Vehicle tracking data for calibrating microscopic traffic simulation models

摘要

This paper applies object detection in a microscopic traffic model calibration process and analyses the outcome. To cover a large and versatile amount of real world data for calibration and validation processes this paper proposes semi automated data acquisition by video analysis. This work concentrates mainly on the aspects of a automatic annotation tool applied to create trajectories of traffic participants over space and time. The acquired data is analyzed with a view towards calibrating vehicle models, which navigate through a road's surface and interact with the environment. The applied vehicle tracking algorithms for automated data extraction provide many trajectories not applicable for model calibration. Therefore, we applied an additional automated processing step to filter out faulty trajectories. With this process chain, the trajectory data can be extracted from videos automatically in a quality sufficient for the model calibration of speeds, the lateral positioning and vehicle interactions in a mixed traffic environment.