Open Transport Data Switzerland

It is the confederation's single official platform for public-transport data, operated by the SKI business office on behalf of the Federal Office of Transport - less a file drop than a small country's entire moving timetable. The catalog holds 74 datasets organised into Timetable data (HRDF, GTFS, NeTEx - 18 products), Master data and metadata (11), Accessibility (8), Real-time and forecast data (7), Historical and statistical (6), Incidents (5) and Journey planner and pricing (5).

Six families carry most of the rail value. Timetable 2026 (GTFS2020) ships the standard GTFS Static tables - agency, stops, routes, trips, stop_times, calendar - covering 14 December 2025 to 12 December 2026 across 117 dated snapshots. NeTEx XML publishes the 2026 and 2027 timetable years in the European exchange format. GTFS-RT Trip Updates and Service Alerts carry the live layer, with SIRI SX / SIRI ET equivalents alongside. ist-daten (Actual data v2) records the journeys that actually ran - the raw material of punctuality analysis. Atlas v2 anchors it all with reference series: service points, stops, platforms, relations, parking, zones and sublines. Coverage spans every mode - SBB plus all Swiss railways, buses, boats and cableways. Get a sample of this dataset and we return rows shaped exactly like the dictionary below.

The fastest way to judge a transit dataset is to look down its timetable spine. Every GTFS-based product here hangs off the same six-file grammar, so one parser covers the whole family:

# shape preview -- exact values pinned to your requested snapshot
# one grammar, six files: parse one GTFS product and you parse them all

# stops.txt
stop_id,stop_name,stop_lat,stop_lon,location_type,parent_station

# routes.txt
route_id,agency_id,route_short_name,route_long_name,route_type

# trips.txt -> stop_times.txt (the timetable spine)
trip_id,arrival_time,departure_time,stop_id,stop_sequence

# calendar.txt
service_id,monday,tuesday,wednesday,thursday,friday,saturday,sunday,start_date,end_date
# Timetable 2026 service window: 20251214 .. 20261212

# ist-daten (Actual data v2) -- realised journeys
journey_id,scheduled_departure,realised_departure,delay_class,stop_id

# catalogue footprint (recorded August 2026)
products: 74 | timetable 18 - master data 11 - accessibility 8 -
real-time & forecast 7 - historical 6 - incidents 5 - journey planner 5
dated snapshots in the flagship timetable product: 117

Those lines are a structural preview, not projections: the file and column grammar comes straight from the GTFS specification this platform implements, the service window matches the recorded Timetable 2026 coverage, and the footprint figures are the catalog's own counts as researched in August 2026. When we cut your sample we pin exact stop identifiers, coordinates and realised-journey values to the named snapshot you will actually receive.

Six fields anchor the dictionary, and each definition below reflects what the product actually carries rather than what a brochure promises.

Additional fields on request: SIRI SX and SIRI ET service-interface feeds, the Occupancy Forecast running over SIRI, HRDF timetable data, the accessibility collection, historical timetable years before 2026, and the journey-planner and pricing products. Whether Vehicle Positions ride along with the Trip Updates stream needs confirmation against live records - a flag we attach to every sample. Column-level naming locks down when we prepare your extract, which is also where join keys between scheduled and realised records get fixed for your pipeline.

Three chips summarise the footprint:

• Geography: all of Switzerland - SBB plus every Swiss railway, bus, boat and cableway in one frame, which makes it equally useful for domestic network work and cross-border Alpine corridor analysis.
• Time frame: the current timetable year, 14 December 2025 to 12 December 2026, carried across 117 dated snapshots in the flagship product; snapshot history reaches back to mid-2025 and earlier timetable years sit in a dedicated archive, so multi-year reliability studies do not require stitching.
• Granularity: stop-level scheduled and realised events, station and master-data records, and network-wide incident alerts - three grains that join on stop and journey identifiers.

Set against the wider Datadory catalog - where the average quality score across all 1,744 datasets is 7.81 - this slice scores 9/10, a band shared by only 534 datasets. Verified field definitions and a full timetable year already snapshotted carry the score.

API, files, or your warehouse. Daily, weekly, or hourly.

You choose the channel and the cadence; the field dictionary above travels unchanged across all three. Structured payloads suit live dashboards and deviation alerting, bulk files suit overnight warehouse loads of full timetable snapshots, and SQL-friendly extracts suit analysts who never want to see a feed again. Cadence changes are a settings conversation, not a re-integration project: tell us which products you need - planned timetable, realised journeys, master data - and how often, and the feed follows.

Ranked by how directly the platform answers their day job:

1. Developers & builders. One documented grammar - GTFS, NeTEx, SIRI - spanning every Swiss mode turns journey-planner integration into a parsing exercise rather than a negotiation; recipes live in these developers builders use cases.
2. Data scientists & ML engineers. Realised-journey records joined to scheduled stop_times give delay models a clean labelled target across a full timetable year; see what else fits in these data scientists use cases.
3. Market researchers & consultants. Network scope across rail, bus, boat and cableway quantifies Swiss mobility coverage for sizing and market-entry work, citable to a federal source.
4. Journalists, academics & students. Dated timetable snapshots make Swiss schedule change a studyable series rather than an anecdote; more angles in these journalists academics use cases.
5. Sales & growth teams. Stop- and platform-level master data scopes station catchments when sizing last-mile, retail or advertising opportunities along the network.

For contrast inside the same industry: the head-to-head vs DB Open Data and DB Developer Portal scores the German operator's estate against this national platform, and the rail transportation data hub shows where both sit among the 15 slices we catalog for the industry.

Notes that pair well with this page:

• Rail transportation data hub - the pooled view of the industry, from this national platform to North American track geometry and UK performance statistics.
• vs DB Open Data and DB Developer Portal - Switzerland's multimodal catalog against Germany's operator-published estate, scored on one page.
• Best rail transportation datasets - where this slice ranks in the industry and why the 9/10 band matters.
• Persona pages - what developer, data-science, journalist and analyst teams each do with this slice.

Every one of them ends the same way this page does: amaze with what exists, prove it with rows and fields, then route to a sample request.