.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/assisted/plot_b05alt_spec_segmentation.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_assisted_plot_b05alt_spec_segmentation.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_assisted_plot_b05alt_spec_segmentation.py:


Segmentations and alternative specific specification
====================================================

We consider 4 specifications for the constants:

- Not segmented
- Segmented by GA (yearly subscription to public transport)
- Segmented by luggage
- Segmented both by GA and luggage

We consider 6 specifications for the time coefficients:

- Generic and not segmented
- Generic and segmented with first class
- Generic and segmented with trip purpose
- Alternative specific and not segmented
- Alternative specific and segmented with first class
- Alternative specific and segmented with trip purpose

We consider 2 specifications for the cost coefficients:

- Generic
- Alternative specific

We obtain a total of 48 specifications.
See `Bierlaire and Ortelli (2023)
<https://transp-or.epfl.ch/documents/technicalReports/BierOrte23.pdf>`_.

:author: Michel Bierlaire, EPFL
:date: Thu Jul 13 16:18:10 2023

.. GENERATED FROM PYTHON SOURCE LINES 35-42

.. code-block:: default

    import numpy as np
    import biogeme.biogeme as bio
    from biogeme import models
    from biogeme.expressions import Beta
    from biogeme.catalog import segmentation_catalogs, generic_alt_specific_catalogs
    from biogeme.results import compile_estimation_results, pareto_optimal








.. GENERATED FROM PYTHON SOURCE LINES 43-44

See :ref:`swissmetro_data`.

.. GENERATED FROM PYTHON SOURCE LINES 44-59

.. code-block:: default

    from swissmetro_data import (
        database,
        CHOICE,
        SM_AV,
        CAR_AV_SP,
        TRAIN_AV_SP,
        TRAIN_TT_SCALED,
        TRAIN_COST_SCALED,
        SM_TT_SCALED,
        SM_COST_SCALED,
        CAR_TT_SCALED,
        CAR_CO_SCALED,
    )









.. GENERATED FROM PYTHON SOURCE LINES 60-61

Definition of the segmentations.

.. GENERATED FROM PYTHON SOURCE LINES 61-73

.. code-block:: default

    segmentation_ga = database.generate_segmentation(
        variable='GA', mapping={0: 'noGA', 1: 'GA'}
    )

    segmentation_luggage = database.generate_segmentation(
        variable='LUGGAGE', mapping={0: 'no_lugg', 1: 'one_lugg', 3: 'several_lugg'}
    )

    segmentation_first = database.generate_segmentation(
        variable='FIRST', mapping={0: '2nd_class', 1: '1st_class'}
    )








.. GENERATED FROM PYTHON SOURCE LINES 74-76

We consider two trip purposes: 'commuters' and anything else. We
need to define a binary variable first.

.. GENERATED FROM PYTHON SOURCE LINES 76-82

.. code-block:: default

    database.data['COMMUTERS'] = np.where(database.data['PURPOSE'] == 1, 1, 0)

    segmentation_purpose = database.generate_segmentation(
        variable='COMMUTERS', mapping={0: 'non_commuters', 1: 'commuters'}
    )








.. GENERATED FROM PYTHON SOURCE LINES 83-84

Parameters to be estimated.

.. GENERATED FROM PYTHON SOURCE LINES 84-89

.. code-block:: default

    ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)
    ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)
    B_TIME = Beta('B_TIME', 0, None, None, 0)
    B_COST = Beta('B_COST', 0, None, None, 0)








.. GENERATED FROM PYTHON SOURCE LINES 90-91

Catalogs for the alternative specific constants.

.. GENERATED FROM PYTHON SOURCE LINES 91-102

.. code-block:: default

    ASC_TRAIN_catalog, ASC_CAR_catalog = segmentation_catalogs(
        generic_name='ASC',
        beta_parameters=[ASC_TRAIN, ASC_CAR],
        potential_segmentations=(
            segmentation_ga,
            segmentation_luggage,
        ),
        maximum_number=2,
    )









.. GENERATED FROM PYTHON SOURCE LINES 103-107

Catalog for the travel time coefficient.
Note that the function returns a list of catalogs. Here, the list
contains only one of them.  This is why there is a comma after
"B_TIME_catalog".

.. GENERATED FROM PYTHON SOURCE LINES 107-118

.. code-block:: default

    (B_TIME_catalog_dict,) = generic_alt_specific_catalogs(
        generic_name='B_TIME',
        beta_parameters=[B_TIME],
        alternatives=['TRAIN', 'SM', 'CAR'],
        potential_segmentations=(
            segmentation_first,
            segmentation_purpose,
        ),
        maximum_number=1,
    )








.. GENERATED FROM PYTHON SOURCE LINES 119-120

Catalog for the travel cost coefficient.

.. GENERATED FROM PYTHON SOURCE LINES 120-124

.. code-block:: default

    (B_COST_catalog_dict,) = generic_alt_specific_catalogs(
        generic_name='B_COST', beta_parameters=[B_COST], alternatives=['TRAIN', 'SM', 'CAR']
    )








.. GENERATED FROM PYTHON SOURCE LINES 125-126

Definition of the utility functions.

.. GENERATED FROM PYTHON SOURCE LINES 126-141

.. code-block:: default

    V1 = (
        ASC_TRAIN_catalog
        + B_TIME_catalog_dict['TRAIN'] * TRAIN_TT_SCALED
        + B_COST_catalog_dict['TRAIN'] * TRAIN_COST_SCALED
    )
    V2 = (
        B_TIME_catalog_dict['SM'] * SM_TT_SCALED
        + B_COST_catalog_dict['SM'] * SM_COST_SCALED
    )
    V3 = (
        ASC_CAR_catalog
        + B_TIME_catalog_dict['CAR'] * CAR_TT_SCALED
        + B_COST_catalog_dict['CAR'] * CAR_CO_SCALED
    )








.. GENERATED FROM PYTHON SOURCE LINES 142-143

Associate utility functions with the numbering of alternatives.

.. GENERATED FROM PYTHON SOURCE LINES 143-145

.. code-block:: default

    V = {1: V1, 2: V2, 3: V3}








.. GENERATED FROM PYTHON SOURCE LINES 146-147

Associate the availability conditions with the alternatives.

.. GENERATED FROM PYTHON SOURCE LINES 147-149

.. code-block:: default

    av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}








.. GENERATED FROM PYTHON SOURCE LINES 150-152

Definition of the model. This is the contribution of each
observation to the log likelihood function.

.. GENERATED FROM PYTHON SOURCE LINES 152-154

.. code-block:: default

    logprob = models.loglogit(V, av, CHOICE)








.. GENERATED FROM PYTHON SOURCE LINES 155-156

Create the Biogeme object.

.. GENERATED FROM PYTHON SOURCE LINES 156-161

.. code-block:: default

    the_biogeme = bio.BIOGEME(database, logprob)
    the_biogeme.modelName = 'b05alt_spec_segmentation'
    the_biogeme.generate_html = False
    the_biogeme.generate_pickle = False








.. GENERATED FROM PYTHON SOURCE LINES 162-163

Estimate the parameters.

.. GENERATED FROM PYTHON SOURCE LINES 163-165

.. code-block:: default

    dict_of_results = the_biogeme.estimate_catalog()








.. GENERATED FROM PYTHON SOURCE LINES 166-167

Number of estimated models.

.. GENERATED FROM PYTHON SOURCE LINES 167-169

.. code-block:: default

    print(f'A total of {len(dict_of_results)} models have been estimated')





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    A total of 48 models have been estimated




.. GENERATED FROM PYTHON SOURCE LINES 170-171

All estimation results

.. GENERATED FROM PYTHON SOURCE LINES 171-175

.. code-block:: default

    compiled_results, specs = compile_estimation_results(
        dict_of_results, use_short_names=True
    )








.. GENERATED FROM PYTHON SOURCE LINES 176-178

.. code-block:: default

    compiled_results






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Model_000000</th>
          <th>Model_000001</th>
          <th>Model_000002</th>
          <th>Model_000003</th>
          <th>Model_000004</th>
          <th>Model_000005</th>
          <th>Model_000006</th>
          <th>Model_000007</th>
          <th>Model_000008</th>
          <th>Model_000009</th>
          <th>Model_000010</th>
          <th>Model_000011</th>
          <th>Model_000012</th>
          <th>Model_000013</th>
          <th>Model_000014</th>
          <th>Model_000015</th>
          <th>Model_000016</th>
          <th>Model_000017</th>
          <th>Model_000018</th>
          <th>Model_000019</th>
          <th>Model_000020</th>
          <th>Model_000021</th>
          <th>Model_000022</th>
          <th>Model_000023</th>
          <th>Model_000024</th>
          <th>Model_000025</th>
          <th>Model_000026</th>
          <th>Model_000027</th>
          <th>Model_000028</th>
          <th>Model_000029</th>
          <th>Model_000030</th>
          <th>Model_000031</th>
          <th>Model_000032</th>
          <th>Model_000033</th>
          <th>Model_000034</th>
          <th>Model_000035</th>
          <th>Model_000036</th>
          <th>Model_000037</th>
          <th>Model_000038</th>
          <th>Model_000039</th>
          <th>Model_000040</th>
          <th>Model_000041</th>
          <th>Model_000042</th>
          <th>Model_000043</th>
          <th>Model_000044</th>
          <th>Model_000045</th>
          <th>Model_000046</th>
          <th>Model_000047</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Number of estimated parameters</th>
          <td>6</td>
          <td>8</td>
          <td>14</td>
          <td>9</td>
          <td>13</td>
          <td>8</td>
          <td>15</td>
          <td>11</td>
          <td>13</td>
          <td>10</td>
          <td>4</td>
          <td>7</td>
          <td>13</td>
          <td>11</td>
          <td>11</td>
          <td>5</td>
          <td>15</td>
          <td>10</td>
          <td>5</td>
          <td>9</td>
          <td>13</td>
          <td>9</td>
          <td>17</td>
          <td>10</td>
          <td>7</td>
          <td>8</td>
          <td>6</td>
          <td>9</td>
          <td>17</td>
          <td>7</td>
          <td>8</td>
          <td>13</td>
          <td>7</td>
          <td>10</td>
          <td>6</td>
          <td>11</td>
          <td>12</td>
          <td>9</td>
          <td>11</td>
          <td>15</td>
          <td>12</td>
          <td>11</td>
          <td>13</td>
          <td>15</td>
          <td>9</td>
          <td>11</td>
          <td>12</td>
          <td>11</td>
        </tr>
        <tr>
          <th>Sample size</th>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
        </tr>
        <tr>
          <th>Final log likelihood</th>
          <td>-5312.894223</td>
          <td>-5013.830192</td>
          <td>-4986.71867</td>
          <td>-5011.211361</td>
          <td>-5117.991547</td>
          <td>-5075.704346</td>
          <td>-4970.741164</td>
          <td>-4928.268572</td>
          <td>-4935.433409</td>
          <td>-5022.276564</td>
          <td>-5331.252007</td>
          <td>-5048.818199</td>
          <td>-4924.149131</td>
          <td>-5042.929071</td>
          <td>-5008.156443</td>
          <td>-5234.708233</td>
          <td>-4939.052885</td>
          <td>-5011.135042</td>
          <td>-5331.250708</td>
          <td>-5240.921463</td>
          <td>-4986.698765</td>
          <td>-5160.079285</td>
          <td>-4865.971435</td>
          <td>-5045.571884</td>
          <td>-5080.952424</td>
          <td>-5241.011928</td>
          <td>-5050.677696</td>
          <td>-4945.30006</td>
          <td>-4912.396818</td>
          <td>-5031.333906</td>
          <td>-5047.955187</td>
          <td>-5136.058347</td>
          <td>-4976.118642</td>
          <td>-5226.651348</td>
          <td>-5083.499937</td>
          <td>-4952.546476</td>
          <td>-4989.598481</td>
          <td>-5204.910152</td>
          <td>-4962.315058</td>
          <td>-4899.835708</td>
          <td>-5020.14451</td>
          <td>-4995.5015</td>
          <td>-4890.815071</td>
          <td>-4920.638996</td>
          <td>-5206.882927</td>
          <td>-4957.612234</td>
          <td>-5038.915413</td>
          <td>-5020.027091</td>
        </tr>
        <tr>
          <th>Akaike Information Criterion</th>
          <td>10637.788446</td>
          <td>10043.660383</td>
          <td>10001.43734</td>
          <td>10040.422722</td>
          <td>10261.983094</td>
          <td>10167.408692</td>
          <td>9971.482329</td>
          <td>9878.537145</td>
          <td>9896.866819</td>
          <td>10064.553128</td>
          <td>10670.504014</td>
          <td>10111.636399</td>
          <td>9874.298263</td>
          <td>10107.858141</td>
          <td>10038.312885</td>
          <td>10479.416466</td>
          <td>9908.10577</td>
          <td>10042.270084</td>
          <td>10672.501415</td>
          <td>10499.842927</td>
          <td>9999.397531</td>
          <td>10338.158569</td>
          <td>9765.94287</td>
          <td>10111.143768</td>
          <td>10175.904849</td>
          <td>10498.023855</td>
          <td>10113.355392</td>
          <td>9908.60012</td>
          <td>9858.793636</td>
          <td>10076.667813</td>
          <td>10111.910373</td>
          <td>10298.116695</td>
          <td>9966.237283</td>
          <td>10473.302695</td>
          <td>10178.999875</td>
          <td>9927.092951</td>
          <td>10003.196963</td>
          <td>10427.820304</td>
          <td>9946.630115</td>
          <td>9829.671416</td>
          <td>10064.28902</td>
          <td>10013.003</td>
          <td>9807.630143</td>
          <td>9871.277992</td>
          <td>10431.765855</td>
          <td>9937.224469</td>
          <td>10101.830826</td>
          <td>10062.054183</td>
        </tr>
        <tr>
          <th>Bayesian Information Criterion</th>
          <td>10678.708211</td>
          <td>10098.22007</td>
          <td>10096.916793</td>
          <td>10101.80237</td>
          <td>10350.642586</td>
          <td>10221.968379</td>
          <td>10073.781742</td>
          <td>9953.556715</td>
          <td>9985.526311</td>
          <td>10132.752737</td>
          <td>10697.783857</td>
          <td>10159.376125</td>
          <td>9962.957754</td>
          <td>10182.877711</td>
          <td>10113.332455</td>
          <td>10513.51627</td>
          <td>10010.405183</td>
          <td>10110.469693</td>
          <td>10706.60122</td>
          <td>10561.222575</td>
          <td>10088.057022</td>
          <td>10399.538218</td>
          <td>9881.882206</td>
          <td>10179.343377</td>
          <td>10223.644575</td>
          <td>10552.583543</td>
          <td>10154.275157</td>
          <td>9969.979768</td>
          <td>9974.732971</td>
          <td>10124.407539</td>
          <td>10166.47006</td>
          <td>10386.776186</td>
          <td>10013.97701</td>
          <td>10541.502304</td>
          <td>10219.91964</td>
          <td>10002.112521</td>
          <td>10085.036493</td>
          <td>10489.199952</td>
          <td>10021.649685</td>
          <td>9931.97083</td>
          <td>10146.128551</td>
          <td>10088.02257</td>
          <td>9896.289635</td>
          <td>9973.577405</td>
          <td>10493.145503</td>
          <td>10012.244039</td>
          <td>10183.670357</td>
          <td>10137.073753</td>
        </tr>
        <tr>
          <th>ASC_CAR (t-test)</th>
          <td>-0.271  (-2.29)</td>
          <td>-0.464  (-5.75)</td>
          <td>-0.423  (-3.67)</td>
          <td>-0.452  (-5.6)</td>
          <td>-0.364  (-2.75)</td>
          <td>-0.367  (-3.32)</td>
          <td>-0.579  (-5.03)</td>
          <td>-0.383  (-2.95)</td>
          <td>-0.602  (-5.23)</td>
          <td>-0.293  (-3.93)</td>
          <td>-0.155  (-2.66)</td>
          <td>-0.246  (-3.77)</td>
          <td>-0.67  (-7.27)</td>
          <td>-0.463  (-5.33)</td>
          <td>-0.556  (-5.04)</td>
          <td>-0.187  (-3.23)</td>
          <td>-0.488  (-3.9)</td>
          <td>-0.388  (-3.42)</td>
          <td>-0.155  (-2.53)</td>
          <td>-0.237  (-3.13)</td>
          <td>-0.489  (-5.53)</td>
          <td>-0.24  (-3.36)</td>
          <td>-0.446  (-3.68)</td>
          <td>-0.475  (-5.51)</td>
          <td>-0.415  (-5.38)</td>
          <td>-0.238  (-3.26)</td>
          <td>-0.249  (-3.97)</td>
          <td>-0.662  (-7.79)</td>
          <td>-0.615  (-5.2)</td>
          <td>-0.576  (-7.15)</td>
          <td>-0.332  (-2.72)</td>
          <td>-0.455  (-3.77)</td>
          <td>-0.281  (-4.53)</td>
          <td>-0.342  (-2.82)</td>
          <td>-0.427  (-5.55)</td>
          <td>-0.298  (-4.12)</td>
          <td>-0.502  (-5.71)</td>
          <td>-0.313  (-2.47)</td>
          <td>-0.475  (-3.86)</td>
          <td>-0.395  (-2.94)</td>
          <td>-0.365  (-2.94)</td>
          <td>-0.603  (-6.73)</td>
          <td>-0.434  (-3.72)</td>
          <td>-0.423  (-3.5)</td>
          <td>-0.407  (-3.48)</td>
          <td>-0.405  (-3.52)</td>
          <td>-0.407  (-3.59)</td>
          <td>-0.29  (-3.77)</td>
        </tr>
        <tr>
          <th>ASC_TRAIN (t-test)</th>
          <td>-0.202  (-1.82)</td>
          <td>-0.655  (-5.1)</td>
          <td>-1.22  (-7.89)</td>
          <td>-0.633  (-4.58)</td>
          <td>-0.999  (-7.28)</td>
          <td>-0.0754  (-0.712)</td>
          <td>-0.747  (-5.69)</td>
          <td>-0.965  (-7.29)</td>
          <td>-0.88  (-6.42)</td>
          <td>-1.75  (-15.1)</td>
          <td>-0.701  (-8.49)</td>
          <td>-1.28  (-13)</td>
          <td>-1.36  (-8.42)</td>
          <td>-0.459  (-3.2)</td>
          <td>-0.121  (-1.15)</td>
          <td>-0.814  (-9.45)</td>
          <td>-1.57  (-11.2)</td>
          <td>-0.743  (-5.53)</td>
          <td>-0.701  (-7.69)</td>
          <td>-1.54  (-12.8)</td>
          <td>-1.11  (-6.79)</td>
          <td>-1.58  (-13.8)</td>
          <td>-1.07  (-6.72)</td>
          <td>-0.483  (-3.66)</td>
          <td>0.213  (1.97)</td>
          <td>-1.54  (-13.5)</td>
          <td>-1.28  (-14)</td>
          <td>-0.938  (-6.76)</td>
          <td>-1.32  (-8.47)</td>
          <td>0.0242  (0.236)</td>
          <td>-1.09  (-8.35)</td>
          <td>-1.12  (-8.51)</td>
          <td>-1.37  (-14.7)</td>
          <td>-1.08  (-8.08)</td>
          <td>0.189  (2.06)</td>
          <td>-1.79  (-15.4)</td>
          <td>-1.13  (-7.25)</td>
          <td>-0.128  (-1.13)</td>
          <td>-1.14  (-9.1)</td>
          <td>-1.45  (-9.88)</td>
          <td>-1.57  (-11)</td>
          <td>-0.615  (-4.49)</td>
          <td>-0.593  (-4.28)</td>
          <td>-0.561  (-4.08)</td>
          <td>-0.337  (-3.07)</td>
          <td>0.0774  (0.705)</td>
          <td>-0.708  (-5.4)</td>
          <td>-1.74  (-14.6)</td>
        </tr>
        <tr>
          <th>B_COST (t-test)</th>
          <td>-1.07  (-16)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.09  (-15.8)</td>
          <td></td>
          <td></td>
          <td>-1.13  (-15)</td>
          <td></td>
          <td>-1.1  (-14.8)</td>
          <td>-1.08  (-15.9)</td>
          <td>-1.1  (-14.9)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.23  (-16.6)</td>
          <td>-1.22  (-14.3)</td>
          <td></td>
          <td>-1.08  (-16)</td>
          <td>-1.09  (-15.8)</td>
          <td></td>
          <td>-1.22  (-16.3)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.09  (-15.7)</td>
          <td>-1.1  (-14.8)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.1  (-14.9)</td>
          <td>-1.17  (-15.3)</td>
          <td>-1.26  (-15.3)</td>
          <td>-1.08  (-15.8)</td>
          <td></td>
          <td>-1.25  (-15.3)</td>
          <td></td>
          <td>-1.09  (-16.1)</td>
          <td>-1.22  (-14.3)</td>
          <td>-1.12  (-15)</td>
          <td>-1.1  (-14.8)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.17  (-15.5)</td>
          <td></td>
          <td></td>
          <td>-1.1  (-14.8)</td>
        </tr>
        <tr>
          <th>B_TIME_CAR (t-test)</th>
          <td>-1.12  (-10.3)</td>
          <td></td>
          <td>-1.3  (-7.92)</td>
          <td></td>
          <td>-1.4  (-16.9)</td>
          <td>-1.29  (-7.92)</td>
          <td>-0.614  (-2.98)</td>
          <td>-1.4  (-16.8)</td>
          <td>-0.623  (-3)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.617  (-3)</td>
          <td></td>
          <td>-0.401  (-2.53)</td>
          <td>-1.3  (-7.95)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.55  (-11.3)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.618  (-2.98)</td>
          <td></td>
          <td>-1.12  (-10.1)</td>
          <td>-0.403  (-2.55)</td>
          <td></td>
          <td>-1.11  (-10.1)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.42  (-17.1)</td>
          <td>-0.41  (-2.55)</td>
          <td>-1.39  (-16.7)</td>
          <td>-1.11  (-10.1)</td>
          <td></td>
          <td>-1.55  (-11.4)</td>
          <td>-1.54  (-11.3)</td>
          <td>-0.416  (-2.57)</td>
          <td>-1.54  (-11.4)</td>
          <td>-1.29  (-7.88)</td>
          <td></td>
        </tr>
        <tr>
          <th>B_TIME_SM (t-test)</th>
          <td>-1.17  (-6.42)</td>
          <td></td>
          <td>-1.11  (-6.13)</td>
          <td></td>
          <td>-1.79  (-16.5)</td>
          <td>-1.11  (-6.25)</td>
          <td>-0.36  (-2.02)</td>
          <td>-1.8  (-16.6)</td>
          <td>-0.375  (-1.99)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.37  (-2.02)</td>
          <td></td>
          <td>-0.41  (-2.1)</td>
          <td>-1.12  (-6.21)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.73  (-15.3)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.365  (-2)</td>
          <td></td>
          <td>-1.18  (-6.46)</td>
          <td>-0.411  (-2.09)</td>
          <td></td>
          <td>-1.15  (-6.35)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.81  (-16.6)</td>
          <td>-0.419  (-2.09)</td>
          <td>-1.78  (-16.4)</td>
          <td>-1.17  (-6.39)</td>
          <td></td>
          <td>-1.74  (-15.4)</td>
          <td>-1.73  (-15.3)</td>
          <td>-0.426  (-2.04)</td>
          <td>-1.74  (-15.5)</td>
          <td>-1.1  (-6.16)</td>
          <td></td>
        </tr>
        <tr>
          <th>B_TIME_TRAIN (t-test)</th>
          <td>-1.57  (-14.4)</td>
          <td></td>
          <td>-1.05  (-8.59)</td>
          <td></td>
          <td>-1.81  (-20.2)</td>
          <td>-0.889  (-7.51)</td>
          <td>-0.469  (-3.75)</td>
          <td>-1.61  (-17.3)</td>
          <td>-0.568  (-4.49)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.444  (-3.6)</td>
          <td></td>
          <td>-0.682  (-5.35)</td>
          <td>-1.06  (-8.66)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.34  (-12.7)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.565  (-4.48)</td>
          <td></td>
          <td>-1.31  (-11.5)</td>
          <td>-0.848  (-6.65)</td>
          <td></td>
          <td>-1.51  (-13.6)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.87  (-21.2)</td>
          <td>-0.689  (-5.39)</td>
          <td>-1.58  (-17)</td>
          <td>-1.29  (-11.2)</td>
          <td></td>
          <td>-1.35  (-12.8)</td>
          <td>-1.18  (-11.4)</td>
          <td>-0.853  (-6.66)</td>
          <td>-1.16  (-11.4)</td>
          <td>-0.912  (-7.57)</td>
          <td></td>
        </tr>
        <tr>
          <th>ASC_CAR_GA (t-test)</th>
          <td></td>
          <td>-0.218  (-1.13)</td>
          <td>-0.211  (-1.07)</td>
          <td>-0.218  (-1.12)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.217  (-1.14)</td>
          <td>-0.0613  (-0.306)</td>
          <td>-0.291  (-1.49)</td>
          <td></td>
          <td>-0.298  (-1.55)</td>
          <td>-0.0449  (-0.227)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.136  (-0.676)</td>
          <td>-0.223  (-1.14)</td>
          <td></td>
          <td></td>
          <td>-0.203  (-1.04)</td>
          <td></td>
          <td>-0.145  (-0.739)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.301  (-1.56)</td>
          <td>-0.0761  (-0.389)</td>
          <td>-0.0233  (-0.115)</td>
          <td></td>
          <td>-0.272  (-1.41)</td>
          <td></td>
          <td>-0.231  (-1.19)</td>
          <td></td>
          <td></td>
          <td>-0.206  (-1.05)</td>
          <td>-0.204  (-1.04)</td>
          <td></td>
          <td>-0.166  (-0.84)</td>
          <td>-0.193  (-0.996)</td>
          <td>-0.264  (-1.35)</td>
          <td></td>
          <td>-0.173  (-0.891)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.287  (-1.48)</td>
        </tr>
        <tr>
          <th>ASC_TRAIN_GA (t-test)</th>
          <td></td>
          <td>1.34  (9.64)</td>
          <td>1.19  (8.41)</td>
          <td>1.35  (9.62)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>2.05  (21.8)</td>
          <td>1.45  (9.92)</td>
          <td>1.78  (19.4)</td>
          <td></td>
          <td>1.99  (22.6)</td>
          <td>1.4  (10.3)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>1.74  (18.5)</td>
          <td>1.32  (9.09)</td>
          <td></td>
          <td></td>
          <td>1.22  (8.85)</td>
          <td></td>
          <td>1.26  (8.67)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>1.97  (22.3)</td>
          <td>1.52  (11.1)</td>
          <td>1.31  (9.12)</td>
          <td></td>
          <td>1.95  (21.5)</td>
          <td></td>
          <td>1.91  (21.5)</td>
          <td></td>
          <td></td>
          <td>1.75  (19.1)</td>
          <td>1.21  (8.85)</td>
          <td></td>
          <td>1.89  (20.7)</td>
          <td>1.87  (19.2)</td>
          <td>1.76  (18.7)</td>
          <td></td>
          <td>1.38  (9.3)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>1.8  (19.6)</td>
        </tr>
        <tr>
          <th>B_COST_CAR (t-test)</th>
          <td></td>
          <td>-0.887  (-7.6)</td>
          <td>-0.756  (-4.97)</td>
          <td>-0.893  (-7.75)</td>
          <td></td>
          <td>-0.786  (-5.27)</td>
          <td>-0.762  (-4.93)</td>
          <td></td>
          <td>-0.767  (-4.98)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.843  (-7.16)</td>
          <td>-0.93  (-8.06)</td>
          <td>-0.78  (-5.11)</td>
          <td></td>
          <td></td>
          <td>-0.764  (-5.06)</td>
          <td></td>
          <td></td>
          <td>-0.891  (-7.69)</td>
          <td></td>
          <td>-0.836  (-5.28)</td>
          <td>-0.924  (-7.89)</td>
          <td>-0.945  (-8.28)</td>
          <td></td>
          <td></td>
          <td>-0.848  (-7.25)</td>
          <td>-0.756  (-4.86)</td>
          <td>-0.898  (-7.77)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.939  (-8.1)</td>
          <td></td>
          <td>-0.885  (-7.53)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.881  (-7.54)</td>
          <td>-0.845  (-5.37)</td>
          <td>-0.849  (-5.39)</td>
          <td></td>
          <td>-0.862  (-5.52)</td>
          <td>-0.771  (-5.12)</td>
          <td></td>
        </tr>
        <tr>
          <th>B_COST_SM (t-test)</th>
          <td></td>
          <td>-1.11  (-14.8)</td>
          <td>-1.14  (-13.8)</td>
          <td>-1.1  (-14.9)</td>
          <td></td>
          <td>-1.12  (-14.2)</td>
          <td>-1.28  (-14.1)</td>
          <td></td>
          <td>-1.31  (-13.9)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.29  (-16)</td>
          <td>-1.09  (-15.5)</td>
          <td>-1.28  (-14.3)</td>
          <td></td>
          <td></td>
          <td>-1.14  (-13.8)</td>
          <td></td>
          <td></td>
          <td>-1.11  (-14.9)</td>
          <td></td>
          <td>-1.15  (-14)</td>
          <td>-1.1  (-15.3)</td>
          <td>-1.08  (-15.6)</td>
          <td></td>
          <td></td>
          <td>-1.3  (-16.1)</td>
          <td>-1.31  (-14)</td>
          <td>-1.23  (-16.4)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.09  (-15.5)</td>
          <td></td>
          <td>-1.11  (-14.8)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.23  (-16.2)</td>
          <td>-1.15  (-14.1)</td>
          <td>-1.14  (-14.3)</td>
          <td></td>
          <td>-1.13  (-14.5)</td>
          <td>-1.13  (-14.1)</td>
          <td></td>
        </tr>
        <tr>
          <th>B_COST_TRAIN (t-test)</th>
          <td></td>
          <td>-1.93  (-10.7)</td>
          <td>-1.96  (-9.57)</td>
          <td>-1.94  (-10.8)</td>
          <td></td>
          <td>-3.08  (-16)</td>
          <td>-3  (-14.3)</td>
          <td></td>
          <td>-1.98  (-8.84)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.78  (-10.1)</td>
          <td>-2.74  (-16.3)</td>
          <td>-3.19  (-15)</td>
          <td></td>
          <td></td>
          <td>-2.02  (-9.74)</td>
          <td></td>
          <td></td>
          <td>-1.88  (-10.5)</td>
          <td></td>
          <td>-2.03  (-9.61)</td>
          <td>-2.73  (-16.3)</td>
          <td>-2.94  (-17.4)</td>
          <td></td>
          <td></td>
          <td>-1.83  (-10.3)</td>
          <td>-1.96  (-8.86)</td>
          <td>-2.91  (-16.6)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-2.93  (-17.4)</td>
          <td></td>
          <td>-1.87  (-10.5)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-2.73  (-15.7)</td>
          <td>-2.09  (-9.76)</td>
          <td>-2.97  (-14.8)</td>
          <td></td>
          <td>-3.18  (-15.9)</td>
          <td>-2.87  (-14.9)</td>
          <td></td>
        </tr>
        <tr>
          <th>B_TIME (t-test)</th>
          <td></td>
          <td>-1.18  (-9.63)</td>
          <td></td>
          <td>-1.15  (-11.5)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.17  (-11.2)</td>
          <td>-1.28  (-12.3)</td>
          <td>-1.16  (-13.6)</td>
          <td>-0.692  (-4.53)</td>
          <td>-1.09  (-11.1)</td>
          <td></td>
          <td>-0.647  (-4.69)</td>
          <td></td>
          <td></td>
          <td>-1.28  (-15.1)</td>
          <td>-1.24  (-14.6)</td>
          <td>-1.14  (-11.4)</td>
          <td>-0.656  (-4.64)</td>
          <td></td>
          <td>-1.12  (-9.29)</td>
          <td>-1.08  (-11.1)</td>
          <td>-1.24  (-11.9)</td>
          <td>-1.18  (-11.3)</td>
          <td>-0.69  (-4.56)</td>
          <td></td>
          <td>-0.686  (-4.58)</td>
          <td></td>
          <td></td>
          <td>-0.621  (-4.46)</td>
          <td></td>
          <td>-1.12  (-9.3)</td>
          <td>-0.622  (-4.42)</td>
          <td>-1.18  (-9.58)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.697  (-4.58)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.14  (-13.5)</td>
        </tr>
        <tr>
          <th>ASC_CAR_one_lugg (t-test)</th>
          <td></td>
          <td></td>
          <td>0.0684  (1.03)</td>
          <td></td>
          <td>0.0672  (1.02)</td>
          <td></td>
          <td>0.0266  (0.4)</td>
          <td></td>
          <td></td>
          <td>0.0744  (1.13)</td>
          <td></td>
          <td></td>
          <td>0.0193  (0.291)</td>
          <td>0.0592  (0.918)</td>
          <td></td>
          <td></td>
          <td>0.0286  (0.427)</td>
          <td></td>
          <td></td>
          <td>0.104  (1.57)</td>
          <td>0.0633  (0.967)</td>
          <td>0.0616  (0.923)</td>
          <td>0.0264  (0.394)</td>
          <td>0.0582  (0.9)</td>
          <td></td>
          <td>0.103  (1.56)</td>
          <td></td>
          <td></td>
          <td>0.0208  (0.309)</td>
          <td></td>
          <td></td>
          <td>0.063  (0.953)</td>
          <td></td>
          <td>0.102  (1.56)</td>
          <td></td>
          <td>0.0324  (0.486)</td>
          <td>0.0626  (0.954)</td>
          <td></td>
          <td></td>
          <td>0.0293  (0.44)</td>
          <td>0.0715  (1.08)</td>
          <td>0.0295  (0.453)</td>
          <td></td>
          <td>0.026  (0.394)</td>
          <td></td>
          <td></td>
          <td>0.0619  (0.941)</td>
          <td>0.0749  (1.14)</td>
        </tr>
        <tr>
          <th>ASC_CAR_several_lugg (t-test)</th>
          <td></td>
          <td></td>
          <td>-0.247  (-1.02)</td>
          <td></td>
          <td>-0.25  (-1.06)</td>
          <td></td>
          <td>-0.445  (-1.8)</td>
          <td></td>
          <td></td>
          <td>-0.252  (-1.06)</td>
          <td></td>
          <td></td>
          <td>-0.403  (-1.66)</td>
          <td>-0.261  (-1.11)</td>
          <td></td>
          <td></td>
          <td>-0.397  (-1.65)</td>
          <td></td>
          <td></td>
          <td>-0.252  (-1.07)</td>
          <td>-0.241  (-1.01)</td>
          <td>-0.432  (-1.83)</td>
          <td>-0.299  (-1.23)</td>
          <td>-0.249  (-1.06)</td>
          <td></td>
          <td>-0.25  (-1.06)</td>
          <td></td>
          <td></td>
          <td>-0.429  (-1.73)</td>
          <td></td>
          <td></td>
          <td>-0.376  (-1.58)</td>
          <td></td>
          <td>-0.218  (-0.928)</td>
          <td></td>
          <td>-0.437  (-1.82)</td>
          <td>-0.23  (-0.966)</td>
          <td></td>
          <td></td>
          <td>-0.285  (-1.2)</td>
          <td>-0.236  (-0.999)</td>
          <td>-0.393  (-1.63)</td>
          <td></td>
          <td>-0.319  (-1.31)</td>
          <td></td>
          <td></td>
          <td>-0.274  (-1.14)</td>
          <td>-0.261  (-1.1)</td>
        </tr>
        <tr>
          <th>ASC_TRAIN_one_lugg (t-test)</th>
          <td></td>
          <td></td>
          <td>0.666  (6.68)</td>
          <td></td>
          <td>1.14  (12)</td>
          <td></td>
          <td>0.784  (7.79)</td>
          <td></td>
          <td></td>
          <td>0.712  (7.23)</td>
          <td></td>
          <td></td>
          <td>0.607  (6.05)</td>
          <td>0.807  (8.08)</td>
          <td></td>
          <td></td>
          <td>0.639  (6.36)</td>
          <td></td>
          <td></td>
          <td>1.15  (12.3)</td>
          <td>0.67  (6.72)</td>
          <td>1.05  (11.1)</td>
          <td>0.674  (6.7)</td>
          <td>0.807  (8.06)</td>
          <td></td>
          <td>1.15  (12.3)</td>
          <td></td>
          <td></td>
          <td>0.632  (6.31)</td>
          <td></td>
          <td></td>
          <td>1.04  (10.7)</td>
          <td></td>
          <td>1.14  (12)</td>
          <td></td>
          <td>0.635  (6.4)</td>
          <td>0.666  (6.67)</td>
          <td></td>
          <td></td>
          <td>0.716  (7.18)</td>
          <td>0.708  (7.16)</td>
          <td>0.772  (7.72)</td>
          <td></td>
          <td>0.797  (7.87)</td>
          <td></td>
          <td></td>
          <td>0.793  (7.87)</td>
          <td>0.717  (7.3)</td>
        </tr>
        <tr>
          <th>ASC_TRAIN_several_lugg (t-test)</th>
          <td></td>
          <td></td>
          <td>0.503  (2.23)</td>
          <td></td>
          <td>0.888  (3.85)</td>
          <td></td>
          <td>0.515  (2.32)</td>
          <td></td>
          <td></td>
          <td>0.593  (2.67)</td>
          <td></td>
          <td></td>
          <td>0.386  (1.78)</td>
          <td>0.565  (2.48)</td>
          <td></td>
          <td></td>
          <td>0.435  (2.01)</td>
          <td></td>
          <td></td>
          <td>0.976  (4.43)</td>
          <td>0.49  (2.19)</td>
          <td>0.799  (3.74)</td>
          <td>0.495  (2.3)</td>
          <td>0.581  (2.54)</td>
          <td></td>
          <td>0.978  (4.47)</td>
          <td></td>
          <td></td>
          <td>0.408  (1.85)</td>
          <td></td>
          <td></td>
          <td>0.744  (3.33)</td>
          <td></td>
          <td>0.913  (3.97)</td>
          <td></td>
          <td>0.431  (2)</td>
          <td>0.504  (2.23)</td>
          <td></td>
          <td></td>
          <td>0.595  (2.81)</td>
          <td>0.591  (2.65)</td>
          <td>0.505  (2.28)</td>
          <td></td>
          <td>0.547  (2.47)</td>
          <td></td>
          <td></td>
          <td>0.573  (2.55)</td>
          <td>0.584  (2.65)</td>
        </tr>
        <tr>
          <th>B_TIME_commuters (t-test)</th>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.218  (-0.963)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.183  (-0.799)</td>
          <td></td>
          <td>-0.217  (-0.984)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.00469  (-0.0222)</td>
          <td>-0.0396  (-0.184)</td>
          <td>-0.23  (-1.01)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.212  (-0.97)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.202  (-0.874)</td>
        </tr>
        <tr>
          <th>B_TIME_CAR_commuters (t-test)</th>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>0.692  (3.56)</td>
          <td></td>
          <td></td>
          <td>0.699  (3.61)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>0.682  (3.48)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>0.71  (3.65)</td>
          <td></td>
          <td>0.687  (3.54)</td>
          <td></td>
          <td></td>
          <td>0.692  (3.54)</td>
          <td>0.678  (3.46)</td>
          <td></td>
          <td>0.686  (3.51)</td>
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th>B_TIME_SM_commuters (t-test)</th>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>1.67  (8.64)</td>
          <td></td>
          <td></td>
          <td>1.66  (8.62)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>1.6  (8.06)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>1.7  (8.78)</td>
          <td></td>
          <td>1.64  (8.53)</td>
          <td></td>
          <td></td>
          <td>1.62  (8.15)</td>
          <td>1.61  (8.07)</td>
          <td></td>
          <td>1.61  (8.14)</td>
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th>B_TIME_TRAIN_commuters (t-test)</th>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>0.365  (2.79)</td>
          <td></td>
          <td></td>
          <td>0.178  (1.3)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>0.116  (0.848)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>0.412  (3.14)</td>
          <td></td>
          <td>0.155  (1.14)</td>
          <td></td>
          <td></td>
          <td>0.13  (0.956)</td>
          <td>0.175  (1.33)</td>
          <td></td>
          <td>0.184  (1.4)</td>
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th>B_TIME_CAR_1st_class (t-test)</th>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.2  (-8.01)</td>
          <td></td>
          <td>-1.2  (-7.73)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.19  (-7.86)</td>
          <td></td>
          <td>-1.14  (-7.27)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.21  (-7.89)</td>
          <td></td>
          <td></td>
          <td>-1.13  (-7.2)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.14  (-7.12)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.13  (-6.98)</td>
          <td></td>
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th>B_TIME_SM_1st_class (t-test)</th>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.42  (-6.36)</td>
          <td></td>
          <td>-1.41  (-6.11)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.42  (-6.24)</td>
          <td></td>
          <td>-1.44  (-6.28)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.42  (-6.25)</td>
          <td></td>
          <td></td>
          <td>-1.41  (-6.13)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.43  (-6.12)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.4  (-5.85)</td>
          <td></td>
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th>B_TIME_TRAIN_1st_class (t-test)</th>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.843  (-6.52)</td>
          <td></td>
          <td>-1.02  (-7.66)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.859  (-6.6)</td>
          <td></td>
          <td>-1.12  (-8.57)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.998  (-7.53)</td>
          <td></td>
          <td></td>
          <td>-1.17  (-8.95)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.16  (-8.72)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.25  (-9.3)</td>
          <td></td>
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th>B_TIME_1st_class (t-test)</th>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.91  (-8.39)</td>
          <td></td>
          <td></td>
          <td>-1.02  (-9.87)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.943  (-8.88)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.925  (-8.62)</td>
          <td></td>
          <td>-0.795  (-7.45)</td>
          <td></td>
          <td></td>
          <td>-0.914  (-8.6)</td>
          <td></td>
          <td></td>
          <td>-0.891  (-8.26)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.784  (-7.25)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 179-180

Glossary

.. GENERATED FROM PYTHON SOURCE LINES 180-183

.. code-block:: default

    for short_name, spec in specs.items():
        print(f'{short_name}\t{spec}')





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Model_000000    ASC:no_seg;B_COST_gen_altspec:generic;B_TIME:no_seg;B_TIME_gen_altspec:altspec
    Model_000001    ASC:GA;B_COST_gen_altspec:altspec;B_TIME:no_seg;B_TIME_gen_altspec:generic
    Model_000002    ASC:GA-LUGGAGE;B_COST_gen_altspec:altspec;B_TIME:no_seg;B_TIME_gen_altspec:altspec
    Model_000003    ASC:GA;B_COST_gen_altspec:altspec;B_TIME:COMMUTERS;B_TIME_gen_altspec:generic
    Model_000004    ASC:LUGGAGE;B_COST_gen_altspec:generic;B_TIME:COMMUTERS;B_TIME_gen_altspec:altspec
    Model_000005    ASC:no_seg;B_COST_gen_altspec:altspec;B_TIME:no_seg;B_TIME_gen_altspec:altspec
    Model_000006    ASC:LUGGAGE;B_COST_gen_altspec:altspec;B_TIME:FIRST;B_TIME_gen_altspec:altspec
    Model_000007    ASC:GA;B_COST_gen_altspec:generic;B_TIME:COMMUTERS;B_TIME_gen_altspec:altspec
    Model_000008    ASC:GA;B_COST_gen_altspec:altspec;B_TIME:FIRST;B_TIME_gen_altspec:altspec
    Model_000009    ASC:GA-LUGGAGE;B_COST_gen_altspec:generic;B_TIME:no_seg;B_TIME_gen_altspec:generic
    Model_000010    ASC:no_seg;B_COST_gen_altspec:generic;B_TIME:no_seg;B_TIME_gen_altspec:generic
    Model_000011    ASC:GA;B_COST_gen_altspec:generic;B_TIME:COMMUTERS;B_TIME_gen_altspec:generic
    Model_000012    ASC:GA-LUGGAGE;B_COST_gen_altspec:altspec;B_TIME:FIRST;B_TIME_gen_altspec:generic
    Model_000013    ASC:LUGGAGE;B_COST_gen_altspec:altspec;B_TIME:COMMUTERS;B_TIME_gen_altspec:generic
    Model_000014    ASC:no_seg;B_COST_gen_altspec:altspec;B_TIME:FIRST;B_TIME_gen_altspec:altspec
    Model_000015    ASC:no_seg;B_COST_gen_altspec:generic;B_TIME:FIRST;B_TIME_gen_altspec:generic
    Model_000016    ASC:GA-LUGGAGE;B_COST_gen_altspec:generic;B_TIME:FIRST;B_TIME_gen_altspec:altspec
    Model_000017    ASC:GA;B_COST_gen_altspec:altspec;B_TIME:no_seg;B_TIME_gen_altspec:altspec
    Model_000018    ASC:no_seg;B_COST_gen_altspec:generic;B_TIME:COMMUTERS;B_TIME_gen_altspec:generic
    Model_000019    ASC:LUGGAGE;B_COST_gen_altspec:generic;B_TIME:COMMUTERS;B_TIME_gen_altspec:generic
    Model_000020    ASC:GA-LUGGAGE;B_COST_gen_altspec:altspec;B_TIME:COMMUTERS;B_TIME_gen_altspec:generic
    Model_000021    ASC:LUGGAGE;B_COST_gen_altspec:generic;B_TIME:FIRST;B_TIME_gen_altspec:generic
    Model_000022    ASC:GA-LUGGAGE;B_COST_gen_altspec:altspec;B_TIME:COMMUTERS;B_TIME_gen_altspec:altspec
    Model_000023    ASC:LUGGAGE;B_COST_gen_altspec:altspec;B_TIME:no_seg;B_TIME_gen_altspec:generic
    Model_000024    ASC:no_seg;B_COST_gen_altspec:altspec;B_TIME:COMMUTERS;B_TIME_gen_altspec:generic
    Model_000025    ASC:LUGGAGE;B_COST_gen_altspec:generic;B_TIME:no_seg;B_TIME_gen_altspec:generic
    Model_000026    ASC:GA;B_COST_gen_altspec:generic;B_TIME:no_seg;B_TIME_gen_altspec:generic
    Model_000027    ASC:GA;B_COST_gen_altspec:altspec;B_TIME:FIRST;B_TIME_gen_altspec:generic
    Model_000028    ASC:GA-LUGGAGE;B_COST_gen_altspec:altspec;B_TIME:FIRST;B_TIME_gen_altspec:altspec
    Model_000029    ASC:no_seg;B_COST_gen_altspec:altspec;B_TIME:FIRST;B_TIME_gen_altspec:generic
    Model_000030    ASC:GA;B_COST_gen_altspec:generic;B_TIME:no_seg;B_TIME_gen_altspec:altspec
    Model_000031    ASC:LUGGAGE;B_COST_gen_altspec:generic;B_TIME:FIRST;B_TIME_gen_altspec:altspec
    Model_000032    ASC:GA;B_COST_gen_altspec:generic;B_TIME:FIRST;B_TIME_gen_altspec:generic
    Model_000033    ASC:LUGGAGE;B_COST_gen_altspec:generic;B_TIME:no_seg;B_TIME_gen_altspec:altspec
    Model_000034    ASC:no_seg;B_COST_gen_altspec:altspec;B_TIME:no_seg;B_TIME_gen_altspec:generic
    Model_000035    ASC:GA-LUGGAGE;B_COST_gen_altspec:generic;B_TIME:FIRST;B_TIME_gen_altspec:generic
    Model_000036    ASC:GA-LUGGAGE;B_COST_gen_altspec:altspec;B_TIME:no_seg;B_TIME_gen_altspec:generic
    Model_000037    ASC:no_seg;B_COST_gen_altspec:generic;B_TIME:COMMUTERS;B_TIME_gen_altspec:altspec
    Model_000038    ASC:GA;B_COST_gen_altspec:generic;B_TIME:FIRST;B_TIME_gen_altspec:altspec
    Model_000039    ASC:GA-LUGGAGE;B_COST_gen_altspec:generic;B_TIME:COMMUTERS;B_TIME_gen_altspec:altspec
    Model_000040    ASC:GA-LUGGAGE;B_COST_gen_altspec:generic;B_TIME:no_seg;B_TIME_gen_altspec:altspec
    Model_000041    ASC:LUGGAGE;B_COST_gen_altspec:altspec;B_TIME:FIRST;B_TIME_gen_altspec:generic
    Model_000042    ASC:GA;B_COST_gen_altspec:altspec;B_TIME:COMMUTERS;B_TIME_gen_altspec:altspec
    Model_000043    ASC:LUGGAGE;B_COST_gen_altspec:altspec;B_TIME:COMMUTERS;B_TIME_gen_altspec:altspec
    Model_000044    ASC:no_seg;B_COST_gen_altspec:generic;B_TIME:FIRST;B_TIME_gen_altspec:altspec
    Model_000045    ASC:no_seg;B_COST_gen_altspec:altspec;B_TIME:COMMUTERS;B_TIME_gen_altspec:altspec
    Model_000046    ASC:LUGGAGE;B_COST_gen_altspec:altspec;B_TIME:no_seg;B_TIME_gen_altspec:altspec
    Model_000047    ASC:GA-LUGGAGE;B_COST_gen_altspec:generic;B_TIME:COMMUTERS;B_TIME_gen_altspec:generic




.. GENERATED FROM PYTHON SOURCE LINES 184-185

Estimation results of the Pareto optimal models.

.. GENERATED FROM PYTHON SOURCE LINES 185-190

.. code-block:: default

    pareto_results = pareto_optimal(dict_of_results)
    compiled_pareto_results, pareto_specs = compile_estimation_results(
        pareto_results, use_short_names=True
    )








.. GENERATED FROM PYTHON SOURCE LINES 191-193

.. code-block:: default

    compiled_pareto_results






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Model_000000</th>
          <th>Model_000001</th>
          <th>Model_000002</th>
          <th>Model_000003</th>
          <th>Model_000004</th>
          <th>Model_000005</th>
          <th>Model_000006</th>
          <th>Model_000007</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Number of estimated parameters</th>
          <td>17</td>
          <td>7</td>
          <td>6</td>
          <td>5</td>
          <td>13</td>
          <td>11</td>
          <td>4</td>
          <td>9</td>
        </tr>
        <tr>
          <th>Sample size</th>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
          <td>6768</td>
        </tr>
        <tr>
          <th>Final log likelihood</th>
          <td>-4865.971435</td>
          <td>-4976.118642</td>
          <td>-5050.677696</td>
          <td>-5234.708233</td>
          <td>-4890.815071</td>
          <td>-4928.268572</td>
          <td>-5331.252007</td>
          <td>-4945.30006</td>
        </tr>
        <tr>
          <th>Akaike Information Criterion</th>
          <td>9765.94287</td>
          <td>9966.237283</td>
          <td>10113.355392</td>
          <td>10479.416466</td>
          <td>9807.630143</td>
          <td>9878.537145</td>
          <td>10670.504014</td>
          <td>9908.60012</td>
        </tr>
        <tr>
          <th>Bayesian Information Criterion</th>
          <td>9881.882206</td>
          <td>10013.97701</td>
          <td>10154.275157</td>
          <td>10513.51627</td>
          <td>9896.289635</td>
          <td>9953.556715</td>
          <td>10697.783857</td>
          <td>9969.979768</td>
        </tr>
        <tr>
          <th>ASC_CAR (t-test)</th>
          <td>-0.446  (-3.68)</td>
          <td>-0.281  (-4.53)</td>
          <td>-0.249  (-3.97)</td>
          <td>-0.187  (-3.23)</td>
          <td>-0.434  (-3.72)</td>
          <td>-0.383  (-2.95)</td>
          <td>-0.155  (-2.66)</td>
          <td>-0.662  (-7.79)</td>
        </tr>
        <tr>
          <th>ASC_CAR_GA (t-test)</th>
          <td>-0.145  (-0.739)</td>
          <td>-0.231  (-1.19)</td>
          <td>-0.301  (-1.56)</td>
          <td></td>
          <td>-0.173  (-0.891)</td>
          <td>-0.217  (-1.14)</td>
          <td></td>
          <td>-0.0761  (-0.389)</td>
        </tr>
        <tr>
          <th>ASC_CAR_one_lugg (t-test)</th>
          <td>0.0264  (0.394)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th>ASC_CAR_several_lugg (t-test)</th>
          <td>-0.299  (-1.23)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th>ASC_TRAIN (t-test)</th>
          <td>-1.07  (-6.72)</td>
          <td>-1.37  (-14.7)</td>
          <td>-1.28  (-14)</td>
          <td>-0.814  (-9.45)</td>
          <td>-0.593  (-4.28)</td>
          <td>-0.965  (-7.29)</td>
          <td>-0.701  (-8.49)</td>
          <td>-0.938  (-6.76)</td>
        </tr>
        <tr>
          <th>ASC_TRAIN_GA (t-test)</th>
          <td>1.26  (8.67)</td>
          <td>1.91  (21.5)</td>
          <td>1.97  (22.3)</td>
          <td></td>
          <td>1.38  (9.3)</td>
          <td>2.05  (21.8)</td>
          <td></td>
          <td>1.52  (11.1)</td>
        </tr>
        <tr>
          <th>ASC_TRAIN_one_lugg (t-test)</th>
          <td>0.674  (6.7)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th>ASC_TRAIN_several_lugg (t-test)</th>
          <td>0.495  (2.3)</td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th>B_COST_CAR (t-test)</th>
          <td>-0.836  (-5.28)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.845  (-5.37)</td>
          <td></td>
          <td></td>
          <td>-0.848  (-7.25)</td>
        </tr>
        <tr>
          <th>B_COST_SM (t-test)</th>
          <td>-1.15  (-14)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.15  (-14.1)</td>
          <td></td>
          <td></td>
          <td>-1.3  (-16.1)</td>
        </tr>
        <tr>
          <th>B_COST_TRAIN (t-test)</th>
          <td>-2.03  (-9.61)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-2.09  (-9.76)</td>
          <td></td>
          <td></td>
          <td>-1.83  (-10.3)</td>
        </tr>
        <tr>
          <th>B_TIME_CAR (t-test)</th>
          <td>-1.55  (-11.3)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.55  (-11.4)</td>
          <td>-1.4  (-16.8)</td>
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th>B_TIME_CAR_commuters (t-test)</th>
          <td>0.682  (3.48)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>0.692  (3.54)</td>
          <td>0.699  (3.61)</td>
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th>B_TIME_SM (t-test)</th>
          <td>-1.73  (-15.3)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.74  (-15.4)</td>
          <td>-1.8  (-16.6)</td>
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th>B_TIME_SM_commuters (t-test)</th>
          <td>1.6  (8.06)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>1.62  (8.15)</td>
          <td>1.66  (8.62)</td>
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th>B_TIME_TRAIN (t-test)</th>
          <td>-1.34  (-12.7)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-1.35  (-12.8)</td>
          <td>-1.61  (-17.3)</td>
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th>B_TIME_TRAIN_commuters (t-test)</th>
          <td>0.116  (0.848)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>0.13  (0.956)</td>
          <td>0.178  (1.3)</td>
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th>B_COST (t-test)</th>
          <td></td>
          <td>-1.26  (-15.3)</td>
          <td>-1.1  (-14.8)</td>
          <td>-1.23  (-16.6)</td>
          <td></td>
          <td>-1.13  (-15)</td>
          <td>-1.08  (-15.9)</td>
          <td></td>
        </tr>
        <tr>
          <th>B_TIME (t-test)</th>
          <td></td>
          <td>-0.621  (-4.46)</td>
          <td>-1.18  (-11.3)</td>
          <td>-0.647  (-4.69)</td>
          <td></td>
          <td></td>
          <td>-1.28  (-12.3)</td>
          <td>-0.69  (-4.56)</td>
        </tr>
        <tr>
          <th>B_TIME_1st_class (t-test)</th>
          <td></td>
          <td>-0.914  (-8.6)</td>
          <td></td>
          <td>-1.02  (-9.87)</td>
          <td></td>
          <td></td>
          <td></td>
          <td>-0.925  (-8.62)</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 194-195

Glossary.

.. GENERATED FROM PYTHON SOURCE LINES 195-197

.. code-block:: default

    for short_name, spec in pareto_specs.items():
        print(f'{short_name}\t{spec}')




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Model_000000    ASC:GA-LUGGAGE;B_COST_gen_altspec:altspec;B_TIME:COMMUTERS;B_TIME_gen_altspec:altspec
    Model_000001    ASC:GA;B_COST_gen_altspec:generic;B_TIME:FIRST;B_TIME_gen_altspec:generic
    Model_000002    ASC:GA;B_COST_gen_altspec:generic;B_TIME:no_seg;B_TIME_gen_altspec:generic
    Model_000003    ASC:no_seg;B_COST_gen_altspec:generic;B_TIME:FIRST;B_TIME_gen_altspec:generic
    Model_000004    ASC:GA;B_COST_gen_altspec:altspec;B_TIME:COMMUTERS;B_TIME_gen_altspec:altspec
    Model_000005    ASC:GA;B_COST_gen_altspec:generic;B_TIME:COMMUTERS;B_TIME_gen_altspec:altspec
    Model_000006    ASC:no_seg;B_COST_gen_altspec:generic;B_TIME:no_seg;B_TIME_gen_altspec:generic
    Model_000007    ASC:GA;B_COST_gen_altspec:altspec;B_TIME:FIRST;B_TIME_gen_altspec:generic





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 12.526 seconds)


.. _sphx_glr_download_auto_examples_assisted_plot_b05alt_spec_segmentation.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_b05alt_spec_segmentation.py <plot_b05alt_spec_segmentation.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_b05alt_spec_segmentation.ipynb <plot_b05alt_spec_segmentation.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_