Geospatial Technologies for Ride-Hailing and Emergency Vehicle Fleets
Extensive literature now exists on methods for pricing and matching in ride-hailing platforms, such as Uber, Lyft, Didi Chuxing, and Ola. However, less attention has been paid to the complex geospatial inputs required for these systems. For example, carpool matching methods require predictions of the time required to travel between any two locations in the road network. Similar mapping inputs are required in the context of decision systems for emergency vehicle fleets. We describe geospatial technologies, including those for travel time prediction and route optimization, that can be used in the context of such large-scale vehicle decision systems. We showcase the challenges, such as data sparsity on parts of the road network, and the fact that highly accurate predictions need to take into account the detailed dynamics of a physical system (traffic patterns in a road network). We also demonstrate machine learning architectures for travel time prediction that incorporate this contextual information.
Dawn Woodard leads data science for platforms including Uber Maps, which is the mapping platform used in Uber’s rider and driver app and decision systems. The team’s technologies include road map and points of interest definition, map search, route optimization, travel time prediction, and navigation. Dr. Woodard earned her PhD in statistics from Duke University, after which she was a faculty member in the School of Operations Research and Information Engineering at Cornell. There she developed forecasting methods for emergency vehicle decision support systems, in collaboration with several ambulance organizations. After receiving tenure at Cornell, she joined Microsoft Research for her sabbatical, where she created travel time prediction methods for use in Bing Maps. She then transitioned to Uber, to build and lead data science for the pricing and matching teams, eventually transitioning to her current role in Platforms.