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Scope

TNPs should check all files for data quality before submission and verify that they pass at least the following tests. Additional tests as each TNP sees fit are highly encouraged, as is reaching out to the City of Chicago for any discussion each TNP thinks useful in this effort.

Errors detected should be fixed before file submission. If it is impossible or unreasonably difficult to fix the errors, TNPs should inform BACP of the nature and degree (such as number of records affected) of the known errors in a file.

File Format

Standard # File(s) Standard Test
1 Driver Driver ID columns complete D1, D2, and D3 contain no blanks.
2 Session Driver ID columns complete S1 and S2 contain no blanks.
3 Trip Driver ID columns complete T1 and T2 contain no blanks.
4 Vehicle Driver ID columns complete V8 and V9 contain no blanks.
5 Trip Shared trip columns complete T14 and T15 contain no blanks.
6 Vehicle Vehicle ID columns complete V1, V2, and V3 contain no blanks.
7 Trip Vehicle ID columns complete T3 contains no blanks.
8 Vehicle Vehicle description columns complete V4, V5, V6, and V7 contain no blanks.
9 Vehicle WAV column complete V10 contains either Y or N for every record.
10 Vehicle Valid inspection date V11 contains a date in the past for every record.
11 Driver No duplicates No combination of D2/D3 values appears more than once.
12 Vehicle No duplicates No V1 value or combination of V2/V3 values appears more than once, except if due to another driver (distinct V8/V9) also using the vehicle.
13 Driver
Session
Session and Driver files match All S1/S2 values have corresponding D2/D3 values.
14 Driver
Trip
Trip and Driver files match All T1/T2 values have corresponding D2/D3 values.
15 Driver
Vehicle
Vehicle and Driver files match All V8/V9 values have corresponding D2/D3 values.
16 Trip
Vehicle
Trip and Vehicle files match All T3 values have a corresponding V1 value.
17 All Valid data types All non-string columns contain values valid for the column type.

File Content

The above tests are designed to detect structural errors in the data files. TNPs should also make reasonable efforts to check for situations in which data are structurally valid but incorrect. While no list can be comprehensive, examples of things to check might include: