def DistancePriorityPickup(c, meetingCount):
'''
param c: Taxi id.
if success, taxi will have non 0 passengerid, path, i=0
'''
InfVeh = AKIVehTrackedGetInf(c)
currentpos = str(InfVeh.idSection)
steps = []
# a tuple list storing (no.of steps needed to get passenger, passenger id )
for d in range(1, len(Passengerlist)):
if Passengerlist[d] == None:
continue
if Passengerlist[d].assigned == 0:
path = na.dijkstra_path_length(G, currentpos, Passengerlist[d].origin)
steps.append((path, d))
steps = sorted(steps, key=lambda x: (x[0], x[1]))
# sorted by : firstly, smallest steps to get,
# secondly passenger id (if steps equal, first come first serve)
if len(steps) > 0:
d = steps[0][1]
Taxilist[c].passengerid = d
Passengerlist[d].assigned = 1
Taxilist[
c].origin = currentpos # since after drop off, taxi's place is actually its destination. Hence new origin = its currentplace.
Taxilist[c].destination = Passengerlist[Taxilist[c].passengerid].origin
if Taxilist[c].origin == Taxilist[
c].destination: # if the taxi happen to be on the same section as the passenger
Passengerlist[Taxilist[c].passengerid].pickuptime = Taxilist[c].stoptime
Taxilist[c].destination = Passengerlist[Taxilist[c].passengerid].destination
Taxilist[c].occupy = 1
meetingCount += 1
Passengerlist[Taxilist[c].passengerid].take = 1
Taxilist[c].path = na.dijkstra_path(G, str(Taxilist[c].origin), str(
Taxilist[c].destination)) # go to passenger's destination
Taxilist[c].i = 0
else:
Taxilist[c].path = na.dijkstra_path(G, str(Taxilist[c].origin), str(
Taxilist[c].destination)) # go to passenger's origin
Taxilist[c].i = 0
else:
Taxilist[c].passengerid = 0
Taxilist[c].path = []
Taxilist[c].origin = 0
Taxilist[c].destination = 0
print(
'................................Currently no waiting passenger to assign for taxi :',
c)
def shortest_path_and_len(self, src, dst):
path = dijkstra_path(self.g, src, dst, 'weight')
path_len = self.path_len(path)
if path_len >= max_int:
return None, None
log.debug('path_len: %s, path: %s' % (path_len, path))
return path, path_len
def generate_random_new_way(self):
og = min_weighted_dominating_set(self.graph, "weight")
start_node = og.pop()
holder = set()
while og:
curr_node = og.pop()
holder.update(dijkstra_path(self.graph, start_node, curr_node))
if holder and is_valid_network(self.graph,
self.graph.subgraph(holder)):
self.network = nx.minimum_spanning_tree(
self.graph.subgraph(holder))
else:
self.generate_random_old_way(high_degree=True)
print ("Average Shortest Path")
print (algorithms.all_pairs_shortest_path(G))
print (algorithms.average_shortest_path_length(G))
print ()
# Graph Distance:
# Unweighted distance - number of edges between two nodes
# Weighted distance - sum of weights between two nodes.
# so shortest path is least combined weight, not necessarily the fewest nodes
# Euclidean distance - between the two nodes in the adjacency matrix: which is
# proportional to the number of common neighbors shared between the nodes.
#
# Dijkstra's Algorithm:
# for a given vertex it finds the lowest cost path to all other vertices,
# where “cost” is determined by summing edge weights. In graphs where edge
# weights correspond to distance (in unweighted graphs the weights are
# assumed to be one) the found path is the shortest.
# The algorithm can also determine the lowest cost path between two given
# vertices.
print (algorithms.dijkstra_path(G, 0, 3))
print (algorithms.dijkstra_predecessor_and_distance(G, 0, 3))
pos=networkx.spring_layout(G)
networkx.draw_networkx_nodes(G,pos)
networkx.draw_networkx_edges(G,pos)
networkx.draw_networkx_labels(G,pos)
matplotlib.pyplot.axis('off')
matplotlib.pyplot.savefig("graphTraversals.png")
matplotlib.pyplot.show()
def AAPIEnterVehicle(idveh, idsection):
#print('Car enters with', idveh)
import networkx.algorithms as na
import traceback
try:
global CARS, waitingTimeSteps, vacantTimeSteps, clock
#and str(idsection) in secIDs
if len(
Taxilist
) <= CARS or clock % 120 == 0: # and not CARcondition : #only introduce tracked vehicle when it enters connected G.
AKIVehSetAsTracked(idveh)
InfVeh = AKIVehTrackedGetInf(idveh)
if str(InfVeh.idSection) not in list(G.nodes):
#print('Taxi coming from non SCP, have to throw it away')
AKIVehSetAsNoTracked(idveh)
else:
Taxi_current_pos = str(InfVeh.idSection)
steps = []
# a tuple list storing (no.of steps needed to get passenger, passenger id )
for d in range(1, len(Passengerlist)):
if Passengerlist[d] == None:
continue
if Passengerlist[d].assigned == 0:
steps.append(
(na.dijkstra_path_length(G, Taxi_current_pos,
Passengerlist[d].origin),
d))
#print(steps)
if len(steps) == 0:
Taxilist[idveh] = Taxi(0, 0, 0, 0, 0, 0, True, False, 0, 0,
0)
Taxilist[idveh].path = []
print(
idveh,
"initialized, does not have unassigned waiting passenger"
)
else:
steps = sorted(steps, key=lambda x:
(x[0], x[1])) #distance priority pick up
# sorted by : firstly, smallest steps to get,
# secondly passenger id (if steps equal, first come first serve)
if len(steps) > 0:
d = steps[0][1]
Taxilist[idveh] = Taxi(0, 0, 0, 0, 0, 0, True, False,
0, 0, 0)
Taxilist[idveh].passengerid = d
Passengerlist[d].assigned = 1
Taxilist[idveh].origin = str(InfVeh.idSection)
Taxilist[idveh].destination = str(
Passengerlist[Taxilist[idveh].passengerid].origin)
Taxilist[idveh].path = na.dijkstra_path(
G, Taxilist[idveh].origin,
Taxilist[idveh].destination)
#print(idveh, "is assigned to passenger", d, 'at section', Passengerlist[d].origin, 'with path', Taxilist[idveh].path)
if idveh not in Taxilist.keys():
AKIVehSetAsNoTracked(idveh)
except Exception:
traceback.print_exc()
return 0
def AAPIManage(time, timeSta, timeTrans, acycle):
import traceback, random
import networkx.algorithms as na
global clock
try:
'''
Taxi Pickup & Drop off management
'''
global Passengerlist, Taxilist, CARS, meeting, \
meetingCount, vacant, waitAccumulation, vacantAccumulation, vacantTaxi, waitingTimeSteps, vacantTimeSteps, clock
clock += 1 # this steps
for p in range(1, len(Passengerlist)):
if Passengerlist[p] == None:
continue
#print('Passenger', p, 'Has origin', Passengerlist[p].origin, 'destination', Passengerlist[p].destination, 'Is taken?', Passengerlist[p].take, 'Is assigned', Passengerlist[p].assigned)
def DistancePriorityPickup(c, meetingCount):
'''
param c: Taxi id.
if success, taxi will have non 0 passengerid, path, i=0
'''
InfVeh = AKIVehTrackedGetInf(c)
currentpos = str(InfVeh.idSection)
steps = []
# a tuple list storing (no.of steps needed to get passenger, passenger id )
for d in range(1, len(Passengerlist)):
if Passengerlist[d] == None:
continue
if Passengerlist[d].assigned == 0:
path = na.dijkstra_path_length(G, currentpos,
Passengerlist[d].origin)
steps.append((path, d))
steps = sorted(steps, key=lambda x: (x[0], x[1]))
# sorted by : firstly, smallest steps to get,
# secondly passenger id (if steps equal, first come first serve)
if len(steps) > 0:
d = steps[0][1]
Taxilist[c].passengerid = d
Passengerlist[d].assigned = 1
Taxilist[
c].origin = currentpos # since after drop off, taxi's place is actually its destination. Hence new origin = its currentplace.
Taxilist[c].destination = Passengerlist[
Taxilist[c].passengerid].origin
if Taxilist[c].origin == Taxilist[
c].destination: # if the taxi happen to be on the same section as the passenger
Passengerlist[Taxilist[
c].passengerid].pickuptime = Taxilist[c].stoptime
Taxilist[c].destination = Passengerlist[
Taxilist[c].passengerid].destination
Taxilist[c].occupy = 1
meetingCount += 1
Passengerlist[Taxilist[c].passengerid].take = 1
Taxilist[c].path = na.dijkstra_path(
G, str(Taxilist[c].origin),
str(Taxilist[c].destination
)) # go to passenger's destination
Taxilist[c].i = 0
else:
Taxilist[c].path = na.dijkstra_path(
G, str(Taxilist[c].origin),
str(Taxilist[c].destination
)) # go to passenger's origin
Taxilist[c].i = 0
else:
Taxilist[c].passengerid = 0
Taxilist[c].path = []
Taxilist[c].origin = 0
Taxilist[c].destination = 0
print(
'................................Currently no waiting passenger to assign for taxi :',
c)
def removeTaxi(c, meetingCount):
'''
Param: c is the taxi idveh
return 0 if successful. return 1 if not successful
'''
if Taxilist[c] == None:
print('Taxi', c, 'is already deleted!',
Taxilist[c].passengerid)
return 1
if Taxilist[c].passengerid != 0:
#sometimes you really can't control it when it's taking a turn........This is logically incorrect, but rarely happens, due to Aimsun
if Passengerlist[Taxilist[c].passengerid].take == 1:
#But at least we know if the passenger is taken, meeting is successful.
Passengerlist[Taxilist[c].passengerid] = None
else:
Passengerlist[Taxilist[c].passengerid].assigned = 0
Taxilist.pop(c, None)
AKIVehSetAsNoTracked(c)
print(
'Decrease in Taxi Number!--------------------------------------------------------------------------'
)
print('Should not delete taxi', c,
'! Currently carrying passenger!',
Taxilist[c].passengerid)
Taxilist[c] = None # discard this taxi.
return 1
TaxiAveSpeed = '/Users/shirley/TaxiResults/TaxiAveSpeed_{}_{}.txt'.format(
str(ANGConnGetReplicationId()), testID)
file = open(TaxiAveSpeed, "a")
file.write(str(c))
file.write(',')
file.write(
str(format(Taxilist[c].speedAccumulation / clock, '.2f')))
file.write(',')
file.write(str(clock))
file.write('\n')
file.close() # only discard one taxi at a time.
Taxilist[c] = None #discard this taxi.
Taxilist.pop(c, None)
AKIVehSetAsNoTracked(c)
print(
'Deleted Taxi', c,
'-----------------------------------------------------------------------------------------------------------'
)
return 0
for c in Taxilist.keys():
if Taxilist[c] == None:
continue
InfVeh = AKIVehTrackedGetInf(c)
print("Taxi", c, "position", InfVeh.idSection,
"is currently assigned with", Taxilist[c].passengerid,
" and Has completed the trip of",
Taxilist[c].completedPassenger)
if len(Taxilist[c].path) != 0:
#print('Taxi',c, 'has path', Taxilist[c].path,'i',Taxilist[c].i,'next section',Taxilist[c].path[Taxilist[c].i])
#AKIVehTrackedModifyNextSection(c, int(Taxilist[c].path[Taxilist[c].i])) # Track vehicle by using its (key) Vechicle id c
# get the information of the vehicle again.
# InfVeh = AKIVehTrackedGetInf(c)
if InfVeh.idSection == -1:
pass
# if AKIVehTrackedModifyNextSection(c, int(Taxilist[c].path[Taxilist[c].i]))!=0:
# print('Error 1 : Taxi', c, 'currently on', InfVeh.idSection,'has error modifying turning into the correct next step',
# Taxilist[c].path[Taxilist[c].i])
#elif Taxilist[c].i != Taxilist[c].path.index(str(InfVeh.idSection))-1:
#print('i should be ',Taxilist[c].i, 'and index should be',Taxilist[c].path.index(str(InfVeh.idSection)))
#Taxilist[c].i == Taxilist[c].path.index(str(InfVeh.idSection))
#print('Error__________________3 : Taxi', c, 'Currently on', InfVeh.idSection,
#' has error modifying to the correct next step',
#Taxilist[c].path[Taxilist[c].i])
elif InfVeh.idSection == int(
Taxilist[c].path[Taxilist[c].i]
) and Taxilist[c].i < len(Taxilist[c].path) - 1:
Taxilist[
c].i += 1 # move on to next step. This is incremented every for step.
if AKIVehTrackedModifyNextSection(
c, int(Taxilist[c].path[Taxilist[c].i])) != 0:
print('Error_____________2 : Taxi', c,
'Currently on', InfVeh.idSection,
' has error modifying to the correct next step',
Taxilist[c].path[Taxilist[c].i])
# move on to the next step
#InfVeh = AKIVehTrackedGetInf(c)
Taxilist[c].speedAccumulation += InfVeh.CurrentSpeed
# Taxilist[c].path == 0 and
if str(InfVeh.idSection) not in G.nodes(
) and InfVeh.idSection != -1:
Passengerlist[Taxilist[c].passengerid].assigned = 0
AKIVehSetAsNoTracked(c)
print(
"^^^^^^^Taxi", c,
" is discarded for wondering out of the graph while not on a node^^^^^^^^^^^^^"
)
removeTaxi(c, meetingCount)
continue
if (Taxilist[c].passengerid == 0 or len(Taxilist[c].path)
== 0) and InfVeh.idSection != -1: #case no customer.
DistancePriorityPickup(c, meetingCount)
InfVeh = AKIVehTrackedGetInf(c)
'''
Passenger pick up & droppoff management
'''
if len(Taxilist[c].path) != 0 and InfVeh.idSection == int(
Taxilist[c].path[-1]):
#print('Car', c, 'is enterrring last step~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
Taxilist[c].i = 0
A2KSectionInf = AKIInfNetGetSectionANGInf(
int(Taxilist[c].path[-1]))
Taxilist[c].sectionlength = A2KSectionInf.length
Taxilist[c].midsection = Taxilist[c].sectionlength * 0.5
if Taxilist[c].midsection - 40 < InfVeh.distance2End < Taxilist[
c].midsection + 40:
#print('Car', c, 'should be stopped!!!!!!!!!!!!!!!---------------------------')
Taxilist[
c].stopping = True # stop it on the midsection, as if the passenger emerges at midsection.
if Taxilist[c].stopping == True:
if Taxilist[
c].firststop == True: # only record the first stopping time-- otherwise this value could be re-write up to 5sec.
Taxilist[c].stoptime = AKIGetCurrentSimulationTime(
) # current time.
Taxilist[c].firststop = False
AKIVehTrackedModifySpeed(c, 0) # temporary stop
#print('Car', c, 'is stopped!!!!!!!!!!!!!!!---------------------------')
if clock - Taxilist[c].stoptime > 3 and Taxilist[
c].stopping == True: # if taxi restarts.
# current senario: taxi is stopped.
# Decide if it is a drop off or pick up senario.
Taxilist[c].stopping = False
#print('Car', c, 'should go back to movement!!!!!!!!!!!!!!!---------------------------')
Taxilist[
c].firststop = True # change firststop back until next time the taxi stops.
# counter = counter + 1
# print('Taxi',c,'should Passenger that has origin ',Passengerlist[Taxilist[c].passengerid].origin, 'and destination',Passengerlist[Taxilist[c].passengerid].destination)
'''
A searching and pickup senario: taxi reaching for passenger: taxi's destination = current passenger's origin.
A taxi sending passenger reaching its destination : taxi's destinations = current passenger's destination.
'''
Pickup = (Taxilist[c].path[-1] == Passengerlist[
Taxilist[c].passengerid].origin)
Dropoff = (Taxilist[c].path[-1] == Passengerlist[
Taxilist[c].passengerid].destination)
if Pickup:
Passengerlist[Taxilist[c].passengerid].take = 1
Passengerlist[Taxilist[
c].passengerid].pickuptime = Taxilist[c].stoptime
# Taxilist[c].empty_distance=Taxilist[c].currentPathLength
'''
vacant_time, a list.
=: the taxi's drop off time of the LAST passenger) - (the taxi's pick up time of the NEXT passenger)
'''
# Taxilist[c].vacant_time.append(Passengerlist[Taxilist[c].passengerid].pickuptime - Taxilist[c].droptime)
Taxilist[c].occupy = 1
Taxilist[c].origin = Passengerlist[
Taxilist[c].passengerid].origin
Taxilist[c].destination = Passengerlist[
Taxilist[c].passengerid].destination
Taxilist[c].path = na.dijkstra_path(
G, str(Taxilist[c].origin),
str(Taxilist[c].destination))
print(
'Taxi ', c, "has picked up",
Taxilist[c].passengerid, "on desination to",
Taxilist[c].destination
) # have arrived the destination of the passenger and then go to take the next passenger
meetingCount += 1
Passengerlist[Taxilist[c].passengerid].take = 1
elif Dropoff:
Passengerlist[
Taxilist[c].passengerid].dropofftime = Taxilist[
c].stoptime # current time
# Taxilist[c].droptime = Taxilist[c].stoptime #first stop time.
# Taxilist[c].occupied_distance=Taxilist[c].occupied_distance + Taxilist[c].currentPathLength
Passengerlist[Taxilist[
c].passengerid].distance_travelled = Taxilist[
c].currentPathLength
print('Taxi', c, 'dropped off passenger',
Taxilist[c].passengerid,
"________________________________")
Taxilist[c].completedPassenger.append(
Taxilist[c].passengerid)
Taxilist[c].occupy = 0 # becomes vacant
Taxilist[c].passengerid = 0
Taxilist[c].i = 0
# Taxilist[c].occupied_time=Taxilist[c].occupied_time + (Passengerlist[Taxilist[c].passengerid].dropofftime - Passengerlist[Taxilist[c].passengerid].pickuptime)
# accummulate occupied time.
'''
After dropping off, this part is our main concern:
what pick up strategy is the best?
'''
# SequentialPickup()
DistancePriorityPickup(c, meetingCount)
'''
Define Vacant and Meeting.
'''
wait = 0
for s in range(1, len(Passengerlist)):
if Passengerlist[s] == None:
continue
if Passengerlist[s].take == 0:
wait = wait + 1
print('Number of passenger waiting: ', wait)
waitingTimeSteps.append(wait)
'''
unassigned = 0
for s in range(1, len(Passengerlist)):
if Passengerlist[s]==None:
continue
if Passengerlist[s].assigned == 0:
unassigned = unassigned + 1
#print ('Number of passenger unassigned: ', unassigned)
'''
vacant = 0
for c in Taxilist.keys():
if Taxilist[c] == None:
#Taxilist.pop(c,None)
continue
elif Taxilist[c].occupy == 0:
vacant += 1
print('Number of vacant taxi: ', vacant)
vacantTimeSteps.append(vacant)
waitAccumulation += wait
vacantAccumulation += vacant
print("Meeting happened in this interval:", meetingCount)
'''
In test small network, no complicated sin distribution is tested.
Manages the introducing of new passengers. and the reduction of existing vacant taxis
'''
def decision(time, sinscale):
import random
return random.random() < np.sin(time / sinscale)
if clock % 60 == 0: #and decision(clock+100, 200): #introduces new passenger every DI sec.
start = random.choice(secIDs)
while (True):
end = random.choice(secIDs)
if start != end and end in innerSec.keys():
break
appeartime = AKIGetCurrentSimulationTime() # get current time.
Passengerlist.append(
Passenger(start, end, 0, 0, appeartime, 0, 0, 0))
global sinscale
if (clock % 10 == 0) and decision(clock, sinscale):
for c in Taxilist.keys():
if Taxilist[c] == None or Taxilist[c].passengerid != 0:
continue
if (removeTaxi(c, meetingCount) == 0):
break
'''
Manages the calculation of meeting function statistics.
'''
'''
MFD generation
'''
global accumulation, flow, flows, accumulations, completion, completions, sectionid, junctionid, totallength, DiscreteInterval
if clock % DiscreteInterval == 1:
for id in sectionid:
estad2 = AKIEstGetParcialStatisticsSection(id, timeSta, 0)
A2KSectionInf = AKIInfNetGetSectionANGInf(id)
sectionlength = A2KSectionInf.length
if (estad2.report == 0): # default, refer to manual.
flow += estad2.Flow * sectionlength
if flow != 0:
flows.append(flow / 3600)
flow = 0
print('flows:', flows)
if clock % DiscreteInterval == 0:
meeting.append([
meetingCount, waitAccumulation / DiscreteInterval,
vacantAccumulation / DiscreteInterval
])
# flows: a list of normalised production/output over time.
completions.append(completion) #see exitvehicle
print('Completionss:', completions)
completion = 0
for sectionnumbmer in sectionid:
numbersinsection = AKIVehStateGetNbVehiclesSection(
sectionnumbmer,
True) #total number of vehicle on the section
accumulation = accumulation + numbersinsection #sums all vehicles on all sections.
for junctionnumber in junctionid:
numbersinjunction = AKIVehStateGetNbVehiclesJunction(
junctionnumber)
accumulation = accumulation + numbersinjunction #similarly, sums all vehicles on all junctions.
accumulations.append(accumulation)
#print(accumulations)
#print('Accumulations:', accumulations)
accumulation = 0
waitAccumulation = 0
vacantAccumulation = 0
meetingCount = 0
except Exception:
traceback.print_exc()
return 0
def short_distance():
from networkx import algorithms
g=generate_graph()
print algorithms.shortest_path(g,0,5)
print algorithms.dijkstra_path(g,0,5)
print("Average Shortest Path")
print(algorithms.all_pairs_shortest_path(G))
print(algorithms.average_shortest_path_length(G))
print()
# Graph Distance:
# Unweighted distance - number of edges between two nodes
# Weighted distance - sum of weights between two nodes.
# so shortest path is least combined weight, not necessarily the fewest nodes
# Euclidean distance - between the two nodes in the adjacency matrix: which is
# proportional to the number of common neighbors shared between the nodes.
#
# Dijkstra's Algorithm:
# for a given vertex it finds the lowest cost path to all other vertices,
# where “cost” is determined by summing edge weights. In graphs where edge
# weights correspond to distance (in unweighted graphs the weights are
# assumed to be one) the found path is the shortest.
# The algorithm can also determine the lowest cost path between two given
# vertices.
print(algorithms.dijkstra_path(G, 0, 3))
print(algorithms.dijkstra_predecessor_and_distance(G, 0, 3))
pos = networkx.spring_layout(G)
networkx.draw_networkx_nodes(G, pos)
networkx.draw_networkx_edges(G, pos)
networkx.draw_networkx_labels(G, pos)
matplotlib.pyplot.axis('off')
matplotlib.pyplot.savefig("graphTraversals.png")
matplotlib.pyplot.show()
def shortest_path_and_len(self, src, dst):
path = dijkstra_path(self.g, src, dst, 'weight')
path_len = self.path_len(path)
if path_len >= max_int:
return None, None
return path, path_len
def AAPIManage(time, timeSta, timeTrans, acycle):
#print('Current Manage step -------------------------------------------------------------------')
import traceback, random
import networkx.algorithms as na
try:
global clock, numberofpassenger, Passengerlist, Taxilist, CARS, clock2, accumulation, numbersinsection, numbersinjunction, astring, meeting, \
meetingCount, waitNumber, vacant, waitAccumulation, vacantAccumulation
global counter
numberofpassenger = len(Passengerlist)
wait = 0
for s in range(0, numberofpassenger):
if Passengerlist[s].take == 0:
wait = wait + 1
print('Number of passenger waiting: ', wait)
vacant = 0
for c in Taxilist:
if Taxilist[c].occupy == 0:
vacant += 1
print('Number of vacant taxi: ', vacant)
waitAccumulation += wait
vacantAccumulation += vacant
if clock == 10: #introduces new passenger every 90 sec.
start = random.choice(secIDs)
while (True):
end = random.choice(secIDs)
if start != end:
break
appeartime = AKIGetCurrentSimulationTime() # get current time.
Passengerlist.append(Passenger(start, end, 0, appeartime, 0, 0, 0))
clock = 0
else:
clock = clock + 1
#print clock
if clock2 == 90:
print('In this 900sec, there were ', waitAccumulation,
'passengers were waiting and there were ',
vacantAccumulation, 'Became vacant')
meeting.append([
meetingCount, wait, vacant, waitAccumulation,
vacantAccumulation
])
waitAccumulation = 0
vacantAccumulation = 0
meetingCount = 0
clock2 = 0
else:
clock2 = clock2 + 1
#print('We have ----------------------- Taxi.taxiCount:',Taxi.taxiCount)
for c in Taxilist:
if Taxilist[c].path == 0:
'''
Make this vacant taxi go to the passenger.
'''
InfVeh = AKIVehTrackedGetInf(c)
'''
AKIVehTrackedGetInf: Read the informaiton of a tracked vehicle.
'''
Taxilist[c].origin = str(InfVeh.idSection)
Taxilist[c].destination = str(
Passengerlist[Taxilist[c].passengerid].origin)
#print('Taxi origin:', Taxilist[c].origin)
#print('Taxi destination:', Taxilist[c].destination)
Taxilist[c].path = na.dijkstra_path(G, Taxilist[c].origin,
Taxilist[c].destination)
Taxilist[c].currentPathLength = na.dijkstra_path_length(
G, Taxilist[c].origin, Taxilist[c].destination)
Taxilist[c].vacant_distance.append(
Taxilist[c].currentPathLength)
'''
idSection = 32766 because it exceeded visible network.
'''
#print('Taxi',c,' has Current Section ID', InfVeh.idSection, 'its next step is', weirdValue)
AKIVehTrackedModifyNextSection(
c, int(Taxilist[c].path[
Taxilist[c].i])) #Vechicle id starting from 1 instead of 0
#get the information of the vehicle again.
InfVeh = AKIVehTrackedGetInf(c)
if InfVeh.idSection == int(Taxilist[c].path[Taxilist[c].i]):
#Taxilist[c].noofpath=len(Taxilist[c].path) #length of all steps
if Taxilist[c].i < len(
Taxilist[c].path) - 1: #if number of steps < all steps
Taxilist[c].i = Taxilist[
c].i + 1 #move on to next step. This is incremented every for step.
AKIVehTrackedModifyNextSection(
c, int(Taxilist[c].path[
Taxilist[c].i])) #move on to next step
InfVeh = AKIVehTrackedGetInf(c)
'''
Passenger pick up.
'''
if InfVeh.idSection == int(Taxilist[c].destination):
Taxilist[c].i = 0
A2KSectionInf = AKIInfNetGetSectionANGInf(
int(Taxilist[c].destination))
Taxilist[c].sectionlength = A2KSectionInf.length
Taxilist[c].midsection = Taxilist[c].sectionlength * 0.5
if Taxilist[c].midsection - 10 < InfVeh.distance2End < Taxilist[
c].midsection + 10:
Taxilist[
c].stopping = True #stop it on the midsection, as if the passenger emerges at midsection.
if Taxilist[c].stopping == True:
if Taxilist[
c].firststop == True: #only record the first stopping time-- otherwise this value could be re-write up to 5sec.
Taxilist[c].stoptime = AKIGetCurrentSimulationTime(
) #current time.
Taxilist[c].firststop = False
AKIVehTrackedModifySpeed(c, 0) #temporary stop
print(
'Taxi ', c, "has its passenger ", Taxilist[c].passengerid,
" now on trip to ",
Passengerlist[Taxilist[c].passengerid].destination
) # have taken the passenger, then send the passenger to his destination
if time - Taxilist[c].stoptime > 5 and Taxilist[
c].stopping == True: #if taxi restarts.
#current senario: taxi is stopped.
#Decide if it is a drop off or pick up senario.
Taxilist[c].stopping = False
Taxilist[
c].firststop = True #change firststop back until next time the taxi stops.
counter = counter + 1
print('Taxi', c, 'should Passenger that has origin ',
Passengerlist[Taxilist[c].passengerid].origin,
'and destination',
Passengerlist[Taxilist[c].passengerid].destination)
'''
A searching and pickup senario: taxi reaching for passenger: taxi's destination = current passenger's origin.
A taxi sending passenger reaching its destination : taxi's destinations = current passenger's destination.
'''
Pickup = Taxilist[c].destination == Passengerlist[
Taxilist[c].passengerid].origin
Dropoff = Taxilist[c].destination == Passengerlist[
Taxilist[c].passengerid].destination
if Pickup:
Passengerlist[Taxilist[
c].passengerid].pickuptime = Taxilist[c].stoptime
#Taxilist[c].noofpath=len(Taxilist[c].path)
Taxilist[c].empty_distance = Taxilist[
c].currentPathLength
'''
vacant_time, a list.
=: the taxi's drop off time of the LAST passenger) - (the taxi's pick up time of the NEXT passenger)
'''
Taxilist[c].vacant_time.append(
Passengerlist[Taxilist[c].passengerid].pickuptime -
Taxilist[c].droptime)
Taxilist[c].occupy = 1
Taxilist[c].origin = Passengerlist[
Taxilist[c].passengerid].origin
Taxilist[c].destination = Passengerlist[
Taxilist[c].passengerid].destination
Taxilist[c].path = na.dijkstra_path(
G, str(Taxilist[c].origin),
str(Taxilist[c].destination))
print(
'Taxi ', c, "has picked up",
Taxilist[c].passengerid, "on desination to",
Taxilist[c].destination
) # have arrived the destination of the passenger and then go to take the next passenger
meetingCount += 1
elif Dropoff:
Passengerlist[
Taxilist[c].passengerid].dropofftime = Taxilist[
c].stoptime #current time
Taxilist[c].droptime = Taxilist[c].stoptime #
#Taxilist[c].noofpath=len(Taxilist[c].path)
Taxilist[c].occupied_distance = Taxilist[
c].occupied_distance + Taxilist[c].currentPathLength
Passengerlist[Taxilist[
c].passengerid].distance_travelled = Taxilist[
c].currentPathLength
Taxilist[c].occupy = 0 #becomes vacant
Taxilist[c].occupied_time = Taxilist[c].occupied_time + (
Passengerlist[Taxilist[c].passengerid].dropofftime
-
Passengerlist[Taxilist[c].passengerid].pickuptime)
#accummulate occupied time.
def SequentialPickup():
for d in range(0, numberofpassenger):
if Passengerlist[
d].take == 0: # subsequently take on the next waiting passenger on the given list.
Taxilist[c].passengerid = d
Passengerlist[d].take = 1
break
def DistancePriorityPickup():
steps = []
# a tuple list storing (no.of steps needed to get passenger, passenger id )
for d in range(0, numberofpassenger):
if Passengerlist[d].take == 0:
steps.append((na.dijkstra_path_length(
G, Taxilist[c].destination,
Passengerlist[d].origin), d))
steps = sorted(steps, key=lambda x: (x[0], x[1]))
# sorted by : firstly, smallest steps to get,
# secondly passenger id (if steps equal, first come first serve)
if len(steps) > 0:
d = steps[0][1]
Taxilist[c].passengerid = d
Passengerlist[d].take = 1
#SequentialPickup()
DistancePriorityPickup()
Taxilist[c].origin = Taxilist[
c].destination #since after drop off, taxi's place is actually its destination. Hence new origin = its currentplace.
Taxilist[c].destination = Passengerlist[
Taxilist[c].passengerid].origin
if Taxilist[c].origin == Taxilist[
c].destination: #if the taxi happen to be on the same section as the passenger
Passengerlist[
Taxilist[c].
passengerid].pickuptime = Taxilist[c].stoptime
Taxilist[c].vacant_time.append(0)
Taxilist[c].vacant_distance.append(0)
Taxilist[c].destination = Passengerlist[
Taxilist[c].passengerid].destination
Taxilist[c].path = na.dijkstra_path(
G, str(Taxilist[c].origin),
str(Taxilist[c].destination)
) #go to passenger's destination
else:
Taxilist[c].path = na.dijkstra_path(
G, str(Taxilist[c].origin),
str(Taxilist[c].destination)
) #go to passenger's origin
Taxilist[c].vacant_distance.append(
Taxilist[c].currentPathLength)
except Exception:
traceback.print_exc()
return 0
from networkx import algorithms
import networkx
from networkx import *
import itertools
g = networkx.generators.krackhardt_kite_graph()
print(algorithms.shortest_path(g, 5, 1))
print(algorithms.dijkstra_path(g, 5, 1))
#print(dict(algorithms.all_pairs_shortest_path(g)))
#print(dict(algorithms.all_pairs_shortest_path(g))[5])
#并排显示
#lst1=[1,2]
#lst2=[5,6]
#print(lst1,lst2)
#print(*lst1)
nodes = list(g.nodes())[:8]
node_pairs = list(itertools.combinations(nodes, 2))
#print(node_pairs)
for pair in node_pairs:
print(algorithms.shortest_path(g, *pair),
algorithms.dijkstra_path(g, *pair))
print(node_pairs)
def addItem(self, dst_url):
try:
path = dijkstra_path(self._graph, self.url, dst_url)
self._table[dst_url] = path[1] if len(path) > 1 else path[0]
except Exception as e:
print(e)