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Monthly Validation of Truck Parking Predictions
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How to read the numbers
The page uses percentages and percentage points so planners can understand the result without converting a 0-to-1 score.
The model’s average percentage error for the month. Lower is better.
The share of predictions no more than 10 percentage points away from the matched driver report.
The share of predictions no more than 20 percentage points away from the matched driver report.
Monthly results
Monthly update: add one new row at the bottom of the table. Everything above updates automatically.
| Month | MAPE | Within 10 points | Within 20 points |
|---|---|---|---|
| Jan 2026 | 18.0% | 42.1% | 59.6% |
| Feb 2026 | 16.7% | 51.9% | 72.2% |
| Mar 2026 | 22.7% | 36.7% | 53.1% |
| Apr 2026 | 23.8% | 20.8% | 56.6% |
| May 2026 | 26.1% | 22.5% | 47.5% |
| Jun 2026 | 23.7% | 16.7% | 50.0% |
Prediction coverage and evidence
Every location has a weekly prediction pattern, but the amount of direct evidence differs by site.
Total locations
Every location has a complete weekly pattern covering 672 fifteen-minute periods.
Site-differentiated
These patterns include direct observations from the individual site.
Cluster-inferred
These patterns rely more heavily on operationally similar facilities.
How the prediction baseline works
Locations are grouped using characteristics such as capacity, facility type, fees, amenities, and nearby truck traffic.
Each group supplies a typical weekly availability pattern. Direct driver observations are then used to check or adjust individual locations where evidence is available.
How monthly validation works
Each driver report is matched to the model prediction for the same parking location and the same fifteen-minute period.
- MAPE summarizes monthly prediction error.
- Within 10 points shows tighter agreement.
- Within 20 points shows broader agreement.
The bottom line
The model provides national truck parking availability patterns without requiring sensors at every facility. Monthly validation makes performance visible, while facility-level confidence information helps planners decide where the data is appropriate for screening or deeper investment analysis.
Need confidence-graded parking analytics?
Trucking Lab provides structured availability patterns, evidence-density information, and corridor-level analytics for freight plans and truck parking studies.
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