Calibrating microscopic car following models for adaptive cruise control vehicles: a multi-objective approach

TL;DR

This paper investigates how to calibrate car-following models for adaptive cruise control vehicles using a multi-objective approach, balancing speed and spacing accuracy to improve traffic flow modeling.

Contribution

It introduces a multi-objective calibration method for ACC vehicle models, analyzing tradeoffs between speed and spacing errors for better traffic simulation accuracy.

Findings

Calibrating for low spacing error does not compromise speed accuracy.

Multi-objective calibration reveals tradeoffs between speed and spacing errors.

Results align with recent literature on ACC vehicle modeling.

Abstract

Adaptive cruise control (ACC) vehicles are the first step toward comprehensive vehicle automation. However, the impacts of such vehicles on the underlying traffic flow are not yet clear. Therefore, it is of interest to accurately model vehicle-level dynamics of commercially available ACC vehicles so that they may be used in further modeling efforts to quantify the impact of commercially available ACC vehicles on traffic flow. Importantly, not only model selection but also the calibration approach and error metric used for calibration are critical to accurately model ACC vehicle behavior. In this work, we explore the question of how to calibrate car following models to describe ACC vehicle dynamics. Specifically, we apply a multi-objective calibration approach to understand the tradeoff between calibrating model parameters to minimize speed error vs. spacing error. Three different car-following models are calibrated for data from six vehicles. The results are in line with recent literature and verify that targeting a low spacing error does not compromise the speed accuracy whether the opposite is not true for modeling ACC vehicle dynamics.