bt-gtfs-rt-labels-2026-04-18

Ground-truth arrival labels inferred from Bloomington Transit's public GTFS-RT feed, for evaluating delay prediction quality at a small agency that publishes only trip-level delays.

Rows

Two parquet files:

1. ground_truth_arrivals.parquet - 11,283 rows. One row per (trip_id, stop_sequence). Columns:

   • trip_id, route_id, stop_id, stop_sequence, vehicle_id, service_id, service_date_local
   • scheduled_arrival_utc (ISO UTC)
   • inferred_actual_arrival_utc (ISO UTC, null if confidence == low)
   • inference_method ∈ {stopped_at, midpoint, excluded}
   • confidence ∈ {high, medium, low}
   • excluded_reason (empty for high/medium)
   • first_pos_sample_utc, last_pos_sample_utc

2. bt_prediction_error.parquet - 693,648 rows. One row per prediction (per snapshot, per labelled (trip_id, stop_sequence)). Columns:

   • trip_id, route_id, stop_id, stop_sequence, service_id, ground_truth_confidence
   • snapshot_ts_utc, snapshot_epoch
   • bt_delay_seconds (BT's published trip-level delay at the snapshot)
   • bt_predicted_arrival_utc, inferred_actual_arrival_utc
   • error_seconds = bt_predicted − inferred_actual (signed; positive = BT late)
   • horizon_seconds = inferred_actual − snapshot_ts
   • horizon_bucket ∈ {0-60, 60-180, 180-600, 600-1800, 1800+}

Data collection

Logger polls position_updates.pb + trip_updates.pb + alerts.pb every 10 s. Raw .pb snapshots are not in this dataset (refreshable from S3) but the derived labels are.

Window: 2026-04-17 through 2026-04-19 (~50 h, spanning Friday evening, Saturday full day, and Sunday).

Inference method for actuals

Per (trip_id, vehicle_id):

1. Exclude the trip if consecutive current_stop_sequence values go backwards, OR gap > 3 min between samples.
2. For each scheduled stop on that trip:
   • HIGH: first sample with current_stop_sequence == N AND current_status == STOPPED_AT -> that vehicle.timestamp is the actual arrival.
   • MEDIUM: midpoint between last sample at N−1 and first sample at N+1.
   • LOW / excluded: neither condition satisfied.
3. Scheduled arrival from static stop_times.txt (HH:MM:SS, HH≥24 allowed for overnight wrap), combined with service_date = local_date(first_pos_sample), localised in America/New_York and converted to UTC.

Label quality: 18.3 % high, 53.2 % medium, 28.5 % low (excluded).

Known limitations

• Temporal coverage: ~50 h across 2026-04-17 to 2026-04-19 (Friday evening + Saturday + Sunday). No Mon-Thu coverage; no holiday / severe-weather windows.
• Route coverage: 12 of BT's 16 routes have labels; routes 12 / 13 / 14 / 122927 are unobserved.
• 460 unique trips contributed labels across 487 (trip, vehicle) instances.
• Timezone note: BT's agency.txt declares America/New_York (not the canonical America/Indiana/Indianapolis). Both have the same UTC offset in 2026 but use with care.

Licence

MIT. The underlying GTFS-RT feed is publicly available from ETA Transit Systems at s3.amazonaws.com/etatransit.gtfs/bloomingtontransit.etaspot.net/. Their terms of service govern redistribution of raw bytes; the labels derived here are a transformation and are released under MIT.

Citation

@misc{btgtfslabels2026,
  title  = {bt-gtfs-rt-labels-2026-04-18: inferred arrival-time labels for Bloomington Transit},
  author = {Sk, Ayan and Dodia, Chirag and Patel, Omkar and <Naishal>},
  year   = 2026,
  url    = {https://huggingface.co/datasets/Ayansk11/bt-gtfs-rt-labels-2026-04-18}
}