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

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.

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