# 300: Itinerary Choice Data¶

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.

In this example we will import the air itinerary choice example dataset, starting from a csv text file in idca format. Suppose that data file is gzipped, named “arc.csv.gz” and is located in the current directory (use os.getcwd() to see what is the current directory).

Tip

If you want to practice with this example, you can put this file into the current directory by using the command:

larch.DB.Example('AIR').export_idca("arc.csv.gz", exclude={'caseid','altid'})


We can take a peek at the contents of the file, examining the first 70 lines:

>>> import gzip
>>> with gzip.open("arc.csv.gz", '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 import this data easily:

>>> d = larch.DB.CSV_idca("arc.csv.gz", caseid="id_case", altid="id_alt", choice="choice")


We can then look at some of the attibutes of the imported data:

>>> d.variables_ca()
('caseid', 'altid', 'id_case', 'id_alt', 'choice', 'nb_cnxs', 'elapsed_time', 'fare_hy', 'fare_ly', 'equipment', 'carrier', 'timeperiod')
>>> d.variables_co()
('caseid', 'id_case', 'traveler', 'origin', 'destination')
>>> d.alternative_codes()
(1, 2, 3, 4, 5, 6, 7, ..., 125, 126, 127)


Larch automatically analyzed the data file to find variables that do not vary within cases, and transformed those into idco format variables. If you would prefer that Larch not do this you can set the keyword argument tablename_co to None. 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 seperate idca and idco parts is a relatively expensive operation, and it is not actually required for most model structures.:

>>> d1 = larch.DB.CSV_idca("arc.csv.gz", tablename_co=None, caseid="id_case", altid="id_alt", choice="choice")
>>> d1.variables_ca()
('caseid', 'altid', 'id_case', 'id_alt', 'choice', 'traveler', 'origin', 'destination', 'nb_cnxs', 'elapsed_time', 'fare_hy', 'fare_ly', 'equipment', 'carrier', 'timeperiod')
>>> d1.variables_co()
('caseid',)
>>> d.alternative_codes()
(1, 2, 3, 4, 5, 6, 7, ..., 125, 126, 127)


In this case the set of variables in the idco table isn’t actually empty, because that table is actually now expressed as a special view of the single idca table:

>>> d1.queries.qry_idco()
'SELECT DISTINCT caseid AS caseid FROM (SELECT id_case AS caseid, id_alt AS altid, * FROM data)'


In either case, the set of all possible alternatives is deduced automatically from all the values in the altid column. You will note that, while the sample case we have peeked at in the beginning of the raw data file has 67 alternatives, there are other observations in the file with alternatives numbering up to 127.