Bayesian Calibration for Activity Based Models
- URL: http://arxiv.org/abs/2203.04414v1
- Date: Tue, 8 Mar 2022 21:38:07 GMT
- Title: Bayesian Calibration for Activity Based Models
- Authors: Laura Schultz and Joshua Auld and Vadim Sokolov
- Abstract summary: ABMs rely on statistical models of traveler's behavior to predict travel patterns in a metropolitan area.
We develop an approach that uses Gaussian process emulator to calibrate an activity-based model of a metropolitan transplantation system.
- Score: 1.7403133838762443
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: We consider the problem of calibration and uncertainty analysis for
activity-based transportation simulators. ABMs rely on statistical models of
traveler's behavior to predict travel patterns in a metropolitan area. Input
parameters are typically estimated from traveler's surveys using maximum
likelihood. We develop an approach that uses Gaussian process emulator to
calibrate an activity-based model of a metropolitan transplantation system. Our
approach extends traditional emulators to handle high-dimensional and
non-stationary nature of the transportation simulator. Our methodology is
applied to transportation simulator of Bloomington, Illinois. We calibrate key
parameters of the model and compare to the ad-hoc calibration process.
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