---
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 |

Readers that understand Hive partitioning also surface `year` and `month` as
columns, recovered from the directory names rather than stored in the files.

## 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.