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

Vladimir Kuzmanovski*, Jaakko Hollmen

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

9 Lataukset (Pure)

Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoAdvances in Intelligent Data Analysis XIX - 19th International Symposium on Intelligent Data Analysis, IDA 2021, Proceedings
ToimittajatPedro Henriques Abreu, Pedro Pereira Rodrigues, Alberto Fernández, João Gama
KustantajaSpringer Science and Business Media Deutschland GmbH
Sivut171-183
Sivumäärä12
ISBN (elektroninen)978-3-030-74251-5
ISBN (painettu)978-3-030-74250-8
DOI - pysyväislinkit
TilaJulkaistu - 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Symposium on Intelligent Data Analysis - Online, Porto, Portugali
Kesto: 26 huhtikuuta 202128 huhtikuuta 2021

Julkaisusarja

NimiLecture Notes in Computer Science
KustantajaSpringer
Vuosikerta12695
ISSN (painettu)0302-9743

Conference

ConferenceInternational Symposium on Intelligent Data Analysis
MaaPortugali
KaupunkiPorto
Ajanjakso26/04/202128/04/2021

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