四支路環(huán)形交叉口的信號(hào)控制與仿真主要方案?jìng)鹘y(tǒng)環(huán)形交叉口在車流量較小的情況下具有一定的自我調(diào)節(jié)能力,但入環(huán)車輛與繞環(huán)車輛優(yōu)先通行權(quán)并不明確。隨著我國(guó)城市汽車保有量的驟增,無(wú)信號(hào)調(diào)節(jié)的環(huán)形交叉口環(huán)道內(nèi)的車輛與入環(huán)車輛同時(shí)到達(dá)交織點(diǎn)形成沖突車流,無(wú)法及時(shí)駛出環(huán)道,導(dǎo)致交叉口延誤增大、排隊(duì)長(zhǎng)度增加,出現(xiàn)了擁堵以及鎖死的狀況。在這樣的背景下,如何對(duì)環(huán)形交叉口開(kāi)展改造成為了一個(gè)重要問(wèn)題。需要指出的是，國(guó)內(nèi)大多數(shù)學(xué)者通常采用的解決方式有以下三種:①拆掉原有的環(huán)島,改為正常的有信號(hào)燈調(diào)節(jié)的十字交叉口;②拓寬出入口,采用左轉(zhuǎn)、右轉(zhuǎn)、直行專用車道;③采用信號(hào)控制。進(jìn)一步地，

本文就采用信號(hào)控制這一解決方式,以大連數(shù)碼廣場(chǎng)的實(shí)際數(shù)據(jù)為例,運(yùn)用道路交通VISSIM仿真軟件,經(jīng)過(guò)大量的仿真實(shí)驗(yàn),對(duì)環(huán)形交叉口的幾種調(diào)節(jié)方式的適用性開(kāi)展了探討。

本文首先對(duì)環(huán)形交叉口的基本概念進(jìn)行了簡(jiǎn)單的介紹,闡述了環(huán)形交叉口的發(fā)展歷史、定義與構(gòu)成以及環(huán)形交叉口的分類。其次介紹了優(yōu)先權(quán)控制、單進(jìn)口放行調(diào)節(jié)、左轉(zhuǎn)兩步控制這3種控制方式以及配時(shí)模型,并提出了一種新的左轉(zhuǎn)兩步調(diào)節(jié)方法——Sazi配時(shí)原型,并對(duì)控制方法開(kāi)展了相位調(diào)整,隨后進(jìn)行了疊加提升。然后,利用實(shí)地調(diào)研的流量數(shù)據(jù)對(duì)幾種調(diào)節(jié)方式進(jìn)行實(shí)例配時(shí)計(jì)算,并利用VISSIM軟件進(jìn)行原型選取以及建模。由此可見(jiàn)，最后,

本文設(shè)計(jì)了大量仿真實(shí)驗(yàn)方案,并通過(guò)分析仿真檢測(cè)數(shù)據(jù),對(duì)疊加優(yōu)化開(kāi)展了肯定;確認(rèn)了平均延誤隨左轉(zhuǎn)車流量的增加而增加,并得到了以下重要結(jié)論:(1)當(dāng)總流量小于4200veh/h時(shí)優(yōu)先權(quán)控制最優(yōu),當(dāng)總流量處于4200-5500veh/h時(shí),單進(jìn)口放行控制方式較優(yōu),當(dāng)流量大于5500veh/h時(shí),左轉(zhuǎn)兩步調(diào)節(jié)最為適合;(2)對(duì)左轉(zhuǎn)兩步楊曉光的調(diào)節(jié)原型與新模型進(jìn)行了比較,發(fā)現(xiàn)兩者在不同左轉(zhuǎn)車道劃分依據(jù)下各自的左轉(zhuǎn)流量適應(yīng)性也不同,并得出了不同情況下兩者的優(yōu)缺點(diǎn);(3)對(duì)HCM2000左轉(zhuǎn)車道劃分依據(jù)闡述了質(zhì)疑,得出了Yang配時(shí)模型左轉(zhuǎn)車道數(shù)的劃分閾值在379-400veh/h之間,Sazi配時(shí)原型的閾值在408-448veh/h之間。綜上所述，論文提出了一種新的環(huán)形交叉口的左轉(zhuǎn)兩步調(diào)節(jié)方式模型,并為環(huán)形交叉口左轉(zhuǎn)車道的劃分提供了理論依據(jù),并為環(huán)形交叉口控制方式的選擇提供參考。?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_13:

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_13(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_13(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_13(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_13(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_13(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_13(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_13()

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

processed=cess_data(input_data)

system.data=processed

metrics=system.calculate_metrics()

print(