For state DOTs, MPOs, and freight analysts, the truck parking crisis is a complex challenge of safety, efficiency, and infrastructure. Traditional solutions are reactive. The next leap is a proactive, predictive strategy powered by Artificial Intelligence (AI). Here are five AI trends reshaping our approach.

1. Predictive Parking Availability Modeling (TPIMS 2.0)

Moving beyond static occupancy data, AI analyzes historical trends, real-time traffic, weather, and economic indicators to forecast parking probability hours in advance. It answers the driver’s core question: “Will there be a spot when I arrive?” It leverages existing TPIMS data and camera/sensor feeds, making it highly feasible.^1,3

• Application: The predictive model’s output, often a probability score, is integrated into backend systems. This data can be broadcast to in-cab navigation systems and fleet management platforms, enabling dynamic routing and providing drivers with actionable, forward-looking guidance.
• Key Benefit: Shifts the paradigm from real-time reporting to future-state forecasting, drastically reducing dangerous parking and search time while optimizing network utilization.

2. Utilization Analysis via Sensor Fusion

Advanced AI models now leverage sensor fusion, combining data from a suite of technologies like computer vision (from existing traffic cameras), LiDAR², radar, and in-ground sensors. This multi-layered approach delivers hyper-accurate, 24/7 detection of vehicle types, occupancy rates, and precise dwell times.¹

• Application: Public agencies can deploy this system to conduct continuous, automated studies. The AI doesn’t just provide a snapshot; it reveals patterns—peak demand windows, illegal usage by passenger vehicles, staging behaviors, and rates of fill—providing a rich, longitudinal dataset for planning and policy justification.
• Key Benefit: Eliminates the need for manual counts and delivers continuous, high-fidelity data that reveals not just occupancy, but usage patterns and trends critical for strategic decision-making.

3. Dynamic Demand Forecasting for Freight Planning

AI creates a “living” demand model by continuously analyzing vast, anonymized datasets from truck GPS/telematics, fused with economic trends, land use, and port activity. This reveals not only where trucks currently park but, crucially, where latent and unmet demand exists by understanding intended stops and routes.

• Application: Agencies can use this dynamic model to move beyond outdated, static studies. It enables them to precisely pinpoint corridors with chronic, evidence-based supply-demand gaps, building compelling, data-driven cases for targeted public or private investment.
• Key Benefit: Transforms truck parking planning from a periodic exercise into a continuous, data-driven process, ensuring infrastructure investments are precisely targeted and justified with current freight movement patterns.

4. Generative AI for “What-If” Scenario Planning

Planners use generative AI and agent-based modeling to simulate the complex freight ecosystem. By running “what-if” scenarios, they can understand the secondary and tertiary impacts of policy changes, new regulations, or major infrastructure projects on parking demand.

• Application: Model the impact of a proposed HOS rule change or a new logistics hub. The AI can predict specific spikes in regional parking demand, allowing agencies and consultants to develop proactive mitigation strategies before policies are finalized.
• Key Benefit: Enables a shift from reactive analysis to proactive simulation, allowing planners to stress-test policies and minimize unintended consequences on parking infrastructure.

5. Digital Twins for Multi-Objective Optimization

An AI-powered digital twin synthesizes massive datasets—including network flow, crash statistics, land use regulations, environmental justice zones, and construction costs—to optimize every aspect of parking investment: location, scale, design, and phasing.

• Application: For a statewide master plan update, a digital twin can compare countless investment scenarios (public, private, PPP). It provides a ranked list of candidate sites, phased roll-out plans, and detailed impact analyses on safety, equity, and traffic efficiency.
• Key Benefit: Moves site selection beyond simple spatial analysis to a multi-objective optimization process, maximizing return on investment and ensuring solutions balance safety, efficiency, and community goals.

Case Study: Identification of Truck Parking Gaps

In Utah, four counties were identified through our utilization data and truck volume as critical areas where truck parking demand is expected to grow.

The Bottom Line

AI is fundamentally shifting truck parking management from a reactive to a predictive and proactive discipline. These tools are viable, the data is available, and the benefits—enhanced safety, operational efficiency, and optimized capital planning—are now within reach.