chaosskill/tisseo-stop-frequencies-toulouse

Tisséo Stop Frequencies — Toulouse Public Transit

Departure-frequency data for every serviced Tisséo (Toulouse's public transit operator) stop — metro, tram, and bus — computed from the official GTFS feed rather than left as a raw, hard-to-use timetable dump.

• 3,817 stops with active service
• Point locations (WGS84 — lat/lon fields, not GeoJSON geometry — see Format below)
• Format: single JSON object, keyed by GTFS stop_id
• Geographic scope: the Tisséo network (Toulouse + surrounding métropole communes it serves)

Why this exists

Raw GTFS feeds are five separate files (routes.txt, trips.txt, stops.txt, calendar_dates.txt, stop_times.txt) that need joining before "how frequent is this stop" is answerable. This extract does that join once: for a representative weekday, it counts actual scheduled departures per route at every stop and expresses it as departures/hour — the metric people actually want ("is this a 2-minutes-apart metro stop or a twice-an-hour bus stop") instead of a timetable you'd have to parse yourself.

What's inside

"stop_point:SP_1002": {
  "name": "Lalande",
  "lat": 43.557031130685445,
  "lon": 1.5291974348203419,
  "routes": [
    { "type": 3, "type_label": "Bus", "name": "L7",  "dep_per_hr": 4.7 },
    { "type": 3, "type_label": "Bus", "name": "109", "dep_per_hr": 1.9 }
  ]
}

Stops with active service	3,817
Route-at-stop entries (metro)	76
Route-at-stop entries (tram)	51
Route-at-stop entries (bus, incl. 6 cable-car entries — see limitations)	5,051
Metro lines present	A, B
Tram lines present	T1
dep_per_hr range	0.2 – 35.8

How the frequency figure is computed

Departures/hour is not a live or peak-hour figure — it's derived from a single representative weekday (the date in the feed's calendar_dates.txt with the most active services, i.e. a normal full-service weekday, not a Sunday or holiday), counting every scheduled departure at that stop for each route and dividing by an assumed 18-hour service window (roughly first to last service of the day). A stop showing dep_per_hr: 4.7 had roughly 4.7×18 ≈ 85 scheduled departures that day on that route — averaged across the day, so actual frequency is higher at rush hour and lower late evening/early morning.

Known limitations

• Only the top 3 routes per stop are kept, prioritized metro > tram > cable-car > bus, then by descending frequency. A busy stop serving 4+ routes will have its least-frequent route(s) silently dropped from this extract.
• Routes under 0.2 departures/hour are excluded (effectively single-digit-departures-per-day services) rather than shown as near-zero.
• 6 entries are cable-car (Téléo) stops mislabeled "Bus". Root cause: the build script maps GTFS route type 5 to "Téléphérique" explicitly, but Tisséo's feed apparently codes the cable car as GTFS type 6 (aerial lift, per the GTFS spec) — which falls through to the script's default label, "Bus". The frequency figure itself is correct; only the label is wrong for these 6 entries.
• 1,506 stop names appear more than once (e.g. "Lalande" ×4) — this is normal, not a duplicate-data bug: named stops (e.g. a single intersection) commonly have multiple physical stop_id points, one per direction of travel, each with its own coordinates and frequency.
• Single-day snapshot, not averaged or live. The exact reference date chosen isn't stored in the output (only the resulting counts are) — re-running against a fresh GTFS export could select a different representative weekday. GTFS feed vintage: downloaded ~early January 2026.

Format note

This is plain JSON (a dict keyed by stop ID), not GeoJSON — chosen because the source build groups multiple routes under one stop. If you need GeoJSON or CSV, flattening is a short loop: one row/feature per stop, with routes either kept as a nested list (GeoJSON properties support nesting) or exploded into one row per stop×route (CSV-friendly).

License & attribution

Source: Tisséo Collectivités GTFS feed (public transit schedule data for the Toulouse metropolitan area), published as open data. Reuse, including commercial reuse, is permitted with attribution to Tisséo. This extract adds the departures/hour computation and stop-level aggregation on top of that public schedule data.

Suggested uses

• Transit-access scoring for a neighborhood (frequency, not just presence of a stop)
• Identify high-frequency corridors (metro/tram) vs. low-frequency bus-only areas
• Combine with a walking-distance layer for a realistic "how easy is this to use" metric, rather than straight-line distance to the nearest stop

About La Ville Rose

This extract was built for lavillerose.com, a free interactive map of Toulouse that helps five kinds of people — families, students, companies, property investors, and seniors — find the right neighborhood for their specific situation. Instead of one generic map, each persona gets its own layer set: students and families see transit frequency alongside schools/rent budget, seniors see it alongside healthcare access, and so on. All the underlying data (this transit-frequency extract included) comes from public open-data sources, cleaned and joined into something usable on a map.

• Map: lavillerose.com — pick a persona, explore ~15 data layers per profile for free, no signup.
• Guides & reports: guide.lavillerose.com — per-neighborhood guides, plus paid detailed/custom reports for anyone who wants a specific recommendation rather than exploring themselves.

This transit-frequency extract is one dataset in a series of open Toulouse layers released alongside the map. A paid, cadastral-section-level dataset merging this and other domains (price, schools, safety, zoning) into per-neighborhood scores is available on the same site.