先,

本文研判了十字路口信號(hào)燈相位的應(yīng)用情況,基于對(duì)相位的分析,利用MATLAB對(duì)交通仿真平臺(tái)VISSIM展開(kāi)了二次開(kāi)發(fā),搭建了信號(hào)配時(shí)和車(chē)輛軌跡協(xié)同優(yōu)化的仿真環(huán)境,實(shí)現(xiàn)了外部掌控范式與仿真環(huán)境的數(shù)據(jù)交互。之后以動(dòng)態(tài)規(guī)劃思想為基礎(chǔ),

本文提出了以路網(wǎng)中車(chē)輛總體延誤值最小為優(yōu)化目標(biāo)的路口兩相位信號(hào)燈配時(shí)的優(yōu)化方法,并將優(yōu)化算法實(shí)踐到仿真環(huán)境中,將優(yōu)化后的配時(shí)作為車(chē)輛軌跡優(yōu)化的基礎(chǔ)。其次,

本文建立了車(chē)輛在單車(chē)道上行駛時(shí)的縱向運(yùn)動(dòng)學(xué)模型,以?xún)?yōu)化時(shí)刻車(chē)輛到路口的距離和優(yōu)化信號(hào)配時(shí)作為約束,使車(chē)輛在綠燈開(kāi)始時(shí)到達(dá)十字路口,以此來(lái)實(shí)現(xiàn)無(wú)障礙通行,在此過(guò)程中,把時(shí)間約束內(nèi)的加速度時(shí)長(zhǎng)取得極小值作為優(yōu)化目標(biāo),以此來(lái)達(dá)到優(yōu)化燃油經(jīng)濟(jì)性的目的,使用最優(yōu)掌控中的燃油最優(yōu)控制原理對(duì)目標(biāo)函數(shù)進(jìn)行求解,得到了針對(duì)不同初始狀態(tài)車(chē)輛的控制策略。同時(shí),考慮到十字路口環(huán)境中車(chē)流量較大的特征,

本文提出了車(chē)道上車(chē)輛隊(duì)列的劃分方法,將車(chē)輛隊(duì)列劃分為領(lǐng)頭車(chē)和跟隨車(chē)的模式,隊(duì)列中領(lǐng)頭車(chē)通過(guò)燃油最優(yōu)掌控原理優(yōu)化軌跡,跟隨車(chē)輛按照跟馳范式中的約束跟隨前車(chē)行駛,以此來(lái)實(shí)現(xiàn)隊(duì)列的可控。最后,對(duì)協(xié)同優(yōu)化方法進(jìn)行了仿真,分析比較了優(yōu)化前后的總體延誤值,并通過(guò)燃油瞬時(shí)消耗模型對(duì)仿真過(guò)程中的燃油消耗進(jìn)行了計(jì)算,結(jié)果表明了協(xié)同優(yōu)化方法的有效性。在驗(yàn)證環(huán)節(jié),利用STM32單片機(jī)搭建了智能小車(chē)隊(duì)列,用以驗(yàn)證文中隊(duì)列模型在多車(chē)協(xié)同控制環(huán)境中的運(yùn)行效果,將小車(chē)隊(duì)列行駛過(guò)程中采集到的素材擬合之后,對(duì)小車(chē)運(yùn)行效果展開(kāi)了研判。?簡(jiǎn)介：擅長(zhǎng)數(shù)據(jù)搜集與處理、建模仿真、程序設(shè)計(jì)、仿真代碼、論文寫(xiě)作與指導(dǎo)，畢業(yè)論文、期刊論文經(jīng)驗(yàn)交流。

?具體問(wèn)題可以聯(lián)系QQ或者微信：30040983。仿真代碼importnumpyasnp

importrandom

fromcollectionsimportdeque

classTrafficSystem_30:

def__init__(self):

self.data=[]

self.parameters={}

self.state='initialized'

self.metrics={}

defprocess_data(self,inputs):

processed=[]

foritemininputs:

value=item*random.uniform(0.8,1.2)

processed.append(max(0,value))

returnprocessed

defcalculate_metrics(self):

ifnotself.data:

return{}

values=np.array(self.data)

return{

'mean':np.mean(values),

'std':np.std(values),

'min':np.min(values),

'max':np.max(values),

'median':np.median(values)

}

defoptimize(self,objective='efficiency'):

best_value=0

best_params={}

foriterationinrange(100):

param_a=random.uniform(10,100)

param_b=random.uniform(0.1,1.0)

score=param_a*param_b+random.gauss(0,5)

ifscore>best_value:

best_value=score

best_params={'param_a':param_a,'param_b':param_b}

self.parameters=best_params

returnbest_params,best_value

defsimulation_function_30(duration=1000,seed=42):

np.random.seed(seed)

results=[]

fortinrange(duration):

arrival_rate=500+300*np.sin(2*np.pi*t/duration)

service_rate=np.random.normal(600,50)

ifarrival_rate<service_rate:

delay=(duration-t)/(2*duration)

else:

delay=(arrival_rate-service_rate)/arrival_rate

results.append({

'time':t,

'arrivals':arrival_rate,

'service':service_rate,

'delay':delay

})

returnresults

defoptimization_algorithm_30(data,iterations=200):

population_size=50

population=[]

for_inrange(population_size):

individual={

'x':random.uniform(0,100),

'y':random.uniform(0,100),

'z':random.uniform(0,100)

}

population.append(individual)

forgeninrange(iterations):

fitness_scores=[]

forindinpopulation:

fitness=-(ind['x']-50)**2-(ind['y']-50)**2-(ind['z']-50)**2

fitness_scores.append(fitness)

best_idx=np.argmax(fitness_scores)

best_individual=population[best_idx]

new_population=[best_individual]

for_inrange(population_size-1):

parent1=population[random.randint(0,population_size-1)]

parent2=population[random.randint(0,population_size-1)]

child={

'x':(parent1['x']+parent2['x'])/2+random.gauss(0,5),

'y':(parent1['y']+parent2['y'])/2+random.gauss(0,5),

'z':(parent1['z']+parent2['z'])/2+random.gauss(0,5)

}

new_population.append(child)

population=new_population

returnbest_individual,max(fitness_scores)

defpredictive_model_30(historical_data,horizon=10):

iflen(historical_data)<10:

return[0]*horizon

recent=historical_data[-20:]

trend=(recent[-1]-recent[0])/len(recent)

predictions=[]

last_value=historical_data[-1]

forhinrange(horizon):

predicted=last_value+trend*(h+1)

noise=random.gauss(0,abs(predicted)*0.1)

predictions.append(max(0,predicted+noise))

returnpredictions

defcontrol_strategy_30(state,parameters):

ifstate['congestion_level']>0.7:

action='increase_capacity'

control_value=parameters.get('max_control',100)

elifstate['congestion_level']>0.4:

action='moderate_control'

control_value=parameters.get('moderate_control',60)

else:

action='maintain'

control_value=parameters.get('min_control',30)

return{

'action':action,

'value':control_value,

'expected_improvement':random.uniform(5,20)

}

defperformance_evaluation_30(strategy_results):

total_delay=sum(r.get('delay',0)forrinstrategy_results)

total_throughput=sum(r.get('throughput',0)forrinstrategy_results)

avg_speed=np.mean([r.get('speed',50)forrinstrategy_results])

efficiency_score=total_throughput/(total_delay+1)*100

return{

'total_delay':total_delay,

'total_throughput':total_throughput,

'average_speed':avg_speed,

'efficiency_score':efficiency_score

}

defdata_preprocessing_30(raw_data):

cleaned=[]

foriteminraw_data:

ifitemisNoneoritem<0:

continue

iflen(cleaned)>0:

ifabs(item-cleaned[-1])>cleaned[-1]*2:

item=cleaned[-1]

cleaned.append(item)

iflen(cleaned)>5:

window_size=5

smoothed=[]

foriinrange(len(cleaned)):

start=max(0,i-window_size//2)

end=min(len(cleaned),i+window_size//2+1)

window=cleaned[start:end]

smoothed.append(np.mean(window))

returnsmoothed

returncleaned

defmain():

system=TrafficSystem_30()

input_data=np.random.randint(100,1000,50)

processed=cess_data(input_data)

system.data=processed

metrics=system.calculate_metrics()

print(f"System