San Joaquin Schedule#

GTFS Stop Time Updates San Joaquin 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

San Joaquin 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)
2025-06-01 372,278 96.1% 54.8% 0.2 2.9 75.0% 74.0% 7.6 3.7 0.8
2025-06-02 1,884,330 96.6% 66.9% 0.2 3.0 77.0% 69.0% 6.6 2.7 0.0
2025-06-03 1,866,406 96.5% 66.4% 0.2 3.0 80.0% 69.0% 6.2 2.6 0.2
2025-06-04 1,961,126 96.7% 66.9% 0.2 3.0 83.0% 69.0% 6.1 2.6 0.2
2025-06-05 1,946,440 96.6% 65.0% 0.2 3.1 81.0% 67.0% 6.1 2.8 0.1
2025-06-06 1,907,276 96.7% 65.7% 0.2 3.0 82.0% 67.0% 6.3 2.7 0.2
2025-06-07 457,094 96.1% 53.9% 0.3 3.0 87.0% 78.0% 8.0 3.8 1.1
2025-06-08 425,754 96.5% 52.3% 0.3 3.0 84.0% 75.0% 7.6 3.8 1.2
2025-06-09 1,866,536 96.7% 66.3% 0.3 3.1 79.0% 67.0% 6.2 2.7 0.2
2025-06-10 1,652,058 96.6% 66.5% 0.2 3.0 70.0% 68.0% 6.0 2.8 0.0
2025-06-11 1,926,838 96.6% 66.8% 0.2 3.0 80.0% 66.0% 6.0 2.8 0.0
2025-06-12 1,621,624 95.7% 63.9% 0.2 3.0 73.0% 66.0% 6.2 2.9 0.1
2025-06-13 1,940,570 96.6% 62.7% 0.3 3.0 81.0% 66.0% 6.8 3.1 0.2
2025-06-14 389,048 96.3% 51.4% 0.3 3.0 74.0% 83.0% 8.4 4.0 1.4
2025-06-15 306,482 96.2% 51.3% 0.3 3.0 82.0% 74.0% 9.3 4.4 1.0