Redding Schedule#

GTFS Stop Time Updates Redding 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

Redding 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 3,042 97.4% 29.6% 0.2 1.9 100.0% 29.0% 8.1 7.0 -2.1
2025-06-02 1,392,096 98.6% 64.1% 0.2 2.9 100.0% 80.0% 6.7 3.2 1.5
2025-06-03 1,374,555 98.6% 59.4% 0.2 2.9 100.0% 82.0% 8.2 3.6 1.8
2025-06-04 1,385,373 98.4% 61.9% 0.2 2.9 99.0% 80.0% 7.2 3.5 1.6
2025-06-05 1,425,258 98.6% 64.0% 0.2 3.0 100.0% 78.0% 7.1 3.3 1.4
2025-06-06 1,417,893 98.6% 60.6% 0.2 2.9 100.0% 81.0% 8.0 3.6 1.6
2025-06-07 747,609 98.5% 73.5% 0.2 2.9 100.0% 61.0% 5.0 2.4 0.4
2025-06-08 2,877 97.9% 27.3% 0.2 2.6 100.0% 29.0% 8.2 7.4 -1.3
2025-06-09 1,384,083 98.6% 65.5% 0.2 2.9 100.0% 73.0% 6.7 3.2 1.1
2025-06-10 1,363,926 98.7% 65.4% 0.2 2.9 97.0% 78.0% 7.3 3.1 1.4
2025-06-11 1,426,377 98.4% 64.7% 0.2 2.9 100.0% 75.0% 6.6 3.2 1.3
2025-06-12 1,340,385 97.3% 67.1% 0.2 2.8 100.0% 74.0% 6.9 3.1 1.2
2025-06-13 1,411,041 98.5% 64.0% 0.2 2.9 100.0% 80.0% 7.2 3.4 1.4
2025-06-14 724,395 98.1% 68.5% 0.2 2.9 100.0% 71.0% 7.1 2.7 0.9
2025-06-15 2,778 97.8% 34.6% 0.2 2.4 75.0% 36.0% 9.1 4.7 0.0