EU-funded project · CEF Transport

eAOP: the extended Airport Operations Plan at Brussels Airport

The eAOP programme aims to strengthen Brussels Airport as a hub: better connections and intermodality, better punctuality, more efficient operations and more reliable service. Jetpack.AI builds the forecasting, demand-capacity balancing and simulation engines at the heart of the programme.

Why eAOP

Better hub performance for passengers, airlines and the European network

A hub airport works when flights leave on time, connections are made and every process has enough capacity at the right moment. The eAOP targets four levers of hub performance.

Connections & intermodality

Passengers and bags make their onward flight or their train, with transfer processes sized to demand.

Punctuality

More predictable turnarounds and runway operations raise on-time performance.

Efficiency

Shorter taxi times and less holding on taxiways and in the air reduce fuel burn and local emissions.

Reliability

Disruptions are anticipated, and operations recover faster from adverse conditions such as winter weather.

The programme

From the initial AOP to the extended AOP

The Airport Operations Plan (AOP) is the single, shared plan that the airport, airlines, ground handlers and air traffic control use to run each day of operations. Brussels Airport's initial AOP laid the foundations: it began exchanging flight trajectory, airport resource and meteorological data with EUROCONTROL's Network Operations Plan (NOP), and prepared the ground for demand-capacity balancing.

The extended AOP builds on those foundations, in line with the European Common Project 1 (CP1) regulation and the SESAR Airport Operations Management concept. It adds more airside and landside data that affect flight predictability, a data quality process, a demand-capacity balancing tool, and a wider data exchange with the EUROCONTROL Network Manager. Together these deliver four airport performance services.

Steer Long and medium term
Set performance goals, KPI thresholds and priorities, agreed together with airport partners.
Monitor Short term
Compare current performance with the forecast and alert partners and the Network Manager automatically.
Manage Real time
Assess the impact of deviations, decide with what-if scenarios and share changes to the AOP.
Post-operations After the day
Compare the day with the plan and identify the root cause of each deviation.

Jetpack.AI's role

Forecasting, demand-capacity balancing and simulation

Jetpack.AI and Brussels Airport's Data & Analytics team build the analytical engines of the eAOP. From historical and operational data, the engines forecast demand, check it against planned capacity and simulate how the airport will behave under different scenarios. Jetpack.AI also provides simple visualisations and ad-hoc analysis support for operational teams.

The engines are built as modules that plug into Brussels Airport's Business Platform, the airport's operational platform. Operations teams get one place for real-time monitoring, simulation and demand-capacity balancing.

Capability Delivered by Data used

Forecasting Jetpack.AI Post-operational and analytically enriched data

Demand-capacity balancing Jetpack.AI Post-operational and analytically enriched data

Simulation Jetpack.AI Post-operational and analytically enriched data

Advanced visualisation Brussels Airport with Jetpack.AI Enriched data

Real-time monitoring Brussels Airport with Jetpack.AI Operational data

Post-operations analytics Brussels Airport Post-operational data

How the engines work

Forecast demand
Start from the forecast for a process: flights, passengers, bags or weather.
Simulate the process
Replay a past day of operations or anticipate a future one.
Check against capacity
Test whether planned capacity handles the demand within the agreed service level.
Optimise
If it does not, an optimisation model computes the minimum capacity needed to meet the demand.

The same engines serve the four performance services: forecasts to set targets (Steer), next-hours simulation to spot problems early (Monitor), what-if scenarios for decisions (Manage), and replays of past days to find root causes (Post-operations).

Use cases

Four engines, each tied to a hub performance lever

AIR-DCB

Airside demand and capacity balancing

Departures and arrivals compete for the same runway slots, and in winter de-icing resources add a second constraint. AIR-DCB simulates runway slot allocation and de-icing demand together and shows where and why delays occur.

What-if scenarios: another runway configuration, fewer de-icing trucks, a partial or full runway closure
Estimates of delays and necessary cancellations from the resources available
Flight priority planning when capacity falls short of demand
KPIs for bottleneck analysis and communication to partners

PunctualityEfficiency

BAG DCB

Baggage demand and capacity balancing

Bag volumes swing sharply across the day and the seasons, while belts, sorters, screening and storage have fixed limits. BAG DCB learns the behaviour of the baggage system from historical data and models demand against capacity end to end.

Next-hours simulation from the current situation, to anticipate problems before they happen
Disruption scenarios (extra flights, equipment downtime, peak-season surges) to prepare contingency plans
Evidence for operational planning and asset investment decisions

ConnectionsReliability

APOC

Tracing how disruptions spread

Trouble at one step of the passenger or flight journey ripples into the next. This engine studies how disruptions propagate so they can be fixed where they start, before they cascade.

First chain: security screening, border control and flight punctuality
Once validated, extension to other airport processes
Goal: an end-to-end picture of how disruptions propagate

ConnectionsPunctuality

WINCON / ICECON

Winter conditions

Winter weather changes runway and aircraft surface conditions quickly, and forecasts hours ahead are uncertain. The engine simulates how surfaces will evolve from weather forecasts and turns the result into the condition ratings operations already use.

Explores many possible weather scenarios, not a single forecast
Side-by-side comparison of scenarios
De-icing and runway treatment prepared ahead of time

ReliabilityPunctuality

Expected results

What the eAOP brings

Predictable, flexible operations

Proactive management means less waiting for passengers and a plan that partners and the European network can rely on.

Lower emissions

Shorter taxi times and less holding on taxiways and in holding patterns reduce fuel burn and CO₂.

More from existing capacity

Better use of today's runway, baggage and terminal capacity allows new infrastructure investment to be postponed.

Better passenger experience

Higher punctuality and smoother connections improve passenger satisfaction.

Faster recovery

Operations return to normal sooner after planned or unplanned adverse conditions.

Network integration

A full exchange between the airport plan and EUROCONTROL's Network Operations Plan supports flight efficiency across Europe.

Who is involved

Partners

Brussels Airport Company

Operator of Brussels Airport and owner of the eAOP programme. Its operational teams define the needs and use the tools; its Data & Analytics team co-develops with Jetpack.AI.

Jetpack.AI

Data science and AI company specialised in airport operations. Designs and builds the forecasting, demand-capacity balancing and simulation engines.

Questions about the project: contact Jetpack.AI.