202: Exampville Mode Choice Logsums¶

Welcome to Exampville, the best simulated town in this here part of the internet!

Exampville is a demonstration provided with Larch that walks through some of the data and tools that a transportation planner might use when building a travel model.

import os
import numpy as np
import xarray as xr
from addicty import Dict
from larch import P, X
import larch.numba as lx

In this example notebook, we will walk through the creation of logsums from an existing tour mode choice model. First, let’s load the data files from our example.

hh, pp, tour, skims = lx.example(200, ['hh', 'pp', 'tour', 'skims'])

We’ll also load the saved model from the mode choice estimation.

exampville_mode_choice_file = lx.example(201, output_file='exampville_mode_choice.html', estimate=True)
m = lx.read_metadata(exampville_mode_choice_file)

We’ll replicate the pre-processing used in the mode choice estimation, to merge the household and person characteristics into the tours data, add the index values for the home TAZ’s, filter to include only work tours, and merge with the level of service skims. (If this pre-processing was computationally expensive, it would probably have been better to save the results to disk and reload them as needed, but for this model these commands will run almost instantaneously.)

Mode = Dict(
    DA = 1,
    SR = 2,
    Walk = 3,
    Bike = 4,
    Transit = 5,
).freeze()

tour_dataset = lx.Dataset.construct.from_idco(tour.set_index('TOURID'), alts=Mode)
od_skims = lx.Dataset.construct.from_omx(skims)

dt = lx.DataTree(
    tour=tour_dataset.dc.query_cases("TOURPURP == 1"),
    hh=hh.set_index('HHID'),
    person=pp.set_index('PERSONID'),
    od=od_skims,
    do=od_skims,
    relationships=(
        "tours.HHID @ hh.HHID",
        "tours.PERSONID @ person.PERSONID",
        "hh.HOMETAZ @ od.otaz",
        "tours.DTAZ @ od.dtaz",
        "hh.HOMETAZ @ do.dtaz",
        "tours.DTAZ @ do.otaz",
    ),
)

Then we bundle the raw data into the larch.DataFrames structure, as we did for estimation, and attach this structure to the model as its dataservice.

We’ll also initialize a DataArray to hold the computed logsums. This data will have one row for each case in our source data, and a column for each possible destination zone.

logsums = lx.DataArray.zeros(
    dt.caseids(),
    skims.TAZ_ID,
    name='logsums',
)

The logsums from a Model can be computed using the Model.logsums method. However, if we want the logsums for each possible destination, we’ll need to replace the part of our data that depends on the destination zone, writing in the appropriate values for each. We can simply iterate over the zones, dropping in the zone as the destination and computing the logsums.

for i,dtaz in enumerate(logsums.TAZ_ID):
    m.datatree = dt.replace_datasets(
        tour=dt.root_dataset.assign(
            DTAZ=xr.full_like(dt['DTAZ'], dtaz)
        ),
    )
    logsums.loc[dict(TAZ_ID=dtaz)] = m.logsums()

Then we can persist the logsums dataframe to disk, for use in the next example, where we will estimate a destination choice model.

logsums.to_zarr('logsums.zarr.zip')

To recover the DataArray later, we can read it using the DataArray.from_zarr method.

lx.DataArray.from_zarr('logsums.zarr.zip')

<xarray.DataArray 'logsums' (TOURID: 7564, TAZ_ID: 40)>
dask.array<open_dataset-284278a5bbed5f5b042effd153aca686logsums, shape=(7564, 40), dtype=float64, chunksize=(1891, 20), chunktype=numpy.ndarray>
Coordinates:
  * TAZ_ID   (TAZ_ID) int64 1 2 3 4 5 6 7 8 9 10 ... 32 33 34 35 36 37 38 39 40
  * TOURID   (TOURID) int64 0 1 3 7 10 13 ... 20728 20729 20731 20734 20736

v5.7.0

202: Exampville Mode Choice Logsums

202: Exampville Mode Choice Logsums¶