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ptv2gtfs

This is a conversion script to translate from the standard Metlink/PTV SQLite3 timetable database format used by PTV applications (e.g. iPhone app) to GTFS.

Requirements

  • Python v2.7
  • [Google Transitfeed] transitfeed
  • A MetLink/PTV database (SQLite3 DB)

This has only been tested on Linux and Mac OS X using Python 2.7. It should probably work on any platform.

Obtaining the data

Currently, obtaining and using this database is up to you, and may or may not be legal. I believe the Department of Transport may not like it.

You may be better off to use the [PTV Timetable API] timetable_api.

Usage

Command line help looks like this:

Usage: ptv2gtfs.py --file <input db> --service <service> --output <output file>

Options:
  -h, --help                     show this help message and exit
  -f INPUTDB, --file=INPUTDB     PTV SQLite3 database file
  -s SERVICE, --service=SERVICE  Should be train, tram or bus
  -o OUTPUT, --output=OUTPUT     Path of output file (should end in .zip)

An example of the output used for tram timetable data looks like this:

$ ./ptv2gtfs.py --file Metlink-Tram.sql --service tram --output tram_gtfs.zip
Processing timetable: fri
Processing timetable: monfri
Processing timetable: monthur
Processing timetable: sat
Processing timetable: sun
All services are defined on a weekly basis from 2014-01-01 to 2014-12-31 with
no single day variations. If there are exceptions such as holiday service
dates please ensure they are listed in calendar_dates.txt
The stops "Batmans Hill Dr/Collins St" (ID 2701) and "Batman's Hill/700
Collins St" (ID 2489) are 0.00m apart and probably represent the same
location.
The stops "Cotham Rd/Burke Rd" (ID 2695) and "1219 Burke Rd" (ID 2436) are
0.00m apart and probably represent the same location.

The output zip file (tram_gtfs.zip) in this case is our resulting GTFS data file.

Performance: This script can take up to 10 minutes to run on a large DB, such as Victoria's bus network. It will also require over 100MB of RAM (e.g., between 800MB - 1GB for the bus network).

Once the data has been processed, you can use the schedule_viewer.py script provided with Google's Transitfeed package to visualise the data:

$ schedule_viewer.py train_gtfs.zip
Loading data from feed "train_gtfs.zip"...
(this may take a few minutes for larger cities)
routes.txt:1 column route_short_name
Missing column route_short_name in file routes.txt
To view, point your browser at http://localhost:8765/

Limitations

Currently this script does NOT deal with special timetable days, like public holidays.

You may also find inconsistancies within the data provided. This is up to you to resolve.

Contact

Feel free to contact me at [email protected] for any questions.

If you find this useful, please let me know. Pull requests appreciated.