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Parameter values obtained by calibrating a car-following model using trajectory observations can provide important new insights into longitudinal driving behavior that cannot be derived from double loop detector data. However, that holds only if these behavioral parameters can be reliably estimated. This important aspect of calibration has been generally neglected in scientific research. In this contribution, therefore, calibrations are done to analyze several aspects influencing the degree to which behavioral parameters can be reliably estimated. In these calibrations trajectory data created by the authors with known characteristics as well as empirical trajectories are used. It is shown that traffic conditions during observation determine which model parameters can be reliably estimated. That measurement errors do negatively influence the reliability of parameter estimates is also demonstrated. The same negative effect appears to occur when a car-following model not fully able to capture the dynamics of the observed driver is calibrated. Finally, it is found that the extent to which a specific parameter can be reliably estimated is dependent on the degree to which it affects model predictions and thereby the value of the calibration objective. From this contribution it is concluded that to answer a specific research question on longitudinal driving behavior, it is essential to collect trajectories during appropriate traffic conditions. The results furthermore stress that even when trajectories contain enough information to calibrate the behavioral parameters of interest, whether it is reliable enough to draw inferences on car-following behavior needs to be determined for every parameter value separately.

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.

A dedicated trajectory data collection method using a helicopter enabled a range of in-depth empirical studies of car-following behavior. These studies found a high degree of heterogeneity in car-following behavior; that is, drivers' driving styles turned out to be highly different because different modeling approaches were needed to model these behaviors satisfactorily. Therefore the impact of heterogeneity in car following on modeling traffic dynamics is examined to gain insight into the effect of incorporating different types and degrees of heterogeneity in car-following behavior on the dynamics of a simulated traffic flow. The microsimulation approach that was adopted focused on two case studies: the first case study focused on heterogeneity in parameter values by comparing stability results for heterogeneous platoons and homogeneous platoons, which could in fact be seen as a solid preparation for the second case study. The second study was a simulation of a fixed stretch of road on which, from a certain point on, a speed limit was imposed for the drivers. In this case study the link between the empirical results and the simulations was strengthened as several types of heterogeneity (e.g., different model specifications for different drivers and different parameter settings and combinations of them) were explored and compared with the empirically estimated parameters. Both analyses show clear differences between simulations with homogeneous drivers and simulations with heterogeneous drivers.

This paper examines the car-following behavior of individual drivers in real traffic on the basis of vehicle trajectory data extracted from high-resolution digital images collected at a high frequency from a helicopter. These data are used to cross-compare seven car-following models regarding their average performances as well as their specific performances for all individual drivers observed using a simulation approach. The prime objective of this cross-comparison is to study interdriver differences; both optimal parameter settings and model performances are compared between drivers. Average model performances reveal that the simplest models are generally not able to capture the dynamics of car-following behavior correctly, whereas individual estimates show that the performances of more elaborate models differ between drivers. The most important contribution of this paper is that analysis shows that interdriver differences cannot be caught by different parameter settings alone; driving styles of individual drivers appear to be inherently different in that various car-following models are needed to model them satisfactorily.