To develop advanced motorcycle assistance systems, the focus is shifting towards the rider's abilities. A model in (Scherer et al. 2022) predicts motorcycle dynamics influenced by riders without specific rider or vehicle parameters. It employs mathematical functions to describe speed and roll angle changes, revealing differences among riders. Unlike previous stochastic approaches, this model allows clear interpretation of measurement data with rider-specific parameters like correction amplitudes and trends, aiding critical maneuver identification. The paper investigates applying this rider model to real traffic data. For this purpose, three riders (two experienced frequent riders and one inexperienced infrequent rider) on two different vehicles (Honda CBF 1000 and BMW K1200R Sport) were recorded and examined on a sample basis using a validated low-cost measurement technique with a total amount of n = 40 measurements. Taking into account evaluation curves suitable for proving the methodology, two consecutive country road curves were selected with a respective change in direction (equivalent to a yaw angle change of the vehicle between entering and exiting the curve) of approx. 180°. These were each driven through 5 times by all three riders under constant conditions in good, summer weather and road conditions. In addition, one of the riders drove through them in wintry and less than optimal road conditions at the beginning of the season. Initial findings assess the model's transferability to real traffic. The investigation results show its applicability, with rider-specific riding styles and parameterization functions, as well as the need to repeat the study with a large number of samples. The model accurately predicts future positions, with over 85% of maneuvers having less than a 2% lateral deviation. This demonstrates applicability under real conditions, confirming its efficacy beyond the closed terrain test in (Scherer et. al., 2022). In the future, this model will enable rider-dependent trajectory predictions with uncertainty intervals in real traffic situations.