Fast-Time Simulation
Airport Ground-Movement Digital Twin Simulator
Fast-time airport surface simulation — stand allocation, pushback, taxi routing, runway sequencing, wake and runway-dependency separation, and turnaround — with editable scenarios, terminal airspace design, and 3D replay.
Testing a new stand plan, a runway configuration, or a peak-day schedule on the real airport is not an option. Fast-Time Simulation is a digital twin of airport ground movement that runs a whole operating day in minutes, so planners can try alternatives and compare them with numbers.
It simulates stand allocation, pushback, taxi routing, runway sequencing, wake and runway-dependency separation to ICAO and RECAT-EU standards, and turnaround. Scenarios are edited on the map — taxiway directions and costs, runway entries and exits, dependency rules, and terminal airspace procedures — and every run can be replayed in 2D or 3D.
Capabilities
An airport operating day, simulated fast-time
Surface movement simulation
Stand rules, pushback, taxi kinematics with per-direction segment cost, node-reservation conflicts, and hold-short queues.
Runway sequencing and separation
Runway dependencies with max-wins separation, and wake separation from named ICAO and RECAT-EU matrices.
Turnaround and demand
Stand occupancy and turnaround from flight schedules; design-day and peak-day demand that balance arrivals against departures.
Scenario editing on the map
Taxiway direction rules and cost weights, runway entries and exits, and dependency parameters, saved back to the scenario.
Terminal airspace design
PBN / ARINC-424 procedure legs, SIDs, STARs, approaches, vectoring legs, and holdings.
Replay and KPIs
To-scale aircraft footprints in 2D or 3D, with run KPIs and the inputs, flights, events, and trajectories of every kept run.
Architecture
How it works
1 · Ingest
An airport from GeoJSON — taxiway centerlines, apron and runway polygons, gate positions — or a native scenario, plus schedules and reference data for aircraft, airlines, and separation standards.
2 · Model
The taxiway network becomes a weighted routing graph, and each flight is simulated through stand, pushback, taxi, runway, and turnaround with conflicts and separation enforced.
3 · Deliver
KPIs, replay, and stored runs to compare alternatives — in a web application on the same data platform.
Technical profile
At a glance
- Inputs
- GeoJSON airport layouts, native JSON scenarios, legacy CSV projects, flight schedules
- Reference data
- 344 aircraft types (wingspan, length, ADG), ~900 airlines, ICAO and RECAT-EU separation matrices
- Core methods
- Discrete fast-time simulation, weighted taxi routing, node-reservation conflict resolution, runway-dependency separation
- Outputs
- Run KPIs, flight and event logs, trajectories, 2D/3D replay
- Delivery
- Web application (deck.gl map) and API
- Platform
- Helios — Cignus’s AWS-native aviation data and AI platform
- Access
- Role-based, enforced per request at the Helios API layer — not inside the application
FAQ
Common questions
Which airports can be simulated?
Any airport with a layout. An airport imported from GeoJSON is derived once into an editable scenario; scenarios carry selectable flow configurations such as north and south flow.
How is access controlled?
By role, at the API layer. Every request to Helios passes through interceptors that check the caller’s role and scope before any data is returned, so the same rules apply whether the request comes from the application, an API client, or an integration. Nothing is governed inside the application itself.
See Fast-Time Simulation in action
We will walk through it on data from your airport or airspace and discuss how it fits your operation.