GE Flight Quest Winners improve gate/runway arrival predictions by 40-45 pct
The winners used a mixture of gradient boosting and random forest models to reduce prediction errors for gate and runway arrival times to 4.2 and 3.2 minutes. Careful feature selection played a key role in their success.
GE Flight Quest contest phase 1 has concluded.
The winning team used a mixture of gradient boosting and random forest models to predict gate and runway arrival times. With average errors of 4.2 and 3.2 minutes for gate and runway arrivals, respectively, this translates to 40% and 45% improvements over the standard industry benchmark estimates. Key to their success was careful feature selection with their final models using only 58 and 84 features for gate and runway arrivals, respectively, from the total 258 features they painstakingly constructed and optimized.
Winning team is composed of 4 researchers from Singapore and one from France:
- Xavier is a French actuary with more than 15 years of working experience in France, Brazil, China and Singapore
- Clifton Phua (Singapore) is currently a Senior Consultant in SAS Institute Pte Ltd.
- Hong Cao (Singapore) is currently a data analytics scientist in Institute for Infocomm Research (I2R) of Singapore's Agency of Science, Technology and Research (A*STAR)
- Ghim-Eng Yap (Singapore) is a Principal Investigator with the Institute for Infocomm Research (I2R)
- Hon Nian "Kenny" Chua (Singapore) is currently a data analytics scientist in Institute for Infocomm Research (I2R)
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