Prediction of Overnight Truck Parking Availability
Key Takeaways
- Problem: Planners lack real-time visibility into lot-level availability, utilization, and demand patterns.
- Solution: Developed a cost-effective, scalable model using clustering and crowdsourced feedback to predict parking availability without sensors.
- Practitioner Commentary: Seven TRB Reviewers praised its practical utility, national scope, and alignment with real-world driver behavior.
How We Built a Scalable, Sensor-Free Truck Parking 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
- Can parking availability be predicted without installing costly sensors?
- Can a real-time, nationwide parking information system be built?
Data Collection, Processing, and Modeling
- Data Fusion: Combined satellite imagery, highway traffic data, and real-time availability reports submitted by truck drivers through a mobile app.
- Truck Parking Analytic Cluster: Clustered over 10,000 parking facilities into five types based on traffic volume, amenities, fees, and more.
- Missing Data & AI: Missing values were completed using Singular Value Decomposition (SVD), and future availability was forecasted using Long Short-Term Memory (LSTM) neural networks.
What This Means for Policy and Planning
- Accurate Availability Prediction: The model was validated against real-time sensor and camera data for 50+ parking facilities, achieving strong alignment with observed utilization.
- Policy Utility: The system supports demand forecasting, performance monitoring, and infrastructure planning—helping agencies prioritize investment and identify high-need locations.
- Cost-Effective Alternative: Combines AI forecasting with crowdsourced data to offer a scalable, low-cost substitute for sensor-based solutions.
Practitioner Insights (TRB Reviewers’ Comments)
- Grounded in Reality: “Rarely does a study reflect the lived experience of drivers so accurately. As one reviewer highlighted, truckers begin their day already planning where they’ll shut down—this study understands that.”
- Nationwide, Practical Scope: “This study fills the gap left by fragmented, state-level systems by integrating multiple data sources at national scale.”
- Smart Clustering: “The five-category framework based on operational traits offers more actionable insight than legacy classifications.”
- Scalable Hybrid Model: “Combining AI predictions with user reports is a cost-effective, scalable alternative to expensive infrastructure.”
- Policy-Ready: “This is not just research—it’s immediately useful for drivers, companies, and public agencies alike.”
Conclusion
This model demonstrates how planners can forecast parking availability without costly infrastructure. It offers a scalable, cost-efficient alternative that can inform policy and investment decisions nationwide.
Access the Full Paper