Think you can change the future of flight?

Flight Quest Phase 1 Winners

1. • 

     1. Xavier Conort

   • 

     1. Hong Cao

   • 

     1. Clifton Phua

   • 

     1. Ghim-Eng Yap

   • 

     1. Kenny Chua

   Team Gxav &* 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.
2. • 

     2. Jonathan Peters

   • 

     2. Pawel Jankiewicz

   Team As High As Honor used a two-step approach that combined the results of a generalized linear model that encoded intuition about important variables with refinements derived from a random forest model. The team capitalized on the success of the linear model to add the effects of multiple variables and cleanly resolve issues of missing data.
3. 

   3. Gabor Takacs

   Team Taki used a six layer model relying on successive ridge regressions and gradient boosting machines to model both gate and runway arrival times. This approach used 56 features extracted from the raw data, with all but two coming from the test day data.
4. 

   4. Sergey Kozub

   Team Sun’s approach to predicting gate and runway arrival times relied on creating a derived data set with new variables encoding information about the aircraft, airport, airway, gate, hour, and flight path times. Important features used in this model include aircraft GPS position, ASDI flight plans the direction from which airplanes approached airport runways.
5. 

   5. Jacques Kvam

   Jacques Kvam’s approach for predicting runway and gate arrival times used gradient boosting for a model using 10,000 trees and a whopping 1,102 features trained on 260,000 flights. Most significant among these included the distance between the final waypoint and the arrival airport. Many weather features were important as well including temporary vertical visibility and wind speed at the arrival airport.
6. 

   Honorable Mention: Matt Berseth

   Team __mtb__ used random forest and gradient boosted models to estimate runway and gate arrival times. The final solution included over 100 different individual models, each focused on a narrow set of features (i.e. wind/weather, flight plan, aircraft's current location, etc.). These individual models were blended together to generate the final estimates. The training data was created by randomly selecting eight cutoff times for each day in the training period. A separate cross validation data set was used to select hyper-parameters.

Think you can change the future of flight?

Did you know airlines are constantly looking for ways to make flights more efficient? From gate conflicts to operational challenges to air traffic management, the dynamics of a flight can change quickly and lead to costly delays.

There is good news. Advancements in real-time big data analysis are changing the course of flight as we know it. Imagine if the pilot could augment their decision-making process with “real time business intelligence,”—information available in the cockpit that would allow them to make adjustments to their flight patterns. 

Your challenge, should you decide to accept it: 

Use the different data sets found on this page under Get the Data to develop a usable and scalable algorithm that delivers a real-time flight profile to the pilot, helping them make flights more efficient and reliably on time.

Started: 6:36 pm, Wednesday 28 November 2012 UTC
Ended: 11:59 pm, Monday 11 March 2013 UTC (103 total days)
Points: this competition awarded standard ranking points
Tiers: this competition counted towards tiers