In this talk, we consider high-dimensional transportation problems that arise in congested metropolitan urban areas. We focus on the use of high-resolution urban mobility stochastic simulators and formulate the optimization problems as high-dimensional continuous simulation-based optimization (SO) problems. We discuss the opportunities and challenges of designing SO algorithms for these problems. An important component in high-dimensional problems is the exploration-exploitation tradeoff. We discuss work that has focused on improving the exploitation capabilities of SO algorithms. We then present novel exploration techniques suitable for high-dimensional spaces. We consider a Bayesian optimization (BO) setting, and present various ways in which a simple analytical traffic model is used to enhance both the scalability and the sample efficiency of BO. We illustrate the methods with simple toy network as well as with a New York City case study.

BIO:

Carolina Osorio is an Associate Professor in the Department of Decision Sciences at HEC Montreal, where Osorio holds the SCALE AI Research Chair in Artificial Intelligence for Urban Mobility and Logistics. Osorio is also a Staff Research Scientist at Google Research. Osorio's work develops operations research techniques to inform the design and operations of urban mobility systems. It focuses on simulation-based optimization algorithms for, and analytical probabilistic modeling of, congested urban mobility networks. Osorio was recognized as one of the outstanding early-career engineers in the US by the National Academy of Engineering's EU-US Frontiers of Engineering Symposium, and is the recipient of a US National Science Foundation CAREER Award, an MIT CEE Maseeh Excellence in Teaching Award, an MIT Technology Review EmTech Colombia TR35 Award, an IBM Faculty Award and a European Association of Operational Research Societies (EURO) Doctoral Dissertation Award.