GTFS and GTFS Realtime feed generator toolkit for DPMP

Toolkit to create and maintain GTFS and GTFS Realtime feeds for Pardubice public transportation.

It aims to provide a bridge between (semi-)propriatary data formats and standard transit open data for publication to Google as well as general analysis.

Note: still a work in progress.

How this works?

This tool bundle builds beforementioned feeds out of CIS data, bus stop geo coordinates in DPMP proprietary format and also REST API that provides latest vehicle locations and deviations from the schedule.

The static data (GTFS) needs to be updated whenever bus schedule changes. Not so often.

The realtime data (GTFS - Realtime) needs to be updates as often as possible so that feed consumers (and ultimately passengers) don't end up with stale info. Ideally, you should rebuilt the feed with same frequency buses send their location updates. At the time of this writing, this would be something like every 15 seconds.

General workflow is following:

1. Create GTFS feed out of CIS data and bus stop coords. Update it when schedule changes.
2. Create & frequently update GTFS-Realtime feed using GTFS feed and realtime API as input.

Both feeds have to be exposed publicly to let consumers grab them at will. Exposing the feeds is out of scope of this toolkit but should be fairly simple using NGINX for example.

In order to integrate Google, an agency representative has to register the feed URLs as well.

To start using this tool bundle, you'll first need to get hold of following stuff:

• Transit agency data in a JDF format (accepted by CIS). Extract it and put it somewhere on your filesystem wrapped in jdf/1/ so that you have files like jdf/1/Spoje.txt.
• Stop location geodata in raw form (STANICE.ZS).
• An API key for the MHD REST API. Ask DPMP representatives to provide you with one.

Using Docker

If you don't want to hassle with installing .NET or Python, there are prebuilt Docker images you can use easily.

1. Copy over JDF source and STANICE.ZS to a single parent directory, let' say /tmp/data and cd into it:

   cd /tmp/data

2. Prepare stop location CSV to feed jrutil later on:

   docker run -v $PWD:/data xaralis/dpmp-gtfs-bridge:latest ./convert-stop-locations.sh  --src_file /data/STANICE.ZS --dest_file /data/stop_locations.csv

   This will create /tmp/data/stop_locations.csv.

3. Convert JDF feed to GTFS using jrutil:

   mkdir gtfs-out
   docker run -v $PWD:/data xaralis/dpmp-gtfs-jrutil:latest dotnet run -- jdf-to-gtfs --stop-coords-by-id /data/stop_locations.csv /data/jdf/1 /data/gtfs-out
   

   This will create your GTFS feed in /tmp/data/gtfs-out. You should re-run this command whenever your transi schedule changes.

4. Start GTFS Realtime daemon:

   docker run -it -v $PWD:/data xaralis/dpmp-gtfs-bridge:latest ./start-gtfsr-generator.sh --mhd_api_key=[API_KEY] --gtfs_trips_src_file=/data/gtfs-out/trips.txt --dest_file=/data/gtfs-out/gtfsr.pb

   The daemon is meant to be up all the time as the realtime feed needs to be fresh.

Running directly

1. Install & build jrutil locally, follow instructions at the library home page. Simply put: you'll need to install .NET support (v5) on your machine and also clone linked repositories in the thirdparty directory (refrer to Dockerfile.jrutil for details). Then build the jrutil-multitool:

   dotnet build jrutil-multitool

2. Install this app and its dependencies:

   git clone git@github.com:xaralis/dpmp-gtfs.git && cd dpmp-gtfs
   poetry install

3. Prepare stop location CSV to feed jrutil later on:

   poetry run src/convert_stop_locations.py --src_file STANICE.ZS

4. Convert JDF feed to GTFS using jrutil:

   cd path/to/jrutil/jrutil-multitool
   dotnet run -- jdf-to-gtfs --stop-coords-by-id path/to/stop_locations.csv path/to/jdf/1 path/to/gtfs/output/dir

   This will create your GTFS feed in specified location. You should re-create the feed whenever your transit schedule changes.

5. Start GTFS Realtime daemon:

   cd path/to/dpmp-gtfs
   poetry run src/generate_gtfs_realtime.py --mhd_api_key=[API_KEY] --gtfs_trips_src_file=path/to/gtfs/output/dir/trips.txt --dest_file=path/to/gtfs/output/dir//gtfsr.pb

   The daemon is meant to be up all the time as the realtime feed needs to be fresh. Refer to further config options with:

   poetry run src/generate_gtfs_realtime.py --help
   

Verifying GTFS feed

First, create a Google Maps API key. Follow the official Google guide:

https://developers.google.com/maps/documentation/maps-static/get-api-key#creating-api-keys

Now you can visualise the feed using Google's transitfeed toolkit. I've added some basic fixes as the original repository is old and unmaintained. Now, assuming your previously generated GTFS feed lives at /tmp/data/gtfs-out, you can run the schedule viewer with this command:

docker run -it -v ~/tmp/data/gtfs-out:/data -p 8765:8765 xaralis/dpmp-gtfs-transitfeed:latest python schedule_viewer.py --key [API_KEY] --feed_filename /data

After couple seconds, this will expose the schedule viewer web app on http://localhost:8765.

Credits

I would like to give thanks to the author of jrutil David Koňařík. Without his work on the JDF -> GTFS conversion library, everything would be a lot harder.