SAPPAO - Optimizing the flight times of airplanes using data science
The SAPPAO project aims to optimise the accuracy and reliability of predicting scheduled flight times. The full name of the project is 'A Systems Approach towards Data Mining and Prediction in Airlines Operations'.
- 2016 - 2020
- Matthijs van Leeuwen
On average, every plane experiences a delay of about 45 minutes due to the lack of predictability in flight operations. This, in turn, is caused by variability due to weather, congestion of planes and other factors. As a result, thousands of airplanes stay in the air every day for more time than strictly necessary.
Optimise accuracy and reliability
We are analysing historical flight data and data on the associated disruptive events on the flight network. By doing that, SAPPAO will optimise the accuracy and reliability of predicting scheduled flight times. It will result in significant savings on better utilisation of airplanes, decreased fuel consumption, CO2 emissions, ambient noise, and a better use of time for passengers, airports and carriers.
SAPPAO is a Dutch-Indian collaboration between Leiden University, IIT Roorkee and GE Aviation in Bangalore. It focuses on developing predictive algorithms and tools for managing, mining, learning and optimization of flight operations data. With those, we will enable airline operations in their endeavours to take relevant mitigation measures 4 hours prior to any flight. The project is a collaboration with professor Dhish Saxena, and PhD candidates Sarang Kapoor and Divyam Aggarwal at IIT Roorkee.
Picture: Heathrow Airport London. 2008 Complaint locations overlaid on map showing a typical day of westerly operations. The holding areas are clearly visible. (Source: Heathrow Airport: Environmental Noise Directive Noise Action Plan 2010-2015.)