MTA Scan

New York City / Transit observability

Subway signal desk

View deployed source ↗

Inspect gaps between live arrival predictions, narrow the network and explore constructed incident replays.

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0.00.60 alert0.851.0 station with no score in this window

Live gaps use the first snapshot of two distinct upcoming trips at the same route, stop and direction. Departure-only updates are excluded. Repeated trip pairs are suppressed within a four-hour window; this cache resets on restart. Feed freshness is separate from the age of the latest scored pair. Scores describe deviation in arrival estimates; they are not measured train passages, calibrated probabilities or official MTA incident reports.

Counters describe the live-source window; replay has separate measurements.

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stops scored
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rows scored
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anomalies ≥ 0.60
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high score ≥ 0.85
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newest data

Observation queue

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    Pipeline

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    Continuous collection / chronological evaluation

    History & forecasting

    Loading history…Waiting for a validated worker snapshot

    Follow the evidence as the dataset grows. The target is a future feed-predicted arrival-spacing proxy, not actual train passages or passenger waiting time.

    Retained history span
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    Complete fresh windows
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    Five-minute windows
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    Stable route/direction groups
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    Forecasts appear only after history, coverage and freshness checks pass.

    Inspect temporal evaluation & collection health

    No measured temporal evaluation is available yet.

    Lower error is better. Missing 24-hour seasonal values mean no comparable lag was available. Validation-selected rows can combine different algorithms across route/direction groups; the test set does not choose the method. Platform N/S uses the documented MTA stop suffix, not an inferred compass bearing.

    Source health at publication

        Inspect the protocol & source ↗

        Frozen replay · inspect a recorded scenario

        Measured on the frozen labelled replay (216 rows, 16 labelled, 3 incidents, 6 stations — small by design): recall@20 1.00 vs 0.625 for the best of three baselines, 3/3 incidents caught, 6.7 min average lead time — at the highest false-alarm rate of the four (0.030 vs 0.000–0.010). Full table below.

        online model (River) rolling z-score EWMA fixed threshold labelled incident window alert threshold 0.60

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        Evaluation & method notes Inspect the measured trade-offs

        Replay evaluation — every number below comes from the frozen artifact

        modelfalse-alarm ratep@20recall@20 incidents foundavg lead (min)avg time-to-detect (min)MRR