Cut taxi time.
Save the fuel.
Hand the events to ATOMS.
Airfield-movement optimisation. Fuses surface radar, MLAT, A-CDM, and the ATOMS turnaround stream. Surfaces congestion hot-spots, predicts taxi time per gate–runway pair, nudges TSAT 30–90s so the aircraft meets the gap, not the queue. 10–20% taxi-time reduction; fuel + CO₂ on the same dashboard.
A minute on the ground
is kilograms in the air.
Narrow-body burns ~8–15 kg/min at taxi idle. A hub runs 1,000+ movements a day. The spread between best and worst cycle on the same route can be a sector's fuel bill. Taxi time is the cleanest fuel saving — invisible because nobody watches the network, only the single radio call.
From radio relay
to network view.
- Pushback on gate's schedule, not what the taxi network can absorb.
- Aircraft idle at the holding point — fuel burning, crew clock ticking.
- Congestion hot-spots known to controllers, unknown to the airline.
- Sustainability uses fleet-wide average, not per-flight measurement.
- TSAT advised — pushback nudged 30–90s so aircraft meets the gap, not the queue.
- Per-flight VTT published to airline before pushback.
- Hot-spot heatmap — duty controller sees where the network is bleeding minutes.
- Events streamed into ATOMS — gate-to-gate record continuous.
Data we fuse.
AI that learns.
Events that act.
We collect.
SMR for position; MLAT for tail attribution; A-CDM for intent; ATOMS for the pushback event; weather, schedule, runway config — all time-aligned on one clock.
The AI learns.
Congestion model learns where the network bleeds time. VTT predictor publishes expected taxi duration per gate–runway pair. TSAT advisor nudges pushback so the holding point is met at the gap.
We emit events.
Each inference is a structured event on the ATOMS bus. Tower sees the advisory. Airline sees per-flight VTT before pushback. Sustainability officer sees per-movement fuel + CO₂ saved.
The duty controller's view.
Flowing segment
Below threshold
Slowing segment
Watch · advisory armed
Hot-spot
Threshold breached · event
Ground target
MLAT + SMR fused
What the duty manager tracks.
Who gains, and how.
Every minute pulled out of the average cycle is a minute back on a slot, every shift.
- 10–20% taxi-time reduction on the average cycle.
- Hot-spot visibility the duty controller did not have before.
- Sustainability evidence — per-movement, audit-ready.
One of the cleanest sustainability wins in the airline P&L — no new aircraft, no new SAF.
- Per-flight fuel saved attributed to each tail.
- Per-flight CO₂ avoided for sustainability / CORSIA.
- Predictive VTT — block fuel decisions get tighter.
Built to the operating standards.
Common questions.
Do we need ATOMS to run AeroTaxi?
No. Standalone. With ATOMS, taxi events land on the same bus as turnaround events and the record is continuous gate-to-gate.
Will this conflict with our A-CDM platform?
No. Reads from A-CDM and emits TSAT advisories as suggestions, not commands. Watch manager accepts or overrides. Acceptance climbs to 85–90% in a few weeks.
How long until we see the 10–20%?
Congestion model needs a few weeks of priors. TSAT advisory becomes useful in the first month. Full 10–20% characterised during commissioning against your baseline.
How is the fuel / CO₂ attribution audited?
Per-flight fuel against a published baseline; CO₂ via IATA 3.16× factor. Methodology signed off during commissioning; every saving traceable to the underlying taxi events. Trust Center →
Start with one runway, scale across the airfield.
Integrations production-tested. Scoped in days, integrated in weeks, characterised in a month.
Bring AeroTaxi
to your airfield.
A 30-minute walkthrough on your runway config and average taxi cycle.