雾天高速匝道控制与可变限速控制论文

雾天高速公路匝道控制与可变限速控制方法研究主要方案大雾天气加深了高速公路框架的随机性、复杂性与不确定性,严重影响了高速公路交通安全。为明显改善雾天高速公路交通安全,

本文结合现有探讨成果较为深入地详细研究了雾天匝道控制方法、可变限速控制方法与协同控制方法,为雾天高速公路交通流控制提供相应的理论依据。在传统高速公路交通流模型的基础上引入基于T-S的雾天交通流模型修正因子,构建雾天高速公路交通流模型,通过仿真检测对该模型的有效性进行验证。根据高速公路系统主线交通流速度、密度与道路能见度之间的关系提出了基于T-S模型的道路临界密度在线修正方法,结合PI-ALINEA机制提出了雾天高速公路匝道控制算法,应用粒子群改进机制对其求解,以道路事故发生率为评价指标借助仿真实验对该算法有效性进行验证。综合考虑雾天因素、道路限速因素对高速公路交通流参数的影响,构建了雾天可变限速控制作用下高速公路交通流模式,以交通流速度与密度的平均绝对误差、平均绝对百分比误差为评价指标通过仿真检测对该模式的有效性进行验证。以雾天可变限速作用下交通流模式为预测模型,依据道路交通安全构建了雾天高速公路可变限速控制命题,并实践灰狼深度优化算法对其求解,通过仿真实验对该方法的有效性进行验证。针对雾天匝道控制与可变限速控制方法作用下匝道流入流量过少、交通流速度波动幅度过大等问题,改进匝道排队模式,结合雾天可变限速控制作用下交通流模型,构建雾天匝道与可变限速协同控制作用下高速公路交通流模式。提出依据模式预测控制的雾天高速公路匝道与可变限速协同控制方法,以道路事故发生率为评价指标借助仿真实验对该方法的有效性进行验证。✅简介：擅长数据搜集与处理、建模仿真、程序设计、仿真代码、论文写作与指导，毕业论文、期刊论文经验交流。

✅具体问题可以联系QQ或者微信：30040983。仿真代码importnumpyasnp

fromdataclassesimportdataclass

@dataclass

classConnectedVehicle:

id:int

position:float

speed:float

acceleration:float

destination:str

eta:float

priority:int=0

classV2XIntersectionController:

def__init__(self,intersection_id):

ersection_id=intersection_id

munication_range=300

self.vehicles={}

self.reservation_table={}

defregister_vehicle(self,vehicle):

self.vehicles[vehicle.id]=vehicle

defcalculate_arrival_time(self,vehicle,distance):

ifvehicle.speed>0:

arrival_time=distance/vehicle.speed

else:

arrival_time=float('inf')

returnarrival_time

defcheck_conflict(self,vehicle1,vehicle2,time_buffer=2):

arrival1=vehicle1.eta

arrival2=vehicle2.eta

time_diff=abs(arrival1-arrival2)

returntime_diff<time_buffer

deffcfs_scheduling(self):

sorted_vehicles=sorted(self.vehicles.values(),key=lambdav:v.eta)

schedule=[]

reserved_times=[]

forvehicleinsorted_vehicles:

crossing_time=vehicle.eta

duration=2.5

conflict=False

forreserved_start,reserved_endinreserved_times:

ifnot(crossing_time+duration<reserved_startorcrossing_time>reserved_end):

conflict=True

break

ifnotconflict:

schedule.append({'vehicle_id':vehicle.id,'crossing_time':crossing_time,'duration':duration})

reserved_times.append((crossing_time,crossing_time+duration))

else:

delay=max([endfor_,endinreserved_times])-crossing_time

new_crossing_time=crossing_time+delay+1

schedule.append({'vehicle_id':vehicle.id,'crossing_time':new_crossing_time,'duration':duration})

reserved_times.append((new_crossing_time,new_crossing_time+duration))

returnschedule

defoptimize_speed_profile(self,vehicle,target_arrival_time):

current_position=vehicle.position

distance_to_intersection=300-current_position

iftarget_arrival_time>0:

optimal_speed=distance_to_intersection/target_arrival_time

optimal_speed=max(10,min(optimal_speed,60))

else:

optimal_speed=vehicle.speed

ifabs(optimal_speed-vehicle.speed)>5:

acceleration=(optimal_speed-vehicle.speed)/5

acceleration=max(-3,min(acceleration,2))

else:

acceleration=0

returnoptimal_speed,acceleration

defplatoon_coordination(vehicles,platoon_size=5):

platoons=[]

current_platoon=[]

sorted_vehicles=sorted(vehicles,key=lambdav:v.position)

forvehicleinsorted_vehicles:

ifnotcurrent_platoon:

current_platoon.append(vehicle)

else:

last_vehicle=current_platoon[-1]

spacing=vehicle.position-last_vehicle.position

speed_diff=abs(vehicle.speed-last_vehicle.speed)

ifspacing<50andspeed_diff<10andlen(current_platoon)<platoon_size:

current_platoon.append(vehicle)

else:

platoons.append(current_platoon)

current_platoon=[vehicle]

ifcurrent_platoon:

platoons.append(current_platoon)

returnplatoons

defcooperative_merging(main_vehicles,ramp_vehicles,merge_point=500):

merge_schedule=[]

main_positions={v.id:v.positionforvinmain_vehicles}

ramp_positions={v.id:v.positionforvinramp_vehicles}

all_vehicles=main_vehicles+ramp_vehicles

all_vehicles.sort(key=lambdav:abs(merge_point-v.position))

time=0

forvehicleinall_vehicles:

ifvehicle.idinmain_positions:

merge_schedule.append({'vehicle_id':vehicle.id,'lane':'main','merge_time':time,'speed_adjustment':0})

else:

merge_schedule.append({'vehicle_id':vehicle.id,'lane':'ramp','merge_time':time+1,'speed_adjustment':-5})

time+=2

returnmerge_schedule

defeco_driving_optimization(vehicle,signal_timing,distance_to_signal):

green_start=signal_timing['green_start']

green_duration=signal_timing['green_duration']

cycle_length=signal_timing['cycle_length']

current_speed=vehicle.speed

time_to_arrival=distance_to_signal/current_speedifcurrent_speed>0elsefloat('inf')

arrival_phase=time_to_arrival%cycle_length

ifarrival_phase>=green_startandarrival_phase<=green_start+green_duration:

optimal_speed=current_speed

eco_benefit=0

else:

time_to_next_green=(green_start-arrival_phase)%cycle_length

optimal_speed=distance_to_signal/(time_to_arrival+time_to_next_green)

optimal_speed=max(20,min(optimal_speed,current_speed))

fuel_saved=(current_speed-optimal_speed)**2*0.01

eco_benefit=fuel_saved

return{'optimal_speed':optimal_speed,'fuel_saving':eco_benefit,'recommendation':'decelerate'ifoptimal_speed<current_speedelse'maintain'}

deftrajectory_planning(vehicle,obstacles,time_horizon=10):

trajectories=[]

fortinrange(time_horizon):

position=vehicle.position+vehicle.speed*t+0.5*vehicle.acceleration*t**2

speed=vehicle.speed+vehicle.acceleration*t

collision=False

forobsinobstacles:

ifabs(position-obs['position'])<obs['size']:

collision=True

break

trajectories.append({'time':t,'position':position,'speed':speed,