def compute_tt_vmt(damaged_graph, demand): start = time.time() it = ita.ITA(damaged_graph,demand) newG = it.assign() print 'time to assign: ', time.time()-start travel_time = util.find_travel_time(damaged_graph) vmt = util.find_vmt(damaged_graph) ''' in the undamaged case, this should be around 172 million (http://www.mtc.ca.gov/maps_and_data/datamart/stats/vmt.htm) over the course of a day, so divide by 0.053 (see demand note in main). BUT our trip table has only around 11 million trips (instead of the 22 million mentioned here: http://www.mtc.ca.gov/maps_and_data/datamart/stats/baydemo.htm because we are looking at vehicle-driver only and not transit, walking, biking, being a passenger in a car, etc. So, that's **8-9 million vehicle-miles divided by 2, which equals around 4 million vehicle-miles!** ''' return travel_time, vmt
def compute_tt_vmt(damaged_graph, demand): start = time.time() it = ita.ITA(damaged_graph,demand) newG = it.assign() print 'time to assign: ', time.time()-start # for n,nbrsdict in newG.adjacency_iter(): # for nbr,keydict in nbrsdict.items(): # for key,eattr in keydict.items(): # if eattr['flow']>0: # print (n, nbr, eattr['flow']) travel_time = util.find_travel_time(damaged_graph) #this should be a little less than 252850.3941hours or **910,261,418.76seconds. ** vmt = util.find_vmt(damaged_graph) #in the undamaged case, this should be around 172 million (http://www.mtc.ca.gov/maps_and_data/datamart/stats/vmt.htm) over the course of a day, so divide by 0.053 (see demand note in main). So, that's **8-9 million vehicle-miles** # print travel_time # print 'vmt: ', vmt return travel_time, vmt
def run_iteration(G, ground_motion, demand): #change edge properties newG = damage_network(G, ground_motion) #call ita start = time.time() # print 'starting iterative travel assignment' it = ita.ITA(G,demand) newG = it.assign() print 'time to assign: ', time.time()-start # for n,nbrsdict in newG.adjacency_iter(): # for nbr,keydict in nbrsdict.items(): # for key,eattr in keydict.items(): # if eattr['flow']>0: # print (n, nbr, eattr['flow']) travel_time = util.find_travel_time(newG) vmt = util.find_vmt(G) # print 'travel time: ', travel_time # print 'vmt: ', util.find_vmt(G) #in the undamaged case, this should be around 172 million (http://www.mtc.ca.gov/maps_and_data/datamart/stats/vmt.htm) newG = util.clean_up_graph(newG) return (travel_time, vmt)
def run_iteration(G, ground_motion, demand, damagedG, clean_up=True):
'''this function runs iterative traffic assignment to find the vehicle miles traveled (VMT) and the travel time'''
if damagedG == None:
print 'damaging network'
damagedG = damage_network(G, ground_motion)
travel_time = -1
vmt = -1
#call ita
start = time.time()
it = ita.ITA(damagedG,demand)
newG = it.assign()
print 'time to assign: ', time.time()-start
# for n,nbrsdict in newG.adjacency_iter():
# for nbr,keydict in nbrsdict.items():
# for key,eattr in keydict.items():
# if eattr['flow']>0:
# print (n, nbr, eattr['flow'])
travel_time = util.find_travel_time(damagedG) #this should be a little less than 252850.3941hours or **910,261,418.76seconds. **
vmt = util.find_vmt(damagedG) #in the undamaged case, this should be around 172 million (http://www.mtc.ca.gov/maps_and_data/datamart/stats/vmt.htm) over the course of a day, so divide by 0.053 (see demand note in main). So, that's **8-9 million vehicle-miles**
if clean_up == True:
damagedG= util.clean_up_graph(damagedG)
return (travel_time, vmt)