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International Symposium on Intelligent Data Analysis

IDA 2021: Advances in Intelligent Data Analysis XIX pp 171–183Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNISA,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.

This work has been supported by the European Commission through the H2020 project Finest Twins (grant No. 856602).

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Notes

  1. 1.

    Code available at: https://version.aalto.fi/gitlab/kuzmanv1/bolfiwcsm-preday.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Aalto University, Espoo, Finland

    Vladimir Kuzmanovski & Jaakko Hollmén

  2. Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia

    Vladimir Kuzmanovski

  3. Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden

    Jaakko Hollmén

  4. Smart City Center of Excellence, TalTech, Estonia

    Vladimir Kuzmanovski

Authors
  1. Vladimir Kuzmanovski
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  2. Jaakko Hollmén
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Corresponding author

Correspondence to Vladimir Kuzmanovski .

Editor information

Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    Pedro Henriques Abreu

  2. University of Porto, Porto, Portugal

    Pedro Pereira Rodrigues

  3. University of Granada, Granada, Spain

    Alberto Fernández

  4. University of Porto, Porto, Portugal

    João Gama

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Kuzmanovski, V., Hollmén, J. (2021). Composite Surrogate for Likelihood-Free Bayesian Optimisation in High-Dimensional Settings of Activity-Based Transportation Models. In: Abreu, P.H., Rodrigues, P.P., Fernández, A., Gama, J. (eds) Advances in Intelligent Data Analysis XIX. IDA 2021. Lecture Notes in Computer Science(), vol 12695. Springer, Cham. https://doi.org/10.1007/978-3-030-74251-5_14

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  • DOI: https://doi.org/10.1007/978-3-030-74251-5_14

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  • Online ISBN: 978-3-030-74251-5

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