Trucking Lab

Truck parking data and tools for planning

AI for truck parking planning

Five AI uses reshaping truck parking planning

AI can help agencies move from static parking studies to forward-looking planning. The most useful applications forecast availability, monitor use, identify unmet demand, test policy scenarios, and compare investment options.

01Predict availability
02Measure utilization
03Forecast demand
04Test scenarios
05Optimize investment

Key takeaways

The value is knowing where, when, and why shortages occur

  • Key takeawayThe immediate value of AI is not automation for its own sake. It is helping planners identify where, when, and why parking shortages occur.
  • Why it mattersBetter forecasts can reduce last-minute parking searches, reveal corridor gaps, and strengthen the evidence behind capital investments.
  • Planning approachBegin with a clearly defined parking decision, then select the data and AI method needed to support that decision.

Start here

Start with the planning question

The technology should follow the decision, not the other way around.

Where will parking fill up?

Use predictive availability models based on historical patterns and current operating conditions.

Where is capacity missing?

Use demand modeling to compare freight activity, observed parking, and available supply.

Which investment performs best?

Use scenario modeling or digital-twin methods to compare locations, designs, costs, and expected outcomes.

Applications

Each application addresses a different planning or operational question.

  1. 01

    Predictive parking availability

    AI can combine historical parking patterns with traffic, weather, economic indicators, and available sensor data to estimate whether parking is likely to be available when a driver arrives.

    Best use

    Provide forward-looking availability information through navigation, fleet-management, or agency information systems.

    Planning value

    Moves parking information beyond a current snapshot and toward anticipated future conditions.

    Supporting sources: 1 and 3.

  2. 02

    Utilization analysis through sensor fusion

    Computer vision, LiDAR, radar, and in-ground sensors can be combined to monitor vehicle type, parking occupancy, and dwell time.

    Best use

    Conduct continuous facility studies and identify peak demand, fill rates, staging behavior, and non-truck use.

    Planning value

    Replaces isolated manual counts with a continuous record of how parking facilities are used.

    Supporting sources: 1 and 2.

  3. 03

    Dynamic demand forecasting

    AI can combine anonymized truck GPS or telematics data with economic activity, land use, port operations, and existing parking supply to identify unmet demand.

    Best use

    Identify corridors where freight activity and parking demand consistently exceed available supply.

    Planning value

    Helps agencies update parking priorities as freight patterns and surrounding development change.

  4. 04

    Generative AI for scenario planning

    Generative AI and agent-based models can examine how changes in policy, regulation, development, or freight activity may affect future parking demand.

    Best use

    Test proposed hours-of-service changes, logistics hubs, or major transportation projects before implementation.

    Planning value

    Helps planners identify possible secondary effects and prepare mitigation strategies before a decision is finalized.

  5. 05

    Digital twins for investment analysis

    A digital twin can combine freight flows, crash data, land-use constraints, community impacts, and construction costs to compare parking investment strategies.

    Best use

    Compare candidate sites, facility sizes, public-private options, and phased implementation plans.

    Planning value

    Supports site selection based on several objectives, not location or available land alone.

Utah example

Identifying future parking gaps

Trucking Lab combined parking utilization and truck-volume data to identify four Utah counties where truck parking demand is expected to grow.

What the analysis shows

  • Parking pressure is concentrated along major freight corridors.
  • Current statewide totals can hide localized supply-demand gaps.
  • Corridor screening can identify where a deeper site study is most useful.

Why it matters: demand forecasting helps agencies look beyond current shortages and identify where future freight growth may require additional capacity.

Utah map showing projected truck parking shortages along I-15 and I-80
Projected truck parking gaps along major Utah freight corridors, including I-15 and I-80.

Summary

The bottom line

AI can make truck parking planning more forward-looking, but the planning question should stay clear. Forecast availability when drivers need better information. Model demand when agencies need investment priorities. Use broader scenario tools when several policy or capital options must be compared.

Sources

  1. FDOT, Truck Parking and Rail Detection Design Using Machine Learning Concepts (2024).
  2. Gong et al., Marking-Based Perpendicular Parking Slot Detection Algorithm Using LiDAR Sensors (2024).
  3. U.S. DOT, Truck Parking Pattern Aggregation and Availability Prediction by Deep Learning (2021).

Related research

Authored, reviewed, and approved by Troy Choi, Ph.D., P.E. Transportation Systems Optimization and Engineering Research.

Start with the data these methods need

Verified inventory, 24/7 utilization patterns, and demand screening for your state, ready on Day 1.

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