About Calibration Of Car-Following Dynamics Of Automated And Human-Driven Vehicles: Methodology, Guidelines And Codes

A comprehensive literature review reveals that there exist lots of ambiguities, confusion and even contradictions in setting a car-following calibration problem. In particular, confusion arises in the selection of measure of performances and goodness-of-fit functions. In this study, a methodology to compare and rank objective functions is thus proposed, which is based on Pareto-efficiency and on indifference curves. The methodology has been applied to all objective functions used in the field literature so far (and to new ones), in a vast set of calibration experiments. The experiments involved two car-following models and two adaptive cruise control (ACC) algorithms, and four different datasets, including both automated and human-driven vehicles trajectories. Since results are consistent among all the calibration experiments, a sound and robust guideline to calibrate car-following dynamics has been proposed. It includes recommendation about what calibration settings should be avoided and what are to be adopted. On the one hand, a general agreement on a sound calibration setting for car-following models is deemed necessary for comparing results from different studies which use different models and datasets. On the other hand, any new carfollowing model or objective function being developed in the future shall be compared with existing ones in a fair and impartial manner. For these reasons, and to promote and enable transparent and reproducible research, codes and data from this study are shared with the community.