Is more always better? The impact of vehicular trajectory completeness on car-following model calibration and validation

This paper investigates the impact of vehicular trajectory completeness on car-following (CF) model calibration and validation. Synthetic data with different levels of trajectory completeness, i.e., different number of driving regimes, generated from carefully designed numerical experiments are mainly used to calibrate and validate the Intelligent Driver Model (IDM) and the Newell's CF model. Model calibration results suggest that some driving regimes in a trajectory impact calibration errors and the particular regime and its exact impact are model-specific, e.g., the presence of the standstill and the absence of the cruising regimes impacts IDM and Newell's CF model calibration errors, respectively. However, level of trajectory completeness has no impact. The acceleration behaviour of IDM drivers in different driving regimes is determined by more than one parameter, i.e., a one-to-one mapping between the parameters and the driving regimes do not exist. On the contrary, for Newell's CF model, there exists a one-to-one mapping between the cruising regime and the desired speed. Furthermore, level of trajectory completeness impacts IDM and Newell's CF model validation. More specifically, the average calibrated parameters obtained from more complete trajectories performs better in validation and leads to smaller validation errors. These findings can have a profound impact on how future research on CF model calibration and validation using trajectories should be planned and implemented. (C) 2019 Elsevier Ltd. All rights reserved.