Reliability of Parameter Values Estimated Using Trajectory Observations

Abstract

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.