10: MTC MNL Mode Choice, Autos per Household

There are strong theoretical and empirical reasons to expect that a variety of decision maker related variables such as income, car availability, residential location, number of workers in the household and others, influence workers’ choice of travel mode.

Model 10 considers the number of automobiles in the household. (pp. 120)

d = larch.examples.MTC()
m = larch.Model(dataservice=d)
from larch.roles import P, X, PX
for a in [2,3]:
        m.utility_co[a] = (
                + P("hhinc#2,3") * X("hhinc")
                + P("numveh#{}".format(a)) * X("numveh")
                )

for a in [4,5,6]:
        m.utility_co[a] = (
                + P("hhinc#{}".format(a)) * X("hhinc")
                + P("numveh#{}".format(a)) * X("numveh")
                )

for a,name in m.dataservice.alternatives[1:]:
        m.utility_co[a] += P("ASC_"+name)
m.utility_ca= (
        + PX("totcost")
        + P("motorized_time") * X("(altnum <= 4) * tottime")
        + P("nonmotorized_time") * X("(altnum > 4) * tottime")
        + P("motorized_ovtbydist") * X("(altnum <= 4) * ovtt/dist")
        )
m.availability_var = '_avail_'
m.choice_ca_var = '_choice_'
m.ordering = (
        ("LOS", ".*cost.*", ".*time.*", ".*dist.*", ),
        ("Income", "hhinc.*", ),
        ("Ownership", "numveh.*"),
        ("ASCs", "ASC.*", ),
)
>>> m.load_data()
>>> m.maximize_loglike()
┣ ...Optimization terminated successfully...
>>> m.calculate_parameter_covariance()
>>> m.loglike()
-3501.642...

>>> print(m.pfo()[['value','std err','t stat','robust std err','robust t stat']])
                                value  std err   t stat  robust std err  robust t stat
Category  Parameter
LOS       totcost             -0.0041   0.0002 -16.9496          0.0003       -15.1707
          motorized_time      -0.0378   0.0036 -10.4850          0.0039        -9.7963
          nonmotorized_time   -0.0475   0.0057  -8.3873          0.0055        -8.6859
          motorized_ovtbydist -0.1785   0.0185  -9.6329          0.0238        -7.5159
Income    hhinc#2,3           -0.0020   0.0014  -1.3649          0.0015        -1.2771
          hhinc#4             -0.0013   0.0020  -0.6512          0.0019        -0.6828
          hhinc#5             -0.0095   0.0054  -1.7495          0.0060        -1.5848
          hhinc#6             -0.0042   0.0034  -1.2370          0.0037        -1.1142
Ownership numveh#2            -0.0353   0.0391  -0.9022          0.0411        -0.8573
          numveh#3             0.0723   0.0570   1.2669          0.0646         1.1191
          numveh#4            -0.5545   0.0690  -8.0405          0.0790        -7.0167
          numveh#5            -0.2292   0.1316  -1.7421          0.1651        -1.3885
          numveh#6            -0.3656   0.0995  -3.6729          0.1160        -3.1508
ASCs      ASC_BIKE            -2.2203   0.3807  -5.8323          0.4709        -4.7152
          ASC_SR2             -2.0540   0.1212 -16.9445          0.1274       -16.1230
          ASC_SR3             -3.6431   0.1798 -20.2644          0.1964       -18.5539
          ASC_TRANSIT          0.5738   0.1807   3.1751          0.1953         2.9381
          ASC_WALK            -0.4403   0.3315  -1.3281          0.3305        -1.3322