Short version: this is free for research, journalism, teaching, and personal
projects. Please credit me, and please don't use it to make money without
asking first. That's really the whole thing.

The MTA's data is theirs, and it's public. GTFS-RT, static GTFS, and the
ridership figures all come from the MTA's open data. I'm not claiming any of
it, and you're very welcome to fetch it straight from the source — if today's
feed is all you need, you don't need this archive at all.

What I'm sharing is the history. The MTA keeps none: every poll overwrites
the last, so nothing upstream remembers what the network looked like at 08:14
last Tuesday. The continuous capture since April 2026, the decoding and
deduplication, the analytical panels, the learned baselines and the labelled
prediction record are my work, and that's what I'm licensing to you under
CC BY-NC 4.0.

What you can do

Use it, share it, adapt it, and publish whatever you find — just credit
Henry Williams / subway.fyi. Academic work, journalism, teaching, civic and
policy analysis, hobby projects: all welcome, and I'd genuinely like to hear
what you build with it.

The one real limit: not for commercial use

Please don't use this, or anything derived from it, to make money without
asking me first. That covers shipping it inside a paid product or service,
training a model that's then deployed or sold commercially (derived weights
carry the same limit), fee-paid consulting or analysis, and for-profit
internal use.

Access here isn't a commercial licence on its own — but do get in touch if you
want one. I'm open to it, and it's usually a short conversation.

Two caveats worth knowing

No warranty. This is an independent capture pipeline, not
infrastructure. It has gaps, and it isn't safety-certified — please don't
rely on it anywhere that being wrong would actually matter.
It isn't an MTA product. Unofficial and unaffiliated, so please don't
present it as an MTA source.

NYC Subway Realtime Archive

Continuous capture of the New York City subway's public realtime feeds, decoded into analysis-ready tables — plus the derived service-quality panels, learned "normal" baselines, and disruption-prediction track record built on top of them.

Collected every 30 seconds since 2026-04-16 across all nine MTA GTFS-RT feeds, by the pipeline behind subway.fyi. Source: github.com/digitalhen/subway-data.

This archive exists because the source data disappears. The MTA publishes realtime feeds but no history: each poll overwrites the last, and our own database drops the raw firehose after 60 days. Everything here is a permanent record of moments that are otherwise gone. It is append-only — new days are added, old days are never rewritten.

What makes this different from GTFS-RT dumps

Three things, roughly in order of how much work they represent:

The 5-minute analytics panel (route_station_5min) — the firehose already reduced to per route × station × 5-minute service quality: headways, dwell times, arrival delays, bunching, and whether an official alert was active. This is the table most research questions actually want, and it is ~250× smaller than the raw feed it came from.
Learned baselines (baselines_headway, baselines_stop_hold) — what "normal" looks like per route × station × day-of-week × hour, rebuilt nightly from months of history with alert-active periods excluded so normal never learns from a bad day. This is what turns a headway into an anomaly.
A labelled disruption-prediction record (delay_predictions) — every evaluation the live delay radar has made, each labelled by a nightly job against the alerts that actually followed. A ready-made benchmark, described below.

Plus the NYCT protobuf extensions most parsers silently drop: train_id, is_assigned, scheduled_track, actual_track, NYCT direction.

Quick start

from datasets import load_dataset

panel = load_dataset("digitalhen/nyc-subway-realtime", "route_station_5min", split="train")

Most of this is better handled as Parquet than as a datasets iterator. For anything analytical, query the files directly:

import duckdb
duckdb.sql("""
  SELECT route_id,
         avg(avg_headway_sec)/60 AS mean_headway_min,
         avg(headway_cv)         AS mean_bunching
  FROM 'data/route_station_5min/*.parquet'
  WHERE NOT alert_active
    AND extract(hour FROM bucket_start AT TIME ZONE 'America/New_York') BETWEEN 8 AND 9
  GROUP BY 1 ORDER BY 2 DESC
""")

hf download digitalhen/nyc-subway-realtime --repo-type dataset --local-dir . \
  --include "data/route_station_5min/*"

Coverage windows differ by table because retention policies differ. Row counts and exact per-file coverage are in manifest.json.

Derived analytics — the long-history core

config	grain	coverage	notes
`route_station_5min`	route × station × 5 min	from 2026-05-14	headways, dwell, delays, bunching, alert flags. The flagship table.
`station_hourly`	station × hour	from 2026-04-29	absolute service quality: avg headway, bunching CV, arrivals, routes served
`station_daily`	station × day	from 2026-04-17	daily "badness"/"awfulness" percentile ranks over 3h rolling windows
`alerts_unique`	alert × content-state	from 2026-05-14	deduplicated service alerts: cause, effect, full text, active periods, informed entities. The label source.
`delay_predictions`	route × evaluation	from 2026-07-30	every radar evaluation + nightly outcome labels. See benchmark below.
`incident_replays`	incident	rolling 30 days	precomputed incident playbacks: contagion grid, origin station, radar-vs-MTA timing

Raw realtime firehose

One Parquet file per UTC day, append-only. The database drops these chunks at 60 days; the files here are permanent.

config	rows/day	what it is
`firehose_stop_time_updates`	~33 M	per-stop predicted arrival/departure inside each trip update. The big one.
`firehose_trip_updates`	~1.6 M	one row per trip entity per snapshot, incl. NYCT extensions
`firehose_vehicle_positions`	~1.4 M	per-vehicle position and status (`STOPPED_AT` / `IN_TRANSIT_TO` / `INCOMING_AT`)
`firehose_alerts`	~150 k	full re-ingest of every active alert, every poll. 95%+ are byte-identical republishes — use `alerts_unique` unless you specifically want the republish timing.
`feed_snapshots`	~26 k	one row per fetch attempt: latency, entity counts, errors. Capture-quality ground truth.

Reference and learned structure

config	what it is
`gtfs_stops`, `gtfs_routes`, `gtfs_trips`, `gtfs_stop_times`, `gtfs_shapes`	static GTFS: geography, schedules, route colours, track shapes
`station_complex`, `station_complex_stops`	MTA station complexes and their GTFS stop mappings
`ridership_hourly`	average ridership per complex × day-of-week × hour (MTA open data)
`baselines_headway`	learned normal headway per route × station × dow × hour, alert periods excluded
`baselines_stop_hold`	routine-hold duration per platform (p50/p95/p99) — a 9-min hold is normal at Canal St, an emergency at 167 St
`station_adjacency`	directed station hops per route and direction, from static schedules
`terminal_stops`	terminal platforms, where long dwells are normal

A ready-made benchmark: predict disruptions before the MTA announces them

delay_predictions is a labelled record of a real prediction task run live in production. Each row is one evaluation of one route at one minute, with the features that drove it, whether it fired, and — from a nightly labelling job — whether an official MTA alert actually followed and how many minutes later.

The production model (digitalhen/nyc-subway-delay-radar) scores, on a holdout it was evaluated on exactly once:

	delay radar v2.7	naive rule
episode recall	0.13	0.053
precision	0.446	0.242
false alarms/day	5.75	5.88
median lead time	43 min	—

Those are the numbers to beat. Recall is genuinely low — about one disruption in eight — because most incidents never produce a legible signature in the public feed before they are announced. That is the research problem, not a bug in the benchmark.

Four rules matter if you want your numbers to mean anything here, each learned by getting it wrong first:

Split on time, never at random. Adjacent 5-minute rows from one incident are nearly identical; a random split leaks and will flatter you enormously.
Score episodes, not rows. Rows within an incident are hugely autocorrelated. Per-row AUC on this data is meaningless. Collapse per route on a ~90-minute window.
Use alert_seen_at, not the MTA's active_period start. The MTA backdates alerts. alert_seen_at is our own first observation — the only timestamp that makes "we saw it first" a real claim rather than an artifact.
Respect suppression. A route with a currently active alert does not fire in production, so it must not train as though it would. Note that planned-work postings sit in the feed 24/7 — presence in the feed is not the same as being active.

Provenance and honesty notes

Timestamps. Everything is TIMESTAMPTZ, stored UTC. NYC service days, rush hours, and weekend effects only make sense in America/New_York — convert before bucketing by hour or day, and note that DST transitions produce a 23- and a 25-hour day.
fetched_at is when we observed it, not when the MTA generated it. That distinction is the whole point of the archive; feed_ts in feed_snapshots carries the feed's own timestamp when you need the other one.
Capture gaps are visible, not hidden. feed_snapshots records every fetch attempt including failures. There is a known ~6-minute gap on 2026-07-30 (19:52–19:58 ET) from a planned database migration. Check feed_snapshots before attributing a quiet period to the subway rather than to us.
JSON columns (active_period, informed_entities, payload) are stored as JSON strings, not nested Parquet structs — parse with json.loads or DuckDB's json functions.
Reproducible cutoff. Every export run is bounded by one instant, recorded in manifest.json. Capture never stops, so a row count only matches a live database when both sides are bounded by the same instant.
Not an MTA product. Unofficial, unaffiliated, not endorsed. Derived from public feeds that remain subject to the MTA's own terms.
No personal data. GTFS-RT describes vehicles, not riders. Ridership figures are pre-aggregated MTA open data.
station_id convention: COALESCE(parent_station, stop_id) — platform rows are rolled up to the station they belong to.

Updates

Updated daily, at roughly 10:30 UTC, publishing the previous day once the upstream nightly jobs have finished computing and labelling it.

History here only ever grows. A partition file stays open — rewritten on each run — until its period has closed and any late-arriving updates have landed; after that it is final and never touched again. Raw firehose days are final immediately, since a captured row never changes. So:

New days append. Old days do not move.
The current month is live and will keep growing until the month ends.
A file that is final stays byte-identical, which is what makes a pinned revision reproducible. Pin one with revision= if you need that guarantee.

The one deliberate exception is alerts_unique, which is republished whole each run because an alert's last_seen_at keeps advancing while it is still live.

Because the source database prunes the firehose at 60 days, this archive becomes the only surviving copy of those days — its history grows past what the database itself can hold.

Citation

@misc{williams2026nycsubway,
  author = {Williams, Henry},
  title  = {NYC Subway Realtime Archive},
  year   = {2026},
  url    = {https://huggingface.co/datasets/digitalhen/nyc-subway-realtime},
  note   = {Captured continuously from MTA GTFS-RT feeds since 2026-04-16}
}

Licence

CC BY-NC 4.0 — use, share, and adapt it freely, with credit to Henry Williams / subway.fyi, for non-commercial purposes.

Two layers, owned differently:

layer	status
upstream MTA GTFS-RT, static GTFS, ridership	the MTA's public open data, under the MTA's own terms. Not claimed here — fetch it from the source if that's all you need.
this archive and everything derived from it — the captured history, the analytical panels, the learned baselines, the labelled predictions	my work, licensed to you under CC BY-NC 4.0

The distinction matters because the MTA keeps no history: each poll overwrites the last. So this isn't a mirror of a public dataset — it's a record that wouldn't otherwise exist.

For commercial use, including training a model that's commercially deployed or sold, please get in touch first — derived weights carry the same limit. Static GTFS and ridership tables are included for reproducibility and remain the MTA's.

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Before you dive in