TITLE: Models and Algorithms for Transportation in the Sharing Economy
The recent (r)evolution of “transportation-as-a-service” has affected commuting patterns in major American cities. Yet the rise of ride-sharing apps, like Uber or Lyft, and bike-sharing systems, like CitiBike or Hubway, not only provides new opportunities for commuters, but also new challenges for operators. Common to all of these challenges are the intricate underlying network effects each ride has on supply in the system. For example, every rental of a bike at a bike-sharing station not only decreases the supply of bikes at that station but simultaneously increases the supply of docks available (for bike returns). A similar phenomenon is present in ride-sharing. The resulting externalities, positive or negative, are of both academic and practical interest.
In this talk, I present the results of two orthogonal pieces of work that combine rigorous mathematical analysis with real data to address these operational challenges. In the first part, we apply an inventory model frequently used in routing problems to inform the system-design of bike-sharing systems. By identifying an underlying discrete convexity in the model, we develop a provably correct, fast optimization algorithm. Applying our algorithm to data-sets from NYC, Chicago, and Boston, we derive proposals for re-designing these systems, which have since been adopted by the operators. In the second part, we study the question of how to optimize prices for ride-sharing systems in the presence of network externalities. Though the underlying stochastic control problem is non-convex, we show that a novel relaxation can be efficiently solved and provides parametric approximation guarantees with relative error bounds close to 0 in realistic regimes. Surprisingly, our analysis extends far beyond the realm of pricing, unifying several results on other controls employed in such systems, which were obtained concurrently.
This talk presents two papers, the first joint with Shane G. Henderson & David B. Shmoys and the second joint with Siddhartha Banerjee & Thodoris Lykouris.
BIO: Daniel Freund is a Ph.D. Candidate in Cornell’s Center for Applied Mathematics where he is advised by Prof. David B. Shmoys. He holds a B.Sc. in Mathematics from the University of Warwick and is an alumnus of the German National Merit Foundation. His research considers optimization problems that arise at the intersection of on-demand transportation and the sharing economy. During his Ph.D., he spent time as a Data Scientist both at Motivate, the operator of America’s largest bike-sharing systems, and at Lyft. While embedded in industry, he developed tools to cope with the operational challenges arising in such transportation systems; these motivated the theoretical models and novel algorithmic advances in his thesis, which in turn had impact on real-world decision-making. He also aims to bring his industry experience to the classroom, having taught as an instructor in Cornell’s School of Operations Research and Information Engineering and received a Yahoo! Graduate Teaching Award for his role as a Teaching Assistant in the Department of Computer Science.