山地城市公交專用道適用性評(píng)價(jià)及優(yōu)化研究主要方案大量實(shí)驗(yàn)證實(shí)，優(yōu)先發(fā)展公共交通是解決城市交通供需矛盾,調(diào)整交通結(jié)構(gòu)的有效途徑。設(shè)立公交專用道是發(fā)展公交優(yōu)先政策最直接有效的措施之一。此外，在各大城市紛紛布設(shè)公交專用道之時(shí),山地城市由于其特殊的地形地貌,用地資源有限且道路資源緊張等環(huán)境因素,導(dǎo)致公交專用道的設(shè)立相比于平原城市更為復(fù)雜,其適用性值得全面研究。

本文對(duì)山城重慶已建成的公交專用道的適用性進(jìn)行評(píng)價(jià),對(duì)其布設(shè)的效果進(jìn)行了深入探究。值得注意的是，

本文首先對(duì)山地城市公交專用道的布設(shè)模式進(jìn)行了詳細(xì)分析。同時(shí)，從山地城市的地形特性和交通特性著手,分析了山地城市獨(dú)特的城市形態(tài)、空間布局、交通組成和居民出行方式,均與平原城市存在較大差異?；诠粚Ｓ玫赖牟荚O(shè)模式,從專用道的車道設(shè)置方式、交叉口進(jìn)口道設(shè)置方式和路段出口的設(shè)置等角度探索了山地城市公交專用道的布設(shè)形式。其次,選取了山地城市公交專用道適用性的評(píng)價(jià)指標(biāo)。同時(shí)，分別從乘客、車輛、路段等方面選取12項(xiàng)評(píng)價(jià)指標(biāo)。在基于平原城市對(duì)公交專用道評(píng)價(jià)的基礎(chǔ)指標(biāo)之上,

本文針對(duì)山地城市獨(dú)特的地理環(huán)境,提出摩托車占道率、右轉(zhuǎn)社會(huì)車輛延誤和專用道空閑率等評(píng)價(jià)指標(biāo)。針對(duì)山地城市的特殊性對(duì)車速模型和時(shí)間模型進(jìn)行了增強(qiáng),為后續(xù)專用道的綜合評(píng)價(jià)提供支撐。接著,經(jīng)對(duì)比分析

本文選取Topsis方法作為山地城市公交專用道的綜合評(píng)價(jià)方法,并引入熵權(quán)法確定指標(biāo)權(quán)重,構(gòu)建了適用于山地城市公交專用道的綜合評(píng)價(jià)模型。最后,以山地城市——重慶市為例,選取重慶市已建成的三條公交專用道,對(duì)其適用性進(jìn)行綜合性評(píng)價(jià)。

本文不僅對(duì)重慶市設(shè)置公交專用道之后的現(xiàn)狀進(jìn)行了考量與評(píng)價(jià),而且利用了Vissim軟件對(duì)設(shè)置專用道之前的交通狀況進(jìn)行了仿真與指標(biāo)計(jì)算,詳細(xì)比較了設(shè)置前后指標(biāo)的變化,并對(duì)山地城市公交專用道的適用性進(jìn)行了考量。結(jié)果表明,山地城市公交專用道的適用性較好,但仍存在區(qū)段擁堵率高、公交車車頭時(shí)距穩(wěn)定性差、右轉(zhuǎn)社會(huì)車輛延誤嚴(yán)重、專用道空閑率高和摩托車占道現(xiàn)象的問題待解決。另外，所以

本文通過建立借道右轉(zhuǎn)交織區(qū)長(zhǎng)度的模型,進(jìn)一步細(xì)化在不同交通量下的交織區(qū)長(zhǎng)度。并從交織區(qū)的設(shè)置形式、右轉(zhuǎn)起點(diǎn)標(biāo)志標(biāo)線設(shè)置、車輛管理和專用道管理等方面,對(duì)山地城市公交專用道的完善提出系統(tǒng)優(yōu)化建議。?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。

?具體問題可以聯(lián)系QQ或者微信：30040983。仿真代碼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':