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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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TITLE: Pricing and Optimization in Shared Vehicle Systems
ABSTRACT:
Shared vehicle systems, such as those for bike-sharing (e.g., Citi Bike in NYC, Velib in Paris), car-sharing (e.g., car2go, Zipcar) and ride-sharing (Uber, Lyft, etc.) are fast becoming essential components of the urban transit infrastructure. The technology behind these platforms enable fine-grained monitoring and control tools, including good demand forecasts, accurate vehicle-availability information, and the ability to do dynamic pricing and vehicle repositioning. However, owing to their on-demand nature and the presence of network externalities (wherein setting prices at one place affects the supply at all other locations), optimizing the operations of such systems is challenging. To this end, I will describe how such systems can be modeled using queueing-network models, and talk about two recent projects in which we have developed the theoretical tools for analyzing such systems.
First, I will present a unifying framework for data-driven pricing and optimization in shared vehicle systems. Our approach provides efficient algorithms with rigorous approximation guarantees under a variety of controls (pricing, empty-vehicle repositioning, demand redirection) and for a wide class of objective functions (including welfare and revenue, and also multi-objective problems such as Ramsey pricing). Next, using the particular example of dynamic pricing in ride-sharing platforms, I will discuss how market mechanisms can help us to go beyond data-driven optimization; in particular, I will show how dynamic pricing is not any better than static pricing in general, but rather, how it allows the platform to realize optimal performance with limited knowledge of system parameters.
Based on joint work with Ramesh Johari and Carlos Riquelme at Stanford, Daniel Freund and Thodoris Lykouris at Cornell, and the data science team at Lyft.
