Validity of Trajectory-Based Calibration Approach of Car-Following Models in Presence of Measurement Errors

Abstract

Interest in calibration of car-following models by using real-life microscopic trajectory data is increasing. However, more information is needed on the influence of methodological issues on calibration results as well as on the influence of practical issues related to the use of real-life data. In particular, the influence of measurement errors on parameter estimates has not yet been considered in detail. To gain insight into the influence of measurement errors on calibration results, synthetic data were created to which several types of measurement error are introduced. These data are input to a validated calibration procedure, after which it is studied how well the parameters used for creating the data can be identified from the erroneous data. The sensitivity of the objective function to small changes in the optimal parameters also is assessed. The calibrations are repeated by using different variables in the objective. The three main findings are that (a) measurement errors can yield a considerable bias in the estimation results, (b) parameters minimizing the objective function do not necessarily capture following dynamics best, and (c) measurement errors substantially reduce the sensitivity of the objective function and consequently reduce the reliability of estimation results. The extent to which these problems caused by measurement errors can be avoided by smoothing the data carefully before use is assessed and discussed.