o2g 0.6.0
A simple tool to extract GTFS feed from OpenStreetMap.
Requires Python: >=3.5
Keywords: osm gtfs

# o2g

[![Build Status](https://travis-ci.org/hiposfer/o2g.svg?branch=master)](https://travis-ci.org/hiposfer/o2g) [![pypi](https://img.shields.io/pypi/v/o2g.svg)](https://pypi.python.org/pypi/o2g)

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](http://osmcode.org/) library converts it to a partial
[GTFS](https://developers.google.com/transit/gtfs/reference/) 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](https://github.com/osmcode/pyosmium).

Afterwards install the script from PyPI:

    $ pip install o2g

Or install it from source (with [flit](https://flit.readthedocs.io/en/latest/)):

    $ 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](https://overpass-api.de/). 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](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](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](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`](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 _colour_ 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