300L: Itinerary Choice Data
300L: Itinerary Choice Data¶
import larch, pandas, os, gzip
larch.__version__
'5.7.0'
The example itinerary choice described here is based on data derived from a ticketing database provided by the Airlines Reporting Corporation. The data represent ten origin destination pairs for travel in U.S. continental markets in May of 2013. Itinerary characteristics have been masked, e.g., carriers are labeled generically as “carrier X” and departure times have been aggregated into categories. A fare is provided but is not completely accurate (a random error has been added to each fare). These modifications were made to satisfy nondisclosure agreements, so that the data can be published freely for teaching and demostration purposes. It is generally representative of real itinerary choice data used in practice, and the results obtained from this data are intuitive from a behavioral perspective, but it is not quite accurate and should not be used for behavioral studies.
from larch.data_warehouse import example_file
with gzip.open(example_file("arc"), 'rt') as previewfile:
print(*(next(previewfile) for x in range(70)))
id_case,id_alt,choice,traveler,origin,destination,nb_cnxs,elapsed_time,fare_hy,fare_ly,equipment,carrier,timeperiod
1,1,0,1,444,222,1,300,470.55658,0,1,3,7
1,2,0,1,444,222,1,345,475.92258,0,2,3,5
1,3,0,1,444,222,1,335,443.48166,0,1,3,2
1,4,0,1,444,222,1,435,433.56735,0,2,3,2
1,5,0,1,444,222,1,710,449.83664,0,2,3,2
1,6,0,1,444,222,1,380,470.45175,0,1,3,5
1,7,0,1,444,222,1,345,440.70526,0,2,3,6
1,8,0,1,444,222,1,320,474.57831,0,2,3,4
1,9,0,1,444,222,1,335,474.97363,0,2,3,3
1,10,0,1,444,222,1,335,481.98392,0,1,3,3
1,11,0,1,444,222,1,320,440.41138,0,1,3,7
1,12,0,1,444,222,1,360,455.11444,0,2,3,1
1,13,0,1,444,222,1,380,470.67239,0,1,3,4
1,14,14,1,444,222,0,215,317.4277,0,2,3,1
1,15,19,1,444,222,0,215,283.96292,0,2,3,1
1,16,19,1,444,222,0,215,285.04138,0,2,3,2
1,17,19,1,444,222,0,215,283.59644,0,2,3,2
1,18,1,1,444,222,0,220,276.40555,0,2,3,3
1,19,8,1,444,222,0,220,285.51282,0,2,3,3
1,20,10,1,444,222,0,215,313.89828,0,2,3,3
1,21,7,1,444,222,0,220,280.06757,0,2,3,4
1,22,1,1,444,222,0,220,294.53979,0,2,3,4
1,23,5,1,444,222,0,220,285.1618,0,2,3,5
1,24,1,1,444,222,0,220,287.32828,0,2,3,5
1,25,22,1,444,222,0,225,274.38611,0,2,3,6
1,26,16,1,444,222,0,225,286.12646,0,2,3,7
1,27,11,1,444,222,0,225,300.91037,0,2,3,6
1,28,5,1,444,222,0,220,301.78799,0,2,3,7
1,29,5,1,444,222,0,220,311.88431,0,2,3,7
1,30,3,1,444,222,0,215,285.65631,0,2,3,8
1,31,4,1,444,222,0,215,283.51544,0,2,3,8
1,32,0,1,444,222,1,512,467.40497,0,1,1,3
1,33,0,1,444,222,1,411,474.33835,0,1,1,2
1,34,0,1,444,222,1,508,433.01563,0,1,1,5
1,35,0,1,444,222,1,387,457.29861,0,1,1,3
1,36,0,1,444,222,1,389,461.02136,0,1,1,4
1,37,0,1,444,222,1,392,465.53665,0,1,1,5
1,38,0,1,444,222,1,389,472.26083,0,1,1,4
1,39,0,1,444,222,1,379,438.02396,0,1,1,4
1,40,0,1,444,222,1,343,474.71518,0,1,1,1
1,41,0,1,444,222,1,389,437.87329,0,1,1,4
1,42,0,1,444,222,1,405,448.78522,0,1,1,6
1,43,0,1,444,222,1,392,473.38318,0,1,1,2
1,44,0,1,444,222,1,434,444.40308,0,1,1,1
1,45,3,1,444,222,0,214,248.23685,0,2,2,6
1,46,0,1,444,222,0,223,255.85193,0,2,2,3
1,47,3,1,444,222,0,214,253.83798,0,2,2,6
1,48,0,1,444,222,0,223,239.98866,0,2,2,3
1,49,0,1,444,222,0,219,282.74249,0,1,2,7
1,50,3,1,444,222,0,221,265.04773,0,1,2,6
1,51,1,1,444,222,0,219,281.88403,0,1,2,7
1,52,0,1,444,222,0,214,252.09259,0,1,2,4
1,53,3,1,444,222,0,214,264.69473,0,1,2,4
1,54,0,1,444,222,0,215,255.55827,0,1,2,7
1,55,0,1,444,222,1,396,423.67627,0,1,2,8
1,56,0,1,444,222,0,215,278.64148,0,1,2,8
1,57,3,1,444,222,0,215,234.55371,0,1,2,1
1,58,0,1,444,222,1,578,268.89609,0,2,4,1
1,59,0,1,444,222,1,477,285.80167,0,2,4,1
1,60,0,1,444,222,1,599,259.35504,0,2,4,4
1,61,1,1,444,222,1,758,262.39859,0,2,4,4
1,62,0,1,444,222,1,476,267.64124,0,2,4,5
1,63,0,1,444,222,1,477,273.67731,0,2,4,7
1,64,0,1,444,222,1,459,283.35803,0,2,4,6
1,65,0,1,444,222,1,586,291.98431,0,2,4,3
1,66,0,1,444,222,1,618,298.26163,0,2,4,6
1,67,0,1,444,222,1,502,259.70834,0,2,4,2
2,1,3,2,444,222,1,300,0,422.4599,1,3,7
2,2,1,2,444,222,1,345,0,415.59332,2,3,5
The first line of the file contains column headers. After that, each line represents
an alternative available to one or more decision makers. In our sample data, we see the first 67
lines of data share a id_case of 1, indicating that they are 67 different itineraries
available to the first decision maker type. An identidier of the alternatives is given by the
number in the column id_alt, although this number is simply a sequential counter within each case
in the raw data, and conveys no other information about the itinerary or its attributes.
The observed choices of the decision maker[s] are indicated in the column choice.
That column counts the number of travelers who face this choice set and chose the itinerary
described by this row in the file.
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.
itin = pandas.read_csv(example_file("arc"), index_col=['id_case','id_alt'])
itin.info()
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 6023 entries, (1, 1) to (105, 51)
Data columns (total 11 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 choice 6023 non-null int64
1 traveler 6023 non-null int64
2 origin 6023 non-null int64
3 destination 6023 non-null int64
4 nb_cnxs 6023 non-null int64
5 elapsed_time 6023 non-null int64
6 fare_hy 6023 non-null float64
7 fare_ly 6023 non-null float64
8 equipment 6023 non-null int64
9 carrier 6023 non-null int64
10 timeperiod 6023 non-null int64
dtypes: float64(2), int64(9)
memory usage: 545.3 KB
itin.head()
| choice | traveler | origin | destination | nb_cnxs | elapsed_time | fare_hy | fare_ly | equipment | carrier | timeperiod | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id_case | id_alt | |||||||||||
| 1 | 1 | 0 | 1 | 444 | 222 | 1 | 300 | 470.55658 | 0.0 | 1 | 3 | 7 |
| 2 | 0 | 1 | 444 | 222 | 1 | 345 | 475.92258 | 0.0 | 2 | 3 | 5 | |
| 3 | 0 | 1 | 444 | 222 | 1 | 335 | 443.48166 | 0.0 | 1 | 3 | 2 | |
| 4 | 0 | 1 | 444 | 222 | 1 | 435 | 433.56735 | 0.0 | 2 | 3 | 2 | |
| 5 | 0 | 1 | 444 | 222 | 1 | 710 | 449.83664 | 0.0 | 2 | 3 | 2 |
d = larch.DataFrames(itin, ch='choice', crack=True, autoscale_weights=True)
d.info(1)
rescaled array of weights by a factor of 2239.980952380952
larch.DataFrames: (not computation-ready)
n_cases: 105
n_alts: 127
data_ce: 6023 rows
- choice (6023 non-null int64)
- nb_cnxs (6023 non-null int64)
- elapsed_time (6023 non-null int64)
- fare_hy (6023 non-null float64)
- fare_ly (6023 non-null float64)
- equipment (6023 non-null int64)
- carrier (6023 non-null int64)
- timeperiod (6023 non-null int64)
data_co:
- traveler (105 non-null int64)
- origin (105 non-null int64)
- destination (105 non-null int64)
data_av: <populated>
data_ch: choice
data_wt: computed_weight (/ 2239.980952380952)