This Python application is designed to collect real-time transit data from the NextBus API and process it into a "retrospective" or "retroactive" GTFS package. Schedule-based GTFS data describes how transit is expected to operate. This produces GTFS that describes how it did operate. The output is not useful for routing actual people on a network, but can be used for a variety of analytical purposes such as comparing routing / accessibility outcomes on the schedule-based vs the retrospective GTFS datasets. Measures can be derived showing the differences between the schedule and the actual operations and these could be interpretted as a measure of performance either for the GTFS package (does it accurately describe reality?) or for the agency in question (do they adhere to their schedules?).
A paper outlining the project and the basic algorithm has been published in the Journal of Transport Geography. Any academic use of this code should please cite that work.
As for actually using the code, please have a look at the wiki, and feel free to email Nate or create an issue if you encounter any problems.
Related projects by other people:
- https://github.com/WorldBank-Transport/Transitime
- https://trid.trb.org/view.aspx?id=1394074 (does anyone have a link to the actual paper?)
- ...