MTC Work Mode Choice Data¶

import os, gzip
import numpy as np, pandas as pd, xarray as xr
import larch.numba as lx

/home/runner/work/larch/larch/larch/larch/numba/model.py:23: UserWarning: 

### larch.numba is experimental, and not feature-complete ###
 the first time you import on a new system, this package will
 compile optimized binaries for your machine, which may take 
 a little while, please be patient 

  warnings.warn( ### EXPERIMENTAL ### )

The MTC sample dataset is the same data used in the Self Instructing Manual [Koppelman and Bhat, 2006] for discrete choice modeling:

The San Francisco Bay Area work mode choice data set comprises 5029 home-to-work commute trips in the San Francisco Bay Area. The data is drawn from the San Francisco Bay Area Household Travel Survey conducted by the Metropolitan Transportation Commission (MTC) in the spring and fall of 1990. This survey included a one day travel diary for each household member older than five years and detailed individual and household socio-demographic information.

In this example we will import the MTC example dataset, starting from a csv text file in idca format. Suppose that data file is gzipped, named “MTCwork.csv.gz” and is located in the current directory (use os.getcwd to see what is the current directory). For this example, we’ll use the example_file method to find the file that comes with Larch.

We can take a peek at the contents of the file, examining the first 10 lines:

with gzip.open(lx.example_file("MTCwork.csv.gz"), 'rt') as previewfile:
    print(*(next(previewfile) for x in range(10)))

casenum,altnum,chose,ivtt,ovtt,tottime,totcost,hhid,perid,numalts,dist,wkzone,hmzone,rspopden,rsempden,wkpopden,wkempden,vehavdum,femdum,age,drlicdum,noncadum,numveh,hhsize,hhinc,famtype,hhowndum,numemphh,numadlt,nmlt5,nm5to11,nm12to16,wkccbd,wknccbd,corredis,vehbywrk,vocc,wgt
 1,1,1,13.38,2,15.38,70.63,2,1,2,7.69,664,726,15.52,9.96,37.26,3.48,1,0,35,1,0,4,1,42.5,7,0,1,1,0,0,0,0,0,0,4,1,1
 1,2,0,18.38,2,20.38,35.32,2,1,2,7.69,664,726,15.52,9.96,37.26,3.48,1,0,35,1,0,4,1,42.5,7,0,1,1,0,0,0,0,0,0,4,1,1
 1,3,0,20.38,2,22.38,20.18,2,1,2,7.69,664,726,15.52,9.96,37.26,3.48,1,0,35,1,0,4,1,42.5,7,0,1,1,0,0,0,0,0,0,4,1,1
 1,4,0,25.9,15.2,41.1,115.64,2,1,2,7.69,664,726,15.52,9.96,37.26,3.48,1,0,35,1,0,4,1,42.5,7,0,1,1,0,0,0,0,0,0,4,1,1
 1,5,0,40.5,2,42.5,0,2,1,2,7.69,664,726,15.52,9.96,37.26,3.48,1,0,35,1,0,4,1,42.5,7,0,1,1,0,0,0,0,0,0,4,1,1
 2,1,0,29.92,10,39.92,390.81,3,1,2,11.62,738,9,35.81,53.33,32.91,764.19,1,0,40,1,0,1,1,17.5,7,0,1,1,0,0,0,1,0,1,1,0,1
 2,2,0,34.92,10,44.92,195.4,3,1,2,11.62,738,9,35.81,53.33,32.91,764.19,1,0,40,1,0,1,1,17.5,7,0,1,1,0,0,0,1,0,1,1,0,1
 2,3,0,21.92,10,31.92,97.97,3,1,2,11.62,738,9,35.81,53.33,32.91,764.19,1,0,40,1,0,1,1,17.5,7,0,1,1,0,0,0,1,0,1,1,0,1
 2,4,1,22.96,14.2,37.16,185,3,1,2,11.62,738,9,35.81,53.33,32.91,764.19,1,0,40,1,0,1,1,17.5,7,0,1,1,0,0,0,1,0,1,1,0,1

The first line of the file contains column headers. After that, each line represents an alternative available to a decision maker. In our sample data, we see the first 5 lines of data share a caseid of 1, indicating that they are 5 different alternatives available to the first decision maker. The identity of the alternatives is given by the number in the column altid. The observed choice of the decision maker is indicated in the column chose with a 1 in the appropriate row.

We can load this data easily using pandas. We’ll also set the index of the resulting DataFrame to be the case and alt identifiers.

df = pd.read_csv(lx.example_file("MTCwork.csv.gz"), index_col=['casenum','altnum'])

df.head(15)

		chose	ivtt	ovtt	tottime	totcost	hhid	perid	numalts	dist	wkzone	...	numadlt	nmlt5	nm5to11	nm12to16	wkccbd	wknccbd	corredis	vehbywrk	vocc	wgt
casenum	altnum
1	1	1	13.38	2.0	15.38	70.63	2	1	2	7.69	664	...	1	0	0	0	0	0	0	4.00	1	1
	2	0	18.38	2.0	20.38	35.32	2	1	2	7.69	664	...	1	0	0	0	0	0	0	4.00	1	1
	3	0	20.38	2.0	22.38	20.18	2	1	2	7.69	664	...	1	0	0	0	0	0	0	4.00	1	1
	4	0	25.90	15.2	41.10	115.64	2	1	2	7.69	664	...	1	0	0	0	0	0	0	4.00	1	1
	5	0	40.50	2.0	42.50	0.00	2	1	2	7.69	664	...	1	0	0	0	0	0	0	4.00	1	1
2	1	0	29.92	10.0	39.92	390.81	3	1	2	11.62	738	...	1	0	0	0	1	0	1	1.00	0	1
	2	0	34.92	10.0	44.92	195.40	3	1	2	11.62	738	...	1	0	0	0	1	0	1	1.00	0	1
	3	0	21.92	10.0	31.92	97.97	3	1	2	11.62	738	...	1	0	0	0	1	0	1	1.00	0	1
	4	1	22.96	14.2	37.16	185.00	3	1	2	11.62	738	...	1	0	0	0	1	0	1	1.00	0	1
	5	0	58.95	10.0	68.95	0.00	3	1	2	11.62	738	...	1	0	0	0	1	0	1	1.00	0	1
3	1	1	8.60	6.0	14.60	37.76	5	1	2	4.10	696	...	3	2	0	0	0	1	0	0.33	1	1
	2	0	13.60	6.0	19.60	18.88	5	1	2	4.10	696	...	3	2	0	0	0	1	0	0.33	1	1
	3	0	15.60	6.0	21.60	10.79	5	1	2	4.10	696	...	3	2	0	0	0	1	0	0.33	1	1
	4	0	16.87	21.4	38.27	105.00	5	1	2	4.10	696	...	3	2	0	0	0	1	0	0.33	1	1
4	1	0	30.60	8.5	39.10	417.32	6	1	2	14.58	665	...	2	1	0	0	1	0	0	1.00	0	1

15 rows × 36 columns

To prepare this data for use with the latest version of Larch, we’ll want to convert this DataFrame into a larch.Dataset. For idca format like this, we can use the from_idca constructor to do so easily.

Larch can automatically analyze the data to find variables that do not vary across alternatives within cases, and transform those into idco format variables. If you would prefer that Larch not do this you can set the crack argument to False. This is particularly important for larger datasets (the data sample included is only a tiny extract of the data that might be available for this kind of model), as breaking the data into separate idca and idco parts is a relatively expensive operation, and it is not actually required for most model structures.

The set of all possible alternative codes is deduced automatically from all the values in the altnum column. However, the alterative codes are not very descriptive when they are set automatically, as the csv data file does not have enough information to tell what each alternative code number means. We can use the set_altnames method to attach more descriptive names.

v5.7.0

MTC Work Mode Choice Data

MTC Work Mode Choice Data¶