The customer operates an inland container yard connected to the national rail network. The yard handles a mix of import/export containers and serves as a consolidation point for regional distribution.

Operations include:

• Rail-served container blocks

• Rubber-tired gantry cranes (RTG)

• Yard stackers and reach stackers

• High daily peak fluctuations driven by train schedules

With increasing volume, manual alignment and visual guidance were no longer sufficient to maintain efficiency.

• Visual alignment limits
  Crane operators depended on experience and markings, which varied by lighting, weather, and operator skill.

• High rehandle cost
  Misplaced containers often required secondary moves, consuming time and fuel.

• Rail-yard synchronization pressure
  Missed alignment during rail loading caused cascading delays.

• Lack of objective precision data
  Management could not quantify positioning accuracy or identify systemic inefficiencies.

GridRTLS implemented an RTK-based crane and vehicle positioning architecture:

1) RTK reference infrastructure

A GNSS Differential Reference Station was installed at a surveyed location to provide real-time correction data across the yard.

2) Crane & stacker positioning terminals

Each gantry crane and yard stacker was equipped with an RTK positioning terminal, delivering:

• Centimeter-level position accuracy

• Continuous heading and movement data

• Real-time coordinates aligned with yard slot maps

3) Yard coordinate model

The yard was modeled as a digital grid with defined:

• Container slots

• Rail alignment points

• Buffer and exclusion zones

RTK positions were mapped directly to this coordinate system, eliminating manual translation.

4) Operations & analytics layer

Supervisors gained:

• Live crane and vehicle positions

• Historical movement replay

• Accuracy and efficiency metrics by block, shift, and operator

Phase 1 — Survey and coordinate definition
The yard was surveyed to establish a common coordinate reference aligned with rail tracks and container rows.

Phase 2 — RTK deployment
RTK terminals were mounted on cranes and stackers with vibration-resistant fixtures and redundant power.

Phase 3 — Integration and calibration
Correction data was distributed via industrial gateways over the yard network. Calibration focused on validating slot-level accuracy rather than abstract coordinates.

Phase 4 — Operator training and tuning
Operators were trained to use RTK guidance for final placement, while supervisors tuned thresholds for acceptable deviation.

After deployment:

• Rehandles dropped by more than 60%, directly improving throughput.

• Placement accuracy approached 97%, reducing corrective moves.

• Rail-yard coordination improved, with fewer missed loading windows.

• Management gained measurable KPIs, enabling data-driven optimization instead of anecdotal adjustments.

The RTK system shifted crane operations from experience-based execution to precision-guided workflows.