transitio

AOI-driven OSM and GTFS acquisition, validation and repair — companion to pyrosm and cafein. transitio moves the raw ingredients of routing — OSM extracts and GTFS timetables — from the open data ecosystem to your area of interest, validated and repaired, ready for cafein to brew into routing results.

Status: early development. Acquisition (Mobility Database catalog + OSM extracts), GTFS validation, repair and cropping are in place, tied together by the one-call transitio.fetch pipeline.

Quick example

import transitio

# One call: OSM extract + validated GTFS feeds for an area of interest.
result = transitio.fetch(helsinki_polygon)        # any shapely geometry,
                                                   # bbox tuple or place name
result.osm_pbf     # cropped OSM extract (path)
result.feeds       # downloaded, cropped and validated GTFS feeds (paths)
result.reports     # per-feed merged validation reports
result.skipped     # (feed id, reason) for anything left out

net = result.to_cafein()   # routable cafein.TransportNetwork
osm = result.to_pyrosm()   # pyrosm.OSM reader over the extract

fetch accepts when="2026-09-01" to pick the dataset versions covering a service day (needs a free Mobility Database API token, passed as refresh_token= or via the MOBILITY_API_REFRESH_TOKEN environment variable), modes=["rail", "tram"] to keep only feeds serving given modes, and repair=True to repair feeds before use. With a token, GTFS downloads are catalogued dataset versions verified against catalog checksums; without one, the latest hosted zips are fetched as-is — unverified moving targets.

Inferring missing route shapes

Many feeds ship without shapes.txt, leaving every consumer to draw straight lines between stops. infer_shapes fills that gap from an OSM extract — matching OSM route relations where they exist, map matching over tram, rail and bus-drivable networks where they do not — and writes a feed carrying real alignments:

report = transitio.infer_shapes(
    "feed.zip", "shaped.zip", pbf, strictness="strict"
)
report["written"]     # shapes written
report["shapes"]      # per shape: method, matched OSM relation, score
report["skipped"]     # per refused pattern: the stage that refused it

How much inference is acceptable is yours to choose. "strict" (the default) writes only unambiguous matches; "relaxed" and "permissive" trade certainty for coverage, which is the trade worth making where a feed has no shapes at all and the alternative is a straight line. Every shape is validated against the pattern's own stops before it is written — each stop must lie on the alignment, in order — so no level writes a shape the feed's own data contradicts.

On the Helsinki tram fixture with the feed's shapes withheld, the levels measured (scripts/validate_shapes.py):

level shapes written median length error worst offset
strict 35/80 0.9% 42 m
relaxed 40/80 0.9% 34 m
permissive 43/80 0.9% 184 m

Helsinki's OSM data is unusually good; expect a worse trade where it is not, and keep the report.

Lower-level access

Each pipeline stage is available on its own:

db = transitio.MobilityDatabase()

feeds = db.search_feeds(aoi=helsinki_polygon)
dataset = db.dataset_for(feeds[0], when="2026-09-01")
path = db.download(dataset)                        # cached, checksum-verified
report = db.validation_report(dataset)             # hosted canonical-validator report

pbf = transitio.fetch_pbf(helsinki_polygon)       # cropped OSM extract
validation = transitio.validate_feed(path)        # canonical-code notices
transitio.repair_feed(path, "repaired.zip")       # gtfstidy-contract repair
transitio.crop_feed(path, "cropped.zip", aoi=helsinki_polygon)

Documentation

The Sphinx site lives in docs/. Building it needs transitio itself installed (autodoc imports the real package) plus the Sphinx toolchain:

pip install . -r docs/requirements.txt
sphinx-build -b html docs docs/_build/html

The hosted version lives at https://transitio.readthedocs.io.

Installation

pip install transitio

Binary wheels cover Linux, macOS and Windows. Building from source instead requires a Rust toolchain (pip install .).

License

MIT

Project links

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  • Released:
Newer release available (0.11.0)

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Credits

Author: Henrikki Tenkanen

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License

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Requires

Python >=3.10

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