def build_year_1(nZones=9,
transit_scope=slice(2, 8),
n_HH=834,
directory=None,
seed=0):
global _cache_1, _directory
if _cache_1 is not None:
return _cache_1
flog("EXAMPVILLE Builder (Year 1)")
flog(" simulating a survey of {} households", n_HH)
flog(" traveling among {} travel analysis zones", nZones)
if directory is None:
if _directory is not None:
directory = _directory
else:
from ..util.temporaryfile import TemporaryDirectory
_directory = directory = TemporaryDirectory()
else:
_directory = directory
if isinstance(transit_scope, tuple):
transit_scope = slice(*transit_scope)
if transit_scope.stop > nZones:
raise TypeError('transit_scope too large for nZones')
# The randomizer seed is reset to zero by default so that we get consistent generated
# Exampville survey results. (High levels of randomness are not very important
# here because we just want to demonstrate the models.)
numpy.random.seed(seed)
## Zones
flog("Zones")
pop_weight = numpy.fmax(numpy.arange(nZones),
numpy.flipud(numpy.arange(nZones))).astype(float)
pop_weight /= pop_weight.sum()
wrk_weight = scipy.stats.binom.pmf(numpy.arange(nZones), nZones - 1, .5)
wrk_weight /= wrk_weight.sum()
zone_lat = (-1)**numpy.arange(nZones)
zone_lon = 11 + numpy.arange(nZones)
## Skims
flog("Skims")
distance = numpy.zeros([nZones, nZones])
gaps = numpy.random.random(nZones) + 1
for z in range(nZones):
for y in range(z, nZones):
distance[y, z] = distance[z, y] = gaps[z:y].sum()
distance[z, z] = numpy.random.random()
drivetime = (numpy.random.exponential(1, [nZones, nZones]) + 1) * distance
drivetime[drivetime < 2.0] = 2.0
hov_time = drivetime.copy()
hov_time[:nZones // 2, nZones // 2:] -= numpy.random.random() * 1.5 + 0.5
hov_time[nZones // 2:, :nZones // 2] -= numpy.random.random() * 1.5 + 0.5
transit_range = transit_scope.stop - transit_scope.start
transittime = numpy.zeros([nZones, nZones])
transittime[
transit_scope,
transit_scope] = (numpy.random.random([transit_range, transit_range]) +
1) * distance[transit_scope, transit_scope]
transitfare = numpy.zeros([nZones, nZones])
transitfare[transit_scope, transit_scope] = 1.5
## Households
flog("HHs")
HHidx = numpy.arange(n_HH, dtype=numpy.int64)
HHid = numpy.asarray([50000 + i for i in HHidx])
HHincome = numpy.round(numpy.random.normal(75000, 25000, [
n_HH,
]), -3).astype(numpy.int64)
HHsize = numpy.floor(
numpy.random.exponential(0.8, [
n_HH,
]) + 1 + numpy.random.random([
n_HH,
])).astype(numpy.int64)
HHhomezone = numpy.random.choice(numpy.arange(1, nZones + 1),
size=[
n_HH,
],
replace=True,
p=pop_weight)
## People
flog("People")
n_PER = numpy.sum(HHsize)
PERidx = numpy.arange(n_PER, dtype=numpy.int64)
PERid = numpy.asarray([60000 + i for i in PERidx])
PERhhid = numpy.zeros(n_PER, dtype=numpy.int64)
PERhhidx = numpy.zeros(n_PER, dtype=numpy.int64)
n2 = 0
for n1 in range(n_HH):
PERhhid[n2:(n2 + HHsize[n1])] = HHid[n1]
PERhhidx[n2:(n2 + HHsize[n1])] = HHidx[n1]
n2 += HHsize[n1]
PERage = (numpy.random.random(n_PER) * 80 + 5).astype(numpy.int64)
PERworks = ((numpy.random.random(n_PER) > 0.2) & (PERage > 16) &
(PERage < 70)).astype(numpy.int64)
zone_employment = numpy.round(PERworks.sum() * wrk_weight, 0) + 1
total_employment = zone_employment.sum()
mean_employment = total_employment / nZones
zone_retail = numpy.fmin(
numpy.round(numpy.random.random(nZones) * mean_employment, 0),
zone_employment)
zone_nonretail = zone_employment - zone_retail
PERnworktours = numpy.random.choice(
[0, 1, 2, 3], size=[
n_PER,
], replace=True, p=[0.1, 0.8, 0.07, 0.03]) * PERworks
PERnothertours = numpy.random.choice([0, 1, 2, 3],
size=[
n_PER,
],
replace=True,
p=[0.2, 0.5, 0.2, 0.1])
PERntours = PERnworktours + PERnothertours
## Tours
flog("Tours")
n_TOUR = PERntours.sum()
TOURid = numpy.arange(n_TOUR, dtype=numpy.int64)
TOURper = numpy.zeros(n_TOUR, dtype=numpy.int64)
TOURperidx = numpy.zeros(n_TOUR, dtype=numpy.int64)
TOURhh = numpy.zeros(n_TOUR, dtype=numpy.int64)
TOURhhidx = numpy.zeros(n_TOUR, dtype=numpy.int64)
TOURdtaz = numpy.zeros(n_TOUR, dtype=numpy.int64)
TOURmode = numpy.zeros(n_TOUR, dtype=numpy.int64)
TOURpurpose = numpy.zeros(n_TOUR, dtype=numpy.int64)
# Work tours, then other tours
n2 = 0
for n1 in range(n_PER):
TOURper[n2:(n2 + PERntours[n1])] = PERid[n1]
TOURperidx[n2:(n2 + PERntours[n1])] = PERidx[n1]
TOURhh[n2:(n2 + PERntours[n1])] = PERhhid[n1]
TOURhhidx[n2:(n2 + PERntours[n1])] = PERhhidx[n1]
TOURpurpose[n2:(n2 + PERnworktours[n1])] = 1
TOURpurpose[(n2 + PERnworktours[n1]):(n2 + PERntours[n1])] = 2
n2 += PERntours[n1]
#### Utility by mode to various destinations
flog("Choice Probability")
nameModes = ['DA', 'SR', 'Walk', 'Bike', 'Transit']
mDA = 0
mSR = 1
mWA = 2
mBI = 3
mTR = 4
nModes = len(nameModes)
nModeNests = 3
paramCOST = -0.312
paramTIME = -0.123
paramNMTIME = -0.246
paramDIST = -0.00357
paramLNDIST = -0.00642
paramMUcar = 0.5
paramMUnon = 0.75
paramMUmot = 0.8
paramMUtop = 1.0
Util = numpy.zeros([n_TOUR, nZones, nModes])
for n in range(n_TOUR):
# Mode
# flog('N {}',n)
# flog('Util[n,:,:] &&&')
# flog('{}',Util[n,:,:])
otazi = HHhomezone[TOURhhidx[n]] - 1
Util[n, :, mDA] += drivetime[otazi, :] * paramTIME + distance[
otazi, :] * 0.20 * paramCOST
if HHincome[TOURhhidx[n]] >= 75000:
Util[n, :, mDA] += 1.0
Util[n, :, mTR] -= 0.5
Util[n, :, mSR] += drivetime[otazi, :] * paramTIME - 1.0 + distance[
otazi, :] * 0.20 * 0.5 * paramCOST
Util[n, :, mWA] += 1.0 + distance[otazi, :] / 2.5 * 60 * paramNMTIME
Util[n, :, mBI] += -1.25 + distance[otazi, :] / 12 * 60 * paramNMTIME
Util[n, :, mTR] += -1.5 + transittime[
otazi, :] * paramTIME + transitfare[otazi, :] * paramCOST
# Destination
Util[n, :, :] += distance[otazi, :, None] * paramDIST + log1p(
distance[otazi, :, None]) * paramLNDIST
if HHincome[TOURhhidx[n]] <= 50000:
Util[n, :, :] += 0.75 * log(zone_retail * 2.71828 +
zone_nonretail)[:, None]
else:
Util[n, :, :] += 0.75 * log(zone_retail +
zone_nonretail * 2.71828)[:, None]
# flog('Util[n,:,:] ...')
# flog('{}',Util[n,:,:])
# Unavails
if PERage[TOURperidx[n]] < 16:
Util[n, :, mDA] = -numpy.inf
Util[n, transitfare[otazi, :] <= 0, mTR] = -numpy.inf
Util[n, distance[otazi, :] >= 3, mWA] = -numpy.inf
Util[n, distance[otazi, :] >= 15, mBI] = -numpy.inf
# flog('Util[n,:,:] +++')
# flog('{}',Util[n,:,:])
CPr_car = numpy.zeros([n_TOUR, nZones, 2]) # [DA,SR]
CPr_non = numpy.zeros([n_TOUR, nZones, 2]) # [WA,BI]
CPr_mot = numpy.zeros([n_TOUR, nZones, 2]) # [TR,Car]
CPr_top = numpy.zeros([n_TOUR, nZones, 2]) # [Non,Mot]
NLS_car = numpy.zeros([
n_TOUR,
nZones,
])
NLS_non = numpy.zeros([
n_TOUR,
nZones,
])
NLS_mot = numpy.zeros([
n_TOUR,
nZones,
])
MLS_top = numpy.zeros([
n_TOUR,
nZones,
]) # Mode choice logsum
DLS_top = numpy.zeros([
n_TOUR,
]) # Dest choice logsum
Pr_modes = numpy.zeros([n_TOUR, nZones, nModes])
Pr_dest = numpy.zeros([n_TOUR, nZones])
with numpy.errstate(divide='ignore', invalid='ignore'):
for n in range(n_TOUR):
NLS_car[n, :] = paramMUcar * log(
exp(Util[n, :, mDA] / paramMUcar) +
exp(Util[n, :, mSR] / paramMUcar))
NLS_non[n, :] = paramMUnon * log(
exp(Util[n, :, mWA] / paramMUnon) +
exp(Util[n, :, mBI] / paramMUnon))
NLS_mot[n, :] = paramMUmot * log(
exp(NLS_car[n, :] / paramMUmot) +
exp(Util[n, :, mTR] / paramMUmot))
MLS_top[n, :] = log(exp(NLS_non[n, :]) + exp(NLS_mot[n, :]))
DLS_top[n] = log(numpy.sum(exp(MLS_top[n, :])))
Pr_dest[n, :] = exp(MLS_top[n, :] - DLS_top[n])
CPr_top[n, :, 0] = exp(
(NLS_non[n, :] - MLS_top[n, :]) / paramMUtop)
CPr_top[n, :, 1] = exp(
(NLS_mot[n, :] - MLS_top[n, :]) / paramMUtop)
CPr_mot[n, :, 0] = exp(
(Util[n, :, mTR] - NLS_mot[n, :]) / paramMUmot)
CPr_mot[n, :, 1] = exp(
(NLS_car[n, :] - NLS_mot[n, :]) / paramMUmot)
CPr_non[n, :, 0] = exp(
(Util[n, :, mWA] - NLS_non[n, :]) / paramMUnon)
CPr_non[n, :, 1] = exp(
(Util[n, :, mBI] - NLS_non[n, :]) / paramMUnon)
CPr_car[n, :, 0] = exp(
(Util[n, :, mDA] - NLS_car[n, :]) / paramMUcar)
CPr_car[n, :, 1] = exp(
(Util[n, :, mSR] - NLS_car[n, :]) / paramMUcar)
Pr_modes[n, :,
mTR] = CPr_mot[n, :, 0] * CPr_top[n, :, 1] * Pr_dest[n, :]
Pr_modes[n, :,
mWA] = CPr_non[n, :, 0] * CPr_top[n, :, 0] * Pr_dest[n, :]
Pr_modes[n, :,
mBI] = CPr_non[n, :, 1] * CPr_top[n, :, 0] * Pr_dest[n, :]
Pr_modes[n, :, mDA] = CPr_car[n, :, 0] * CPr_mot[
n, :, 1] * CPr_top[n, :, 1] * Pr_dest[n, :]
Pr_modes[n, :, mSR] = CPr_car[n, :, 1] * CPr_mot[
n, :, 1] * CPr_top[n, :, 1] * Pr_dest[n, :]
Pr_modes[numpy.isnan(Pr_modes)] = 0
## Choices
flog("Choices")
for n in range(n_TOUR):
try:
ch = numpy.random.choice(nModes * nZones,
replace=True,
p=Pr_modes[n, :, :].ravel())
except:
flog("total prob = {}", Pr_modes[n, :, :].sum())
raise
dtazi = ch // nModes
modei = ch - (dtazi * nModes)
TOURdtaz[n] = dtazi + 1
TOURmode[n] = modei + 1
### Write Out Data
flog("Output")
if not os.path.exists(directory):
os.makedirs(directory)
omx = larch.OMX(os.path.join(directory, 'exampville.omx'), mode='a')
omx.shape = (nZones, nZones)
omx.add_matrix('DIST', distance)
omx.add_matrix('AUTO_TIME', drivetime)
omx.add_matrix('RAIL_TIME', transittime)
omx.add_matrix('RAIL_FARE', transitfare)
omx.add_lookup('TAZID', numpy.arange(1, nZones + 1, dtype=numpy.int64))
omx.add_lookup('EMPLOYMENT', zone_employment)
omx.add_lookup('EMP_RETAIL', zone_retail)
omx.add_lookup('EMP_NONRETAIL', zone_nonretail)
omx.add_lookup('LAT', zone_lat)
omx.add_lookup('LON', zone_lon)
omx.flush()
omx.close()
omx = larch.OMX(os.path.join(directory, 'exampville.omx'), mode='r')
f_hh = larch.DT(os.path.join(directory, 'exampville_hh.h5'), mode='a')
f_hh.new_caseids(HHid)
f_hh.new_idco_from_array('INCOME', HHincome)
f_hh.new_idco_from_array('HHSIZE', HHsize)
f_hh.new_idco_from_array('HOMETAZ', HHhomezone)
f_hh.flush()
f_pp = larch.DT(os.path.join(directory, 'exampville_person.h5'), mode='a')
f_pp.new_caseids(PERid)
f_pp.new_idco_from_array('HHID', PERhhid)
f_pp.new_idco_from_array('AGE', PERage)
f_pp.new_idco_from_array('WORKS',
PERworks,
dictionary={
1: 'Yes',
0: 'No'
},
title='Person has a regular job')
f_pp.new_idco_from_array(
'N_WORKTOURS',
PERnworktours,
title='Number of work tours reported by this person on the survey day')
f_pp.new_idco_from_array(
'N_OTHERTOURS',
PERnothertours,
title=
'Number of non-work tours reported by this person on the survey day')
f_pp.new_idco_from_array(
'N_TOTALTOURS',
PERntours,
title=
'Number of non-work tours reported by this person on the survey day')
f_pp.flush()
f_tour = larch.DT(os.path.join(directory, 'exampville_tours.h5'), mode='a')
f_tour.new_caseids(TOURid)
f_tour.new_idco_from_array('HHID', TOURhh)
f_tour.new_idco_from_array('PERSONID', TOURper)
f_tour.new_idco_from_array('DTAZ', TOURdtaz)
f_tour.new_idco_from_array('TOURMODE',
TOURmode,
dictionary={
1: 'DA',
2: 'SR',
3: 'Walk',
4: 'Bike',
5: 'Transit',
})
f_tour.new_idco_from_array('TOURPURP',
TOURpurpose,
dictionary={
1: 'Work Tour',
2: 'Non-Work Tour',
})
f_tour.flush()
flog("EXAMPVILLE Completed Builder (Year 1)")
flog(" SKIMS : {}", omx.filename)
flog(" HHs : {}", f_hh.source_filename)
flog(" Persons: {}", f_pp.source_filename)
flog(" Tours : {}", f_tour.source_filename)
_cache_1 = (directory, omx, f_hh, f_pp, f_tour)
return directory, omx, f_hh, f_pp, f_tour
def test_ch_av_summary_output():
skims = larch.OMX(larch.exampville.files.skims, mode='r')
hh = pandas.read_csv(larch.exampville.files.hh)
pp = pandas.read_csv(larch.exampville.files.person)
tour = pandas.read_csv(larch.exampville.files.tour)
pp_col = [
'PERSONID', 'HHID', 'HHIDX', 'AGE', 'WORKS', 'N_WORK_TOURS',
'N_OTHER_TOURS', 'N_TOURS', 'N_TRIPS', 'N_TRIPS_HBW', 'N_TRIPS_HBO',
'N_TRIPS_NHB'
]
raw = tour.merge(hh, on='HHID').merge(pp[pp_col], on=('HHID', 'PERSONID'))
raw["HOMETAZi"] = raw["HOMETAZ"] - 1
raw["DTAZi"] = raw["DTAZ"] - 1
raw = raw[raw.TOURPURP == 1]
f_tour = raw.join(skims.get_rc_dataframe(
raw.HOMETAZi,
raw.DTAZi,
))
DA = 1
SR = 2
Walk = 3
Bike = 4
Transit = 5
dfs = larch.DataFrames(
co=f_tour,
alt_codes=[DA, SR, Walk, Bike, Transit],
alt_names=['DA', 'SR', 'Walk', 'Bike', 'Transit'],
)
m = larch.Model(dataservice=dfs)
m.title = "Exampville Work Tour Mode Choice v1"
m.utility_co[DA] = (
+P.InVehTime * X.AUTO_TIME + P.Cost * X.AUTO_COST # dollars per mile
)
m.utility_co[SR] = (
+P.ASC_SR + P.InVehTime * X.AUTO_TIME + P.Cost *
(X.AUTO_COST * 0.5) # dollars per mile, half share
+ P("HighInc:SR") * X("INCOME>75000"))
m.utility_co[Walk] = (+P.ASC_Walk + P.NonMotorTime * X.WALK_TIME +
P("HighInc:Walk") * X("INCOME>75000"))
m.utility_co[Bike] = (+P.ASC_Bike + P.NonMotorTime * X.BIKE_TIME +
P("HighInc:Bike") * X("INCOME>75000"))
m.utility_co[Transit] = (+P.ASC_Transit + P.InVehTime * X.TRANSIT_IVTT +
P.OutVehTime * X.TRANSIT_OVTT +
P.Cost * X.TRANSIT_FARE +
P("HighInc:Transit") * X("INCOME>75000"))
# No choice or avail data set
m.load_data()
q = m.dataframes.choice_avail_summary()
assert numpy.array_equal(q.columns, ['name', 'chosen', 'available'])
assert q.index.identical(
pandas.Index([1, 2, 3, 4, 5, '< Total All Alternatives >'],
dtype='object'))
assert numpy.array_equal(q.values, [
['DA', None, None],
['SR', None, None],
['Walk', None, None],
['Bike', None, None],
['Transit', None, None],
['', 0, ''],
])
# Reasonable choice and avail data set
m.choice_co_code = 'TOURMODE'
m.availability_co_vars = {
DA: 'AGE >= 16',
SR: '1',
Walk: 'WALK_TIME < 60',
Bike: 'BIKE_TIME < 60',
Transit: 'TRANSIT_FARE>0',
}
m.load_data()
q = m.dataframes.choice_avail_summary()
assert numpy.array_equal(q.columns, ['name', 'chosen', 'available'])
assert q.index.identical(
pandas.Index([1, 2, 3, 4, 5, '< Total All Alternatives >'],
dtype='object'))
assert numpy.array_equal(q['name'].values,
['DA', 'SR', 'Walk', 'Bike', 'Transit', ''])
assert numpy.array_equal(q['chosen'].values,
[6052., 810., 196., 72., 434., 7564.])
assert numpy.array_equal(
q['available'].values,
numpy.array([7564.0, 7564.0, 4179.0, 7564.0, 4199.0, ''],
dtype=object))
# Unreasonable choice and avail data set
m.choice_co_code = 'TOURMODE'
m.availability_co_vars = {
DA: 'AGE >= 26',
SR: '1',
Walk: 'WALK_TIME < 60',
Bike: 'BIKE_TIME < 60',
Transit: 'TRANSIT_FARE>0',
}
m.load_data()
q = m.dataframes.choice_avail_summary()
assert numpy.array_equal(
q.columns, ['name', 'chosen', 'available', 'chosen but not available'])
assert q.index.identical(
pandas.Index([1, 2, 3, 4, 5, '< Total All Alternatives >'],
dtype='object'))
assert numpy.array_equal(q['name'].values,
['DA', 'SR', 'Walk', 'Bike', 'Transit', ''])
assert numpy.array_equal(q['chosen'].values,
[6052., 810., 196., 72., 434., 7564.])
assert numpy.array_equal(
q['available'].values,
numpy.array([6376.0, 7564.0, 4179.0, 7564.0, 4199.0, ''],
dtype=object))
assert numpy.array_equal(q['chosen but not available'].values,
[942.0, 0.0, 0.0, 0.0, 0.0, 942.0])
import larch, numpy, pandas, os
import larch.exampville
from matplotlib import pyplot as plt
# 导入数据
skims = larch.OMX(larch.exampville.files.skims, mode='r')
hh = pandas.read_csv(larch.exampville.files.hh)
pp = pandas.read_csv(larch.exampville.files.person)
tour = pandas.read_csv(larch.exampville.files.tour)
tour.TOURPURP.statistics()
df = tour[tour.TOURPURP == 1] # 筛选出步行的数据
# print(df.info()) # df的数据信息
# print(df['TOURPURP']) # 读取df的'TOURPURP'的列
df = df.merge(hh, on='HHID').merge(pp, on=('HHID', 'PERSONID'))
df["HOMETAZi"] = df["HOMETAZ"] - 1
df["DTAZi"] = df["DTAZ"] - 1
df = df.join(
skims.get_rc_dataframe(
df["HOMETAZi"], df["DTAZi"],
)
)
# For clarity, we can define numbers as names for modes
DA = 1
SR = 2
Walk = 3