Real-time arrivals university riders can actually trust.
Campus bus feeds and printed schedules are wrong all the time. Dwell rebuilds the predictions from raw GPS — and proves it. One platform, built end-to-end: GPS capture → GTFS feeds → native iOS & Android apps → a self-correcting accuracy loop.
The whole lifecycle, end-to-end
Most transit systems stitch together a different vendor for each of these stages. Dwell owns every one — and the last stage feeds back into the first.
- 01Capture raw GPS
Buses report raw GPS — the only input I trust. Vendor 'predictions' and schedules copied in as fake live data get thrown out at the door.
Raw GPS ingest Vendor-agnostic - 02Generate GTFS-RT feeds
Each position is snapped to its real route shape and published as standards-compliant VehiclePositions + TripUpdates feeds.
GTFS-RT VehiclePositions TripUpdates - 03Power native rider apps
The same engine drives white-label iOS and Android apps — the arrivals students actually open on the way to class.
iOS Android White-label - 04Capture history
Every observation is stored. Per-segment and per-stop timing is learned from millions of past trips, by time of day and day of week.
Time-series store Per-segment timing - 05Measure accuracy
Predictions are scored against what actually happened — continuously — and published on a public accuracy page that updates daily.
Daily scoring Public report - 06Refine the GTFS static feed
Accuracy gaps reveal where the published schedule and shapes are wrong. I fix the static feed — and split service into summer vs. semester calendars.
GTFS static Summer vs. semester - …and the loop closes
Cleaner static feeds and summer-vs-semester models flow back into the history layer (04), which sharpens the live predictions in 01–03. The system gets more accurate the longer it runs.
A campus in July moves nothing like a campus in October.
Most systems average every day of the year together — and get both seasons wrong. Dwell learns separate timing models per service calendar, so a quiet summer route and a packed semester route each get predictions tuned to how that campus actually moves. It's the clearest payoff of owning the whole loop: the schedule data and the historical data improve each other.
What universities get
Drop-in VehiclePositions + TripUpdates feeds that work with the transit apps riders already use.
Your brand and colors — native iOS and Android apps powered by the same engine.
A public accuracy page, updated hourly, so riders and administrators can see the truth.
Served from the edge via Cloudflare — cached, resilient, and inexpensive to run.
Bring trustworthy arrivals to your campus
See exactly how accurate Dwell is for your system, then get a feed your riders' apps can use.