Ridesharing - Accounting for uncertainty in dispatch decisions to optimize marketplace balance скачать в хорошем качестве

Ridesharing - Accounting for uncertainty in dispatch decisions to optimize marketplace balance

Dispatch is one of the most powerful levers to optimize a two-sided marketplace of physical goods, as it is able to use rider payments to reallocate supply within a network. However, uncertainty of user behavior, such as riders canceling or drivers rejecting dispatches, makes achieving perfect optimality a challenge. In this talk, Parker discusses how Lyft has accounted for uncertainty in ride-sharing networks to achieve better overall outcomes. This talk will dive into modeling challenges with sparsity and non-continuity of various ML models, preventing moral hazard in user behavior from these assumptions, and understanding the biases different model assumptions have on the overall objective. Speaker Bio: Parker Spielman has extensive experience in ridesharing, both at Lyft and previously Uber, where he has worked on a variety of problems including dynamic pricing, dispatch, and incentives. All of these areas contribute to a set of levers focused on better overall control systems for real-time marketplaces. Slides at https://www.slideshare.net/ParkerSpie... Event: https://www.meetup.com/SF-Big-Analyti...