Reconstruction of traffic speed distributions from kinetic models with uncertainties

TL;DR

This paper uses a kinetic model combined with uncertainty quantification to reconstruct and predict traffic speed distributions from real-world data, accounting for driver behavior variability.

Contribution

It introduces a novel calibration method for kinetic traffic models that incorporates uncertainty parameters derived from experimental data.

Findings

The approach accurately reconstructs multimodal speed distributions.

Calibration via constrained optimization effectively captures driver behavior variability.

Results validate the kinetic model's predictive capability for traffic speed distributions.

Abstract

In this work we investigate the ability of a kinetic approach for traffic dynamics to predict speed distributions obtained through rough data. The present approach adopts the formalism of uncertainty quantification, since reaction strengths are uncertain and linked to different types of driver behaviour or different classes of vehicles present in the flow. Therefore, the calibration of the expected speed distribution has to face the reconstruction of the distribution of the uncertainty. We adopt experimental microscopic measurements recorded on a German motorway, whose speed distribution shows a multimodal trend. The calibration is performed by extrapolating the uncertainty parameters of the kinetic distribution via a constrained optimisation approach. The results confirm the validity of the theoretical set-up.