Can Results of car-following Model Calibration Based on Trajectory Data be Trusted?

Abstract

Calibration of car-following models against trajectory data has been widely applied as the basis for studies ranging from model investigation and benchmarking to parameter correlation analysis. Other theoretical issues, such as inter- and intradriver heterogeneity or multianticipative driving behavior, are also addressed in such studies. However, very few of these studies attempted to analyze and quantify the uncertainty entailed in the calibration process and its impacts on the accuracy and reliability of results. A thorough understanding of the whole calibration problem (against trajectory data), as well as of the mutual effect of the specific problems raised in the field literature, does not yet exist. In this view, a general methodology to assess a calibration procedure was proposed and applied to the calibration of the Gipps’ car-following model. Compact indicators were proposed to evaluate the capability of a calibration setting to find the known global solution regarding the accuracy and the robustness against the variation of the starting conditions of the optimization algorithm. Then a graphical inspection method, based on cobweb plots, was proposed to explore the existence and nature of the local minima found by the algorithms, as well as to give insights into the measures of performance and the goodness-of-fit functions used in the calibration experiments. The methodology was applied to all calibration settings (i.e., combinations of algorithms, measures of performance, and goodness-of-fit functions) used in the field literature so far. The study allowed the highlighting and motivation, for the model under investigation, of the limits of some of these calibration settings. Research directions for the definition of robust settings for the problem of car-following model calibration based on real trajectory data are outlined.