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https://github.com/hiposfer/o2g

A simple tool to extract GTFS feed from OpenStreetMap.
https://github.com/hiposfer/o2g

gtfs gtfs-generator openstreetmap osm public-transport transit

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A simple tool to extract GTFS feed from OpenStreetMap.

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README 

        
# 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