Year: 2018
Type: conference-paper
Abstract: Mobility dynamics in urban transportation systems is governed by a large number of travelers that act according to their utilities, preferences and biases. The mobility patterns that we observe are the results of the emerging traveler’s behaviors and, in practice, we develop models that represents mobility patterns and their resulting traffic flows. Models, however, can only approximate the processes they represent and often times do not reproduce exact matches to the true system’s observed data. Systematic adjustments, or calibrations, to the model and its input variables may be required to align the associated outputs more closely with their true values. In this paper we outline a mathematical framework that allows the calibration for parameters of urban transportation models through a distributed, Gaussian Process Bayesian regression with active learning methods and demonstrate using a ground transportation simulation model.
Source: Procedia Computer Science
Authors: Laura Schultz, Vadim Sokolov
Institution: George Mason University
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W2800757231