AC Transit Schedule

AC Transit Schedule#

GTFS Stop Time Updates AC Transit Schedule Summary#

Generally, we want better transit user experience. Specifically, the performance metrics we can derive from GTFS RT Trip Updates distills into the following objectives:

  1. Increase prediction reliability and accuracy

  2. Increase the availability and completeness of GTFS RT

  3. Decrease the inconsistency and fluctuations of predictions

AC Transit Schedule
service_date # Stop Time Update Messages % Minutes with 2+ Stop Time Update Message % Minutes with Accurate Predictions Prediction Spread / Wobble (minutes) Avg Stop Time Updates per Minute % Scheduled Trips with Trip Updates Bus Catch Likelihood (early + ontime predictions) Prediction Padding (minutes added to avoid missing bus) Prediction Error IQR (75th - 25th percentile) (minutes) Prediction Error 50th percentile (minutes)
2025-06-01 52,085,145 98.8% 81.0% 0.2 3.0 100.0% 65.0% 4.4 2.0 0.5
2025-06-02 64,159,749 98.8% 79.0% 0.2 3.0 100.0% 70.0% 4.9 2.1 0.7
2025-06-03 64,816,188 98.7% 77.1% 0.2 3.0 99.0% 73.0% 5.3 2.2 0.8
2025-06-04 72,043,281 98.8% 77.4% 0.2 3.0 100.0% 73.0% 5.2 2.2 0.8
2025-06-05 65,130,783 98.8% 77.4% 0.2 3.0 100.0% 73.0% 5.2 2.2 0.8
2025-06-06 65,046,828 98.8% 76.9% 0.2 3.0 99.0% 72.0% 5.2 2.2 0.8
2025-06-07 52,238,148 98.8% 79.0% 0.2 3.0 99.0% 62.0% 4.6 2.1 0.4
2025-06-08 52,301,580 98.8% 81.6% 0.2 3.0 99.0% 63.0% 4.3 2.0 0.4
2025-06-09 64,956,519 98.8% 80.5% 0.2 3.0 98.0% 71.0% 4.7 2.0 0.7
2025-06-10 71,828,565 98.8% 80.2% 0.2 3.0 100.0% 72.0% 4.8 2.1 0.7
2025-06-11 65,169,408 98.8% 79.4% 0.2 3.0 100.0% 72.0% 4.9 2.1 0.8
2025-06-12 74,299,161 98.3% 78.2% 0.2 2.9 99.0% 72.0% 5.0 2.2 0.8
2025-06-13 70,413,930 98.8% 78.4% 0.2 3.0 99.0% 71.0% 5.0 2.2 0.7
2025-06-14 48,856,698 98.7% 78.8% 0.2 3.0 99.0% 64.0% 5.0 2.1 0.5
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