Efficient search in high-dimensional spaces: the case of Bayesian optimization for larges-scale urban traffic control 
11:00am - 12:00pm
Zoom ID: 934 6546 9779 Password:INTR6800

In this talk, we consider high-dimensional traffic signal control problems that arise in congested metropolitan areas. We focus on the use of high-resolution urban mobility stochastic simulators and formulate the control 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 setting, and propose the use of a simple analytical traffic model to specify the covariance function of a Gaussian process. We show how this enables the Bayesian optimization method to more efficiently sample in high-dimensional spaces. We present validation experiments on synthetic low-dimensional problems. We then apply the method to a high-dimensional traffic control problem for Midtown Manhattan, in New York City.

When
Where
Zoom ID: 934 6546 9779 Password:INTR6800
Recommended For
Alumni, Faculty and staff, PG students, UG students
Language
English
Speakers / Performers:
Dr. Carolina Osorio
Department of Decision Sciences, HEC Montreal
Organizer
Systems Hub, HKUST(GZ)
Science & Technology
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