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Science at Uber: Powering Uber’s Ridesharing Technologies Through Mapping

6 August 2019 / Global

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At Uber, we take advanced research work and use it to solve real world problems. In our Science at Uber video series, Uber employees talk about how we apply data science, artificial intelligence, machine learning, and other innovative technologies in our daily work.
Seamless and reliable transportation on Uber’s ridesharing network is dependent on technologies that can accurately predict both user demand and travel times from any two points on a road network. For Dawn Woodard, Director of Maps Data Science at Uber, calculating accurate travel time predictions is one of the most interesting mapping challenges that her team tackles. To solve these problems in real time, her team must factor in the effects of granular, geographic phenomena, including variance in weekly traffic patterns and road segments with data sparsity.
Check out our other Science at Uber videos:
Science at Uber: Building a Data Science Platform at Uber
Science at Uber: Bringing Research to the Roads
Science at Uber: Powering Uber’s Ridesharing Technologies Through Mapping
Science at Uber: Applying Artificial Intelligence at Uber
Science at Uber: Making a Real-world Impact with Data Science
Science at Uber: Innovating Across Digital and Physical Worlds
Interested in applying the latest research to solving real world problems? Consider joining our team!

Molly Vorwerck

Molly Vorwerck is the Eng Blog Lead and a senior program manager on Uber's Tech Brand Team, responsible for overseeing the company's technical narratives and content production. In a previous life, Molly worked in journalism and public relations. In her spare time, she enjoys scouring record stores for Elvis Presley records, reading and writing fiction, and watching The Great British Baking Show.

Posted by Molly Vorwerck

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Culture

Data / ML