Add dataset card

README.md

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+---
+license: cc-by-4.0
+language:
+  - en
+pretty_name: AC Transit Automatic Passenger Counter Records, 2019-2026
+size_categories:
+  - 100M<n<1B
+task_categories:
+  - time-series-forecasting
+tags:
+  - transit
+  - transportation
+  - mobility
+  - urban
+  - geospatial
+  - bay-area
+configs:
+  - config_name: default
+    data_files: "year=*/month=*/data_0.parquet"
+---
+
+# AC Transit Automatic Passenger Counter Records, 2019-2026
+
+Stop-level boarding and alighting counts for the AC Transit bus network in
+Alameda and Contra Costa counties, California, from January 2019 through
+May 2026. The records come from the automatic passenger counters (APCs)
+mounted at the doors of the buses: one row per stop event, with the number of
+passengers who got on, the number who got off, and the load the bus left with.
+
+89 monthly Parquet files, ~5.9 GB, partitioned `year=/month=`. The span covers
+the pre-pandemic baseline, the March 2020 collapse, and the uneven recovery
+that followed, which is the comparison the data was assembled to support.
+
+## Loading
+
+```python
+import pandas as pd
+
+df = pd.read_parquet(
+    "hf://datasets/somemone/ac-transit-apc/year=2019/month=2/data_0.parquet"
+)
+```
+
+Or the whole thing as a partitioned dataset:
+
+```python
+import pyarrow.dataset as ds
+from huggingface_hub import snapshot_download
+
+path = snapshot_download("somemone/ac-transit-apc", repo_type="dataset")
+table = ds.dataset(path, format="parquet", partitioning="hive")
+```
+
+## Schema
+
+| column | type | notes |
+| --- | --- | --- |
+| `route` | string | route as reported by the vehicle |
+| `route_id` | string | GTFS route identifier |
+| `stop_id` | string | GTFS stop identifier |
+| `event_timestamp` | timestamp[us] | when the doors opened |
+| `service_date` | timestamp[us] | service day the event belongs to |
+| `boardings` | int32 | passengers on |
+| `alightings` | int32 | passengers off |
+| `passenger_load` | int32 | load leaving the stop |
+| `latitude` | double | stop position |
+| `longitude` | double | stop position |
+| `door_lift_flags_possibly` | string | vehicle flag field; its low bit separates the two APC-equipped subfleets |
+
+## Working with it
+
+These are **raw counts, not ridership estimates.** Two corrections matter
+before the numbers mean what you want them to:
+
+- **Partial fleet coverage.** Not every bus carries a working APC, and the
+  equipped share varies by route and by month. Summing raw boardings
+  undercounts actual ridership, unevenly. Scale by the observed capture rate
+  per (route, subfleet) before comparing across routes or over time. The low
+  bit of `door_lift_flags_possibly` separates the two subfleets, which have
+  materially different capture rates.
+- **Drift against reported totals.** Even after capture correction, monthly
+  sums do not match the agency's National Transit Database filings. A
+  per-month calibration factor closes the gap.
+
+`passenger_load` is the counter's running estimate and accumulates error along
+a trip; it is more useful for relative load profiles than as an absolute.
+
+A small number of coordinates are wrong. In a representative month, ~0.1% of
+rows across ~15 stop IDs fall outside the service area, and the extremes land
+hundreds of kilometres away. Most apparent outliers are legitimate -- the
+network reaches Fremont, and the Dumbarton corridor crosses to Palo Alto --
+but do not derive a bounding box from `min`/`max` without filtering first.
+
+## Privacy
+
+The records contain no operator, vehicle, or fare-media identifiers, and no
+rider-level information of any kind. A row is a count at a stop at a time.
+Coordinates are bus stop positions.
+
+## Provenance and license
+
+The underlying records originate with the Alameda-Contra Costa Transit
+District (AC Transit). Released here under
+[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). This dataset is not
+affiliated with or endorsed by AC Transit.
+
+## Related
+
+Built for the **[AC Transit Ridership Explorer](https://github.com/somemone0/ac-transit-ridership)**,
+an interactive map of weekly stop-level ridership and pandemic recovery
+([live](https://ac-transit-ridership-385939155005.us-west1.run.app)). That
+repository carries the capture-correction and calibration pipeline these
+records feed.