  The precise estimation of macroscopic traffic parameters, such as travel time
and fuel consumption, is essential for the optimization of traffic management
systems. Despite its importance, the comprehensive acquisition of vehicle
trajectory data for the calculation of these macroscopic measures presents a
challenge. To bridge this gap, this study aims to calibrate car-following
models capable of predicting both microscopic measures and macroscopic
measures. We conduct a numerical analysis to trace the cumulative process of
model prediction errors across various measurements, and our findings indicate
that macroscopic measures encapsulate the accumulation of model errors. By
incorporating macroscopic measures into vehicle model calibration, we can
mitigate the impact of noise on microscopic data measurements. We compare three
car-following model calibration methods: MiC (using microscopic measurements),
MaC (using macroscopic measurements), and BiC (using both microscopic and
macroscopic measurements): utilizing real-world trajectory data. The BiC method
emerges as the most successful in reconstructing vehicle trajectories and
accurately estimating travel time and fuel consumption, whereas the MiC method
leads to overfitting and inaccurate macro-measurement predictions. This study
underscores the importance of bi-scale calibration for precise traffic and
energy consumption predictions, laying the groundwork for future research aimed
at enhancing traffic management strategies.
