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Asked: 2026-05-31T00:39:15+00:00

I have a PostgreSQL database that contains traffic tickets written by a few jurisdictions.

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I have a PostgreSQL database that contains traffic tickets written by a few jurisdictions.

Some jurisdictions don’t indicate if multiple tickets are written in one traffic stop. However, that can be inferred by analyzing other fields. Consider this data:

ticket_id  timestamp            drivers_license
----------------------------------------------
1          2008-08-07 01:51:00  11111111
2          2008-08-07 01:51:00  11111111
3          2008-08-07 02:02:00  22222222
4          2008-08-07 02:25:00  33333333
5          2008-08-07 04:23:00  44444444
6          2008-08-07 04:23:00  55555555
7          2008-08-07 04:23:00  44444444

I can infer that:

  • Tickets 1 & 2 were written in a single traffic stop because they share driver’s license numbers and timestamps.
  • Same for 5 & 7, but notice how ticket 6 is between them. Perhaps another officer was writing a ticket at the same time somewhere else, or data entry operators entered stuff out of order.

I would like to add another column that has a unique ID for each traffic stop. It will not be a primary key for the table because it will have duplicate values. For example:

ticket_id  timestamp            drivers_license  stop_id
--------------------------------------------------------
1          2008-08-07 01:51:00  11111111         1
2          2008-08-07 01:51:00  11111111         1
3          2008-08-07 02:02:00  22222222         2
4          2008-08-07 02:25:00  33333333         3
5          2008-08-07 04:23:00  44444444         4
6          2008-08-07 04:23:00  55555555         5
7          2008-08-07 04:23:00  44444444         4

I can think of computationally-intensive, greedy algorithm ways of doing this with C#, but is there an efficient SQL query that can work?

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1 Answer

  1. Editorial Team

    Efficient SQL Query FTW!

    I’m not at a computer that I can test this on so there is likely some syntax problems; I will fix in the morning, but it is something like this:

    WITH uniquez as (SELECT timestamp, drivers_license, 
rank() over (ORDER BY timestamp, drivers_license) as counterz 
FROM ticketTable)

UPDATE ticketTable TT
SET stop_id = uniquez.counterz
WHERE uniquez.timestamp = TT.timestamp
AND uniquez.drivers_license = TT.drivers_license

    Basically, you make a select that groups (partitions) by timestamp and drivers_license and have a row counter that goes with it. When you do the update, you use this previous select table’s row counter as your “stop_id” and updates the columns that match the timestamp and drivers license.

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