AI in Aviation. How we use AI in optimizing turnaround?

This case study demonstrates how classical machine learning algorithms combined with domain expertise can solve complex airport operations optimization challenges more effectively than advanced deep learning models. The project achieved doubled predictive accuracy while maintaining cost-effectiveness and operational simplicity.

The Challenge: Airport Stand Management and Aircraft Turnaround

Understanding Airport Turnaround Operations

At any busy airport, the turnaround process represents the critical period between an aircraft’s arrival at a stand and its departure. This tightly choreographed operation involves strict timelines, constrained space, and numerous interdependent tasks. Ground crews must coordinate baggage unloading, cabin cleaning, refueling, supply restocking, and passenger boarding within narrow time windows. A delay in any single step can trigger a chain reaction, disrupting schedules across the entire airport.

The complexity extends beyond individual aircraft to the broader aircraft stand management system. While stands are assigned based on scheduled times, late departures often lead to prolonged occupancy, causing arriving flights to queue even when other stands appear available. The real challenge lies in accurately predicting when a stand will be vacated, taking into account the constant flux of real-time operations.

The Operational Impact

In environments where hundreds of aircraft move daily, even small prediction errors compound into significant operational bottlenecks. This creates cascading effects on flight punctuality, airline profitability, and passenger satisfaction. Moreover, providing a real-time operational view of the airport empowers staff to react quickly to changes, whether rerouting an incoming aircraft or reallocating ground crews. Having up-to-the-minute insight into aircraft movement and stand occupancy transforms reactive chaos into proactive control.

Solution Architecture: Pragmatic AI Implementation

Technical Foundation

The development team implemented a real-time machine learning pipeline using Databricks as the core platform. The architecture enabled real-time processing, seamless airport system integration, and results sharing through an intuitive dashboard where operators could visualize, assess, and use the predictions. Models ingested streaming data and refreshed predictions every minute, providing continuous operational insights.

The initial vision was ambitious: develop a comprehensive system combining computer vision with data analytics. Camera feeds would automatically detect key turnaround events such as fueling, catering, and passenger boarding, feeding these visual inputs into a broader data pipeline for near-real-time stand clearance predictions.

The Strategic Decision: Classical Algorithms Over Deep Learning

The most remarkable decision was NOT pursuing advanced deep learning or transformer models, but reaching out to more modest algorithms enhanced with manually incorporated domain expertise. As Jakub Berezowski, Data Scientist at Addepto, explains: “We deliberately chose simplicity over complexity in selecting algorithms, as it turned out that classical, we can say even old-school statistical algorithms, when applied well, deliver matching results at a fraction of the cost compared to the state-of-the-art ones.”

This approach proved far more cost-effective and pragmatic than pursuing cutting-edge alternatives. While advanced models could achieve similar results, they would demand significantly more computational resources, development time, maintenance complexity, and infrastructure costs. Instead of relying solely on raw event data, the team worked closely with operations staff to understand what truly drives turnaround times.

Domain Expertise Integration

The collaboration with airport operations staff enabled the development of feature engineering strategies rooted in operator intuition and experience. These enriched features gave models the context they needed to make better predictions, with the impact being significant: models using these expert-informed features doubled their predictive accuracy compared to models using raw data alone.

Business Impact and Results

The Cost-Benefit Reality

One of the most important takeaways from this project was the clear trade-off between technical advancement and operational value. While more advanced models might have squeezed out slightly more accuracy, the cost in infrastructure, maintenance, and complexity would have far outweighed the benefit.

For example, a deep learning model might predict a 45-minute turnaround with high confidence that the error wouldn’t exceed 4.5 minutes, while the simpler model provides reassurance of a 5-minute margin. That half-minute gain would come with a 10-100x increase in resource consumption. From a business perspective, the simpler solution wasn’t just good enough—it was exactly right.

Key Operational Insights

While event data from external vision systems didn’t prove as impactful as initially hoped, the project delivered tremendous value. The most predictive variable turned out to be airport congestion, not individual turnaround events. This insight shifted focus from micro-events toward broader operational context, fundamentally changing how the team approached turnaround optimization.

Additionally, deploying on Databricks streamlined the entire process, allowing the team to reuse existing data pipelines, connect with other teams, and rapidly iterate without reinventing infrastructure from scratch.

Technology Implementation and Lessons Learned

Platform Advantages

The Databricks platform provided several key advantages: leveraging existing data pipelines and infrastructure, enabling seamless collaboration with other operational teams, and supporting rapid iteration without infrastructure rebuilding. This choice exemplified the project’s pragmatic approach to technology selection.

The Simplicity Advantage

This case study demonstrates that the most effective solutions are not always the most technically sophisticated. The project reinforced several critical principles for successful AI implementation. Domain expertise can significantly enhance algorithm performance beyond what raw data alone can achieve. Classical machine learning methods remain highly effective for many business problems, often outperforming more complex alternatives when properly applied.

Cost-benefit analysis should drive technology selection decisions rather than pursuing the latest technological trends. Operational constraints and business requirements should guide technical choices, ensuring solutions remain practical and maintainable in real-world environments.

Conclusion

This airport turnaround prediction project exemplifies successful pragmatic AI implementation. By combining classical machine learning algorithms with domain expertise and focusing on real-world operational constraints, the team delivered a cost-effective solution that solved genuine business problems without over-engineering.

The project’s success reinforces that effective AI solutions require balancing technical capability with business practicality, operational feasibility, and long-term sustainability. This approach of leveraging expert knowledge, choosing interpretable models, and focusing on real-world constraints proved that the best solutions are not always the most complex.