Calibrating Microscopic Car-Following Models for Adaptive Cruise Control Vehicles: Multiobjective Approach

Felipe De Souza, Raphael Stern

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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 multiobjective calibration approach to understand the trade-off between calibrating model parameters to minimize speed error versus spacing error. Three different car-following models are calibrated for data from seven 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.

Original languageEnglish (US)
Article number04020150
JournalJournal of Transportation Engineering Part A: Systems
Issue number1
StatePublished - Jan 1 2021

Bibliographical note

Funding Information:
This work is supported by the Faculty Fellows Program at the Center for Transportation Studies at the University of Minnesota and by the US Department of Energy Vehicle Technologies Office under the Systems and Modeling for Accelerated Research in Transportation Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems Program.

Publisher Copyright:
© 2020 American Society of Civil Engineers.


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