Extracts partial GTFS feed from OSM data.
OpenStreeMaps data contain information about bus, tram, train and other public transport means. This information is not enought for providing a complete routing service, most importantly because it lacks timing data. However, it still contains routes, stop positions and some other useful data.
This tool takes an OSM file or URI and thanks to osmium library converts it to a partial GTFS feed. GTFS is the de facto standard for sharing public transport information and there are many tools around it. The resulting feed would not validate if you check it, because it is of course partial. Nevertheless, it is yet valuable to us.
This tool uses osmium which is a C++ library built using boost, so one should install that first. The best way would be using the package manager of your OS and installing pyosmium.
Afterwards install the script from pypi:
$ pip install osmtogtfs
Alternatively you can clone the repo and install it:
$ git clone https://github.com/hiposfer/osmtogtfs & cd osmtogtfs
$ python3 setup.py install
This will install osmtogtfs command and its alias o2g on your system. Alternatively, you can run osmtogtfs/cli.py.
Make sure to run these commands with python 3.
Run the tool over your OSM data source (or whatever osmium accepts):
osmtogtfs <osmfile>
After a while, depending on the file size, a file named gtfs.zip will be produced inside the working directory.
Moreover, if you install the package, you will get an script called osmtogtfs in your python path:
$ osmtogtfs --help
Usage: osmtogtfs [OPTIONS] INPUT
Options:
--outdir PATH Store output in this directory.
--zipfile PATH Save as Zip file if provided.
--loglevel Set the logging level.
--help Show this message and exit.
--outdir defaults to the working directory and if --zipfile is provided, the feed will be zipped and stored in
the outdir with the given name, otherwise feed will be stored as plain text in multiple files.
There is a small web app inside demo folder. It accepts a URL to a osmium supported file. It will then convert it
to a zipped GTFS feed. You will need pipenv command to install and run it:
$ cd demo
$ pip install pipenv
$ pipenv install
$ python3 app.py
Browse to http://localhost:3000 afterwards to use it.
This demo is also running at http://o2g.hiposfer.com. It is possible to directly download a zipped GTFS feed for a given OSM URL too:
http 'http://o2g.hiposfer.com/o2g?url=http://download.geofabrik.de/europe/liechtenstein-latest.osm.bz2' > gtfs.zip
The above command uses the HTTPie tool.
It is alos possible to download the necessary OSM data from overpass-api.de. Passing an area name or a bbox to the web API will trigger this feature:
http 'http://o2g.hiposfer.com/o2g?area=Freiburg&bbox=47.9485,7.7066,48.1161,8.0049' > gtfs.zip
As before, it is possible to get a patched and valid GTFS feed by passing the dummy flag:
http 'http://o2g.hiposfer.com/o2g?area=Freiburg&dummy=True > gtfs.zip
If osmium is not available in your package manager, it could be troublesome to install it manually. So here is a docker image that could be used directly:
$ docker run -it -p 3000:3000 hiposfer/osmtogtfs
Then browse to http://localhost:3000.
We use pipenv to manage dependencies and virtualenvs. Install and activate it before anything:
$ cd osmtogtfs
$ pip install pipenv
$ pipenv install
$ pipenv shell
The last command will give us a shell within the newly created virtualenv.
In order to update the requirements.txt we simply use pipenv:
$ pipenv run pip freeze > requirements.txt
We use the pytest package for testing. If you have followed above instructions, pytest should be
already installed. Otherwise install pytest and run the tests:
$ pip install pytest
$ pytest -s
-s disables capturing and shows us more output (such as print statements and log messages).
In order to profile the code we use cProfile:
# For the `osmtogtfs` script
$ python -m cProfile -s cumtime osmtogtfs/cli.py resources/osm/bremen-latest.osm.pbf --outdir output/bremen --dummy > output/benchmarks/bremen.txt
$ python -m cProfile -s cumtime osmtogtfs/cli.py resources/osm/saarland-latest.osm.pbf --outdir output/saarland --dummy > output/benchmarks/saarland.txt
You will find the result in output/benchmark.txt.
Theses results are produced on an Archlinux machine with an Intel(R) Core(TM) i5-3210M CPU @ 2.50GHz CPU with 16GB RAM.
Not all of GTFS necessary data are available in OSM files. In order to fill the missing fields with
some dummy data use --dummy CLI option. This will produce trips.txt, stop_times.txt and calendar
feeds. These files will contain dummy data of course.
In this section we describe important aspects of the implementation in order to help understand how the program works.
GTFS feeds could contain up to thirteen different CSV files with .txt extension. Six of these files are required for a valid
feed, including agency.txt, stops.txt, routes.txt, trips.txt, stop_times.txt and calendar.txt.
Each file contains a set of comumns. Some columns are required and some are optional.
Most importantly, not all the fields necessary to build a GTFS feed are available in OSM data.
Therefore we have to generate some fileds ourselves or leave them blank.
Below we cover how the values for each column of the files that we produce at the moment are produced.
We use operator tag on OSM relations which are tagged as relation=route to extract agency information.
However, there are some routes without operator tags. In such cases we use a dummy agency:
{'agency_id': -1, 'agency_name': 'Unkown agency', 'agency_timezone': ''}
- agency_id: we use the operator value to produce the agency_id:
agency_id = int(hashlib.sha256(op_name.encode('utf-8')).hexdigest(), 16) % 10**8 - agency_name: the value of the operator tag
- agency_timezone: we guess it based on the coordinates of the elements in the relation
- stop_id: value of the node id from OSM
- stop_name: value of name tag or Unknown
- stop_lon: longitute of the node
- stop_lat: latitute of the node
- route_id: id of the OSM relation element
- route_short_name: value of name or ref tag of the relation
- route_long_name: a combination of from and to tags on the relation otherwise empty
- route_type: we map OSM route types to GTFS
- route_url: link to the relation on openstreetmaps.org
- route_color: value of the color tag if present otherwise empty
- agency_id: ID of the agency otherwise -1
Below is the mapping that we use, the left column is the OSM value and the right column is the corresponding value from GTFS specification (make sure the see the code for any changes):
tram: 0
light_rail: 0
subway: 1
rail: 2
railway: 2
train: 2
bus: 3
ex-bus: 3
ferry: 4
cableCar: 5
gondola: 6
funicular: 7
In order to decrease the necessary memory, we use mostly namedtuples (which are basically tuples) to store data.
MIT