Data source
Data from Transport for London, delivered clean.
1 dataset pulled from Transport for London's releases, checked field by field and shipped the way you want them — daily, weekly, or hourly, your call.
- 1 dataset
- 1 industry
- Real rows on request
What Datadory delivers from Transport for London
1TfL Open Data & Unified API
Pick a catch, see the rows.
Name any Transport for London dataset and we send real rows from it — not a screenshot of rows. 1,744 datasets. Pick your catch.
Get a sampleAPI, files, or your warehouse. Daily, weekly, or hourly.
Straight answers about Transport for London data
Can TfL arrival predictions really be tracked per vehicle?
Yes. Each prediction carries a vehicleId such as LA19KAE, observed on route 214 toward Highgate Village during verification, so individual vehicles can be followed through a corridor instead of being averaged into a headway figure.
What is NUMBAT and why does it matter?
TfL's most comprehensive rail-demand study: boarders, alighters, link loads and station entries/exits for Underground, Overground, Elizabeth line and DLR, measured in 15-minute bands by day-type and published annually from 2016 onward. It is built for the network's own capacity planning, which is exactly what makes it valuable outside TfL.
How far back do London ridership records go?
The RODS predecessor survey carries demand records from 1996 through 2017, and NUMBAT with Annual Counts continues the series annually from 2016. Between the two programmes, London rail demand is measurable across three decades with a documented overlap period for reconciliation.
Which London transport modes are covered?
Bus, tube, Overground, DLR, Elizabeth line, tram, IFS Cloud Cable Car, river bus and coach, plus Santander Cycles and the road network. All share one stop and line identifier scheme, so cross-mode queries need no vocabulary reconciliation.