High quality map data powers many aspects of the Uber trip experience. Services such as Search, Routing, and Estimated Time of Arrival (ETA) prediction rely on accurate map data to provide a safe, convenient, and efficient experience for riders, drivers, eaters, and delivery-partners. However, map data can become stale over time, reducing its quality.
As a customer-obsessed company, Uber reviews and addresses feedback in customer support tickets, which are submitted by riders, driver-partners, eaters, and delivery-partners on the Uber platform. Some of these tickets point out location problems, giving us one means of identifying and fixing errors in our map data. We serve over 15 million trips per day, so if even a small percentage of those trips trigger a customer support ticket, we end up with a large quantity of tickets.
Manually poring over the tickets to find those that point out inaccurate map data would not be scalable. Therefore, we use machine learning (ML) and big data processing to automate this workflow. To address the problem of large-scale ticket analysis, we built a natural language processing (NLP) platform that looks for map data-related issues in the text of tickets.
This platform can then specify which specific type of map data triggered the ticket, so that the appropriate team within our maps organization can assess the issue and determine a solution.