疏港公路干線交通信號協(xié)調(diào)控制研究主要方案近年來港口發(fā)展迅速蓬勃,港口周轉(zhuǎn)量激增,但港區(qū)交通流的現(xiàn)狀與其基礎(chǔ)設(shè)施情況不相適應(yīng),嚴(yán)重限制港區(qū)的發(fā)展?；诖?，疏港公路作為城市通向港口的主要道路骨架,交通負(fù)荷巨大,從而導(dǎo)致疏港干線公路的擁堵情況日趨嚴(yán)重,阻礙港口經(jīng)濟(jì)發(fā)展。在港區(qū)路網(wǎng)中,干線道路承載著港區(qū)交通的主要壓力,而港口毗鄰交叉口作為港區(qū)路網(wǎng)的重要節(jié)點(diǎn),其交通管控的合理性影響著港區(qū)道路的通達(dá)性,其通行能力大小影響著道路運(yùn)行效率。因此,需要對疏港公路干線實(shí)施合理的交通控制,防止交通溢流現(xiàn)象甚至交通網(wǎng)絡(luò)癱瘓狀況的發(fā)生,從而增大疏港公路的通行能力,促進(jìn)集卡行駛暢通,緩解眾多交通問題,并且,在交通發(fā)展中必須堅(jiān)持綠色可持續(xù)發(fā)展的理念,減少由于交通因素所造成的環(huán)境污染。

本文在現(xiàn)有系統(tǒng)研究的基礎(chǔ)上,分別從港口毗鄰交叉口信號配時(shí)全面優(yōu)化以及疏港公路干線協(xié)調(diào)相位差優(yōu)化出發(fā),建立信號配時(shí)優(yōu)化范式。

本文首先根據(jù)港口交通特點(diǎn),構(gòu)建港口毗鄰交叉口信號管控范式?？紤]港口閘口處車輛排隊(duì)情況、港口毗鄰交叉口通行能力、延誤以及車輛尾氣排放量等因素,構(gòu)建港口毗鄰交叉口信號控制模型,自變量為各相位綠燈時(shí)間。改進(jìn)遺傳算法進(jìn)行求解范式,并通過相關(guān)案例證明模型的顯著性。其次建立疏港公路干線協(xié)調(diào)優(yōu)化模型。梳理了已有干線協(xié)調(diào)控制方法的文獻(xiàn),根據(jù)車流在下游交叉口遇到紅燈時(shí)的狀態(tài),考慮干線行駛過程中的車輛延誤,包括集卡車隊(duì)頭部遇紅燈受阻和集卡車隊(duì)尾部遇紅燈受阻;以及干線上各個(gè)交叉口車輛遇到紅燈時(shí)的延誤車輛數(shù)量和尾氣排放;建立疏港公路干線協(xié)調(diào)相位差優(yōu)化范式,利用遺傳模擬退火算法進(jìn)行求解。由此可見，通過車速引導(dǎo)策略,引導(dǎo)集卡車隊(duì)加速或減速,從而實(shí)現(xiàn)集卡車輛盡可能多地在綠燈期間通過交叉口,減少車輛的延誤。在此基礎(chǔ)上，最后進(jìn)行案例全面分析。選用大連市大窯灣港區(qū)的三個(gè)干線交叉口作為案例交叉口,現(xiàn)場調(diào)研干線各交叉口現(xiàn)狀情況。通過

本文模型,確定單交叉口信號配時(shí)方案,以及疏港公路干線協(xié)調(diào)管控方案,并通過VISSIM軟件搭建仿真平臺,進(jìn)行仿真分析,對比現(xiàn)狀方案、

本文所闡述考慮車輛延誤的相位差優(yōu)化方案、基于車速引導(dǎo)的考慮車輛延誤的相位差優(yōu)化方案的輸出評價(jià)指標(biāo)。結(jié)果表明,相比于現(xiàn)狀控制方案,

本文的在...基礎(chǔ)上車速引導(dǎo)的考慮車輛延誤的相位差優(yōu)化方案的干線平均延誤減少32%,平均停車次數(shù)減少20.5%,平均排隊(duì)長度減少29.4%,最大排隊(duì)長度減少20.7%。從而驗(yàn)證了

本文所提出疏港公路信號管控優(yōu)化模型的有效性,在一定程度上提高疏港公路干線的服務(wù)水平。?簡介：擅長數(shù)據(jù)搜集與處理、建模仿真、程序設(shè)計(jì)、仿真代碼、論文寫作與指導(dǎo)，畢業(yè)論文、期刊論文經(jīng)驗(yàn)交流。

?具體問題可以聯(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()