Winning the Kaggle Taxi destination prediction
Alex Auvolat, Étienne Simon and myself recently took part in a Kaggle competition organized by the conference ECML/PKDD. The task was simple: given a partial trajectory of a taxi, we were asked to predict its destination.
We stopped working on the competition about two weeks before the deadline as 20 teams were ahead on the public leaderboard and we thought that we had no chance left. Finally we had the good surprise to see that we obtained the first-place on the private leaderboard: many teams were heavily overfitting the public test set. After the deadline, we actually trained our winning model a bit longer (until convergence…) and obtained significantly better results!
As expected, the approaches we tried are all based on neural networks, whereas most published competitor solutions heavily rely on hand-engineering with little to no machine learning. By comparison our approaches have very little pre-processing, no post-processing and no ensembling.
The full description of our models can be found in our paper, which we presented in September at ECML/PKDD in Porto. Kaggle has also published a blog post with other details.
The following video is an example of real-time prediction of our best model as we were in the taxi going from the airport to the conference center!