def __init__(self, code=0, name=""):
self.code = code
if name == "":
self.name = "Node" + str(code)
else:
self.name = name
# graph features
self.out_links = []
self.in_links = []
# demand features
self.s_interval = stochastic.Stochastic()
# parameters of "not stochastic" demand
self.direct_in = []
self.direct_out = []
self.back_in = []
self.back_out = []
# result parameters
self.pass_out = []
self.pass_in = []
def generate_demand(self, duration):
self.in_psgs = []
self.out_psgs = []
self.transfer_psgs = []
self.transfered_psgs = []
# define the arrival moments
for ln in self.lines:
ln.transfer_node = self
ln.define_schedule(duration)
# generate origin passengers
st = 0
s_int = stochastic.Stochastic(law=2, scale=60.0/self.origin_pass_number)
while st < duration:
st += s_int.get_value()
psg = passenger.Passenger()
psg.t_appear = st
psg.to_transfer = False
psg.next_line = random.choice(self.lines) # passengers part isn't considered
self.in_psgs.append(psg)
# generate out and transfer passengers
for ln in self.lines:
for tm in ln.arrivals:
pn = int(ln.out_psgs_number.get_value())
if pn < 0:
pn = 0
for _ in range(pn):
p = passenger.Passenger()
p.t_appear = tm
p.to_transfer = random.random() < self.transfer_prob
if p.to_transfer:
line_to_transfer = random.choice(self.lines)
while line_to_transfer is ln:
line_to_transfer = random.choice(self.lines)
p.next_line = line_to_transfer
self.transfer_psgs.append(p)
# print tm, ln.name, p.next_line.name
# else:
# print tm, ln.name, "end"
self.out_psgs.append(p)
def gen_demand(self, duration, is_stochastic=True):
""""
Generates demand for trips in the network
duration - duration of the simulation period, min.
"""
self.demand = []
if is_stochastic:
for source in self.nodes:
time = 0
while time <= duration:
interval = round(source.s_interval.get_value(), 1)
time += interval
# generating a new passenger
new_passenger = passenger.Passenger()
new_passenger.m_appearance = time
new_passenger.origin_node = source
# defining the target node - random choice rule (can't be the same as origin node)
target = random.choice(self.nodes)
while target == source:
target = random.choice(self.nodes)
new_passenger.destination_nodes = self.define_destinations(
source, target)
# print [new_passenger.origin_node.code] + [nd.code for nd in new_passenger.destination_nodes]
new_passenger.m_boarding = [
0 for _ in range(new_passenger.transits_number + 1)
]
new_passenger.m_disembarkation = [
0 for _ in range(new_passenger.transits_number + 1)
]
new_passenger.current_destination_node = new_passenger.destination_nodes[
0]
# adding the passenger to the origin node collection
source.pass_out.append(new_passenger)
self.demand.append(new_passenger)
else:
#
for nd in self.nodes:
time = 0
for din in nd.direct_in:
if din > 0:
st = stochastic.Stochastic(law=2, scale=60.0 / din)
t = st.get_value()
while t <= 60:
t += st.get_value()
new_passenger = passenger.Passenger()
new_passenger.m_appearance = time + t
new_passenger.origin_node = nd
destination_node = random.choice(self.nodes)
find_next = True
while find_next:
if destination_node == nd:
destination_node = random.choice(
self.nodes)
find_next = True
continue
idx = time // 60 if time // 60 < len(
nd.direct_in) else len(nd.direct_in) - 1
# print "sum:", sum(dnd.direct_out[idx] for dnd in self.nodes)
if sum(dnd.direct_out[idx] for dnd in
self.nodes) - nd.direct_out[idx] >= 1:
if destination_node.direct_out[idx] > 0:
destination_node.direct_out[idx] -= 1
find_next = False
else:
destination_node = random.choice(
self.nodes)
find_next = True
else:
find_next = False
new_passenger.destination_nodes = self.define_destinations(
nd, destination_node)
new_passenger.current_destination_node = new_passenger.destination_nodes[
0]
nd.pass_out.append(new_passenger)
self.demand.append(new_passenger)
# print new_passenger.origin_node.code, new_passenger.destination_node.code
time += 60
time = 0
for bin in nd.back_in:
if bin > 0:
st = stochastic.Stochastic(law=2, scale=60.0 / bin)
t = st.get_value()
while t <= 60:
t += st.get_value()
new_passenger = passenger.Passenger()
new_passenger.m_appearance = time + t
new_passenger.origin_node = nd
destination_node = random.choice(self.nodes)
find_next = True
while find_next:
if destination_node == nd:
destination_node = random.choice(
self.nodes)
find_next = True
continue
idx = time // 60 if time // 60 < len(
nd.back_in) else len(nd.back_in) - 1
# print "sum:", sum(dnd.direct_out[idx] for dnd in self.nodes)
if sum(dnd.back_out[idx] for dnd in
self.nodes) - nd.back_out[idx] >= 1:
if destination_node.back_out[idx] > 0:
destination_node.back_out[idx] -= 1
find_next = False
else:
destination_node = random.choice(
self.nodes)
find_next = True
else:
find_next = False
new_passenger.destination_nodes = self.define_destinations(
nd, destination_node)
new_passenger.current_destination_node = new_passenger.destination_nodes[
0]
nd.pass_out.append(new_passenger)
self.demand.append(new_passenger)
time += 60
from stochastic import stochastic from transportnet import net # generation of a random transport network # given: num_nodes - number of nodes in a net # num_links - number of links in a net # link_weight - stochastic variable # # num_nodes max(num_links) # 1 0 0*1 # 2 2 1*2 # 3 6 2*3 # 4 12 3*4 # 5 ? 4*5(?) # ... ... # n n*(n-1) # forming the network # n = net.Net() # stochastic variable of the links weight sw = stochastic.Stochastic(1, 1, 0.2) # stochastic variable of the number of line stops sn = stochastic.Stochastic(0, 3, 4) # generating the links n.generate(6, 30, sw) # generating the lines n.gen_lines(5, sn) # print out simulation results n.print_characteristics()
for veh_number in range(1, 9 + 2, 2):
for mean_int in range(1, 9 + 2, 2):
for stops_number in range(2, 10 + 2, 2):
for mean_dist in range(1, 3 + 1, 1):
for capacity in range(10, 70 + 30, 30):
# runs of the experiment
twt = []
mean_twt = 0
for runs in range(100):
# forming the network
n = net.Net()
for i in range(2, stops_number + 1):
n.add_link(
i - 1, i,
stochastic.Stochastic(law=1,
location=mean_dist,
scale=0.2 *
mean_dist).get_value())
# define the route path
l = line.Line(n, range(1, stops_number + 1))
# defining the route fleet
# vehicles = []
# for i in range(veh_number):
# vehicles.append(vehicle.Vehicle(capacity))
l.add_vehicles([
vehicle.Vehicle(capacity)
for _ in range(veh_number)
])
# add the line to the network
n.lines.append(l)
# generating the mean intervals for passengers arrivals to the network (line) nodes
for node in n.nodes:
from transfernode import node
from transfernode import line
from stochastic import stochastic
from genetics import ga
wislicka = node.Node()
line125 = line.Line(14, 20, stochastic.Stochastic(1, 2.50, 1.02), 0.0149,
"125")
line129 = line.Line(10, 15, stochastic.Stochastic(1, 3.30, 1.65), 0.0301,
"129")
line132 = line.Line(13, 20, stochastic.Stochastic(1, 2.08, 1.41), 0.0302,
"132")
line138 = line.Line(1, 20, stochastic.Stochastic(1, 3.47, 2.03), 0.0542, "138")
line152 = line.Line(7, 20, stochastic.Stochastic(1, 3.77, 2.00), 0.0471, "152")
line159 = line.Line(4, 15, stochastic.Stochastic(1, 8.83, 2.25), 0.0696, "159")
line172 = line.Line(1, 20, stochastic.Stochastic(1, 4.85, 1.60), 0.0836, "172")
line192 = line.Line(19, 20, stochastic.Stochastic(1, 3.43, 1.45), 0.0282,
"192")
line429 = line.Line(4, 15, stochastic.Stochastic(1, 1.83, 1.34), 0.0235, "429")
line501 = line.Line(12, 15, stochastic.Stochastic(1, 3.47, 1.61), 0.1442,
"501")
line502 = line.Line(1, 10, stochastic.Stochastic(1, 4.75, 2.03), 0.2569, "502")
line511 = line.Line(5, 15, stochastic.Stochastic(1, 5.11, 2.05), 0.1113, "511")
line572 = line.Line(12, 20, stochastic.Stochastic(1, 2.96, 1.49), 0.1060,
"572")
wislicka.origin_pass_number = 402 #[pas./h]
wislicka.lines = [
line125, line129, line132, line138, line152, line159, line172, line192,
line429, line501, line502, line511, line572
]
# # n.add_link(18, 19, s_weight.get_value())
# # n.add_link(20, 21, s_weight.get_value())
# # n.add_link(21, 10, s_weight.get_value())
# # n.add_link(10, 22, s_weight.get_value())
# # n.add_link(22, 23, s_weight.get_value())
# # n.add_link(23, 5, s_weight.get_value())
# # n.add_link(5, 24, s_weight.get_value())
# # n.add_link(25, 5, s_weight.get_value())
# # n.add_link(5, 26, s_weight.get_value())
# # n.add_link(26, 27, s_weight.get_value())
# # n.add_link(27, 17, s_weight.get_value())
# # n.add_link(17, 28, s_weight.get_value())
# # n.add_link(28, 29, s_weight.get_value())
# # n.add_link(29, 30, s_weight.get_value())
s_mean_interval = stochastic.Stochastic(law=0, location=1, scale=4)
for nd in n.nodes:
nd.s_interval = stochastic.Stochastic(law=2, scale=s_mean_interval.get_value())
# # define a set of public transport lines
# bochnia_lines = [line.Line(n, [27, 28, 39, 18, 23, 26, 15, 31, 32,
# 31, 15, 26, 23, 18, 35, 25, 27], [27, 32]),
# line.Line(n, [27, 28, 39, 18, 9, 11, 7, 8, 10, 4, 5, 6,
# 5, 4, 10, 8, 7, 11, 9, 18, 35, 25, 27], [27, 6]),
# line.Line(n, [18, 35, 20, 21, 22, 19, 14, 12, 13,
# 12, 14, 19, 22, 21, 20, 35, 18], [18, 13]),
# line.Line(n, [27, 28, 39, 18, 23, 24, 17, 16, 40, 41, 30, 42, 43, 2, 3, 33, 34, 29,
# 34, 1, 2, 36, 37, 38, 37, 36, 43, 42, 30, 41, 40, 16, 17, 24, 23, 18, 35, 25, 27], [27, 29])]
# # conf #2
# # lines = [line.Line(n, [1, 3, 4, 6, 9, 12, 14]),
# # line.Line(n, [2, 4, 5, 8, 11, 13]),
for ln in lns_back_in:
ps_back_in.append([int(el) for el in ln.split('\t')])
# matrix of passengers' arrivals in main direction
ps_direct_out = []
for ln in lns_direct_out:
ps_direct_out.append([int(el) for el in ln.split('\t')])
# matrix of passengers' arrivals in back direction
ps_back_out = []
for ln in lns_back_out:
ps_back_out.append([int(el) for el in ln.split('\t')])
# simulate the average demand interval for the nodes
for node in n.nodes:
node.s_interval = stochastic.Stochastic(law=2,
scale=stochastic.Stochastic(law=1,
location=2,
scale=0.5).get_value())
# set a trace of the line #129
line129 = line.Line(n, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
# add vehicles operating at the line #129
line129.add_vehicles([vehicle.Vehicle(veh_capacity) for __ in range(veh_num)])
line129.velocity = velocity
line129.intermediate_stop_duration = 1.0*stop_dur/60 # [min]
# add tne line #129 to the net
n.lines.append(line129)
# simulate the line operation
n.simulate(duration=18*60)
# print out characteristics of the net
# n.print_characteristics()
# n.print_od_matrix()
wait_coef += 1.0 * n.total_wait_time / n.duration