Composite Surrogate for Likelihood-Free Bayesian Optimisation in High-Dimensional Settings of Activity-Based Transportation Models

Vladimir Kuzmanovski*, Jaakko Hollmen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Activity-based transportation models simulate demand and supply as a complex system and therefore large set of parameters need to be adjusted. One such model is Preday activity-based model that requires adjusting a large set of parameters for its calibration on new urban environments. Hence, the calibration process is time demanding, and due to costly simulations, various optimisation methods with dimensionality reduction and stochastic approximation are adopted. This study adopts Bayesian Optimisation for Likelihood-free Inference (BOLFI) method for calibrating the Preday activity-based model to a new urban area. Unlike the traditional variant of the method that uses Gaussian Process as a surrogate model for approximating the likelihood function through modelling discrepancy, we apply a composite surrogate model that encompasses Random Forest surrogate model for modelling the discrepancy and Gaussian Mixture Model for estimating the its density. The results show that the proposed method benefits the extension and improves the general applicability to high-dimensional settings without losing the efficiency of the Bayesian Optimisation in sampling new samples towards the global optima.
Original languageEnglish
Title of host publicationAdvances in Intelligent Data Analysis XIX - 19th International Symposium on Intelligent Data Analysis, IDA 2021, Proceedings
EditorsPedro Henriques Abreu, Pedro Pereira Rodrigues, Alberto Fernández, João Gama
Number of pages13
ISBN (Electronic)978-3-030-74251-5
Publication statusPublished - 2021
MoE publication typeA4 Article in a conference publication
EventInternational Symposium on Intelligent Data Analysis - Online, Porto, Portugal
Duration: 26 Apr 202128 Apr 2021

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


ConferenceInternational Symposium on Intelligent Data Analysis


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