山地城市公交专用道适用性评价论文

山地城市公交专用道适用性评价及优化研究主要方案大量实验证实，优先发展公共交通是解决城市交通供需矛盾,调整交通结构的有效途径。设立公交专用道是发展公交优先政策最直接有效的措施之一。此外，在各大城市纷纷布设公交专用道之时,山地城市由于其特殊的地形地貌,用地资源有限且道路资源紧张等环境因素,导致公交专用道的设立相比于平原城市更为复杂,其适用性值得全面研究。

本文对山城重庆已建成的公交专用道的适用性进行评价,对其布设的效果进行了深入探究。值得注意的是，

本文首先对山地城市公交专用道的布设模式进行了详细分析。同时，从山地城市的地形特性和交通特性着手,分析了山地城市独特的城市形态、空间布局、交通组成和居民出行方式,均与平原城市存在较大差异。基于公交专用道的布设模式,从专用道的车道设置方式、交叉口进口道设置方式和路段出口的设置等角度探索了山地城市公交专用道的布设形式。其次,选取了山地城市公交专用道适用性的评价指标。同时，分别从乘客、车辆、路段等方面选取12项评价指标。在基于平原城市对公交专用道评价的基础指标之上,

本文针对山地城市独特的地理环境,提出摩托车占道率、右转社会车辆延误和专用道空闲率等评价指标。针对山地城市的特殊性对车速模型和时间模型进行了增强,为后续专用道的综合评价提供支撑。接着,经对比分析

本文选取Topsis方法作为山地城市公交专用道的综合评价方法,并引入熵权法确定指标权重,构建了适用于山地城市公交专用道的综合评价模型。最后,以山地城市——重庆市为例,选取重庆市已建成的三条公交专用道,对其适用性进行综合性评价。

本文不仅对重庆市设置公交专用道之后的现状进行了考量与评价,而且利用了Vissim软件对设置专用道之前的交通状况进行了仿真与指标计算,详细比较了设置前后指标的变化,并对山地城市公交专用道的适用性进行了考量。结果表明,山地城市公交专用道的适用性较好,但仍存在区段拥堵率高、公交车车头时距稳定性差、右转社会车辆延误严重、专用道空闲率高和摩托车占道现象的问题待解决。另外，所以

本文通过建立借道右转交织区长度的模型,进一步细化在不同交通量下的交织区长度。并从交织区的设置形式、右转起点标志标线设置、车辆管理和专用道管理等方面,对山地城市公交专用道的完善提出系统优化建议。✅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':