def gen_taxi_wt_dict(): ##a:tt_dual
## importing the taz
taz = pd.read_csv('taz_2012.csv', delimiter=',', index_col=False)
##creates a df with the same columns as the tt_dual, that are the 288 5 min interval in seconds eg (0,299),...
taz_wt = pd.DataFrame(
columns=index_column(5).columns
) ##creates a df with the same columns as the tt_dual, that are the 288 5 min interval in seconds eg (0,299),...
taz_wt['zone'] = taz.zone_id ##adding a new column with the zone id
taz_wt.zone = taz_wt.zone.astype(str) ## converting the zone id to str
taz_wt = taz_wt.set_index(
'zone') ## sets the zone id as the index of taz_wt
for column in taz_wt.columns:
if column[0] >= 27000 and column[
1] <= 34200: ## peak hours 7:30-09:30 am (between 4-8 min)
mu, sigma = 360, 60 # mean and standard deviation
taz_wt[column] = np.round(np.random.normal(mu, sigma, len(taz_wt)),
0)
elif column[0] >= 61200 and column[
1] <= 72000: ##05:00-08:00 pm (between 3-7 min)
mu, sigma = 300, 60 # mean and standard deviation
taz_wt[column] = np.round(np.random.normal(mu, sigma, len(taz_wt)),
0)
else: ## off peak hours: the rest of intervals
mu, sigma = 240, 60 ## between 1-5 min
taz_wt[column] = np.round(np.random.normal(mu, sigma, len(taz_wt)),
0)
taz_wt = taz_wt.reset_index(level='zone')
taz_wt.zone = 't' + taz_wt.zone.astype(str)
taz_wt = taz_wt.set_index(['zone'])
taz_wt = taz_wt.T.to_dict()
return (taz_wt)
def gen_on_demand_taxi_shared_tt_dict():
a = pd.read_csv('taz_2012.csv',
delimiter=',',
usecols=[0],
index_col=False)
a['incoming_node'] = a.zone_id
b = pd.read_csv('taz_2012.csv',
delimiter=',',
usecols=[0],
index_col=False)
b['outgoing_node'] = b.zone_id
taz_pairs = pd.DataFrame(
columns=['from_node', 'to_node', 'from_zone', 'to_zone'])
for i in b.index:
for j in a.index:
taz_pairs = taz_pairs.append(
{
'from_node': a.incoming_node[j],
'to_node': b.outgoing_node[i],
'from_zone': a.zone_id[j],
'to_zone': b.zone_id[i]
},
ignore_index=True)
taz_tt = pd.DataFrame(columns=index_column(5).columns)
taz_tt['from_node'] = taz_pairs['from_node']
taz_tt['to_node'] = taz_pairs['to_node']
taz_tt = taz_tt.set_index(['from_node', 'to_node'])
trips = pd.read_csv('traveltime.csv', delimiter=',', index_col=False)
trips.columns = [
'person_id', 'trip_origin_node', 'trip_dest_node', 'from_zone',
'to_zone', 'subtrip_origin_type', 'subtrip_dest_type', 'travel_mode',
'arrival_time', 'travel_time', 'pt_line'
]
allcartrips = trips.loc[(trips.travel_mode == 'ON_TAXI') |
(trips.travel_mode == 'ON_SMS_Veh') |
(trips.travel_mode == 'ON_RAIL_SMS_Veh') |
(trips.travel_mode == 'ON_SMS_Pool_Veh') |
(trips.travel_mode == 'ON_RAIL_SMS_Pool_Veh') |
(trips.travel_mode == 'ON_CAR')]
allcartrips.from_zone = allcartrips.from_zone.astype(int)
allcartrips.to_zone = allcartrips.to_zone.astype(int)
allcartrips = allcartrips.reset_index(drop=True)
y = pd.read_csv('node_taz_mapping.csv',
delimiter=',',
usecols=[0, 8],
index_col=False)
allcartrips.from_zone = allcartrips.from_zone.replace(
list(y.node_id), list(y.taz))
allcartrips.to_zone = allcartrips.to_zone.replace(list(y.node_id),
list(y.taz))
allcartrips[['hh_s', 'mm_s', 'ss_s']] = allcartrips.arrival_time.str.split(
":",
expand=True,
) ## Splitting the start_time into h, m and s
#
## Converting the h, m and s into integers
allcartrips.hh_s = allcartrips.hh_s.astype(int)
allcartrips.mm_s = allcartrips.mm_s.astype(int)
allcartrips.ss_s = allcartrips.ss_s.astype(int)
#
allcartrips['arrival_time_s'] = (
allcartrips.hh_s * 60 + allcartrips.mm_s
) * 60 + allcartrips.ss_s ## Adding a new column to store the start_time in seconds
allcartrips = allcartrips.drop(['hh_s', 'mm_s', 'ss_s'], axis=1)
allcartrips = allcartrips.sort_values(
['arrival_time_s']) ## Removing the h, m and s columns
allcartrips = allcartrips.reset_index(drop=True)
x = allcartrips.loc[(allcartrips.travel_mode == 'ON_SMS_Pool_Veh') |
(allcartrips.travel_mode == 'ON_RAIL_SMS_Pool_Veh')]
x = x.reset_index(drop=True)
for interval in index_column(5).columns:
c = pd.DataFrame(index=taz_tt.index)
# taz_tt[interval]=0
c[interval] = 0
d = x[x.arrival_time_s >= interval[0]]
d = d[d.arrival_time_s <= interval[1]]
if len(d) != 0:
d = d.set_index(['from_zone', 'to_zone'])
for index in d.index.unique():
c.loc[index] = round(d.travel_time[index].mean(), 0)
taz_tt[interval] = c[interval]
for index in taz_tt.index:
taz_tt.loc[index] = taz_tt.loc[index].where(
taz_tt.loc[index] != 0,
np.sum(np.array(taz_tt.loc[index])) /
np.count_nonzero(np.array(taz_tt.loc[index])))
taz_tt = taz_tt.fillna(0)
x = allcartrips.loc[(allcartrips.travel_mode == 'ON_TAXI') |
(allcartrips.travel_mode == 'ON_SMS_Veh') |
(allcartrips.travel_mode == 'ON_RAIL_SMS_Veh')]
x = x.reset_index(drop=True)
taz_tt_aux = pd.DataFrame(columns=index_column(5).columns)
taz_tt_aux['from_node'] = taz_pairs['from_node']
taz_tt_aux['to_node'] = taz_pairs['to_node']
taz_tt_aux = taz_tt_aux.set_index(['from_node', 'to_node'])
for index in taz_tt.index:
if np.count_nonzero(np.array(taz_tt.loc[index])) != 0:
taz_tt_aux = taz_tt_aux.drop([index])
r = pd.DataFrame()
for index in taz_tt_aux.index:
s = x[(x.from_zone == index[0]) & (x.to_zone == index[1])]
r = r.append(s)
for interval in index_column(5).columns:
c = pd.DataFrame(index=taz_tt_aux.index)
# taz_tt[interval]=0
c[interval] = 0
d = r[r.arrival_time_s >= interval[0]]
d = d[d.arrival_time_s <= interval[1]]
if len(d) != 0:
d = d.set_index(['from_zone', 'to_zone'])
for index in d.index.unique():
c.loc[index] = round(d.travel_time[index].mean(), 0)
taz_tt_aux[interval] = c[interval]
for index in taz_tt_aux.index:
taz_tt_aux.loc[index] = taz_tt_aux.loc[index].where(
taz_tt_aux.loc[index] != 0,
np.sum(np.array(taz_tt_aux.loc[index])) /
np.count_nonzero(np.array(taz_tt_aux.loc[index])))
taz_tt_aux = taz_tt_aux.fillna(0)
for index in taz_tt_aux.index:
taz_tt.loc[index] = taz_tt_aux.loc[index]
x = allcartrips.loc[(allcartrips.travel_mode == 'ON_CAR')]
x = x.reset_index(drop=True)
taz_tt_aux = pd.DataFrame(pd.DataFrame(columns=index_column(5).columns))
taz_tt_aux['from_node'] = taz_pairs['from_node']
taz_tt_aux['to_node'] = taz_pairs['to_node']
taz_tt_aux = taz_tt_aux.set_index(['from_node', 'to_node'])
for index in taz_tt.index:
if np.count_nonzero(np.array(taz_tt.loc[index])) != 0:
taz_tt_aux = taz_tt_aux.drop([index])
r = pd.DataFrame()
for index in taz_tt_aux.index:
s = x[(x.from_zone == index[0]) & (x.to_zone == index[1])]
r = r.append(s)
for interval in index_column(5).columns:
c = pd.DataFrame(index=taz_tt_aux.index)
# taz_tt[interval]=0
c[interval] = 0
d = r[r.arrival_time_s >= interval[0]]
d = d[d.arrival_time_s <= interval[1]]
if len(d) != 0:
d = d.set_index(['from_zone', 'to_zone'])
for index in d.index.unique():
c.loc[index] = round(d.travel_time[index].mean(), 0)
taz_tt_aux[interval] = c[interval]
for index in taz_tt_aux.index:
taz_tt_aux.loc[index] = taz_tt_aux.loc[index].where(
taz_tt_aux.loc[index] != 0,
np.sum(np.array(taz_tt_aux.loc[index])) /
np.count_nonzero(np.array(taz_tt_aux.loc[index])))
taz_tt_aux = taz_tt_aux.fillna(0)
for index in taz_tt_aux.index:
taz_tt.loc[index] = taz_tt_aux.loc[index]
for index in taz_tt.index:
if np.count_nonzero(np.array(taz_tt.loc[index])) == 0:
taz_tt.loc[index] = taz_tt.loc[index[1], index[0]]
## Travel time for the missing zone to zone
taz_tt.loc[(
2,
2)] = (taz_tt.loc[(2, 21)] + taz_tt.loc[(21, 2)] + taz_tt.loc[(2, 6)] +
taz_tt.loc[(6, 2)] + taz_tt.loc[(2, 7)] + taz_tt.loc[(7, 2)] +
taz_tt.loc[(2, 1)] + taz_tt.loc[(1, 2)]) / 8
taz_tt.loc[(6, 6)] = (
taz_tt.loc[(6, 21)] + taz_tt.loc[(21, 6)] + taz_tt.loc[(2, 6)] +
taz_tt.loc[(6, 2)] + taz_tt.loc[(6, 7)] + taz_tt.loc[(7, 6)] +
taz_tt.loc[(6, 3)] + taz_tt.loc[(3, 6)] + taz_tt.loc[(6, 12)] +
taz_tt.loc[(12, 6)] + taz_tt.loc[(6, 13)] + taz_tt.loc[(13, 6)]) / 12
taz_tt.loc[(8, 8)] = (taz_tt.loc[(8, 5)] + taz_tt.loc[(5, 8)] + taz_tt.loc[
(8, 3)] + taz_tt.loc[(3, 8)] + taz_tt.loc[(8, 4)] + taz_tt.loc[
(4, 8)] + taz_tt.loc[(8, 1)] + taz_tt.loc[(1, 8)] +
taz_tt.loc[(8, 14)] + taz_tt.loc[(14, 8)]) / 10
taz_tt.loc[(9, 9)] = (taz_tt.loc[(9, 23)] + taz_tt.loc[(23, 9)] +
taz_tt.loc[(9, 10)] + taz_tt.loc[(10, 9)] +
taz_tt.loc[(9, 3)] + taz_tt.loc[(3, 9)] +
taz_tt.loc[(9, 4)] + taz_tt.loc[(4, 9)]) / 8
taz_tt.loc[(11, 11)] = (taz_tt.loc[(11, 12)] + taz_tt.loc[(12, 11)] +
taz_tt.loc[(11, 13)] + taz_tt.loc[(13, 11)] +
taz_tt.loc[(11, 15)] + taz_tt.loc[(15, 13)]) / 6
taz_tt.loc[(18, 18)] = (taz_tt.loc[(18, 19)] + taz_tt.loc[(19, 18)] +
taz_tt.loc[(18, 16)] + taz_tt.loc[(16, 18)] +
taz_tt.loc[(18, 17)] + taz_tt.loc[(17, 18)]) / 6
taz_tt.loc[(22, 22)] = (taz_tt.loc[(22, 21)] + taz_tt.loc[(21, 22)] +
taz_tt.loc[(22, 1)] + taz_tt.loc[(1, 22)] +
taz_tt.loc[(22, 14)] + taz_tt.loc[(14, 22)]) / 6
taz_tt = taz_tt.reset_index()
taz_tt.from_node = 'shar' + taz_tt.from_node.astype(str) + 'i'
taz_tt.to_node = 'shar' + taz_tt.to_node.astype(str) + 'o'
taz_tt = taz_tt.set_index(['from_node', 'to_node'])
taz_tt = taz_tt.round(0)
taz_tt = taz_tt.T.to_dict()
return (taz_tt)
def gen_on_demand_taxi_shared_wt_dict(): ##a:tt_dual
## importing the taz
taz = pd.read_csv('taz_2012.csv', delimiter=',', index_col=False)
##creates a df with the same columns as the tt_dual, that are the 288 5 min interval in seconds eg (0,299),...
taz_wt = pd.DataFrame(
columns=index_column(5).columns
) ##creates a df with the same columns as the tt_dual, that are the 288 5 min interval in seconds eg (0,299),...
taz_wt['zone'] = taz.zone_id ##adding a new column with the zone id
taz_wt.zone = taz_wt.zone.astype(str) ## converting the zone id to str
taz_wt = taz_wt.set_index(
'zone') ## sets the zone id as the index of taz_wt
# trips=pd.read_csv('traveltime.csv', delimiter=',',index_col=False)
trips = pd.read_csv('traveltime.csv', delimiter=',', index_col=False)
trips.columns = [
'person_id', 'trip_origin_node', 'trip_dest_node', 'from_zone',
'to_zone', 'subtrip_origin_type', 'subtrip_dest_type', 'travel_mode',
'arrival_time', 'travel_time', 'pt_line'
]
y = pd.read_csv('node_taz_mapping.csv',
delimiter=',',
usecols=[0, 8],
index_col=False)
x = trips.loc[(trips.travel_mode == 'WAIT_SMS_Pool') |
(trips.travel_mode == 'WAIT_RAIL_SMS_Pool')]
x = x.reset_index(drop=True)
x.from_zone = x.from_zone.astype(int)
x = x.drop(['to_zone'], axis=1)
x.from_zone = x.from_zone.replace(list(y.node_id), list(y.taz))
x.from_zone = x.from_zone.astype(str)
x = x.rename(columns={'from_zone': 'zone', 'travel_time': 'waiting_time'})
x[['hh_s', 'mm_s', 'ss_s']] = x.arrival_time.str.split(
":",
expand=True,
) ## Splitting the start_time into h, m and s
## Converting the h, m and s into integers
x.hh_s = x.hh_s.astype(int)
x.mm_s = x.mm_s.astype(int)
x.ss_s = x.ss_s.astype(int)
x['arrival_time_s'] = (
x.hh_s * 60 + x.mm_s
) * 60 + x.ss_s ## Adding a new column to store the start_time in seconds
x = x.drop(['hh_s', 'mm_s', 'ss_s'], axis=1)
x = x.sort_values(['arrival_time_s']) ## Removing the h, m and s columns
x = x.reset_index(drop=True)
for interval in index_column(5).columns:
c = pd.DataFrame(index=taz_wt.index)
c[interval] = 0
d = x[x.arrival_time_s >= interval[0]]
d = d[d.arrival_time_s <= interval[1]]
if len(d) != 0:
d = d.set_index(['zone'])
for index in d.index:
c.loc[index, :] = round(d.waiting_time[index].mean(), 0)
# else:
# continue
# taz_tt.loc[(index[0],index[1]),(interval[0],interval[1])]
# taz_tt.loc[:,(0,299)]
taz_wt[interval] = c[interval]
for index in taz_wt.index:
taz_wt.loc[index, :] = taz_wt.loc[index, :].where(
taz_wt.loc[index, :] != 0,
np.sum(np.array(taz_wt.loc[index, :])) /
np.count_nonzero(np.array(taz_wt.loc[index, :])))
taz_wt = taz_wt.round(0)
taz_wt = taz_wt.reset_index(level='zone')
taz_wt.zone = 'shar' + taz_wt.zone.astype(str)
taz_wt = taz_wt.set_index(['zone'])
taz_wt = taz_wt.T.to_dict()
return (taz_wt)