Uploaded August 2019 | Updated September 2026, 2 weeks ago
Seamless and reliable transportation on Uber’s ridesharing network is dependant on forecasting technologies that can accurately predict both user demand and travel times from any two points on a road network. For Dawn Woodard, Director of Data Science, Maps 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.
Learn more: eng.uber.com
Seamless and reliable transportation on Uber’s ridesharing network is dependant on forecasting technologies that can accurately predict both user demand and travel times from any two points on a road network. For Dawn Woodard, Director of Data Science, Maps 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.
Learn more: eng.uber.com
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