Downloading Data

The MTC example included with traveler includes a small test data set directly in the Git repository. This small dataset is suitable for testing and use training purposes, but for evaluating runtime performance it is much better to have the “full” MTC data. The full data is much larger, and not suitable for inclusion or tracking directly in the Git repository. Instead, it can be downloaded separately using tools available in the mtc package.

import mtc

store = mtc.full(download=True)

/Users/jpn/Git/traveler/packages/mtc/src/mtc/tables/skims.py:66: ZoneReindexWarning: Zones length does not match OMX file shape, will reindex.
  return OmxSkims(

The skims in the full data encompass 1,454 zones.

store.skims.info()

<Skims>
- DISTWALK             (1454, 1454) float32
- HOV2TOLL_BTOLL       (1454, 1454, 5) float32
- HOV2TOLL_DIST        (1454, 1454, 5) float32
- HOV2TOLL_TIME        (1454, 1454, 5) float32
- HOV2_BTOLL           (1454, 1454, 5) float32
- HOV2_DIST            (1454, 1454, 5) float32
- HOV2_TIME            (1454, 1454, 5) float32
- HOV3TOLL_BTOLL       (1454, 1454, 5) float32
- HOV3TOLL_DIST        (1454, 1454, 5) float32
- HOV3TOLL_TIME        (1454, 1454, 5) float32
- HOV3_BTOLL           (1454, 1454, 5) float32
- HOV3_DIST            (1454, 1454, 5) float32
- HOV3_TIME            (1454, 1454, 5) float32
- SOVTOLL_BTOLL        (1454, 1454, 5) float32
- SOVTOLL_DIST         (1454, 1454, 5) float32
- SOVTOLL_TIME         (1454, 1454, 5) float32
- SOV_BTOLL            (1454, 1454, 5) float32
- SOV_DIST             (1454, 1454, 5) float32
- SOV_TIME             (1454, 1454, 5) float32
- otaz                 (1454,) categorical: (1454 categories)
- dtaz                 (1454,) categorical: (1454 categories)

The land use data is a matching table with 1,454 rows.

store.land_use.info()

<JaxTable id_col=TAZ>
- DISTRICT             (1454,) int8
- SD                   (1454,) int8
- TOTHH                (1454,) int32
- TOTPOP               (1454,) int32
- TOTACRE              (1454,) float32
- RESACRE              (1454,) float32
- CIACRE               (1454,) float32
- TOTEMP               (1454,) int32
- AGE0519              (1454,) int32
- RETEMPN              (1454,) int32
- FPSEMPN              (1454,) int32
- HEREMPN              (1454,) int32
- OTHEMPN              (1454,) int32
- AGREMPN              (1454,) int32
- MWTEMPN              (1454,) int32
- PRKCST               (1454,) float32
- OPRKCST              (1454,) float32
- area_type            (1454,) int32
- HSENROLL             (1454,) float32
- COLLFTE              (1454,) float32
- COLLPTE              (1454,) float32
- TOPOLOGY             (1454,) int32
- TERMINAL             (1454,) float32
- TAZ                  (1454,) int32
- county_id            (1454,) int8

There is also a synthetic population with 2.8 million households.

store.households.info()

<JaxTable>
- household_id         (2875192,) int32
- hhsize               (2875192,) int8
- num_workers          (2875192,) int8
- auto_ownership       (2875192,) int8
- TAZ                  (2875192,) int32
- HHT                  (2875192,) int8
- income               (2875192,) int32
- home_zone_id         (2875192,) categorical: (1454 categories)

Those households vary in size, containing a total of 7.5 million persons.

store.persons.info()

<JaxTable>
- person_id            (7566527,) int32
- household_id         (7566527,) int32
- age                  (7566527,) int32
- PNUM                 (7566527,) int32
- sex                  (7566527,) int32
- pemploy              (7566527,) int32
- pstudent             (7566527,) int32
- ptype                (7566527,) int32
- _household_idx_      (7566527,) int32