Multi-fidelity and multi-level Monte Carlo methods for kinetic models of traffic flow

TL;DR

This paper introduces multi-fidelity and multi-level Monte Carlo methods to efficiently analyze uncertainties in kinetic traffic flow models, significantly improving computational accuracy and efficiency over traditional approaches.

Contribution

It develops and compares multi-fidelity and multi-level Monte Carlo techniques tailored for kinetic traffic models, demonstrating their effectiveness in reducing computational costs.

Findings

Multi-fidelity methods outperform multi-level Monte Carlo in certain scenarios.

Both methods significantly improve accuracy over standard Monte Carlo.

Numerical results validate the efficiency of the proposed approaches.

Abstract

In traffic flow modeling, incorporating uncertainty is crucial for accurately capturing the complexities of real-world scenarios. In this work we focus on kinetic models of traffic flow, where a key step is to design effective numerical tools for analyzing uncertainties in vehicles interactions. To this end we discuss space-homogeneous Boltzmann-type equations, employing a non intrusive Monte Carlo approach both on the physical space, to solve the kinetic equation, and on the stochastic space, to investigate the uncertainty. To address the high dimensional challenges posed by this coupling, control variate approaches such as multi-fidelity and multi-level Monte Carlo methods are particularly effective. While both methods leverage models of varying accuracy to reduce computational demands, multi-fidelity methods exploit differences in model fidelity, while multi-level methods utilize a hierarchy of discretizations. Numerical simulations indicate that these approaches provide substantial accuracy improvements over standard Monte Carlo methods. Moreover, by using appropriate low-fidelity surrogates based on approximated steady state solutions or simplified BGK interactions, multi-fidelity methods can outperform multilevel Monte Carlo methods.