Basin Transit GMV Trip Updates

Basin Transit GMV Trip Updates#

Schedule: Basin Transit GMV Schedule#

Data reflecting: Jan 2026 weekdays

One of the most common transit user behaviors is to consult an app (Google Maps, Apple Maps, NextBus, etc) to find out when the bus or train is going to arrive.

That widely desired piece of information is powered by GTFS Real-Time Trip Updates, specifically the Stop Time Updates specification. The underlying data produced here is huge. Imagine every instance a bus arrives at a stop in California. Multiply that by 30 for the 30 minutes before the bus arrives, and that’s the dataset we’re working to distill into usable performance metrics for all transit operators.

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

Map of Stop Metrics#

The following layers are available:

  • Stops with too late predictions (riders miss bus) or too early predictions (riders wait longer).

  • Stops needing more real-time arrival information (<90% of minutes).

  • All stops along routes that meet either of the 1st two criteria.

  • All stops plotted by average prediction error

Priority Stops#

Stops that are too late (3-5 min late) or too early (3-5 min early)

< 90% minutes with stop time updates#

This metric is the easiest to achieve. For starters, having information is better than no information.

  • Goal: at least 90% of minutes has predicted arrival information.

  • Note: Newmark paper shows that among four CA operators, this metric is fairly easy to reach and operators can even reach up to 90% completeness.

Make this Notebook Trusted to load map: File -> Trust Notebook

Search by stop#

Due to size limitations, the routes that have at least one priority stop are presented in an interactive table.

Loading ITables v2.7.0 from the init_notebook_mode cell... (need help?)

Stop Metrics by Route#

Average prediction error: The accuracy of the predictions is predicted arrival - actual arrival. Closer to zero or small positive values are better, as you are more likely to catch the bus with minimal wait time.

Goal 1: fewer stops with negative prediction errors. We would rather have transit users follow the predictions and wait for the bus.

Goal 2: tighten the IQR range of prediction errors and have the range move closer to zero for shorter expected wait times.

Update completeness: The% of minutes with 2+ predictions available. Higher is better. Goal: 90% or above

Bus catch likelihood: The % prediction early or on-time predictions, and captures whether you’re likely to catch the bus by following the prediction exactly. Higher is better. Goal: 80% or above

Prediction spread / wobble: this metric tracks whether the predicted arrival time was consistent before the bus arrived. Lower is better, as the predictions are not fluctuating wildly and frustrating for the rider.

Full Route Table#

This table shows all the routes side-by-side, in ascending order by prediction error (more late stops shown first).

The nanoplots show prediction error by stop order, showing there is quite a bit of variation even along the same route.

Average prediction error: The accuracy of the predictions is predicted arrival - actual arrival. Closer to zero or small positive values are better, as you are more likely to catch the bus with minimal wait time.

Goal 1: fewer stops with negative prediction errors, lower bound of IQR not negative. We would rather have transit users follow the predictions and wait for the bus.

Goal 2: small IQR range of prediction errors and have the range move closer to zero for shorter expected wait times.

Weekday Route Summary Metrics
Route-Direction Prediction Error (negative = late; positive = early) Prediction Error (ordered by stop) IQR # Early Stops # Late Stops
6593__15 15 Yucca Valley/Palm Springs MCAGCC Airport_1
-10.15
2.15−1.71−1.10−1.71−0.66−0.37−1.150.080−1.08−0.85−0.83−1.50−1.112.15
-3.84.5
0
0
6596__21 21 Landers Loop_0
-2.99
0−3.76−1.21−0.99−1.28−1.04−1.05−1.15−1.71−1.08−0.48−3.76−2.79−2.85−0.17−1.32−1.24
-2.42
0
1
6590__3B 3B Twentunine Palms_0
-1.66
0−2.79−1.75−1.71−1.66−2.79−0.66−1.37−1.32−1.50−1.35−1.51−1.45−1.36−1.18−1.03−1.71−0.66−0.73−0.39−0.52−0.50−0.31−0.59−1.03−0.91−0.65−0.36−0.46−1.68−2.13
-2.20.8
0
0
6590__3B 3B Twentynine Palms_0
-1.62
0−2.79−1.75−1.71−1.66−2.79−0.66−1.37−1.32−1.50−1.35−1.51−1.45−1.36−1.18−1.03−1.71−0.66−0.73−0.39−0.52−0.50−0.31−0.59−1.03−0.91−0.65−0.36−0.46−1.68−2.13
-1.41.1
0
0
6591__7A 7A Yucca Valley North_0
-0.9
0.35−2.08−1.20−1.21−0.70−1.04−1.05−1.15−2.08−1.32−1.240.080−0.87−1.20−0.84−0.94−0.81−0.85−0.63−1.08−0.60−0.91−0.66−0.70−0.78−0.70−0.69−0.56−0.50−0.37−0.45−0.48−0.53−0.48−0.60−0.58−0.990.35
-1.41.5
0
0
6588__1 1 Yucca Valley - Twentynine Palms_0
-0.66
0.56−1.71−0.85−0.63−0.60−0.66−0.70−0.78−0.70−0.69−0.56−0.50−0.37−0.45−1.08−0.48−1.20−0.53−1.21−0.48−0.70−0.60−0.91−0.58−1.04−0.99−1.05−1.15−0.610.350.040−0.13−0.250.010−0.49−0.0200−0.070−0.30−0.070−0.62−0.81−0.80−0.73−0.67−0.66−0.90−0.67−0.59−0.60−0.88−0.81−0.90−1.01−0.74−0.65−0.48−1.710.560.180.180.180.0900.090−0.060−0.17−0.40−0.18−0.40−1.10
-1.51.9
0
0
6595__12 12 Yucca Valley - Palm Springs_0
-0.45
2.86−2.132.152.862.21−2.04−2.13−1.78−0.85−1.08
-2.61.2
0
0
6595__12 12 Yucca Valley - Palm Springs_1
-0.31
2.15−1.50−1.08−0.85−0.83−1.50−1.112.15
-0.82
0
0
6589__3A 3A Twentynine Palms Marine Base_0
-0.18
0.56−1.710.560.180.180.180.0900.090−0.060−0.17−0.18−0.40−0.40−0.15−1.71−1.10−0.420.0600.0900.01000−0.170.080−0.17−0.070−0.040
-1.11.4
0
0
6588__1 1 Yucca Valley - Twentynine Palms_1
-0.1
0.53−1.71−0.40−1.100.0600.0900.01000−0.17−1.710.0800.53−0.170.53−0.0700.27−0.0400.49−0.480.420.36−0.660.0700.100.0100.090−0.300.0700.15−0.14−0.27−0.49−0.0200.14−0.62−0.37−0.25−0.81−1.15−1.080.0800.35−1.200.270.0400.140.0400.0200.040−0.020−0.010−0.10−0.070−0.85
-1.22
0
0
6594__7B 7B Yucca Valley South_0
0.38
2.29−1.081.301.162.29−0.170.0800.881.080.270.0400.14−1.080.0400.0200.040−0.020−0.010−0.10−0.070−0.850.210.12−1.04
-0.92
0
0
6593__15 15 Yucca Valley/Palm Springs MCAGCC Airport_0
2.88
2.86−2.042.152.862.21−2.04−1.78−1.08−0.17−0.85−0.25−1.71−1.10
-0.15.5
0
0
6646__RR Ready Ride_1
4.19
4.5203.773.77
29.5
2
0
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