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genCar.py
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import numpy as np
import math
class GenCar:
def __init__(self, totalNumber, maxSteps, leftTurn, straight, fileName,scale):
self.totalNumber = totalNumber # 代表车辆总数
self.maxSteps = maxSteps # 代表车辆可出现的时间段
self.idealLeft = leftTurn
self.idealStraight = straight
self.realLeft = 0
self.realStraight = 0
self.realRight = 1 - self.realLeft - self.realStraight
self.fileName = fileName # 代表要写入的路径文件名
# self.sumoAgent = sumoAgent
self.origins = ['gneE4','gneE5','gneE6','gneE7']
self.aims = ['-gneE4', '-gneE5', '-gneE6', '-gneE7']
self.scale = scale #0.8 # 调节生成车辆数 1500 - 0.8
def generate_routefile(self):
if self.idealLeft + self.idealStraight > 9:
print("生成文件失败，请重新检查车流转向比例！两者之和为 ", self.idealLeft + self.idealStraight)
return False
# 汽车的生成根据weibull分布 生成N辆车
timings = np.random.weibull(2, self.totalNumber) # N辆车产生的时间按照weibull分布
timings = np.sort(timings)
# print("timings", len(timings))
# 重新调整 以适应0：maxStep的间隔
car_gen_steps = []
min_old = math.floor(timings[1])
max_old = math.ceil(timings[-1])
# print(min_old)
# print(max_old)
min_new = 0
max_new = self.maxSteps
for value in timings:
tmp = ((max_new - min_new) / (max_old - min_old)) * (value - max_old) + max_new
car_gen_steps = np.append(car_gen_steps, tmp)
car_gen_steps = np.rint(car_gen_steps) # 将数组的元素四舍五入到最接近的整数
# print(car_gen_steps)
# 生成汽车路径文件 每行一辆车
with open(self.fileName, 'w') as routes:
print("""<routes>
<vType id="standard_car" length="3.0" maxSpeed="15" minGap="1.0" accel="1.0" decel="4.0" />
<route id="N_S" edges="road_1_2_3 road_1_1_3"/>
<route id="N_E" edges="road_1_2_3 road_1_1_0"/>
<route id="N_W" edges="road_1_2_3 road_1_1_2"/>
<route id="S_W" edges="road_1_0_1 road_1_1_2"/>
<route id="S_E" edges="road_1_0_1 road_1_1_0"/>
<route id="S_N" edges="road_1_0_1 road_1_1_1"/>
<route id="W_E" edges="road_0_1_0 road_1_1_0"/>
<route id="W_N" edges="road_0_1_0 road_1_1_1"/>
<route id="W_S" edges="road_0_1_0 road_1_1_3"/>
<route id="E_N" edges="road_2_1_2 road_1_1_1"/>
<route id="E_S" edges="road_2_1_2 road_1_1_3"/>
<route id="E_W" edges="road_2_1_2 road_1_1_2"/>
""", file=routes)
for car_counter, step in enumerate(car_gen_steps):
straight_or_turn = np.random.uniform() # 生成一个01随机数、
if straight_or_turn < self.idealStraight * 0.1: # 50%机会直行
route_straight = np.random.randint(1, 5)
if route_straight == 1:
print(
" <vehicle id='W_E_%i' type='standard_car' route='W_E' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, step), file=routes)
elif route_straight == 2:
print(
" <vehicle id='E_W_%i' type='standard_car' route='E_W' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, step), file=routes)
elif route_straight == 3:
print(
" <vehicle id='N_S_%i' type='standard_car' route='N_S' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, step), file=routes)
else:
print(
" <vehicle id='S_N_%i' type='standard_car' route='S_N' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, step), file=routes)
elif straight_or_turn < self.idealLeft * 0.1: # 25%左转
route_turn = np.random.randint(1, 5) # 选择一个随机的源目的地
if route_turn == 1:
print(
" <vehicle id='S_W_%i' type='standard_car' route='S_W' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, step), file=routes)
elif route_turn == 2:
print(
" <vehicle id='W_N_%i' type='standard_car' route='W_N' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, step), file=routes)
elif route_turn == 3:
print(
" <vehicle id='N_E_%i' type='standard_car' route='N_E' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, step), file=routes)
elif route_turn == 4:
print(
" <vehicle id='E_S_%i' type='standard_car' route='E_S' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, step), file=routes)
else: # 25%右转
route_turn = np.random.randint(1, 5) # 选择一个随机的源目的地
if route_turn == 1:
print(
" <vehicle id='S_E_%i' type='standard_car' route='S_E' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, step), file=routes)
elif route_turn == 2:
print(
" <vehicle id='W_S_%i' type='standard_car' route='W_S' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, step), file=routes)
elif route_turn == 3:
print(
" <vehicle id='N_W_%i' type='standard_car' route='N_W' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, step), file=routes)
elif route_turn == 4:
print(
" <vehicle id='E_N_%i' type='standard_car' route='E_N' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, step), file=routes)
print("</routes>", file=routes)
self.update_info()
def gen_dynamic_demand(self): # 不能够指定总共生成车的数量
if self.idealLeft + self.idealStraight > 9:
print("生成文件失败，请重新检查车流转向比例！两者之和为 ", self.idealLeft + self.idealStraight)
return False
sim_len = self.maxSteps # 360*2
t = np.linspace(np.pi, 2 * np.pi, self.maxSteps)
sine = np.sin(t) + 1.55
v_schedule = [] # 为模拟中每秒产生的车辆数量创建时间表
second = 1.0
for t in range(int(self.maxSteps)):
n_veh = 0.0
while second > 0.0:
headway = np.random.exponential(sine[t], size=1)
# 获取指数分布的随机样本并返回numpy数组的样本。scale-比率的倒数（请参阅泊松分布中的lam）默认为1.0。size-返回数组的形状
second -= headway
if second > 0.0:
n_veh += 1
second += 1.0
v_schedule.append(int(n_veh))
### randomly shift traffic pattern as a form of data augmentation
# 随机转移交通模式作为数据增强的一种形式
v_schedule = np.array(v_schedule)
random_shift = np.random.randint(0, sim_len) # 随机产生一个数
v_schedule = np.concatenate((v_schedule[random_shift:], v_schedule[:random_shift])) # 前后交换
###zero out the last minute for better comparisons because of random shift
# 由于随机转移的原因，为了更好地进行比较，在最后一分钟进行清零
v_schedule[-60:] = 0
### randomly select from origins, these are where vehicles are generated
# 从源头随机选择，这些是车辆产生的地方。
origins = self.origins
v_schedule = [np.random.choice(origins, size=int(self.scale * n_veh), replace=True)
if n_veh > 0 else [] for n_veh in v_schedule]
# np.random.choice(a,size,replace=True,p=None) 从a中随机选择数字，构成size大小的结果返回.a必须是一维的
# replace=True 表示可以取相同的数字 False 表示不可以取相同的数字
# p 与数组a对应大小，表示取每个元素的概率,默认情况下取每个元素的概率相同
###fancy iterator, just so we can call next for sequential access
# 花式迭代器，这样我们就可以调用下一个来进行顺序访问。
# v_schedule = [[],array(['gne_1'], dtype='<U5'),[],[],array(['gne_3'], dtype='<U5'),[],array(['gne_5', 'gne_6'], dtype='<U5')]
with open(self.fileName, 'w') as routes:
print("""<routes>
<vType id="standard_car" length="3.0" maxSpeed="15" minGap="1.0" accel="1.0" decel="4.0" />
<route id="N_S" edges="gneE5 gneE1 -gneE3 -gneE7"/>
<route id="N_E" edges="gneE5 gneE1 -gneE0 -gneE4"/>
<route id="N_W" edges="gneE5 gneE1 -gneE2 -gneE6"/>
<route id="S_W" edges="gneE7 gneE3 -gneE2 -gneE6"/>
<route id="S_E" edges="gneE7 gneE3 -gneE0 -gneE4"/>
<route id="S_N" edges="gneE7 gneE3 -gneE1 -gneE5"/>
<route id="W_E" edges="gneE6 gneE2 -gneE0 -gneE4"/>
<route id="W_N" edges="gneE6 gneE2 -gneE1 -gneE5"/>
<route id="W_S" edges="gneE6 gneE2 -gneE3 -gneE7"/>
<route id="E_N" edges="gneE4 gneE0 -gneE1 -gneE5"/>
<route id="E_S" edges="gneE4 gneE0 -gneE3 -gneE7"/>
<route id="E_W" edges="gneE4 gneE0 -gneE2 -gneE6"/>
""", file=routes)
car_counter = -1
for step in range(len(v_schedule)):
# print(v_schedule[step])
for j in range(len(v_schedule[step])): # 遍历每个出发点
car_counter += 1
origin = v_schedule[step][j] # 就是出发点
straight_or_turn = np.random.uniform() # 生成一个01随机数 判断是直行还是左转
if straight_or_turn < self.idealStraight * 0.1:
if origin == 'gneE6': # 根据出发点生成路线
print(
" <vehicle id='W_E_%i' type='standard_car' route='W_E' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, float(step)), file=routes)
elif origin == 'gneE4':
print(
" <vehicle id='E_W_%i' type='standard_car' route='E_W' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, float(step)), file=routes)
elif origin == 'gneE5':
print(
" <vehicle id='N_S_%i' type='standard_car' route='N_S' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, float(step)), file=routes)
else:
print(
" <vehicle id='S_N_%i' type='standard_car' route='S_N' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, float(step)), file=routes)
elif straight_or_turn < self.idealLeft * 0.1: # 25%左转
if origin == 'gneE7':
print(
" <vehicle id='S_W_%i' type='standard_car' route='S_W' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, float(step)), file=routes)
elif origin == 'gneE6':
print(
" <vehicle id='W_N_%i' type='standard_car' route='W_N' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, float(step)), file=routes)
elif origin == 'gneE5':
print(
" <vehicle id='N_E_%i' type='standard_car' route='N_E' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, float(step)), file=routes)
elif origin == 'gneE4':
print(
" <vehicle id='E_S_%i' type='standard_car' route='E_S' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, float(step)), file=routes)
else: # 剩余右转
if origin == 'gneE7':
print(
" <vehicle id='S_E_%i' type='standard_car' route='S_E' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, float(step)), file=routes)
elif origin == 'gneE6':
print(
" <vehicle id='W_S_%i' type='standard_car' route='W_S' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, float(step)), file=routes)
elif origin == 'gneE5':
print(
" <vehicle id='N_W_%i' type='standard_car' route='N_W' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, float(step)), file=routes)
elif origin == 'gneE4':
print(
" <vehicle id='E_N_%i' type='standard_car' route='E_N' depart='%s' departLane='random' departSpeed='10' />" % (
car_counter, float(step)), file=routes)
print("</routes>", file=routes)
self.totalNumber = car_counter
# print(self.totalNumber)
self.update_info()
def get_car_number(self):
return self.totalNumber
def update_info(self):
pass
if __name__ == '__main__':
totalNumber, maxSteps, leftTurn, straight, fileName,scale = 4000,3600*2,4,4,"./sumoNet/test4.rou.xml",1
gencar = GenCar(totalNumber, maxSteps, leftTurn, straight, fileName,scale)
gencar.generate_routefile()
print(gencar.totalNumber)
#每次生成一个车辆分布图