A simple tool to extract GTFS feed from OpenStreetMap.
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README.md

o2g

Build Status pypi

A simple tool to extract GTFS feed from OpenStreetMap.

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.

Installation

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 o2g

Or install it from source (with flit):

$ git clone https://github.com/hiposfer/o2g && cd o2g
$ flit install

This will install o2g package along with o2g, its cli tool, on your system.

Make sure to run these commands with python 3.

Usage

Run the tool over your OSM data source (or whatever osmium accepts):

$ o2g --help
usage: o2g [-h] [--area AREA] [--bbox BBOX] [--outdir OUTDIR]
           [--zipfile ZIPFILE] [--dummy]
           [--loglevel {DEBUG,INFO,WARNING,ERROR,CRITICAL}] [--version]
           [OSMFILE]

Export GTFS feed from OpenStreetMap data.

positional arguments:
  OSMFILE               an OSM data file supported by osmium

optional arguments:
  -h, --help            show this help message and exit
  --area AREA           an OSM area name, e.g. Freiburg (default: None)
  --bbox BBOX           a boundary box, e.g. 47.9485,7.7066,48.1161,8.0049
                        (default: None)
  --outdir OUTDIR       output directory (default: .)
  --zipfile ZIPFILE     save to zipfile (default: None)
  --dummy               fill the missing parts with dummy data (default:
                        False)
  --loglevel {DEBUG,INFO,WARNING,ERROR,CRITICAL}
                        the logging level (default: WARNING)
  --version             show the version 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.

Area and Boundary Box

One can pass an area name or a bbox to o2g and it will download the necessary data from Overpass API. Area should be an OSM area name and bbox should be an OSM boundary box including south, west, north, east separated with comma. Example:

$ o2g --area Freiburg
$ o2g --bbox 47.9485,7.7066,48.1161,8.0049
$ o2g --area Freiburg --bbox 47.9485,7.7066,48.1161,8.0049

Web Demo

There is a small web app inside web folder. It accepts a URL to a osmium supported file. It will then convert it to a zipped GTFS feed.

$ cd web
$ pip install bottle o2g
$ python app.py

Browse to http://localhost:3000 afterwards. Alternatively running flit install --extras web will install web dependencies.

This web app is also running at http://o2g.hiposfer.com. It is possible to directly download a zipped GTFS feed for a given OSM URL too:

$ wget 'http://o2g.hiposfer.com/o2g?url=http://download.geofabrik.de/europe/liechtenstein-latest.osm.bz2' -O gtfs.zip

Web Api with Overpass Query

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:

$ wget 'http://o2g.hiposfer.com/o2g?area=Freiburg&bbox=47.9485,7.7066,48.1161,8.0049' -O gtfs.zip

As before, it is possible to get a patched and valid GTFS feed by passing the dummy flag:

$ wget 'http://o2g.hiposfer.com/o2g?area=Freiburg&dummy=True -O gtfs.zip

With Docker

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/o2g

Then browse to http://localhost:3000.

Tests

We use the pytest package for testing:

$ pip install pytest (or by running `flit install`)
$ pytest -s

-s disables capturing and shows us more output (such as print statements and log messages).

Profiling

In order to profile the code we use cProfile:

# For the `o2g` script
$ python -m cProfile -s cumtime o2g/cli.py resources/osm/freiburg.osm.bz2 --outdir resources/out/freiburg --dummy > resources/out/benchmarks/freiburg.txt

You will find the result in resources/out/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.

Dummy Feed Information

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, calendar and frequencies.txt feeds. These files will contain dummy data of course.

Implementation Notes

In this section we describe important aspects of the implementation in order to help understand how the program works.

Field Mapping

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.

agency.txt

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

stops.txt

  • 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

routes.txt

  • 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

OSM to GTFS Route Type Mapping

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

namedtuples as the preferred data structure

In order to decrease the necessary memory, we use mostly namedtuples (which are basically tuples) to store data.

License

MIT