This talk will focus on a recent prototype we develop of a DeepNet that can accurately predict ETAs of trips without having ever seen a map of the city, just learning from past trajectories of our assets by leveraging embeddings coming from Natural Language Processing (we found that trips are remarkably similar to sentences xD).

I would like to frame it with the experiments we ran in real-life, proving how such system can significantly improve the experience of our riders and drivers (mostly by doing smarter assignments and fairer pricing), and thus helps us build smarter and overall better cities. I will touch also on the technical challenge of bringing such system to production considering the scale Cabify has already.

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Carlos Herrera

Head of Data Science & Research Product Team | Cabify

Carlos got his PhD while pioneering the field of human mobility based on passively collected massive electronic records at the Human Mobility and Networks Labs at MIT. He has also performed relevant research around the concept of data induced trust in the sharing economy while his tenure at Traity. Now he is trying to apply both perspectives at scale at Cabify.

Carlos Herrera

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