AbstractVirtual test driving is a methodology to evaluate vehicle performance and environmental impacts under realistic driving conditions. This approach can generate comprehensive traffic information and trajectory data that reflect the dynamic and stochastic properties of transportation systems. Despite the benefits of virtual test driving, it is challenging to ensure a sufficient correlation between virtual and various real-life traffic situations. In this study, the simulation environment comprises a microscopic traffic network in the district of Aachen and a drivetrain model representing the longitudinal dynamics of a real car. In addressing the research challenge of sufficient correlation between virtual and real traffic, this work aims to develop a methodology to calibrate and validate virtual test driving within a selected study site by comparing simulation results with multi-source field measurements. The comparison between the field data and simulation is threefold: We used traffic counts to evaluate the simulated traffic volume at the macroscopic level; the drone-captured trajectory data to characterize the individual vehicle movements on highways, roundabouts, and intersections; and the real driving data of probe vehicles to investigate the influence of vehicle trajectories on energy demand. We also developed a generic approach to investigate the correlation between driving cycles and drivetrain performance, accounting for different combinations of real and virtual objects in three calibration and validation stages. Furthermore, the concept of mapping jigsaw puzzle scenarios is proposed to replicate the drone-captured concrete scenarios in the simulation and extend the calibrated results to a large-scale network. The results indicate that the proposed toolchain can effectively cope with the validation complexity of virtual test driving. A comparison with additional field measurements reveals the benefits of virtual test driving in providing a wide spectrum of realistic driving conditions in a cost-efficient and time-saving manner.Practical ApplicationsVirtual test driving is a methodology to evaluate vehicle performance and environmental impacts under realistic driving conditions. Compared to laboratory tests and field tests, virtual test driving is cost-efficient and time-saving. However, it is challenging to ensure a sufficient correlation between virtual and various real-life traffic situations. This paper proposes a methodology for calibrating and validating virtual test driving with multi-source field data, such as traffic volume at the macroscopic level; drone-captured trajectory data on highways, roundabouts, and intersections; and naturalistic driving data of probe vehicles. In this study, the simulation environment comprises a microscopic traffic network in the district of Aachen and a drivetrain model representing the longitudinal dynamics of a real car. The simulation results were compared with field data based on different measures, such as average speed, acceleration, standstill time, and energy demand. The calibrated and validated simulation environment can reflect the dynamic nature of transportation systems in a stochastic manner.