Podium Session 10: Uncovering Physics-Regularized Data Generation Processes for Individual Human Mobility: A Multi-Task Gaussian Process Approach Based on Multiple Kernel Learning
Title: Uncovering Physics-Regularized Data Generation Processes for Individual Human Mobility: A Multi-Task Gaussian Process Approach Based on Multiple Kernel Learning
Authors: Ekin Uğurel, Shuai Huang, Cynthia Chen
Abstract: Passively-generated mobile data has grown increasingly popular in the travel behavior (or human mobility) literature. A relatively untapped potential for passively-generated mobile data is synthetic population generation, which is the basis for any large-scale simulations for purposes ranging from state monitoring, policy evaluation, and digital tw...
Keywords: Synthetic mobile data; Gaussian process; Multiple kernel Learning; Physics-regularization; Travel behavior