Modern Intelligent Transportation Systems (ITS) often require large amounts of data for their performance analysis. For instance, the Ghosthunter project aimed to utilize smartphone position data to detect and warn wrong-way drivers on German freeways. This was to be accomplished with assured integrity, which required the generation and evaluation of \(4 \cdot 10^{8}\) vehicle trajectories. It is challenging to assess this number with real trajectories and a straightforward simulation. Since recording real trajectories, as well as manually creating ground truth data with such a large number of required trajectories, would represent an unaffordable time expenditure, a graphics processing unit (GPU) accelerated simulator based on the Monte Carlo method was developed. In the course of this, based on OpenStreetMap road map data, a model for simulating vehicle behavior, and error models for map and global navigation satellite system (GNSS) errors, vehicle trajectories are generated. Subsequently, these are then used to automatically test and evaluate the algorithms for wrong-way driver detection, which encompasses a map matching algorithm and the algorithm for detecting the direction of travel. The simulator facilitated the generation and evaluation of the required trajectories within a three-day period.
