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

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.

A Bayesian Optimization Approach for Calibrating Large-Scale Activity-Based Transport Models

“…RF has been chosen for its robustness and scalability when compared to other machine learning models. Namely, RF performs well in cases of high-dimensional problems with limited dataset sizes, which is not the case with more advanced techniques like neural networks and deep learning [49], [65].…”

“…However, the GP comes with a computation bottleneck when applied to high-dimensional problems, which in turn hinders a wider adoption for complex parameter spaces [37], [45]- [47]. Therefore, the straightforward adoption of the BO method in the calibration of activity-based models is hampered by the large number of parameters to be tuned [15], [49].…”