Trucking Lab

Truck parking data and tools for planning

Research · Availability prediction

Making truck parking predictable: a scalable AI approach without sensors

How we built a scalable, sensor-free truck parking forecasting model.

Accepted for TRBAM 2026 presentation

View technical brief Access the full paper

Key takeaways

Problem, solution, and what reviewers said

Problem

Planners lack real-time visibility into lot-level availability, utilization, and demand patterns.

Solution

A model using clustering and crowdsourced feedback to predict parking availability without sensors.

Practitioner commentary

Seven TRB reviewers responded positively, noting its practical utility, national scope, and alignment with real-world driver behavior.

Method

How we built a scalable, sensor-free forecasting model

Problem defined

Planners face difficulty in assessing actual lot utilization and forecasting demand because they lack reliable, lot-level availability data in real time.
Objectives
1. Can parking availability be predicted without installing costly sensors?
2. Can a real-time, nationwide parking information system be built?
Data collection, processing, and modeling
- Data fusion: satellite imagery, highway traffic data, and real-time availability reports submitted by truck drivers through a mobile app.
- Truck parking analytic cluster: over 10,000 parking facilities clustered into five types based on traffic volume, amenities, fees, and more.
- Missing data processing: missing values completed with Singular Value Decomposition (SVD) to maintain complete availability patterns across facilities.

Implications

What this means for policy and planning

Accurate availability prediction

Validated against sensor, camera, and entry/exit data at 14 public facilities in five states. Mean absolute error of 18.6% after retraining. Ongoing accuracy is reported in the monthly validation.

Policy utility

Supports demand forecasting, performance monitoring, and infrastructure planning, helping agencies prioritize investment and identify high-need locations.

Cost-effective alternative

Combines model-based forecasting with crowdsourced data to offer a scalable, low-cost substitute for sensor-based solutions.

Practitioner insights

TRB reviewers’ comments

Grounded in realityRarely does a study reflect the lived experience of drivers so accurately. Truckers begin their day already planning where they will shut down, and this study understands that.
Nationwide, practical scopeThis study fills the gap left by fragmented, state-level systems by integrating multiple data sources at national scale.
Smart clusteringThe five-category framework based on operational traits offers more actionable insight than legacy classifications.
Scalable hybrid modelCombining model predictions with user reports is a cost-effective, scalable alternative to expensive infrastructure.
Policy-readyThis is not just research. It’s immediately useful for drivers, companies, and public agencies alike.

Reviewer comments are paraphrased from the TRB review.

Conclusion

This model shows how planners can forecast parking availability without costly infrastructure. It offers a lower-cost alternative that can inform policy and investment decisions nationwide.