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

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Conference article in proceedings
Date
2021
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Language
en
Pages
12
171-183
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Advances in Intelligent Data Analysis XIX, Lecture Notes in Computer Science, Volume 12695
Abstract
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.
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Kuzmanovski , V & Hollmen , J 2021 , Composite Surrogate for Likelihood-Free Bayesian Optimisation in High-Dimensional Settings of Activity-Based Transportation Models . in P Henriques Abreu , P Pereira Rodrigues , A Fernández & J Gama (eds) , Advances in Intelligent Data Analysis XIX - 19th International Symposium on Intelligent Data Analysis, IDA 2021, Proceedings . , 14 , Lecture Notes in Computer Science , vol. 12695 , Springer , pp. 171-183 , International Symposium on Intelligent Data Analysis , Porto , Portugal , 26/04/2021 . https://doi.org/10.1007/978-3-030-74251-5_14