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Schiphol - Optimale doorstroom van passagiers op Schiphol dankzij slimme datatoepassingen
- 1. Optimale doorstroom van passagiers op Schiphol dankzij slimme datatoepassingen 20 SEP 2018 Marcel Raas Berend Onnes &
- 2. Agenda 2 Introduction • About Schiphol • Traffic Analysis & Forecasting team Implementation of machine learning • Operational forecasting • Tough ride to get it implemented • What did/do we do to make it a success? Questions
- 3. Some big numbers (before talking big data) 3
- 4. Traffic Analysis & Forecasting Team 4 Reporting & Analysis • Network analysis (connectivity) • Benchmarking • Opportunities / threats • Regional Airports Capacity • Forecasting Short term => hiring staff Mid/Long term => infra planning Long term => spatial planning • Limitations: airport slots, airport & airspace capacity, environment, etc. Commercial • Airline business cases: Route / business development • Cargo cases • Forecasts to set airport charges € €
- 5. Agenda 5 Introduction • About Schiphol • Traffic Analysis & Forecasting team Implementation of machine learning • Operational forecasting • Tough ride to get it implemented • What did/do we do to make it a success? Questions
- 6. Confidential 6 Importance of forecasting
- 7. Confidential 7 Forecasting Flight Schedules Time Destination Aircraft type # Seats Airline Slot-file Market insights ProductDataSource Passengers per movement Added: # passengers Transfer share Airline Planning tool Market insights Flow forecast Added: Routing Reporting patterns Ticket scans Operational insights Planning tool Staff planning Added: Throughput = ‘productivity’ Historic throughput LOWWAITINGTIMES @LOWCOSTS 1 2 3 4
- 8. Confidential 8 Lazy / Lean… Why would an Airport work so hard to predict, while Airlines… • Have insights in sales & searches • ‘manage their yields’ (start promotion / discounts) have best insights on the expected number of passengers per flight Increase the share of airline input
- 9. Confidential 9 … but stay in control… Avoid full dependency of airlines Validate Airlines’ input (no strategic behaviour?) Strong tooling
- 10. Confidencelevel Time 10 Not user friendly and a Lack of faith in ‘machine’ Results were strong, now get organized professionally Make the best & full blown Looks great! Now make it robust Building a GUI is not our strength The ML engine is OK, though btw, legacy tool isn’t perfect either That last 5% is tough Promising results Forget about bureaucracy & go! … hence we developed a tool using machine learning
- 11. Confidential 11 How to build a robust model?
- 12. Confidential Predict Train • Predict the past • Make it realistic: do not use future information Feedback: measure how well it performed How to build a robust model?
- 13. Confidential • What machine learning algorithm to use • What data to include and what not • How to model holdiays • Importance weighting of more recent data/holidays What did we tune? 13
- 14. Confidential Dealing with the Dutch holidays 14 • Holidays appear in a lot of different configurations • Employ time-series analysis and disentangle holidays per region • These predictions: additional inputs to machine learning algorithm • Final tweak: weights, more importance to holiday periods Central +20% North + 15% South + 5%
- 15. Adding weights • Holiday periods • Aircraft size • Historic years • Winter/Summer season trends Weights for Summer 2017 training
- 16. Measure whether it improved 16 Sum of all flights departing in the same 15 minute bracket ML prediction too highML prediction too low Test data
- 17. When is it ‘good enough to go’? 17 Daily totals Grouped by airline Grouped by destination Single flight level
- 18. Confidential Reliable training data doesn’t always appear accurate Don’t rely on historic data to be fully accurate. Things we revealed in our historic data • 100 seats and…. 160 passengers • -/- 61 passengers to Mumbai • Freight shipped from Amsterdam to… Amsterdam!
- 20. Be thankful ☺☺☺☺ for cooperation Test data
- 21. See where you can improve Schedule Load factor Transfer share # seats Test data
- 22. ML Roller coaster learnings 22 Got organized seriously Define “when is it good enough to go?” Hired skilled professionals New tools = new user skills Compare & validate Data accuracy
- 23. Agenda 23 Introduction • About Schiphol • Traffic Analysis & Forecasting team Implementation of machine learning • Operational forecasting • Tough ride to get it implemented • What did/do we do to make it a success? Questions