Monthly Validation of Truck Parking Availability Prediction Accuracy
Key Takeaways
- Validation approach: Monthly model predictions were compared with driver-reported parking availability at the same location and 15-minute time period.
- Main metrics: Accuracy was evaluated using average error and the share of predictions within ±0.10 and ±0.20 of driver-reported availability.
- Important context: Driver reports are more likely to be submitted when predictions do not match actual parking conditions, so reported errors may be conservative.
How Prediction Accuracy Was Evaluated
For freight planners and analysts, truck parking availability is not just a driver information issue. It is also a planning input for identifying demand patterns, evaluating corridor needs, and prioritizing future parking investments. To evaluate the prediction model, monthly model-predicted parking availability was compared with availability reports submitted by drivers.
The model provides parking availability predictions for each truck parking location on a 0 to 1 scale. A value of 0 means very low parking availability, 0.5 means moderate availability, and 1 means high availability. Each driver report was matched to the model prediction for the same parking location and the same 15-minute time period.
If a driver reported availability as 0.25 and the model predicted 0.30 for the same location and time, the prediction error was 0.05. Monthly performance was calculated by comparing all matched prediction and driver-report pairs.
Evaluation Metrics
Three practical metrics were used to summarize model performance.
- Average Error: The average difference between predicted availability and driver-reported ground truth. A lower value means the prediction was closer to the ground truth.
- Accuracy within ±0.10: The percentage of predictions within ±0.10 of the driver-reported value, e.g., reported 0.50 and predicted 0.40-0.60.
- Accuracy within ±0.20: The percentage of predictions within ±0.20 of the driver-reported value, e.g., reported 0.50 and predicted 0.30-0.70.
Monthly Validation Results
| Month | Average Error | Accuracy within ±0.10 | Accuracy within ±0.20 |
|---|---|---|---|
| Jan 2026 | 0.180 | 42.1% | 59.6% |
| Feb 2026 | 0.167 | 51.9% | 72.2% |
| Mar 2026 | 0.227 | 36.7% | 53.1% |
| Apr 2026 | 0.238 | 20.8% | 56.6% |
How to Interpret the Results
In January, the average error was 0.180, meaning the model prediction differed from the driver-reported value by about 18 percentage points on average. In February, the average error decreased to 0.167, and 72.2% of predictions were within ±0.20 of the driver-reported value.
The average error increased in March and April. However, driver reports are not random samples of all parking conditions. Drivers are more likely to submit reports when the actual parking situation is different from the availability information shown in our app. When the prediction is reasonably accurate, drivers have less reason to submit a report.
As a result, the validation data is structurally more likely to include cases where predictions were off. This means the reported average error should be viewed as a conservative measure rather than a complete representation of model performance across all locations and time periods.
Conclusion
The model provides a scalable way to estimate truck parking availability across many locations without relying on full sensor coverage. The validation results reflect an important feature of driver-reported data: reports tend to be submitted more often when field conditions differ from expectations. Even with this reporting pattern, the approach provides one of the most practical ways to support truck parking analysis at scale.
Need Truck Parking Analytics for Planning?
Trucking Lab helps agencies and planning teams reduce the undifferentiated hard lifting behind freight planning, truck parking studies, and corridor-level needs assessments. Instead of manually collecting, cleaning, matching, and interpreting fragmented parking data, planners can use structured analytics to move faster from raw information to actionable planning insights.